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14 pages, 5871 KiB  
Article
Additive Manufacturing for Automotive Radar Sensors Using Copper Inks and Pastes
by Nihesh Mohan, Fabian Steinberger, Sonja Wächter, Hüseyin Erdogan and Gordon Elger
Appl. Sci. 2025, 15(5), 2676; https://doi.org/10.3390/app15052676 - 2 Mar 2025
Viewed by 209
Abstract
Radar sensors are critical for obstacle detection and navigation, especially for automated driving. Using the use-case “printing of heating coils on the inside of the front housing (primary radome)” needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide [...] Read more.
Radar sensors are critical for obstacle detection and navigation, especially for automated driving. Using the use-case “printing of heating coils on the inside of the front housing (primary radome)” needed for de-icing in winter, it is demonstrated that additive manufacturing (AM) can provide economic and functional benefits for manufacturing of the sensors. AM will allow significant cost reduction by eliminating parts and simplifying the manufacturing process. Different AM technologies for the coils were investigated, first, by applying the conductive traces by fused deposition modeling (FDM), and, second, by printing copper particle-free inks and pastes. The metal layers were electrically and mechanically characterized using a profilometer to measure the trace dimension and a four-point probe to measure the resistance. It was revealed that low-cost conductive filaments with low resistivity and current carrying capacity are commercially still not available. The best option sourced was a copper–polyester-based filament with 6000 µΩcm after printing. Therefore, low-cost particle-free copper inks and commercial copper flake paste were selected to print the heating coil. The Cu particle-free inks were amine-based Cu (II) formate complexes, where the Cu exists in an ionic form. Using contactless printing processes such as ink-jet printing or pneumatic dispensing, the traces could be deposited onto the low-melting temperature (225 °C) polymeric radome structure. After printing, the material needed to be sintered to form the conductive copper traces. To avoid damaging the polymer radome during sintering, two different processes were investigated: low-temperature (<150 °C) sintering in an oven for 30 min or fast laser sintering. The sintered Cu layers achieved the following specific electric resistivities when slowly sintered in the oven: paste 4 µΩcm and ink 8.8 µΩcm. Using laser sintering, the ink achieved 3.2 µΩcm because the locally high temperature provides better sintering. Also, the adhesion was significantly increased to (5 B). Therefore, laser sintering is the preferred technology. In addition, it allows fast processing directly after printing. Commercial equipment is available where printing and laser sintering is integrated. The potential of low-cost copper material and the integration in additive manufacturing of electronic systems using radar sensors as an example are demonstrated in this paper. Full article
(This article belongs to the Special Issue Material Evaluation Methods of Additive-Manufactured Components)
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<p>(<b>a</b>) Burst schematic of a modern radar sensor. (<b>b</b>) Additively manufactured component of radar sensor (radome with Cu layer using FDM, heating structure using material deposition, and multilayered PCB with shielding layer).</p>
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<p>(<b>a</b>) Predrying and sintering profile of Cu complex ink. (<b>b</b>) Predrying and sintering profile of Cu paste.</p>
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<p>Transient thermal simulation of radome heating. (<b>a</b>) Radome surface temperature after 10 min for a radome of 90 mm × 58 mm × 3 mm. (<b>b</b>) CAD drawing of the radar sensor. (<b>c</b>) Infrared image of the experimental prototype using wires glued on the radome into CNC-machined grooves. (<b>d</b>) Rise time of average radome surface temperature and average heating wire temperature. The heating power was 4 W. The experimental temperatures were roughly 4 °C below the simulated one which is caused by the fact that the experimental test system was not in a still air box and therefore more air movement could be expected. In addition, the emissivity setting of 0.9 in the simulation might be underestimated.</p>
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<p>FDM five-axis prototype equipment (5axismaker) on the left side, a printed prototype of a meander heating structure in the center, and a parallel heating structure on the right side. The parallel structure was realized because the specific resistance of the conducting FDM filaments was low. A sufficiently small resistance, able to dissipate sufficient power using a 10 V power supply, is not feasible using a single meander structure.</p>
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<p>Cu complex ink printed and sintered onto a PET substrate. (<b>a</b>) Cu complex ink printed onto a PET substrate. (<b>b</b>) Cu complex ink sintered onto a PET substrate. (<b>c</b>) Printing and processing parameters with Cu trace properties.</p>
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<p>Effect of substrate plasma pretreatment on ink wettability and Cu metallization after sintering: (<b>left</b>) without plasma treatment; (<b>middle</b>) with plasma treatment for a duration of 5 min; (<b>right</b>) with plasma treatment for a duration of 10 min.</p>
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<p>Microscopy and profilometry analysis of the dimensions of a printed and sintered Cu ink.</p>
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<p>Cu heating structures prepared using different Cu materials and processes for radome heating structure. (<b>a</b>) Electrifi Cu filament; (<b>b</b>) oven-sintered Cu complex ink (five printed layers); (<b>c</b>) oven-sintered Cu paste; and (<b>d</b>) laser-sintered Cu complex ink.</p>
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<p>SEM image of laser-sintered copper layer.</p>
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12 pages, 20754 KiB  
Article
Development of a New Electric Vehicle Post-Crash Fire Safety Test in Korea (Proposed for the Korean New Car Assessment Program)
by Jeongmin In, Jaehong Ma and Hongik Kim
World Electr. Veh. J. 2025, 16(2), 103; https://doi.org/10.3390/wevj16020103 - 13 Feb 2025
Viewed by 455
Abstract
Recent fire incidents following electric vehicle (EV) collisions have been increasing rapidly in Korea, corresponding to the growing distribution of EVs. While the overall number of EV fires is lower compared to those involving internal combustion engine (ICE) vehicles, EV fires can lead [...] Read more.
Recent fire incidents following electric vehicle (EV) collisions have been increasing rapidly in Korea, corresponding to the growing distribution of EVs. While the overall number of EV fires is lower compared to those involving internal combustion engine (ICE) vehicles, EV fires can lead to more severe outcomes. Current regulations for post-crash fuel system integrity evaluation do not differentiate between EVs and ICE vehicles. However, the causes of fires in these vehicles differ due to variations in the design and construction of their fuel systems. This study analyzed seventeen cases of EV post-crash fires in Korea to derive two representative risk scenarios for EV post-crash fires. The first scenario involves significant intrusion into the EV front-end structure resulting from high-speed frontal collisions, while the second scenario involves direct impacts to the battery pack mounted under the vehicle from road curbs at low speeds (30–40 km/h). Based on these scenarios, we conducted tests to assess battery damage severity under two crash test modes, simulating both high-speed frontal collisions and low-speed curb impacts. The test results led to the development of a draft crash test concept to evaluate EV post-crash fire risks. Furthermore, we assessed the reproducibility of these test modes in relation to actual EV post-crash fires. Our findings indicate that square-shaped impactors provide higher reproducibility in simulating real EV post-crash fire incidents compared to hemisphere-shaped impactors. Additionally, a fire occurred 31 days after the storage of a crash-evaluated battery test specimen, which was determined to be caused by moisture invasion during post-crash storage, accelerating a micro-short circuit. This study aims to contribute to the development of new evaluation methods for the Korean New Car Assessment Program (KNCAP) to enhance EV post-crash fire safety by utilizing these test results to refine collision severity evaluation methods. Full article
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<p>Number of EV fires by period and fire type from ′23 to ′24.7 in Korea.</p>
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<p>Damage shapes of vehicle structures and battery packs in post-collision fires following high-speed frontal collisions in EVs. (Red and yellow cycles are battery impact points).</p>
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<p>Damage shapes of vehicle structures and battery packs in post-collision fires from direct impacts to the battery pack located at the bottom of EVs. (Yellow cycle is battery impact point).</p>
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<p>Two representative fire scenarios after EV collisions in Korea.</p>
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<p>Impact velocity reflecting field accident coverage for frontal pole collisions based on German accident research (GIDAS).</p>
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<p>Comparison of front-end intrusion between EVs and ICEVs following a 56 km/h frontal center-pole impact test. (The red line is the foremost position of the battery and the blue line is the intrusion depth of the vehicle front structure).</p>
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<p>Comparison of battery pack damage between EVs and ICEVs following a 56 km/h frontal center-pole impact test.</p>
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<p>CT scan of battery modules after a 56 km/h frontal center-pole test.</p>
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<p>Battery drop test to reflect crash energy equivalent to the bottom impact (17–18 km/h) of EVs at the curb.</p>
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<p>Damage and penetration of battery packs in the 4.9 m battery drop test.</p>
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<p>Burned battery after 4.9 m battery drop test.</p>
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<p>CT scan analysis of the burned battery following the 4.9 m battery drop test.</p>
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<p>Initial draft of new EV post-crash fire safety protocol concepts for direct impact on the batteries mounted at the bottom of EVs by road curbs or similar road debris at low speeds, derived from this research.</p>
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<p>Frontal view of 30 km/h trolley test using a hemispherical impactor.</p>
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<p>Minor damage to the bottom of the battery packs following 30 km/h trolley test using a hemispherical impactor.</p>
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<p>Frontal view of 30 km/h trolley test using a hemispherical impactor.</p>
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<p>Frontal view of 30 km/h trolley test using a square impactor.</p>
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<p>Excessive damage to the front face and internal frame of the battery packs following 30 km/h trolley test using a square impactor.</p>
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<p>Frontal view of 30 km/h trolley test using a square impactor.</p>
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<p>New draft EV post-crash fire safety protocol concepts for future KNCAP (′26) derived from this research.</p>
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15 pages, 11075 KiB  
Article
The Development and Characteristics of an In-Wheel Assembly Using a Variable Speed-Reducing Device
by Kyeongho Shin, Kyoungjin Ko and Junha Hwang
World Electr. Veh. J. 2025, 16(2), 92; https://doi.org/10.3390/wevj16020092 - 11 Feb 2025
Viewed by 380
Abstract
This study proposes an in-wheel assembly with a variable speed-reduction device designed to maximize torque and vehicle speed, enabling high-performance vehicle-level driving characteristics in front-engine, rear-wheel drive (FR), internal combustion engine (ICE) vehicles, where conventional EV motors cannot facilitate e-4WD. The proposed system [...] Read more.
This study proposes an in-wheel assembly with a variable speed-reduction device designed to maximize torque and vehicle speed, enabling high-performance vehicle-level driving characteristics in front-engine, rear-wheel drive (FR), internal combustion engine (ICE) vehicles, where conventional EV motors cannot facilitate e-4WD. The proposed system integrates a motor and speed reducer within the wheel while avoiding interference from braking, steering, and suspension components. Through various innovative approaches, concepts for an integrated wheel-bearing planetary reducer and a variable speed planetary reducer were derived. The developed system achieved twice the maximum torque and a 35% increase in top speed compared to previously developed in-wheel systems, all without altering the front hard points. Multi-body dynamic analysis and component testing revealed wheel lock-up issues during reverse driving, and instability in the one-way clutch at high speeds. To address these issues, the power transmission structure was improved, and the type of one-way clutch was modified. Additionally, deficiencies in lubrication supply to the friction surface of the one-way clutch were identified through flow analysis and visualization tests, leading to design improvements. The findings of this study demonstrate that even in in-wheel systems where the application of large and complex transmission devices is challenging, it is possible to simultaneously enhance both maximum torque and top vehicle speed to achieve high-performance vehicle-level driving dynamics. Consequently, implementing an in-wheel e-4WD system in ICE FR vehicles is expected to improve fuel efficiency, achieve high-performance vehicle capabilities, and enhance market competitiveness. Full article
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<p>In-wheel e-4WD configuration of G70.</p>
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<p>(<b>a</b>) Concept of hub bearing integrated PGS. (<b>b</b>) Block diagram of HB-PGS.</p>
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<p>(<b>a</b>) The block diagram of power transmission. (<b>b</b>) The target T-N (torque–speed) curve.</p>
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<p>The operational state of each OWC based on the location of torque generation and the relative rotational speeds of the components in forward.</p>
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<p>The operational state of each OWC based on the location of torque generation and the relative rotational speeds of the components in backward.</p>
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<p>The structure of the new design of preventing OWC interlock.</p>
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<p>Overall structure of in-wheel assembly with the HB-PGS and VOWC-PGS.</p>
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<p>(<b>a</b>) Dynamic analysis of the OWC. (<b>b</b>) Dynamic analysis of the PGS.</p>
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<p>(<b>a</b>) Dynamic analysis of the VOWC in low speed. (<b>b</b>) Test results of the VOWC.</p>
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<p>(<b>a</b>) Test of VOWC engagement limits at high rotational speeds. (<b>b</b>) Test results of the engagement limits. (<b>c</b>) Dynamic analysis of the VOWC at high speed.</p>
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<p>(<b>a</b>) Test of VOWC engagement limits at high rotational speeds. (<b>b</b>) Test results of the engagement limits. (<b>c</b>) Dynamic analysis of the VOWC at high speed.</p>
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<p>Thermal stress in rotor shaft and OWC Rollover.</p>
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<p>(<b>a</b>) Oil flow analysis. (<b>b</b>) Supplied oil visualization.</p>
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<p>(<b>a</b>) Oil flow according to rotational speed of rotor. (<b>b</b>) Changes in the amount of inlet/outlet oil over time. (<b>c</b>) Single sided oil holes in rotor shaft.</p>
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<p>(<b>a</b>) Oil flow according to rotational speed of rotor. (<b>b</b>) Changes in the amount of inlet/outlet oil over time. (<b>c</b>) Single sided oil holes in rotor shaft.</p>
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<p>Performance test for variable reducer in-wheel system. (<b>a</b>) Variable reducer in-wheel assay PT dynamo test. (<b>b</b>) Wheel T-N performance.</p>
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<p>Efficiency for variable reducer in-wheel system.</p>
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<p>Longitudinal dynamic performance analysis.</p>
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15 pages, 6130 KiB  
Article
Investigation on the Excitation Force and Cavitation Evolution of an Ice-Class Propeller in Ice Blockage
by Qiaogao Huang, Sijie Zheng, Han Li, Xing He and Xinming Li
Water 2025, 17(3), 295; https://doi.org/10.3390/w17030295 - 22 Jan 2025
Viewed by 500
Abstract
When an ice-class propeller is operating in an ice-covered environment, as some ice blocks slide along the ship hull in front of the propeller blades, the inflow ahead of the propeller will become non-uniform. Consequently, the excitation force applied to the blades will [...] Read more.
When an ice-class propeller is operating in an ice-covered environment, as some ice blocks slide along the ship hull in front of the propeller blades, the inflow ahead of the propeller will become non-uniform. Consequently, the excitation force applied to the blades will increase and massive cavitation bubbles will be generated. In this paper, a hybrid Reynolds-Averaged Navier–Stokes/Large Eddy Simulation method and Schnerr–Sauer cavitation model are used to investigate the hydrodynamics, excitation force, cavitation evolution and flow field characteristics of the propeller in ice blockage conditions. The results show that the numerical method adopted has a relatively high accuracy and the hydrodynamic error is controlled within 3.0%. At low cavitation numbers, although the blockage distance decreases, the cavitation phenomenon is still severe and the hydrodynamic coefficients hardly increase accordingly. Ice blockage causes a sharp increase in cavitation. When the distance is 0.15 times the diameter, the cavitation area amounts to 20% of the propeller blades. As the advance coefficient grows, the total cavitation area diminishes, while the cavitation area of the blade behind ice does not decrease, resulting in an increment in excitation force. Ice blockage also causes backflow in the wake. At this time, the largest backflow appears at the tip of the blade behind the ice. The higher the advance coefficient, the more significant the high-pressure area of the pressure side and the greater the pressure difference, causing the excitation force to rise sharply. This work offers a positive theoretical basis for the anti-cavitation design and excitation force suppression of propellers operating in icy regions. Full article
(This article belongs to the Special Issue Ice and Snow Properties and Their Applications)
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<p>The geometric models of the propeller and ice block.</p>
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<p>The computational domain and its boundary settings.</p>
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<p>The grids of the computational domain.</p>
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<p>Validation of hydrodynamics between CFD and EFD (<span class="html-italic">σ<sub>n</sub></span> = 1.5 and <span class="html-italic">J</span> = 0.35): (<b>a</b>) <span class="html-italic">K<sub>T</sub></span>, 10<span class="html-italic">K<sub>Q</sub></span>, <span class="html-italic">η</span><sub>0</sub>; (<b>b</b>) Errors.</p>
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<p>Comparison of hydrodynamic coefficients: (<b>a</b>) <span class="html-italic">K<sub>T</sub></span>; (<b>b</b>) 10<span class="html-italic">K<sub>Q</sub></span>; (<b>c</b>) <span class="html-italic">η</span><sub>0</sub>.</p>
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<p>Time history curves of excitation force superposition: (<b>a</b>,<b>c</b>,<b>e</b>) <span class="html-italic">L/D</span> = 0.15 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively; (<b>b</b>,<b>d</b>,<b>f</b>) <span class="html-italic">L/D</span> = 0.50 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively.</p>
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<p>Evolution of cavitation shapes: (<b>a</b>–<b>c</b>) <span class="html-italic">L/D</span> = 0.15 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively; (<b>d</b>–<b>f</b>) <span class="html-italic">L/D</span> = 0.50 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively.</p>
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<p>Time history curves of cavitation areas’ superposition: (<b>a</b>,<b>c</b>,<b>e</b>) <span class="html-italic">L/D</span> = 0.15 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively; (<b>b</b>,<b>d</b>,<b>f</b>) <span class="html-italic">L/D</span> = 0.50 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively.</p>
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<p>Axial velocity <span class="html-italic">V<sub>x</sub>/nD</span> in flow field: (<b>a</b>–<b>c</b>) <span class="html-italic">L/D</span> = 0.15 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively; (<b>d</b>–<b>f</b>) <span class="html-italic">L/D</span> = 0.50 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively.</p>
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<p>Pressure <span class="html-italic">C<sub>p</sub></span> in flow field: (<b>a</b>–<b>c</b>) <span class="html-italic">L/D</span> = 0.15 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively; (<b>d</b>–<b>f</b>) <span class="html-italic">L/D</span> = 0.50 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively.</p>
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<p>Pressure <span class="html-italic">C<sub>p</sub></span> on blades: (<b>a</b>,<b>c</b>,<b>e</b>) <span class="html-italic">L/D</span> = 0.15 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively; (<b>b</b>,<b>d</b>,<b>f</b>) <span class="html-italic">L/D</span> = 0.50 and <span class="html-italic">J</span> = 0.35, 0.45 and 0.55, respectively.</p>
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26 pages, 14487 KiB  
Article
Accelerating Die Bond Quality Detection Using Lightweight Architecture DSGβSI-Yolov7-Tiny
by Bao Rong Chang, Hsiu-Fen Tsai and Wei-Shun Chang
Electronics 2024, 13(22), 4573; https://doi.org/10.3390/electronics13224573 - 20 Nov 2024
Viewed by 603
Abstract
The die bonding process is one of the most critical steps in the front-end semiconductor packaging process, as it significantly affects the yield of the entire IC packaging process. This research aims to find an efficient, intelligent vision detection model to identify whether [...] Read more.
The die bonding process is one of the most critical steps in the front-end semiconductor packaging process, as it significantly affects the yield of the entire IC packaging process. This research aims to find an efficient, intelligent vision detection model to identify whether each chip correctly adheres to the IC substrate; by utilizing the detection model to classify the type of defects occurring in the die bond images, the engineers can analyze the leading causes, enabling timely adjustments to key machine parameters in real-time, improving the yield of the die bond process, and significantly reducing manufacturing cost losses. This study proposes the lightweight Yolov7-tiny model using Depthwise-Separable and Ghost Convolutions and Sigmoid Linear Unit with β parameter (DSGβSI-Yolov7-tiny), which we can apply for real-time and efficient detection and prediction of die bond quality. The model achieves a maximum FPS of 192.3, a precision of 99.1%, and an F1-score of 0.97. Therefore, the performance of the proposed DSGβSI-Yolov7-tiny model outperforms other methods. Full article
(This article belongs to the Special Issue Novel Methods for Object Detection and Segmentation)
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<p>IC packaging and testing process.</p>
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<p>Yolov4-tiny architecture.</p>
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<p>Yolov5n architecture.</p>
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<p>Yolov7 architecture.</p>
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<p>Yolov7-tiny architecture. Note: k represents kernel size, and s stands for stride.</p>
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<p>Ghost convolution.</p>
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<p>Depthwise separable convolution.</p>
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<p>Depthwise separable and Ghost convolutions.</p>
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<p>DSG-Yolov7 architecture. Note: k represents kernel size, and s stands for stride.</p>
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<p>Sample images of die bond categories. (<b>a</b>,<b>c</b>,<b>e</b>) bond_good; (<b>b</b>,<b>d</b>,<b>f</b>) bond_bad.</p>
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<p>Sample images of die bond categories. (<b>a</b>,<b>c</b>,<b>e</b>) bond_good; (<b>b</b>,<b>d</b>,<b>f</b>) bond_bad.</p>
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<p>Die bond recognition. (<b>a</b>,<b>c</b>,<b>e</b>) bond_good; (<b>b</b>,<b>d</b>,<b>f</b>) bond_bad.</p>
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<p>Die bond recognition. (<b>a</b>,<b>c</b>,<b>e</b>) bond_good; (<b>b</b>,<b>d</b>,<b>f</b>) bond_bad.</p>
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<p>Judgment of categories with bond_good or bond_bad.</p>
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<p>Any combination of the bond detection of the chip’s sides and corners.</p>
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<p>Prediction classification with type of die bond.</p>
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<p>DSG-Yolov7-tiny architecture. Note: k represents kernel size, and s stands for stride.</p>
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<p>DSGβSI-Yolov7-tiny architecture. Note: k represents kernel size, and s stands for stride.</p>
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<p>The workflow of the system.</p>
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<p>The precision–recall curve for the object detection model. (<b>a</b>) Yolov4-tiny; (<b>b</b>) Yolov5n; (<b>c</b>) Yolov7; (<b>d</b>) Yolov7-tiny; (<b>e</b>) DSG-Yolov7; (<b>f</b>) DSG-Yolov7-tiny; (<b>g</b>) DSGSI-Yolov7-tiny; (<b>h</b>) DSGβSI-Yolov7-tiny.</p>
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<p>The precision–recall curve for the object detection model. (<b>a</b>) Yolov4-tiny; (<b>b</b>) Yolov5n; (<b>c</b>) Yolov7; (<b>d</b>) Yolov7-tiny; (<b>e</b>) DSG-Yolov7; (<b>f</b>) DSG-Yolov7-tiny; (<b>g</b>) DSGSI-Yolov7-tiny; (<b>h</b>) DSGβSI-Yolov7-tiny.</p>
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<p>Loss plot of DSGβSI-Yolov7-tiny. (<b>a</b>) Box loss; (<b>b</b>) objectivity loss; (<b>c</b>) classified losses; (<b>d</b>) verify box loss; (<b>e</b>) verify objectivity loss; (<b>f</b>) verify classification losses.</p>
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<p>Loss plot of DSGβSI-Yolov7-tiny. (<b>a</b>) Box loss; (<b>b</b>) objectivity loss; (<b>c</b>) classified losses; (<b>d</b>) verify box loss; (<b>e</b>) verify objectivity loss; (<b>f</b>) verify classification losses.</p>
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14 pages, 4501 KiB  
Article
Moisture Distribution and Ice Front Identification in Freezing Soil Using an Optimized Circular Capacitance Sensor
by Xing Hu, Qiao Dong, Bin Shi, Kang Yao, Xueqin Chen and Xin Yuan
Sensors 2024, 24(22), 7392; https://doi.org/10.3390/s24227392 - 20 Nov 2024
Viewed by 611
Abstract
As the interface between frozen and unfrozen soil, the ice front is not only a spatial location concept, but also a potentially dangerous interface where the mechanical properties of soil could change abruptly. Accurately identifying its spatial position is essential for the safe [...] Read more.
As the interface between frozen and unfrozen soil, the ice front is not only a spatial location concept, but also a potentially dangerous interface where the mechanical properties of soil could change abruptly. Accurately identifying its spatial position is essential for the safe and efficient execution of large-scale frozen soil engineering projects. Electrical capacitance tomography (ECT) is a promising method for the visualization of frozen soil due to its non-invasive nature, low cast, and rapid response. This paper presents the design and optimization of a mobile circular capacitance sensor (MCCS). The MCCS was used to measure frozen soil samples along the depth direction to obtain moisture distribution and three-dimensional images of the ice front. Finally, the experimental results were compared with the simulation results from COMSOL Multiphysics to analyze the deviations. It was found that the fuzzy optimization design based on multi-criteria orthogonal experiments makes the MCCS meet various performance requirements. The average permittivity distribution was proposed to reflect moisture distribution along the depth direction and showed good correlation. Three-dimensional reconstructed images could provide the precise position of the ice front. The simulation results indicate that the MCCS has a low deviation margin in identifying the position of the ice front. Full article
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<p>Schematic diagram of ECT’s forward and inverse problems [<a href="#B17-sensors-24-07392" class="html-bibr">17</a>].</p>
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<p>Compaction curve of loess.</p>
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<p>The diagram of the testing procedure.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi>C</mi> </mrow> <mrow> <mi>s</mi> </mrow> </msub> </mrow> </semantics></math> and moisture content variation in different specimens: (<b>a</b>) 10% initial moisture content; (<b>b</b>) 15% initial moisture content; (<b>c</b>) 20% initial moisture content.</p>
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<p><math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> and moisture content variation in different specimens: (<b>a</b>) 10% initial moisture content; (<b>b</b>) 15% initial moisture content; (<b>c</b>) 20% initial moisture content.</p>
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<p>Ice fronts of different specimens: (<b>a</b>) 10% initial moisture content; (<b>b</b>) 15% initial moisture content; (<b>c</b>) 20% initial moisture content.</p>
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<p>2D image of the relative permittivity distribution at different specimen heights with 10% initial moisture content.</p>
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<p>Three-dimensional interpolated cloud plot of relative permittivity: (<b>a</b>) 10% initial moisture content; (<b>b</b>) 15% initial moisture content; (<b>c</b>) 20% initial moisture content.</p>
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<p>Simulation results of temperature field after 24 h of freezing: (<b>a</b>) 10% initial moisture content; (<b>b</b>) 15% initial moisture content; (<b>c</b>) 20% initial moisture content.</p>
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<p>Normalized simulation results of moisture content compared with measured <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>ε</mi> </mrow> <mrow> <mi>m</mi> </mrow> </msub> </mrow> </semantics></math> after 24 h of freezing: (<b>a</b>) 10% initial moisture content; (<b>b</b>) 15% initial moisture content; (<b>c</b>) 20% initial moisture content.</p>
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25 pages, 29734 KiB  
Article
Study of Flow Characteristics and Anti-Scour Protection Around Tandem Piers Under Ice Cover
by Pengcheng Gao, Lei Chang, Xianyou Mou, Feng Gao, Haitao Su, Bo Zhang, Zhiqiang Shang, Lina Gao, Haode Qin and Hui Ma
Buildings 2024, 14(11), 3478; https://doi.org/10.3390/buildings14113478 - 31 Oct 2024
Viewed by 656
Abstract
The impact of an ice-covered environment on the local flow characteristics of a bridge pier was studied through a series of flume tests, and the dominant factors affecting the scour pattern were found to grasp the change laws of the local hydrodynamic characteristics [...] Read more.
The impact of an ice-covered environment on the local flow characteristics of a bridge pier was studied through a series of flume tests, and the dominant factors affecting the scour pattern were found to grasp the change laws of the local hydrodynamic characteristics of the bridge pier under the ice cover. At the same time, because the scour problem of the pier foundation is a technical problem throughout the life-cycle of the bridge, to determine the optimal anti-scour protection effect on the foundation of the bridge pier, active protection scour plate was used to carry out scour protection tests, and its structural shape was optimized to obtain better anti-scour performance. The test results show that the jumping movements of sediment particles in the scour hole around the pier are mainly caused by events Q2 and Q4, which are accompanied by events Q1 and Q3 and cause the particle rolling phenomenon, where Q1 and Q3 events are outward and inward interacting flow regimes, and Q2 and Q4 events are jet and sweeping flow regimes, respectively. The power spectral attenuation rate in front of the upstream pier is high without masking effects, while strong circulation at the remaining locations results in strong vorticity and high spectral density, in particular, when the sampling time series is 60 s (i.e., f = 1/60), the variance loss rates under ice-covered conditions at the front of the upstream pier, between the two piers, and at the tail end of the downstream pier are 0.5%, 4.6%, and 9.8%, respectively, suggesting a smaller contribution of ice cover to the variance loss. Full article
(This article belongs to the Special Issue Advances in Soil-Structure Interaction for Building Structures)
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<p>Schematic of flume arrangement for flow measurement around bridge piers.</p>
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<p>Selection of test piers: (<b>a</b>) prototype piers and (<b>b</b>) model piers.</p>
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<p>Anti-scour plate model: (<b>a</b>) single-pier and (<b>b</b>) combined-pier protection.</p>
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<p>Delineation of the anti-scour plate’s impacted protection areas.</p>
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<p>Temporal development curve of relative scour depth.</p>
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<p>Evolution of open-channel flow scour hole: (<b>a</b>) initial scour stage and (<b>b</b>) equilibrium scour stage.</p>
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<p>Evolution of ice-covered flow (smooth) scour hole: (<b>a</b>) initial scour stage and (<b>b</b>) equilibrium scour stage.</p>
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<p>Evolution of ice-covered flow (rough) scour hole: (<b>a</b>) initial scour stage and (<b>b</b>) equilibrium scour stage.</p>
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<p>Schematic diagram of the basic shape of a scour hole.</p>
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<p>Single-pier protection.</p>
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<p>Tandem double-pier protection.</p>
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<p>Surface area of the maximum scour hole and its rate of reduction.</p>
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<p>Schematic of flow measurement lines around bridge piers.</p>
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<p>Quadrant event schematic.</p>
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<p>Schematic of flow velocity measurement points around bridge piers.</p>
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<p>Time series of pulsation velocity characteristics.</p>
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<p>Quadrant analysis and frequency histograms of burst events at different gauges around open-channel flow piers: (<b>a</b>–<b>d</b>) are a-line points, while (<b>e</b>–<b>h</b>) are b-line points.</p>
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<p>Quadrant analysis and frequency histograms of burst events at different gauges around open-channel flow piers: (<b>a</b>–<b>d</b>) are a-line points, while (<b>e</b>–<b>h</b>) are b-line points.</p>
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<p>Contributions to Reynolds stress from different gauge line bursts around open-channel flow piers: (<b>a</b>) line a and (<b>b</b>) line b.</p>
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<p>Quadrant analysis and frequency histograms of bursts at different gauge lines around ice-covered flow piers: (<b>a</b>–<b>d</b>) are a-line points, while (<b>e</b>–<b>h</b>) are b-line points.</p>
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<p>Quadrant analysis and frequency histograms of bursts at different gauge lines around ice-covered flow piers: (<b>a</b>–<b>d</b>) are a-line points, while (<b>e</b>–<b>h</b>) are b-line points.</p>
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<p>Contributions to Reynolds stress from different line-of-sight bursts around an ice-covered flow pier: (<b>a</b>) line a and (<b>b</b>) line b.</p>
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<p>Energy spectral analysis around open-channel flow piers: (<b>a</b>) upstream pier-front, (<b>b</b>) inter-pier, and (<b>c</b>) downstream pier-tail locations.</p>
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<p>Energy spectral analysis around open-channel flow piers: (<b>a</b>) upstream pier-front, (<b>b</b>) inter-pier, and (<b>c</b>) downstream pier-tail locations.</p>
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<p>Energy spectral analysis around ice-covered flow piers: (<b>a</b>) upstream pier-front, (<b>b</b>) inter-pier, and (<b>c</b>) downstream pier-tail locations.</p>
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<p>Normalized comparison of flow velocity components: (<b>a</b>) open-flow conditions versus (<b>b</b>) ice-covered conditions.</p>
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<p>Comparison of standard error distributions: (<b>a</b>–<b>c</b>) are downstream velocities and (<b>d</b>–<b>f</b>) are transverse velocities.</p>
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<p>Spectral densities around the bridge pier under open-flow conditions: (<b>a</b>) power spectrum and (<b>b</b>) cumulative power spectrum.</p>
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<p>Spectral densities around the bridge pier under ice-covered conditions: (<b>a</b>) power spectrum and (<b>b</b>) cumulative power spectrum.</p>
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14 pages, 2377 KiB  
Article
Severe Convection at Burgas Airport: Case Study 17 September 2022
by Bilyana Kostashki, Rosen Penchev and Guergana Guerova
Remote Sens. 2024, 16(21), 4012; https://doi.org/10.3390/rs16214012 - 29 Oct 2024
Viewed by 815
Abstract
Convection monitoring and forecasting are crucial for air traffic management as they can lead to the development of intense thunderstorms and hazards such as severe turbulence and icing, lightning activity, microbursts and hail that affect aviation safety. The airport of Burgas is located [...] Read more.
Convection monitoring and forecasting are crucial for air traffic management as they can lead to the development of intense thunderstorms and hazards such as severe turbulence and icing, lightning activity, microbursts and hail that affect aviation safety. The airport of Burgas is located in southeast Bulgaria on the Black Sea coast and occurrences of intense thunderstorms are mainly observed in the warm season between May and September. This work presents an analysis of severe convection over southeast Bulgaria on 17 September 2022. In the late afternoon, a gust front was formed that reached the Burgas airport with a wind speed exceeding 45 m/s, the record for the past 50 years, damaging the instrument landing system of the airport. To analyse the severe weather conditions, we combine state-of-the-art observations from satellite and radar with the upper-air sounding and surface. The studied period was dominated by the presence of a very unstable air mass over southeast Bulgaria ahead of the atmospheric front. As convection developed and moved east towards Burgas, it had four characteristics of severe deep convection, including gravitational waves at the overshooting cloud top, a cold U-shape, a flanking line and a cloud top temperature below −70 °C. The positive integrated water vapour (IWV) rate of change preceded the lightning activity peak by 30 min. Analysis of integrated vapour transport (IVT) gives higher values by a factor of two compared to climatology associated with the atmospheric river covering the eastern Mediterranean sea. Full article
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<p>Map of Bulgaria and position of Staro Selo GNSS station (black circle), Varna weather radar (blue circle) and Burgas airport (red circle).</p>
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<p>(<b>a</b>) Mean sea level pressure (black lines) at 12:00 UTC on 17 September 2022. (<b>b</b>) Thickness chart at 12:00 UTC on 17 September 2022, with 500 hPa geopotential height (black line), 500 hPa isotherm (dashed line) and thickness of the air layer between 1000 and 500 hPa (colours).</p>
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<p>Skew-t thermodiagram at Burgas airport on 17 September at 12:00 UTC.</p>
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<p>13:26 UTC Mode-S (<b>a</b>) hodograph (blue line) from surface (black arrow) to 2500 m (black arrow in circle) and (<b>b</b>) vertical wind data.</p>
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<p>Radar reflectivity at 14:00 UTC. (<b>a</b>) Radar reflectivity cross-section at 2 km height (dBz) and (<b>b</b>) Z max product from Varna weather radar.</p>
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<p>MSG images of (<b>a</b>) 6.2 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m water vapour at 12:30 UTC and (<b>b</b>) 6.2 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m water vapour at 13:30 UTC. (<b>c</b>) “sandwich” satellite product for East Bulgaria at 13:30 UTC with shown storm elements.</p>
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<p>Detected lightning by LINET at (<b>a</b>) 12:30 UTC and (<b>b</b>) 13:30 UTC.</p>
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<p>(<b>a</b>) IWV values (black line with dots) between 10:00 and 15:00 UTC and number of lightning strikes (grey bars). (<b>b</b>) IWV gradient every 15 min vs. number of lightning strikes.</p>
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<p>(<b>a</b>) IWV values (black line with dots) between 10:00 and 15:00 UTC and number of lightning strikes (grey bars). (<b>b</b>) IWV gradient every 15 min vs. number of lightning strikes.</p>
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<p>(<b>a</b>) Map of IVT index on 17 September 2022 at 12:00 UTC. (<b>b</b>) Mean IVT index for 12:00 UTC on 17 September 1992–2022. (<b>c</b>) IVT anomaly for 12:00 UTC on 17 September 2022. Values of 250 kg/ms are shown by red isoline.</p>
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<p>(<b>a</b>) Map of IVT index on 17 September 2022 at 12:00 UTC. (<b>b</b>) Mean IVT index for 12:00 UTC on 17 September 1992–2022. (<b>c</b>) IVT anomaly for 12:00 UTC on 17 September 2022. Values of 250 kg/ms are shown by red isoline.</p>
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<p>17 September 12 UTC IVT vertical profiles for 1992–2021 (gray line with dots), and mean over the period 1992–2022 (red line with dots) and 2022 (blue line with dots).</p>
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17 pages, 8181 KiB  
Article
Frequency–Time Domain Analysis Based on Electrochemical Noise of Dual-Phase (DP) and Ferrite–Bainite (FB) Steels in Chloride Solutions for Automotive Applications
by Facundo Almeraya-Calderón, Marvin Montoya-Rangel, Demetrio Nieves-Mendoza, Jesús Manuel Jáquez-Muñoz, Miguel Angel Baltazar-Zamora, Laura Landa-Ruiz, Maria Lara-Banda, Erick Maldonado-Bandala, Francisco Estupiñan-Lopez and Citlalli Gaona-Tiburcio
Metals 2024, 14(11), 1208; https://doi.org/10.3390/met14111208 - 23 Oct 2024
Cited by 1 | Viewed by 946
Abstract
The automotive industry uses high-strength (HS), low-alloy (HSLA) steels and advanced high-strength steels (AHSSs) to manufacture front and rear rails and safety posts, as well as the car body, suspension, and chassis components of cars. These steels can be exposed to corrosive environments, [...] Read more.
The automotive industry uses high-strength (HS), low-alloy (HSLA) steels and advanced high-strength steels (AHSSs) to manufacture front and rear rails and safety posts, as well as the car body, suspension, and chassis components of cars. These steels can be exposed to corrosive environments, such as in countries where de-icing salts are used. This research aims to characterize the corrosion behavior of AHSSs based on electrochemical noise (EN) [dual-phase (DP) and ferrite–bainite (FB)]. At room temperature, the steels were immersed in NaCl, CaCl2, and MgCl2 solutions and were studied by frequency–time domain analysis using wavelet decomposition, Hilbert–Huang analysis, and recurrence plots (RPs) related to the corrosion process and noise impedance (Zn). Optical microscopy (OM) was used to observe the microstructure of the tested samples. The results generally indicated that the main corrosion process is related to uniform corrosion. The corrosion behavior of AHSSs exposed to a NaCl solution could be related to the morphology of the phase constituents that are exposed to solutions with chlorides. The Zn results showed that DP780 presented a higher corrosion resistance with 918 Ω·cm2; meanwhile, FB780 presented 409 Ω·cm2 when exposed to NaCl. Also, the corrosion mechanism of materials begins with a localized corrosion process spreading to all the surfaces, generating a uniform corrosion process after some exposition time. Full article
(This article belongs to the Special Issue Recent Advances in Corrosion and Protection of Metallic Materials)
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<p>Classification of advanced high-strength steels (AHSSs).</p>
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<p>Elongation (%) vs. tensile strength (MPa) banana diagrams for the different types of steels.</p>
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<p>Three-electrode cell for electrochemical noise (EN) measurements.</p>
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<p>Microstructures of (<b>a</b>) DP780 and (<b>b</b>) FB780 steels by scanning electron microscopy (SEM).</p>
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<p>Noise impedance (Z<sub>n</sub>) for DP780 in different electrolytes.</p>
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<p>Noise impedance (Z<sub>n</sub>) for FB780 in different electrolytes.</p>
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<p>Energy dispersion plot of DP780 alloy.</p>
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<p>Energy dispersion plot of FB780 alloy.</p>
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<p>Recurrence plots, Hilbert specter, and microscopy analysis for DP780 exposed to (<b>a</b>) NaCl, (<b>b</b>) MgCl<sub>2</sub>, and (<b>c</b>) CaCl<sub>2</sub>.</p>
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<p>Recurrence plots, Hilbert specter, and microscopy analysis for DP780 exposed to (<b>a</b>) NaCl, (<b>b</b>) MgCl<sub>2</sub>, and (<b>c</b>) CaCl<sub>2</sub>.</p>
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<p>Recurrence plots, Hilbert specter, and microscopy analysis for FB780 exposed to (<b>a</b>) NaCl, (<b>b</b>) MgCl<sub>2</sub>, and (<b>c</b>) CaCl<sub>2</sub>.</p>
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<p>Recurrence plots, Hilbert specter, and microscopy analysis for FB780 exposed to (<b>a</b>) NaCl, (<b>b</b>) MgCl<sub>2</sub>, and (<b>c</b>) CaCl<sub>2</sub>.</p>
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<p>Schematic representation of corrosion in (<b>a</b>) ferrite–bainite FB780 steel/CaCl<sub>2</sub>-MgCl<sub>2</sub>, (<b>b</b>) dual-phase DP780 steel/test solutions.</p>
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20 pages, 9945 KiB  
Article
Analysis of the Meteorological Conditions and Atmospheric Numerical Simulation of an Aircraft Icing Accident
by Haoya Liu, Shurui Peng, Rong Fang, Yaohui Li, Lian Duan, Ten Wang, Chengyan Mao and Zisheng Lin
Atmosphere 2024, 15(10), 1222; https://doi.org/10.3390/atmos15101222 - 14 Oct 2024
Viewed by 1372
Abstract
With the rapid development of the general aviation industry in China, the influence of high-impact aeronautical weather events, such as aircraft icing, on flight safety has become more and more prominent. On 1 March 2021, an aircraft conducting weather modification operations crashed over [...] Read more.
With the rapid development of the general aviation industry in China, the influence of high-impact aeronautical weather events, such as aircraft icing, on flight safety has become more and more prominent. On 1 March 2021, an aircraft conducting weather modification operations crashed over Ji’an City, due to severe icing. Using multi-source meteorological observations and atmospheric numerical simulations, we analyzed the meteorological causes of this icing accident. The results indicate that a cold front formed in northwestern China and then moved southward, which is the main weather system in the icing area. Based on the icing index, we conducted an analysis of the temperature, relative humidity, cloud liquid water path, effective particle radius, and vertical flow field, it was found that aircraft icing occurred behind the ground front, where warm-moist airflows rose along the front to result in a rapid increase of water vapor in 600–500 hPa. The increase of water vapor, in conjunction with low temperature, led to the formation of a cold stratiform cloud system. In this cloud system, there were a large number of large cloud droplets. In addition, the frontal inversion increased the atmospheric stability, allowing cloud droplets to accumulate in the low-temperature region and forming meteorological conditions conducive to icing. The Weather Research and Forecasting model was employed to provide a detailed description of the formation process of the atmospheric conditions conducive to icing, such as the uplifting motion along the front and supercooled water. Based on a real case, we investigated the formation process of icing-inducing meteorological conditions under the influence of a front in detail in this study and verified the capability of a numerical model to simulate the meteorological environment of frontal icing, in order to provide a valuable reference for meteorological early warnings and forecasts for general aviation. Full article
(This article belongs to the Special Issue Advance in Transportation Meteorology (2nd Edition))
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<p>Simulation region by using the Weather Research and Forecasting (WRF) model. D1 and D2 denote the outer and inner nested grids, with spatial resolutions of 9 km and 3 km, respectively, and the red triangle represents the location of the aircraft icing accident.</p>
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<p>Atmospheric circulation situation before and at the time of the icing accident: (<b>a</b>–<b>c</b>) 550 hPa and (<b>d</b>–<b>f</b>) 850 hPa geopotential height, temperature, and wind fields, as well as (<b>g</b>–<b>i</b>) surface pressure and temperature fields at (<b>a</b>,<b>d</b>,<b>g</b>) 20:00 BJT (Beijing Standard Time) on 28 February, (<b>b</b>,<b>e</b>,<b>h</b>) 08:00 BJT on 1 March and (<b>c</b>,<b>f</b>,<b>i</b>) 15:00 BJT on 1 March. The blue lines in (<b>a</b>–<b>f</b>) represent the isoheight contours (interval of 4, unit: 10 gpm). The green shaded areas in (<b>a</b>–<b>c</b>) indicate the relative humidity larger than 60%, the purple shaded areas in (<b>d</b>–<b>f</b>) denote the wind speed larger than 12 m/s, and the blue lines in (<b>g</b>–<b>i</b>) represent the isobars (interval of 5, unit: hPa). “H” and “L” represent the high- and low-pressure centers, respectively. The red contours indicate the isotherms (interval of 4, unit: °C), where solid and dotted lines denote the positive and negative values. The red triangles in (<b>g</b>–<b>i</b>) show the location of the aircraft icing accident.</p>
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<p>Spatial distribution of the 550 hPa Ic index at (<b>a</b>) 23:00 BJT on 28 February, (<b>b</b>) 07:00 BJT on 1 March, and (<b>c</b>) 15:00 BJT on 1 March, and time-longitude profiles of the (<b>d</b>) Ic index, (<b>e</b>) temperature and (<b>f</b>) relative humidity at 550 hPa. The green short line in (<b>a</b>) shows the latitudinal position of the time-longitude profiles in (<b>d</b>–<b>f</b>), the black triangles represent the accident location, and the green solids and dotted lines in (<b>d</b>–<b>f</b>) show the longitude of the accident location and the time of the accident, respectively.</p>
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<p>Altitude-time profiles of the (<b>a</b>) Ic index, (<b>b</b>) temperature, and (<b>c</b>) relative humidity. The green solid lines indicate the time of the icing accident, and the red dotted lines represent the heights of 600 hPa and 500 hPa.</p>
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<p>(<b>a</b>) Past 24 h and (<b>b</b>) 3 h temperature difference at 15:00 BJT on 1 March. The black triangles represent the location of the icing accident.</p>
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<p>Spatial distributions of the (<b>a</b>) cloud liquid water path, (<b>b</b>) effective particle radius, (<b>c</b>) cloud top temperature, and (<b>d</b>) optical thickness from the Moderate-resolution Imaging Spectroradiometer observations on 1 March.</p>
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<p>Skew-T Log-P diagram over Nanchang station at 08:00 BJT on 1 March. The thick black line indicates the ambient temperature profile, the thick blue line shows the dew point temperature profile, the red dotted line is the state curve, the gray lines are the isotherms, the brown lines are the dry adiabatic lines, and the green lines are the moist adiabatic lines.</p>
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<p>Height–latitude profiles of the meridional circulation (black streamline) superimposed on the (<b>a</b>) vertical velocity, (<b>b</b>) pseudo-equivalent potential temperature, and (<b>c</b>) water vapor flux across Ji’an City at 15:00 BJT on 1 March. The green lines show the latitude of the accident location, and the red dotted lines show the heights of 600 hPa and 500 hPa.</p>
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<p>(<b>a</b>) Spatial distribution of the simulated Ic index at 500 hPa at 15:00 BJT on 1 March, and the meridional vertical sections (along the green line in (<b>a</b>)) of the corresponding (<b>b</b>) temperature (black solid lines), relative humidity, and (<b>c</b>) vertical velocity over the accident location. The green solid lines in (<b>b</b>,<b>c</b>) represent the latitude of the accident location, and the red dotted lines in (<b>b</b>,<b>c</b>) show the heights of 600 hPa and 500 hPa.</p>
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<p>Meridional vertical section of the simulated (<b>a</b>) Ic index, (<b>b</b>) liquid water content, and (<b>c</b>) ice water content over the accident location at 15:00 BJT on 1 March.</p>
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<p>Same as <a href="#atmosphere-15-01222-f010" class="html-fig">Figure 10</a>b, but with superimposed (<b>a</b>) potential temperature and (<b>b</b>) pseudo-equivalent potential temperature.</p>
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<p>Altitude–time cross-sections of the simulated (<b>a</b>) Ic index, (<b>b</b>) vertical water vapor flux, (<b>c</b>) liquid water content, and (<b>d</b>) ice water content in Ji’an City from 07:00 BJT to 19:00 BJT on 1 March. The green solid lines represent the time of the accident, and the red dotted lines show the altitudes of 600 hPa and 500 hPa.</p>
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<p>Conceptual model of the weather conditions for aircraft icing formation under the influence of a cold front.</p>
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16 pages, 2014 KiB  
Article
Somatostatin Receptor Type 2 as a Potential Marker of Local Myocardial Inflammation in Myocardial Infarction: Morphologic Data on Distribution in Infarcted and Normal Human Myocardium
by Vyacheslav V. Ryabov, Andrey A. Trusov, Maria A. Kercheva, Aleksandra E. Gombozhapova, Julia N. Ilyushenkova, Ivan V. Stepanov, Mikhail V. Fadeev, Anna G. Syrkina and Svetlana I. Sazonova
Biomedicines 2024, 12(10), 2178; https://doi.org/10.3390/biomedicines12102178 - 25 Sep 2024
Viewed by 1130
Abstract
Nuclear imaging modalities can detect somatostatin receptor type 2 (SSTR2) in vivo as a potential marker of local post-MI inflammation. SSTR2+ macrophages are thought to be the main substrate for SSTR-targeted radioimaging. However, the distribution of SSTR2+ cells in the MI patients’ myocardium [...] Read more.
Nuclear imaging modalities can detect somatostatin receptor type 2 (SSTR2) in vivo as a potential marker of local post-MI inflammation. SSTR2+ macrophages are thought to be the main substrate for SSTR-targeted radioimaging. However, the distribution of SSTR2+ cells in the MI patients’ myocardium is unknown. Using immunohistochemistry, we investigated the distribution of SSTR2+ cells in the myocardium of patients who died during the MI inflammatory phase (n = 7) compared to the control group of individuals with fatal trauma (n = 3). Inflammatory cellular landscapes evolve in a wave front-like pattern, so we divided the myocardium into histological zones: the infarct core (IC), the border zone (BZ), the remote zone (RZ), and the peri-scar zone (PSZ). The number of SSTR2+ neutrophils (NPs), SSTR2+ monocytes/macrophages (Mos/MPs), and SSTR2+ vessels were counted. In the myocardium of the control group, SSTR2+ NPs and SSTR2+ Mos/MPs were occasional, SSTR2+ vessels were absent. In the RZ, the picture was similar to the control group, but there was a lower number of SSTR2+ Mos/MPs in the RZ. In the PSZ, SSTR2+ vessel numbers were highest in the myocardium. In the IC, the median number of SSTR2+ NPs was 200 times higher compared to the RZ or control group myocardium, which may explain the selective uptake of SSTR-targeted radiotracers in the MI area during the inflammatory phase of MI. Full article
(This article belongs to the Special Issue Molecular Insights into Myocardial Infarction)
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<p>Immunohistochemical images of SSTR2 in myocardial tissue of patients with MI and myocardium of control group. IHC staining with anti-somatostatin receptor subtype-2 antibody (Clone UMB-1; Abcam, Cambridge, MA, USA), work dilution 1:100, counterstained with hematoxylin. Scale bar: 60 μm. (<b>A</b>) Infarct core, inflammatory phase (Patient No. 1). Coagulative necrosis of cardiomyocytes with SSTR2+ NP infiltration (brown positive staining, black arrows), in smaller quantities—SSTR2+ Mos (white arrows). (<b>B</b>) Border zone, inflammatory phase (Patient No. 1). Viable cardiomyocytes, marked interstitial edema. A small number of SSTR2+ NPs (black arrows), SSTR2+ Mos (white arrow). Perinuclear lipofuscin granules in cardiomyocytes (dashed arrows). (<b>C</b>) Remote zone, inflammatory phase (Patient No. 1). Intact cardiomyocytes. There are no SSTR2+ cells in the myocardial tissue itself. Single SSTR2+ NP (black arrows), SSTR2+ Mo (white arrow) in the lumen of the capillaries. Perinuclear lipofuscin granules in cardiomyocytes (dashed arrows). (<b>D</b>) Myocardium tissue of the control group. Intact cardiomyocytes with preserved nuclei and transverse strips. Perinuclear lipofuscin granules in cardiomyocytes (dashed arrows). An SSTR2+ MP in the upper left part of the slide (white arrow). (<b>E</b>) Peri-scar zone (Patient No. 3). In the upper part of the slide there is mature connective tissue, in the lower part there are hypertrophied cardiomyocytes. In the slide center there is a vessel (red circle) with SSTR2-positive outer layer of cells (pericytes) and SSTR-negative inner layer of cells (endotheliocytes). There are two SSTR-negative vessels above with SSTR2+ NPs in the left vessel lumen (black arrows). (<b>F</b>) Peri-scar zone (Patient No. 3). In the lower right part of the slide there is mature connective tissue, in the upper part there are cardiomyocytes. Many SSTR2+ microvessels are visible (red circles). Numerous hemosiderin-laden macrophages with brown granules in cytoplasm are SSTR2-negative (green arrows). (<b>G</b>) Pancreas. Langerhans islets of the human pancreas were used as a positive control, while the exocrine part of the pancreas was used as a negative control. IC—infarct core; BZ—border zone; RZ–remote zone; PSZ—peri-scar zone; NP–neutrophil; Mo–monocyte; MP–macrophage.</p>
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<p>Number of SSTR2+ cells relative to histological zones. (<b>A</b>) Infarct core; (<b>B</b>) border zone; (<b>C</b>) remote zone; (<b>D</b>) myocardium of the control group. Data are presented as box plot with median, 25th–75th percentiles (boxes), and minimum-maximum (whiskers). NPs–neutrophils; Mos/MPs–monocytes/macrophages; FOV–field of view.</p>
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<p>Quantification of SSTR2+ cells in different MI zones in inflammatory phase of MI. (<b>A</b>) General picture of SSTR2+ cell distribution in MI zones and control myocardium. (<b>B</b>) Distribution of SSTR2+ neutrophils. (<b>C</b>) Distribution of SSTR2+ monocytes/macrophages. (<b>D</b>) Distribution of SSTR2+ vessels. Data are presented as box plot with median, 25th–75th percentiles (boxes) and minimum-maximum (whiskers). IC—Infarct core; BZ—border zone; RZ—remote zone; FOV—field of view.</p>
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12 pages, 4492 KiB  
Article
Numerical Simulation of Water Migration during Soil Freezing and Its Resulting Characterization
by Bicheng Zhou, Anatoly V. Brouchkov, Lidia I. Eremina, Chunguang Xu and Jiabo Hu
Appl. Sci. 2024, 14(18), 8210; https://doi.org/10.3390/app14188210 - 12 Sep 2024
Viewed by 832
Abstract
Water migration behavior is the main cause of engineering disasters in cold regions, making it essential to understand its mechanisms and the resulting mechanical characteristics for engineering protection. This study examined the water migration process during soil freezing through both experimental and numerical [...] Read more.
Water migration behavior is the main cause of engineering disasters in cold regions, making it essential to understand its mechanisms and the resulting mechanical characteristics for engineering protection. This study examined the water migration process during soil freezing through both experimental and numerical simulations, focusing on the key mechanical outcomes such as deformation and pore water pressure. Initially, a series of controlled unidirectional freezing experiments were performed on artificial kaolin soil under various freezing conditions to observe the water migration process. Subsequently, a numerical model of water migration was formulated by integrating the partial differential equations of heat and mass transfer. The model’s boundary conditions and relevant parameters were derived from both the experimental processes and existing literature. The findings indicate that at lower clay water content, the experimental results align closely with those of the model. Conversely, at higher water content, the modeled results of frost heaving were less pronounced than the experimental outcomes, and the freezing front advanced more slowly. This discrepancy is attributed to the inability of unfrozen water to penetrate once ice lenses form, causing migrating water to accumulate and freeze at the warmest ice lens front. This results in a higher ice content in the freezing zone than predicted by the model, leading to more significant freezing expansion. Additionally, the experimental observations of pore water pressure under freeze–thaw conditions corresponded well with the trends and peaks projected by the simulation results. Full article
(This article belongs to the Topic Applied Heat Transfer)
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Figure 1
<p>The apparatus of the one-dimensional freezing test.</p>
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<p>Diagram of water migration at freezing fringe.</p>
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<p>Modeling flowchart. (<b>a</b>) Experimental photo of frozen soil column. (<b>b</b>) Schematic diagram of soil column structure. (<b>c</b>) Modeled temperature contour.</p>
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<p>Comparison between simulation results and experimental data: the relationship between frost heaving and freezing depth over time. Here, sample (<b>a</b>) weight water content was 56%, temperature of cold end was <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math> °C; (<b>b</b>) weight water content was 50%, temperature of cold end was <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math> °C; (<b>c</b>) weight water content was 50%, temperature of cold end was <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math> °C.</p>
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<p>Schematic diagram of the pore pressure after freezing of the kaolin clay column, with the cold end temperature at −5 °C, initial weight water content of 50%. (<b>a</b>) Within 5 h of freezing, no ice lenses were generated; (<b>b</b>) after 20 h of freezing, ice lenses were generated.</p>
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<p>Comparison between simulation results and experimental data: The change mode of pore water pressure during the freezing and thawing of Qinghai–Tibet clay sample SC2.</p>
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18 pages, 1914 KiB  
Article
Transient Shallow Water Wave Interactions with a Partially Fragmented Ice Shelf
by Faraj Alshahrani, Michael H. Meylan and Ben Wilks
Fluids 2024, 9(8), 192; https://doi.org/10.3390/fluids9080192 - 21 Aug 2024
Viewed by 702
Abstract
This work investigates the interaction between water waves and multiple ice shelf fragments in front of a semi-infinite ice sheet. The hydrodynamics are modelled using shallow water wave theory and the ice shelf vibration is modelled using Euler–Bernoulli beam theory. The ensuing multiple [...] Read more.
This work investigates the interaction between water waves and multiple ice shelf fragments in front of a semi-infinite ice sheet. The hydrodynamics are modelled using shallow water wave theory and the ice shelf vibration is modelled using Euler–Bernoulli beam theory. The ensuing multiple scattering problem is solved in the frequency domain using the transfer matrix method. The appropriate conservation of energy identity is derived in order to validate our numerical calculations. The transient scattering problem for incident wave packets is constructed from the frequency domain solutions. By incorporating multiple scattering, this paper extends previous models that have only considered a continuous semi-infinite ice shelf. This paper serves as a fundamental step towards developing a comprehensive model to simulate the breakup of ice shelves. Full article
(This article belongs to the Section Mathematical and Computational Fluid Mechanics)
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Figure 1
<p>Schematic showing the proposed sequential ice shelf breakup model. The upper panel shows the initial state in which there is a semi-infinite ice shelf. The middle and lower panels show the state after the first and second breakup events, respectively.</p>
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<p>Schematic of a finite ice shelf fragment.</p>
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<p>Schematic of the problem for a semi-infinite ice shelf.</p>
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<p>Schematic of the problem of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> ice shelf fragments in front of a semi-infinite ice shelf.</p>
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<p>Diagram outlining the transfer matrix method for the problem of <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> ice shelf fragments in front of a semi-infinite ice shelf; the transfer matrix relates amplitudes to the left and right of the ice shelf. The amplitude to the right of ice shelf 1 is related to the amplitude to the left of ice shelf 2 via the propagating matrix, which is <math display="inline"><semantics> <mrow> <msub> <mi>P</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>=</mo> <mfenced open="(" close=")"> <mtable> <mtr> <mtd> <msup> <mi>e</mi> <mrow> <mo>−</mo> <mi mathvariant="normal">i</mi> <mi>k</mi> <mo>(</mo> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </msup> </mtd> <mtd> <mn>0</mn> </mtd> </mtr> <mtr> <mtd> <mn>0</mn> </mtd> <mtd> <msup> <mi>e</mi> <mrow> <mi mathvariant="normal">i</mi> <mi>k</mi> <mo>(</mo> <msub> <mi>x</mi> <mrow> <mi>i</mi> <mo>+</mo> <mn>1</mn> </mrow> </msub> <mo>−</mo> <msub> <mi>x</mi> <mi>i</mi> </msub> <mo>)</mo> </mrow> </msup> </mtd> </mtr> </mtable> </mfenced> <mo>,</mo> </mrow> </semantics></math> where <math display="inline"><semantics> <mrow> <mi>i</mi> <mo>=</mo> <mn>1</mn> <mo>,</mo> <mn>2</mn> <mo>,</mo> <mo>…</mo> <mo>,</mo> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math>. Repeating this process, we can obtain all the amplitudes in <math display="inline"><semantics> <msub> <mi mathvariant="normal">Ω</mi> <mi>F</mi> </msub> </semantics></math>, and then we can recover the amplitudes under the ice. We seek to construct the right-to-left transfer matrix, so we assume that <math display="inline"><semantics> <mrow> <msubsup> <mi>R</mi> <mn>1</mn> <mrow> <mo>(</mo> <mn>1</mn> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <mn>1</mn> </mrow> </semantics></math>, and then we construct the transfer matrix for the whole array and we solve for <math display="inline"><semantics> <mrow> <msubsup> <mi>α</mi> <mn>3</mn> <mrow> <mo>(</mo> <mi>N</mi> <mo>)</mo> </mrow> </msubsup> <mo>=</mo> <mstyle scriptlevel="0" displaystyle="true"> <mfrac> <mn>1</mn> <msub> <mi>s</mi> <mn>11</mn> </msub> </mfrac> </mstyle> </mrow> </semantics></math>, where <math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mn>1</mn> </msub> <msub> <mi>P</mi> <mn>2</mn> </msub> <msub> <mi>T</mi> <mn>2</mn> </msub> <mo>…</mo> <msub> <mi>P</mi> <mi>N</mi> </msub> <mi>M</mi> <mo>=</mo> <mi>S</mi> </mrow> </semantics></math>. We can determine the amplitudes between the ice shelves using (12) for the <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> ice shelf and (21) for the semi-infinite ice shelf. Once we have obtained those amplitudes, the remaining unknown coefficients can be recovered using (11) for the <math display="inline"><semantics> <mrow> <mi>N</mi> <mo>−</mo> <mn>1</mn> </mrow> </semantics></math> ice shelf and (20) for the semi-infinite ice shelf.</p>
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<p>Time-dependent displacement of the water wave–ice shelf system due to an incident Gaussian wave packet. The blue line segments indicate the displacement of the free surface of the water, while the red line segments indicate the vertical displacement of the ice, with a thickness of <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> m and a same length of 40 km and 20 km water gaps, showing how the ice shelves respond to wave forces.</p>
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<p>As in <a href="#fluids-09-00192-f006" class="html-fig">Figure 6</a>, with <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> m.</p>
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<p>As in <a href="#fluids-09-00192-f006" class="html-fig">Figure 6</a>, with <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> m.</p>
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<p>As in <a href="#fluids-09-00192-f006" class="html-fig">Figure 6</a>, with <math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>150</mn> </mrow> </semantics></math> m.</p>
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<p>The propagation of water waves over ice shelves of varying thicknesses. Panels (<b>a</b>,<b>b</b>) depict the transmitted and reflected energy for a thin ice shelf (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>20</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>), showing significant transmission of wave energy. Panels (<b>c</b>,<b>d</b>) illustrate a thicker ice shelf (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>50</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>), with a balance of transmitted and reflected energy. Panels (<b>e</b>,<b>f</b>) show a substantially thicker ice shelf (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>100</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>), resulting in reduced transmitted energy. Panels (<b>g</b>,<b>h</b>) depict the very thick ice shelf (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>150</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>), which strongly reflects wave energy, demonstrating significant wave attenuation.</p>
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<p>The propagation of water waves over ice shelves of varying thicknesses is illustrated as follows. Panels (<b>a</b>,<b>b</b>) show the reflected energy for a thin ice shelf with a thickness of (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>20</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). Panels (<b>c</b>,<b>d</b>) depict the reflected energy for a moderately thick ice shelf with a thickness of (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>50</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). Panels (<b>e</b>,<b>f</b>) present the reflected energy for a significantly thicker ice shelf with a thickness of (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>100</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). Panels (<b>g</b>,<b>h</b>) illustrate the reflected energy for a very thick ice shelf with a thickness of (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>150</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). The figures on the left have a lower value for Young’s modulus compared to those on the right, as shown in the figure title.</p>
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<p>The propagation of water waves over ice shelves of varying thicknesses is illustrated as follows. Panels (<b>a</b>,<b>b</b>) show the reflected energy for a thin ice shelf with a thickness of (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>20</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). Panels (<b>c</b>,<b>d</b>) depict the reflected energy for a moderately thick ice shelf with a thickness of (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>50</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). Panels (<b>e</b>,<b>f</b>) present the reflected energy for a significantly thicker ice shelf with a thickness of (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>100</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). Panels (<b>g</b>,<b>h</b>) illustrate the reflected energy for a very thick ice shelf with a thickness of (<math display="inline"><semantics> <mrow> <mi>h</mi> <mo>=</mo> <mn>150</mn> <mspace width="3.33333pt"/> <mi mathvariant="normal">m</mi> </mrow> </semantics></math>). Panels (<b>a</b>,<b>c</b>,<b>g</b>) show the reflected energy for multiple ice shelves, while Panels (<b>b</b>,<b>d</b>,<b>f</b>) represent the reflected energy for semi-infinite ice shelves.</p>
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29 pages, 23715 KiB  
Article
Forecasting In-Flight Icing over Greece: Insights from a Low-Pressure System Case Study
by Petroula Louka, Ioannis Samos and Flora Gofa
Atmosphere 2024, 15(8), 990; https://doi.org/10.3390/atmos15080990 - 17 Aug 2024
Viewed by 1525
Abstract
Forecasting in-flight icing conditions is crucial for aviation safety, particularly in regions with variable and complex meteorological configurations, such as Greece. Icing accretion onto the aircraft’s surfaces is influenced by the presence of supercooled water in subfreezing environments. This paper outlines a methodology [...] Read more.
Forecasting in-flight icing conditions is crucial for aviation safety, particularly in regions with variable and complex meteorological configurations, such as Greece. Icing accretion onto the aircraft’s surfaces is influenced by the presence of supercooled water in subfreezing environments. This paper outlines a methodology of forecasting icing conditions, with the development of the Icing Potential Algorithm that takes into consideration the meteorological scenarios related to icing accretion, using state-of-the-art Numerical Weather Prediction model results, and forming a fuzzy logic tree based on different membership functions, applied for the first time over Greece. The synoptic situation of an organized low-pressure system passage, with occlusion, cold and warm fronts, over Greece that creates dynamically significant conditions for icing formation was investigated. The sensitivity of the algorithm was revealed upon the precipitation, cloud type and vertical velocity effects. It was shown that the greatest icing intensity is associated with single-layer ice and multi-layer clouds that are comprised of both ice and supercooled water, while convectivity and storm presence lead to also enhancing the icing formation. A qualitative evaluation of the results with satellite, radar and METAR observations was performed, indicating the general agreement of the method mainly with the ground-based observations. Full article
(This article belongs to the Special Issue Numerical Weather Prediction Models and Ensemble Prediction Systems)
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Figure 1
<p>The membership functions for (<b>a</b>) temperature; (<b>b</b>) cloud top temperature; (<b>c</b>) relative humidity; (<b>d</b>) 3haccumulative precipitation; (<b>e</b>) vertical velocity and (<b>f</b>) cloud liquid water content. Membership functions from (<b>a</b>–<b>e</b>) were adopted from [<a href="#B12-atmosphere-15-00990" class="html-bibr">12</a>], while membership function (<b>f</b>) was adopted from [<a href="#B13-atmosphere-15-00990" class="html-bibr">13</a>].</p>
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<p>The membership functions for (<b>a</b>) temperature; (<b>b</b>) cloud top temperature; (<b>c</b>) relative humidity; (<b>d</b>) 3haccumulative precipitation; (<b>e</b>) vertical velocity and (<b>f</b>) cloud liquid water content. Membership functions from (<b>a</b>–<b>e</b>) were adopted from [<a href="#B12-atmosphere-15-00990" class="html-bibr">12</a>], while membership function (<b>f</b>) was adopted from [<a href="#B13-atmosphere-15-00990" class="html-bibr">13</a>].</p>
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<p>Flowchart of the IPA.</p>
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<p>Integration grid of the (<b>a</b>) COSMO=GR4 (4 km grid spacing) and (<b>b</b>) COSMO-GR1 (1 km grid spacing) models, showing the orography from low heights (blue color) to large heights (red color).</p>
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<p>UKMO analysis charts: (<b>a</b>) 12 March 2019 12UTC; (<b>b</b>) 12 March 2019 18UTC; and (<b>c</b>) 13 March 2019 00UTC (from <a href="https://www1.wetter3.de/archiv_ukmet_dt.html" target="_blank">https://www1.wetter3.de/archiv_ukmet_dt.html</a>, accessed on 30 September 2022).</p>
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<p>COSMO-GR predictions of (<b>a</b>) temperature and (<b>b</b>) relative humidity at 20,000 ft and (<b>c</b>) the 0 °C isotherm height on 12 March 2019 12UTC.</p>
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<p>IMERG 24 h precipitation on 12 March 2019. LGBL (Nea Aghialos) airport is shown with the black dot at the central continental Greece.</p>
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<p>Cloud mask field estimated by the IPA: (<b>a</b>) 12 March 2019 12UTC; (<b>b</b>) 12 March 2019 18UTC; and (<b>c</b>) 13 March 2019 00UTC.</p>
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<p>Cloud base height: (<b>a</b>) 12 March 2019 12UTC; (<b>b</b>) 12 March 2019 18UTC; (<b>c</b>) 13 March 2019 00UTC, and Cloud top height on: (<b>d</b>) 12 March 2019 12UTC; (<b>e</b>) 12 March 2019 18UTC; and (<b>f</b>) 13 March 2019 00UTC, as estimated by the IPA.</p>
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<p>Cloud base temperature: (<b>a</b>) 12 March 201912UTC; (<b>b</b>) 12 March 2019 18UTC; and (<b>c</b>) 13 March 2019 00UTC. Cloud top temperature: (<b>d</b>) 12 March 2019 12UTC; (<b>e</b>) 12 March 2019 18UTC; and (<b>f</b>) 13 March 2019 00UTC, as estimated by the IPA.</p>
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<p>IP<sub>S0</sub> estimated on 12 March 2019 12UTC at different flight levels: (<b>a</b>) 1000 ft; (<b>b</b>) 3000 ft; (<b>c</b>) 5000 ft; (<b>d</b>) 8000 ft; (<b>e</b>) 10,000 ft; (<b>f</b>) 15,000 ft; (<b>g</b>) 20,000 ft; (<b>h</b>) 25,000 ft; and (<b>i</b>) 30,000 ft.</p>
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<p>IP<sub>S0</sub> estimated on 12 March 2019 12UTC at different flight levels: (<b>a</b>) 1000 ft; (<b>b</b>) 3000 ft; (<b>c</b>) 5000 ft; (<b>d</b>) 8000 ft; (<b>e</b>) 10,000 ft; (<b>f</b>) 15,000 ft; (<b>g</b>) 20,000 ft; (<b>h</b>) 25,000 ft; and (<b>i</b>) 30,000 ft.</p>
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<p>Difference between IP<sub>S0</sub> and IP<sub>S2</sub> as calculated on 12 March 2019 12UTC at different flight levels: (<b>a</b>) 5000 ft; (<b>b</b>) 8000 ft; (<b>c</b>) 10,000 ft; (<b>d</b>) 15,000 ft; (<b>e</b>) 20,000 ft; and (<b>f</b>) 25,000 ft.</p>
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<p>Difference between IP<sub>S0</sub> and IP<sub>S2</sub> as calculated on 12 March 2019 12UTC at different flight levels: (<b>a</b>) 5000 ft; (<b>b</b>) 8000 ft; (<b>c</b>) 10,000 ft; (<b>d</b>) 15,000 ft; (<b>e</b>) 20,000 ft; and (<b>f</b>) 25,000 ft.</p>
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<p>Difference between IP<sub>S0</sub> and IP<sub>S3</sub> at (<b>a</b>) 5000 ft; (<b>b</b>) 10,000 ft; (<b>c</b>) 15,000 ft; between IP<sub>S0</sub> and IP<sub>S4</sub> at (<b>d</b>) 5000 ft; (<b>e</b>) 10,000 ft; (<b>f</b>) 15,000 ft; and between IP<sub>S0</sub> and IP<sub>S5</sub> at (<b>g</b>) 5000 ft; (<b>h</b>) 10,000 ft; and (<b>i</b>) 15,000 ft as calculated on 12 March 2019 12UTC.</p>
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<p>Cloud top heightfields as observed by the satellite (top): (<b>a</b>) 12 March 2019 12UTC; (<b>b</b>) 12 March 2019 18UTC; (<b>c</b>) 13 March 2019 00UTC and calculated by IPA (bottom) on: (<b>d</b>) 12 March 2019 12UTC; (<b>e</b>) 12 March 2019 18UTC; and (<b>f</b>) 13 March 2019 00UTC.</p>
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<p>Comparison of (<b>a</b>) satellite cloud phase fields, with the maximum icing severity fields corresponding to “none-light” (cyan), “moderate” (blue) and “severe” (light green) for (<b>b</b>) IP<sub>S0</sub>; (<b>c</b>) IP<sub>S2</sub>; and (<b>d</b>) IP<sub>S3</sub> on 12 March 2019 12UTC.</p>
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<p>Comparison of (<b>a</b>) satellite cloud phase fields, with the maximum icing severity fields corresponding to “none-light” (cyan), “moderate” (blue) and “severe” (light green) for (<b>b</b>) IP<sub>S0</sub>; (<b>c</b>) IP<sub>S2</sub>; and (<b>d</b>) IP<sub>S3</sub> on 12 March 2019 18UTC.</p>
Full article ">Figure 15 Cont.
<p>Comparison of (<b>a</b>) satellite cloud phase fields, with the maximum icing severity fields corresponding to “none-light” (cyan), “moderate” (blue) and “severe” (light green) for (<b>b</b>) IP<sub>S0</sub>; (<b>c</b>) IP<sub>S2</sub>; and (<b>d</b>) IP<sub>S3</sub> on 12 March 2019 18UTC.</p>
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<p>Comparison of (<b>a</b>) satellite cloud phase fields, with the maximum icing severity fields corresponding to “none-light” (cyan), “moderate” (blue) and “severe” (light green) for (<b>b</b>) IP<sub>S0</sub>; (<b>c</b>) IP<sub>S2</sub>; and (<b>d</b>) IP<sub>S3</sub> on 13 March 2019 00UTC.</p>
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<p>Radar reflectivity images on 12 March 2019 from Larissa radar: (<b>a</b>)1155 UTC and (<b>b</b>) 1758UTC. The bold lines indicate the cross sections 1 (left image) and 2 (right image) that were used for extracting further discussion, while the colored dots indicate the location of Larissa radar (white northern dot) and Nea Aghialos airport (black dot).</p>
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<p>Cross section of radar reflectivity fields (in dBz) from Larissa radar, the corresponding satellite CTH (purple line) and the estimated CTH from IPA (black line) on 12 March 2019 at (<b>a</b>) 12UTCand cross section 1 and (<b>b</b>) 18UTCand cross section 2.</p>
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<p>IP images at cross section 1 together with the corresponding satellite CTH (purple line on 12 March 2019 at 12UTC for (<b>a</b>) IP<sub>S0</sub>, (<b>b</b>) IP<sub>S2</sub> and (<b>c</b>) IP<sub>S3</sub>.</p>
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<p>IP images at the cross section 2 together with the corresponding satellite CTH (purple line on 12 March 2019 at 18UTC for (<b>a</b>) IP<sub>S0</sub>, (<b>b</b>) IP<sub>S2</sub> and (<b>c</b>) IP<sub>S3</sub>.</p>
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<p>Vertical velocity ω as predicted by COSMO-GR at (<b>a</b>) cross section 1 on 12 March 2019 at 12 UTC and (<b>b</b>) cross section 2 on 12 March 2019 at 18 UTC together with the corresponding satellite CTH (purple line). Negative values of ω correspond to upward motion of air.</p>
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<p>Vertical profiles at Nea Aghialos location (LGBL) of (<b>a</b>) Larissa radar data and (<b>b</b>) IP<sub>S2</sub> on 12 March 2019 at 12UTC and (<b>c</b>) radar data and (<b>d</b>) IP<sub>S2</sub>on 12 March 2019 at 18UTC within a radius of 25 km. The horizontal axis of the radar data is in dBz and of the IP in percentage, while the vertical axes are in feet.</p>
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44 pages, 2928 KiB  
Article
Exergy Analysis in Highly Hydrogen-Enriched Methane Fueled Spark-Ignition Engine at Diverse Equivalence Ratios via Two-Zone Quasi-Dimensional Modeling
by Dimitrios C. Rakopoulos, Constantine D. Rakopoulos, George M. Kosmadakis, Evangelos G. Giakoumis and Dimitrios C. Kyritsis
Energies 2024, 17(16), 3964; https://doi.org/10.3390/en17163964 - 9 Aug 2024
Cited by 1 | Viewed by 1620
Abstract
In the endeavor to accomplish a fully de-carbonized globe, sparkling interest is growing towards using natural gas (NG) having as vastly major component methane (CH4). This has the lowest carbon/hydrogen atom ratio compared to other conventional fossil fuels used in engines [...] Read more.
In the endeavor to accomplish a fully de-carbonized globe, sparkling interest is growing towards using natural gas (NG) having as vastly major component methane (CH4). This has the lowest carbon/hydrogen atom ratio compared to other conventional fossil fuels used in engines and power-plants hence mitigating carbon dioxide (CO2) emissions. Given that using neat hydrogen (H2) containing nil carbon still possesses several issues, blending CH4 with H2 constitutes a stepping-stone towards the ultimate goal of zero producing CO2. In this context, the current work investigates the exergy terms development in high-speed spark-ignition engine (SI) fueled with various hydrogen/methane blends from neat CH4 to 50% vol. fraction H2, at equivalence ratios (EQR) from stoichiometric into the lean region. Experimental data available for that engine were used for validation from the first-law (energy) perspective plus emissions and cycle-by-cycle variations (CCV), using in-house, comprehensive, two-zone (unburned and burned), quasi-dimensional turbulent combustion model tracking tightly the flame-front pathway, developed and reported recently by authors. The latter is expanded to comprise exergy terms accompanying the energy outcomes, affording extra valuable information on judicious energy usage. The development in each zone, over the engine cycle, of various exergy terms accounting too for the reactive and diffusion components making up the chemical exergy is calculated and assessed. The correct calculation of species and temperature histories inside the burned zone subsequent to entrainment of fresh mixture from the unburned zone contributes to more exact computation, especially considering the H2 percentage in the fuel blend modifying temperature-levels, which is key factor when the irreversibility is calculated from a balance comprising all rest exergy terms. Illustrative diagrams of the exergy terms in every zone and whole charge reveal the influence of H2 and EQR values on exergy terms, furnishing thorough information. Concerning the joint content of both zones normalized exergy values over the engine cycle, the heat loss transfer exergy curves acquire higher values the higher the H2 or EQR, the work transfer exergy curves acquire slightly higher values the higher the H2 and slightly higher values the lower the EQR, and the irreversibility curves acquire lower values the higher the H2 or EQR. This exergy approach can offer new reflection for the prospective research to advancing engines performance along judicious use of fully friendly ecological fuel as H2. This extended and in-depth exergy analysis on the use of hydrogen in engines has not appeared in the literature. It can lead to undertaking corrective actions for the irreversibility, exergy losses, and chemical exergy, eventually increasing the knowledge of the SI engines science and technology for building smarter control devices when fueling the IC engines with H2 fuel, which can prove to be game changer to attaining a clean energy environment transition. Full article
(This article belongs to the Special Issue Internal Combustion Engine Performance 2024)
Show Figures

Figure 1

Figure 1
<p>Calculated and experimental CP and calculated MFB vs. CA diagrams, for the engine fueling with all hydrogen <span class="html-italic">z</span> values, and functioning at EQR values of either 1.00 (<b>a</b>), or 0.80 (<b>b</b>), or 0.70 (<b>c</b>).</p>
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<p>Unburned zone, burned zone and mean-state temperatures and LFS vs. CA diagrams, for the engine fueling with all hydrogen <span class="html-italic">z</span> values, and functioning at EQR values of either 1.00 (<b>a</b>), or 0.80 (<b>b</b>), or 0.70 (<b>c</b>).</p>
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<p>Absolute exergy terms vs. crank angle diagrams of the heat loss transfer, work transfer, cylinder thermomechanical, cylinder chemical, cylinder total, irreversibility, blow-by loss (only for the sum content of both zones) and flow between the zones, for the engine fueling with hydrogen vol. fraction 0.50 and functioning at EQR = 0.70, for each zone discretely (<b>a</b>), and for the sum content of both zones (<b>b</b>).</p>
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<p>For each zone discretely and for the sum content of both zones, entropy terms vs. crank angle diagrams of the heat loss transfer, cylinder content, generation, blow-by loss (only for the sum content of both zones) and flow between the zones, for the engine fueling with hydrogen vol. fraction 0.50 and functioning at EQR = 0.80.</p>
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<p>For the sum content of both zones, normalized exergy terms vs. crank angle diagrams of the cylinder thermomechanical, cylinder chemical and cylinder total (<b>a</b>), and of the heat loss transfer, work transfer, irreversibility and flow between the two zones (<b>b</b>), for the engine fueling with hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50, and functioning at EQR = 1.00.</p>
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<p>For the sum content of both zones, normalized exergy terms vs. crank angle diagrams of the cylinder thermomechanical, cylinder chemical and cylinder total (<b>a</b>), and of the heat loss transfer, work transfer, irreversibility and flow between the two zones (<b>b</b>), for the engine fueling with hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50, and functioning at EQR = 0.80.</p>
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<p>For the sum content of both zones, normalized exergy terms vs. crank angle diagrams of the cylinder thermomechanical, cylinder chemical and cylinder total (<b>a</b>), and of heat loss transfer, work transfer, irreversibility and flow between the two zones (<b>b</b>), for the engine fueling with hydrogen vol. fractions 0.10, 0.30 and 0.50, and functioning at EQR = 0.70.</p>
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<p>For each zone discretely and for the sum content of both zones, normalized exergy terms vs. crank angle diagrams of the heat loss and work transfers, for the engine fueling with hydrogen vol. fractions 0.10 and 0.50, and functioning at EQR = 0.70.</p>
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<p>Burned zone normalized chemical exergy terms vs. crank angle diagrams of diffusion, reactive, and diffusion plus reactive, for the engine fueling with hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50, and functioning at EQR values of either 1.00 (<b>a</b>), or 0.80 (<b>b</b>), or 0.70 (<b>c</b>).</p>
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<p>Carbon monoxide concentration (<b>a</b>) and hydrogen concentration (<b>b</b>) vs. crank angle diagrams, for the engine fueling with hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50, and functioning at EQR = 1.00, 0.80 and 0.70.</p>
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<p>Ratio of the chemical exergy to the total exergy at EVO timing against EQR diagrams, for the engine fueling with hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50.</p>
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<p>Burned zone mass fraction burned rate and normalized irreversibility rate vs. crank angle diagrams, for the engine fueling with hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50, and functioning at EQR = 0.80.</p>
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<p>Burned zone mass fraction burned rate and normalized irreversibility rate vs. crank angle diagrams, for the engine fueling with hydrogen vol. fractions 0.10 and 0.50, and functioning at EQR = 1.00 or 0.70.</p>
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<p>Burned zone normalized irreversibility and flame front radius vs. crank angle diagrams, for the engine fueling with hydrogen vol. fractions 0.10, 0.30 and 0.50, and functioning at EQR = 0.80 or 0.70.</p>
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<p>Normalized irreversibility and nitric oxide concentration at EVO event against peak burned zone temperature diagrams (<b>a</b>), and normalized irreversibility against nitric oxide concentration at EVO event diagram (<b>b</b>), for the engine fueling with hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50, and functioning at EQR = 1.00, 0.80 and 0.70. In subfigure (<b>a</b>), equal hydrogen vol. fraction values are connected by (light blue) dashed lines.</p>
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<p>Distribution balance diagrams of the normalized energy or exergy terms for the closed cycle of the engine against the engine fueling hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50, functioned at EQR values of either 1.00 (<b>a</b>), or 0.80 (<b>b</b>), or 0.70 (<b>c</b>).</p>
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<p>Norm. irreversibility against norm. work transfer exergy for the engine fueling with hydrogen vol. fractions 0.00, 0.10, 0.30 and 0.50, and functioning at EQR = 1.00, 0.80 and 0.70.</p>
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