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Keywords = dynamic closed chamber technology

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18 pages, 24098 KiB  
Article
Analysis of Lubrication Characteristics and Friction Test of Texture Topography of Angular Contact Ball Bearing Based on Computational Fluid Dynamics
by Zhi Li, Shijie Yin, Qisheng Zhang, Xiqing Zhang and Hong Zhang
Lubricants 2025, 13(2), 41; https://doi.org/10.3390/lubricants13020041 - 21 Jan 2025
Viewed by 571
Abstract
A textured surface topography can be used to improve the lubrication performance of bearings. These improvements are closely related to the design of the textured topography. Therefore, studying the effect of the textured topography of rolling bearings on lubrication performance is significant. This [...] Read more.
A textured surface topography can be used to improve the lubrication performance of bearings. These improvements are closely related to the design of the textured topography. Therefore, studying the effect of the textured topography of rolling bearings on lubrication performance is significant. This study used computational fluid dynamics (CFD) technology to simulate and analyze the lubrication of an angular contact ball bearing under different working conditions. We studied the influence of a textured topography with different area occupancy rates on the oil-phase volume fraction, as well as the lubrication effect of the textured surface on the bearing’s inner ring and chamber at different rotational speeds and oil inlet speeds. We conducted friction characteristic experiments on point–contact friction pairs using a friction and wear tester. The effects of different loads and rotational speeds on the friction characteristics and surface wear of textured and smooth surfaces were analyzed. The results indicate that the oil-phase volume fraction is always higher than that of the conventional bearing in the inner ring and chamber of a textured bearing. The textured bearing exhibited better lubrication and friction performance. Different textured topographies have different positive effects on lubrication performance, and the influence of the working conditions should be fully considered to achieve these improvements. Full article
(This article belongs to the Special Issue Tribology of Textured Surfaces)
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Figure 1
<p>Flow field model of bearing.</p>
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<p>Fluid domain meshing model.</p>
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<p>Variation process of oil-phase distribution.</p>
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<p>Oil–air two-phase interface diagram in 800 r/min bearing chamber (oil inlet speeds: 2 m/s, 4 m/s, and 6 m/s).</p>
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<p>Oil-phase distribution in bearing chamber at different rotational speeds and oil inlet speeds. (<b>a</b>) Oil-phase distribution at different rotational speeds (800 r/min, 1000 r/min, and 1200 r/min); (<b>b</b>) oil-phase distribution at different oil inlet speeds (2 m/s, 4 m/s, and 6 m/s).</p>
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<p>Solid model of textured bearing.</p>
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<p>Fluid domain model of textured bearing.</p>
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<p>Comparison of oil-phase distribution of fluid domain section.</p>
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<p>Effect of texture area occupancy ratio on bearing lubrication performance.</p>
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<p>Effect of bearing oil inlet speed and rotational speed on oil-phase volume fraction. (<b>a</b>) Effect of oil inlet speed on oil-phase volume fraction; (<b>b</b>) effect of rotational speed on oil-phase volume fraction.</p>
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<p>Effect of bearing oil inlet speed and rotational speed on oil-phase volume fraction. (<b>a</b>) Effect of oil inlet speed on oil-phase volume fraction; (<b>b</b>) effect of rotational speed on oil-phase volume fraction.</p>
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<p>Laser-processing schematic diagram.</p>
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<p>Textured specimens with different area occupancy ratios.</p>
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<p>Experimental bench and friction pair.</p>
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<p>Friction coefficients under different loads.</p>
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<p>Friction coefficients at different rotational speeds.</p>
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<p>Friction coefficients at different rotational speeds.</p>
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<p>Wear amounts on different surfaces.</p>
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<p>Comparison of surface wear traces.</p>
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25 pages, 5904 KiB  
Article
Start of Injection Influence on In-Cylinder Fuel Distribution, Engine Performance and Emission Characteristic in a RCCI Marine Engine
by Alireza Kakoee, Maciej Mikulski, Aneesh Vasudev, Martin Axelsson, Jari Hyvönen, Mohammad Mahdi Salahi and Amin Mahmoudzadeh Andwari
Energies 2024, 17(10), 2370; https://doi.org/10.3390/en17102370 - 14 May 2024
Cited by 1 | Viewed by 1521
Abstract
Reactivity-controlled compression ignition (RCCI) is a promising new combustion technology for marine applications. It has offered the potential to achieve low NOx emissions and high thermal efficiency, which are both important considerations for marine engines. However, the performance of RCCI engines is [...] Read more.
Reactivity-controlled compression ignition (RCCI) is a promising new combustion technology for marine applications. It has offered the potential to achieve low NOx emissions and high thermal efficiency, which are both important considerations for marine engines. However, the performance of RCCI engines is sensitive to a number of factors, including the start of injection. This study used computational fluid dynamics (CFD) to investigate the effects of start of ignition (SOI) on the performance of a marine RCCI engine. The CFD model was validated against experimental data, and the results showed that the SOI has a significant impact on the combustion process. In particular, the SOI affected the distribution of fuel and air in the combustion chamber, which in turn affected the rate of heat release and the formation of pollutants. Ten different SOIs were implemented on a validated closed-loop CFD model from 96 to 42 CAD bTDC (crank angle degree before top dead center) at six-degree intervals. A chemical kinetic mechanism of 54 species and 269 reactions tuned and used for simulation of in-cylinder combustion. The results show that in early injection, high-reactivity fuel was distributed close to the liner. This distribution was around the center of late injection angles. A homogeneity study was carried out to investigate the local equivalence ratio. It showed a more homogenous mixture in early injection until 66 CAD bTDC, after which point, earlier injection timing had no effect on homogeneity. Maximum indicated mean effective pressure (IMEP) was achieved at SOI 48 CAD bTDC, and minimum amounts of THC (total hydrocarbons) and NOx were observed with middle injection timing angles around 66 CAD bTDC. Full article
(This article belongs to the Special Issue Renewable Fuels for Internal Combustion Engines: 2nd Edition)
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<p>Cylinder closed-loop geometry model positioned at TDC and BDC. XYZ coordinate; x (red), y (green) and z (blue).</p>
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<p>Injector-fixed embedded mesh quality depicted in four various crank angle degrees bTDC in a section through a nozzle axis.</p>
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<p>Pressure rise rate simulated with medium load data engine in various mesh sizes, normalized by maximum pressure in 0.008 m case.</p>
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<p>In-Cylinder pressure (normalized by experimental peak pressure) and heat release rate (Normalized by total fuel energy), experimental and simulation comparison.</p>
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<p>Cumulative heat release and specific heat ratio, compared for experimental and simulation cases.</p>
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<p>Comparison of indicated emissions (NO<sub>x</sub> and THC) in simulations and measured, normalized to measurement data.</p>
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<p>In-cylinder pressure curve and maximum pressure for various injection timing simulations.</p>
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<p>Indicated mean effective pressure (IMEP) upper graph and combustion phasing, bottom graph for various SOI.</p>
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<p>NO<sub>x</sub> and total hydrocarbons (THC) in various selected SOIs.</p>
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<p>Mass fraction of high-reactivity fuel at 16 CAD bTDC with various start of injection angles.</p>
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<p>HRF distribution alongside the cylinder radios in various start of injection.</p>
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<p>In-cylinder fuel distribution alongside cylinder radios divided to 10 equal zones.</p>
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<p>Normal distribution of local equivalence ratio at 16 CAD bTDC for SOI = 66 CAD bTDC.</p>
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<p>Normal distribution of local equivalence ratio for all studied injection timings at 16 CAD bTDC.</p>
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<p>In cylinder temperature distribution at 4-degree aTDC (aprox. CA50) in various injection timing.</p>
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<p>In-cylinder equivalence ratio, a section from top for all case studies at 4 CAD aTDC.</p>
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<p>OH concentration for various injection timing in different crank angle degrees.</p>
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<p>CH4 mass fraction distributed in the cylinder for SOI 66 at various engine crank angles.</p>
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34 pages, 32508 KiB  
Article
Dynamic Modeling of Carbon Dioxide Transport through the Skin Using a Capnometry Wristband
by Pierre Grangeat, Maria-Paula Duval Comsa, Anne Koenig and Ronald Phlypo
Sensors 2023, 23(13), 6096; https://doi.org/10.3390/s23136096 - 2 Jul 2023
Cited by 3 | Viewed by 2594
Abstract
The development of a capnometry wristband is of great interest for monitoring patients at home. We consider a new architecture in which a non-dispersive infrared (NDIR) optical measurement is located close to the skin surface and is combined with an open chamber principle [...] Read more.
The development of a capnometry wristband is of great interest for monitoring patients at home. We consider a new architecture in which a non-dispersive infrared (NDIR) optical measurement is located close to the skin surface and is combined with an open chamber principle with a continuous circulation of air flow in the collection cell. We propose a model for the temporal dynamics of the carbon dioxide exchange between the blood and the gas channel inside the device. The transport of carbon dioxide is modeled by convection–diffusion equations. We consider four compartments: blood, skin, the measurement cell and the collection cell. We introduce the state-space equations and the associated transition matrix associated with a Markovian model. We define an augmented system by combining a first-order autoregressive model describing the supply of carbon dioxide concentration in the blood compartment and its inertial resistance to change. We propose to use a Kalman filter to estimate the carbon dioxide concentration in the blood vessels recursively over time and thus monitor arterial carbon dioxide blood pressure in real time. Four performance factors with respect to the dynamic quantification of the CO2 blood concentration are considered, and a simulation is carried out based on data from a previous clinical study. These demonstrate the feasibility of such a technological concept. Full article
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Figure 1
<p>The operating mode of the capnometry wristband, CAPNO, relies on an NDIR optical measurement cell close to the surface of the skin and an air convection flow through the collection cell, which collects the <math display="inline"><semantics><mrow><mi>C</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math> diffused from the blood through the skin and the measurement cell.</p>
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<p><math display="inline"><semantics><mrow><mi>C</mi><msub><mi>O</mi><mn>2</mn></msub></mrow></semantics></math> desorption between blood and ambient air, and a system model based on the concatenation of our physiological and device models.</p>
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<p>Geometrical model scheme and sample points considered for discretizing the continuous spatial space.</p>
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<p>(<b>a</b>) Delay calculation; (<b>b</b>) rise time calculation. Noiseless observation vector of the Kalman inverse problem.</p>
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<p>System input signal. The variations in blood concentration are considered as input signal for the direct approach. There are two levels of blood concentration: one corresponding to a normocapnia level and one corresponding to a hypercapnia level.</p>
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<p>Input signal for the evaluation of the inverse transport model for the three cases proposed: when the input is a noiseless concentration signal (in dark blue); (<b>a</b>) when the input is characterized by a noise variance of <math display="inline"><semantics><mrow><mn>1</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>8</mn></mrow></msup><mo> </mo><msup><mrow><mrow><mo>(</mo><mrow><mi>mol</mi><mo>/</mo><msup><mi mathvariant="normal">m</mi><mn>3</mn></msup></mrow><mo>)</mo></mrow></mrow><mn>2</mn></msup></mrow></semantics></math> (light blue); (<b>b</b>) when the input is characterized by a noise variance of <math display="inline"><semantics><mrow><mn>1</mn><mo>×</mo><msup><mrow><mn>10</mn></mrow><mrow><mo>−</mo><mn>6</mn></mrow></msup><mo> </mo><msup><mrow><mrow><mo>(</mo><mrow><mi>mol</mi><mo>/</mo><msup><mi mathvariant="normal">m</mi><mn>3</mn></msup></mrow><mo>)</mo></mrow></mrow><mn>2</mn></msup></mrow></semantics></math> (light blue).</p>
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<p>Simulation scheme for a realistic clinical test with different phase durations and levels, as given in <a href="#sensors-23-06096-t006" class="html-table">Table 6</a>.</p>
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<p>Synthetic data generated for 13 spatial sample points: the propagation of input concentration within the system through different media (<b>a</b>) with the initialization phase visible from 0 to normocapnia and (<b>b</b>) with the initialization phase not shown.</p>
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<p>Device performance parameters: (<b>a</b>) the evolution of the time delay and rise time in the collection cell according to the flow of the ambient air entering the cell; (<b>b</b>) the variation of the mean of the hypercapnia measurement level according to the flow of the ambient air entering the cell.</p>
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<p>Noiseless observation of a step function as input. (<b>a</b>) Estimated concentrations using the spatial grid for the direct transport problem, including the variable <math display="inline"><semantics><mrow><msubsup><mi>C</mi><mrow><mi>C</mi><msub><mi>O</mi><mn>2</mn></msub></mrow><mrow><mi>i</mi><mi>n</mi><mo> </mo><mi>b</mi><mi>l</mi><mi>o</mi><mi>o</mi><mi>d</mi></mrow></msubsup></mrow></semantics></math> as a state variable. (<b>b</b>) Estimated (red curve) and real (black curve) blood concentration.</p>
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<p>Low noise added to the observation of a step function as input. (<b>a</b>) Estimated concentrations using the spatial grid for the direct transport problem, including the variable <math display="inline"><semantics><mrow><msubsup><mi>C</mi><mrow><mi>C</mi><msub><mi>O</mi><mn>2</mn></msub></mrow><mrow><mi>i</mi><mi>n</mi><mo> </mo><mi>b</mi><mi>l</mi><mi>o</mi><mi>o</mi><mi>d</mi></mrow></msubsup></mrow></semantics></math> as a state variable. (<b>b</b>) Estimated (red curve) and real (black curve) blood concentration.</p>
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<p>High noise added to the observation of a step function as input. (<b>a</b>) Estimated concentrations using the spatial grid for the direct transport problem, including the variable <math display="inline"><semantics><mrow><msubsup><mi>C</mi><mrow><mi>C</mi><msub><mi>O</mi><mn>2</mn></msub></mrow><mrow><mi>i</mi><mi>n</mi><mo> </mo><mi>b</mi><mi>l</mi><mi>o</mi><mi>o</mi><mi>d</mi></mrow></msubsup></mrow></semantics></math> as a state variable. (<b>b</b>) Estimated (red curve) and real (black curve) blood concentration.</p>
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<p>Result of the compartmental model simulation in the four compartments.</p>
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<p>Result of the Kalman filter in the four compartments with two regularity parameters: (<b>a</b>) <math display="inline"><semantics><mrow><mi>φ</mi><mo>=</mo><mn>0</mn></mrow></semantics></math>; (<b>b</b>) <math display="inline"><semantics><mrow><mi>φ</mi><mo>=</mo><mo>−</mo><mn>0.0036</mn></mrow></semantics></math>.</p>
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<p>Comparison of simulated/estimated values in measurement and blood cell: (<b>a</b>) <math display="inline"><semantics><mrow><mi>φ</mi><mo>=</mo><mn>0</mn></mrow></semantics></math>; (<b>b</b>) <math display="inline"><semantics><mrow><mi>φ</mi><mo>=</mo><mo>−</mo><mn>0.0036</mn></mrow></semantics></math>.</p>
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18 pages, 6967 KiB  
Article
Conceptual Design of a UVC-LED Air Purifier to Reduce Airborne Pathogen Transmission—A Feasibility Study
by Saket Kapse, Dena Rahman, Eldad J. Avital, Nithya Venkatesan, Taylor Smith, Lidia Cantero-Garcia, Fariborz Motallebi, Abdus Samad and Clive B. Beggs
Fluids 2023, 8(4), 111; https://doi.org/10.3390/fluids8040111 - 27 Mar 2023
Cited by 2 | Viewed by 3216
Abstract
Existing indoor closed ultraviolet-C (UVC) air purifiers (UVC in a box) have faced technological challenges during the COVID-19 breakout, owing to demands of low energy consumption, high flow rates, and high kill rates at the same time. A new conceptual design of a [...] Read more.
Existing indoor closed ultraviolet-C (UVC) air purifiers (UVC in a box) have faced technological challenges during the COVID-19 breakout, owing to demands of low energy consumption, high flow rates, and high kill rates at the same time. A new conceptual design of a novel UVC-LED (light-emitting diode) air purifier for a low-cost solution to mitigate airborne diseases is proposed. The concept focuses on performance and robustness. It contains a dust-filter assembly, an innovative UVC chamber, and a fan. The low-cost dust filter aims to suppress dust accumulation in the UVC chamber to ensure durability and is conceptually shown to be easily replaced while mitigating any possible contamination. The chamber includes novel turbulence-generating grids and a novel LED arrangement. The turbulent generator promotes air mixing, while the LEDs inactivate the pathogens at a high flow rate and sufficient kill rate. The conceptual design is portable and can fit into ventilation ducts. Computational fluid dynamics and UVC ray methods were used for analysis. The design produces a kill rate above 97% for COVID and tuberculosis and above 92% for influenza A at a flow rate of 100 L/s and power consumption of less than 300 W. An analysis of the dust-filter performance yields the irradiation and flow fields. Full article
(This article belongs to the Special Issue Biological Fluid Dynamics)
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Figure 1
<p>Conceptual design of the UVC-LED air purifier in (<b>a</b>) isometric view and (<b>b</b>) longitudinal cross-section, where all units are in mm.</p>
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<p>Conceptual design of the louvers ‘cassette’ assembly to provide safe removal of the dust filter. The louvers are closed for the filter’s replacement; the all ‘cassette’ assembly is pulled out from the air purifier, and the filter can be safely removed.</p>
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<p>The dust filter’s (<b>a</b>) efficiency <span class="html-italic">E</span> for several fiber diameters <span class="html-italic">d</span><sub>f</sub> from Equation (3) and (<b>b</b>) pressure drop <span class="html-italic">Δp</span> for <span class="html-italic">d</span><sub>f</sub> = 50 microns and several clogging conditions measured by the particle solidity on the filter <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>p</mi> </msub> </mrow> </semantics></math> using Equation (2), where the filter’s solidity is <math display="inline"><semantics> <mrow> <msub> <mi>α</mi> <mi>f</mi> </msub> <mo>=</mo> <mn>0.05</mn> </mrow> </semantics></math>.</p>
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<p>The UVC chamber LEDs layout where (<b>a</b>) staggering is arranged between the upper and lower LEDs arrays and (<b>b</b>) staggering is also shown in the LED array itself to reduce the number of LEDs. The length unit is mm.</p>
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<p>The irradiance <span class="html-italic">S</span> (mW/cm<sup>2</sup>) at a horizontal plane of (20, 20) cm as emitted by the upper LED array with the LEDs arrangement of <a href="#fluids-08-00111-f005" class="html-fig">Figure 5</a>b, where the plane is (<b>a</b>) 4 cm and (<b>b</b>) 6 cm away from the upper LEDs array.</p>
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<p>The irradiance <span class="html-italic">S</span> (mW/cm<sup>2</sup>) at a horizontal plane of (20, 20) cm as emitted by the upper LED array with the LEDs arrangement of <a href="#fluids-08-00111-f005" class="html-fig">Figure 5</a>b, where the plane is (<b>a</b>) 4 cm and (<b>b</b>) 6 cm away from the upper LEDs array.</p>
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<p>(<b>a</b>) The geometry of the turbulence-generating grid, where the given dimensions are in mm and the overall dimensions are (200, 200) mm. (<b>b</b>) The time-averaged velocity magnitude field at the grid.</p>
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<p>The (<b>a</b>) time-averaged velocity magnitude and (<b>b</b>) turbulent kinetic energy fields at the mid-spanwise plane of the air purifier.</p>
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<p>The (<b>a</b>) time-averaged velocity magnitude and (<b>b</b>) turbulent kinetic energy fields at the mid-vertical plane of the upper UVC chamber.</p>
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<p>The (<b>a</b>) time-averaged velocity magnitude and (<b>b</b>) turbulent kinetic energy fields at the mid-vertical plane of the upper UVC chamber.</p>
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<p>The pressure field at the mid-spanwise plane of the air cleaner and relative to the pressure at a central point after the dust filter.</p>
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19 pages, 3660 KiB  
Article
Numerical Modeling of Laser Heating and Evaporation of a Single Droplet
by Sagar Pokharel, Albina Tropina and Mikhail Shneider
Energies 2023, 16(1), 388; https://doi.org/10.3390/en16010388 - 29 Dec 2022
Cited by 2 | Viewed by 2503
Abstract
Laser technology is being widely studied for controlled energy deposition for a range of applications, including flow control, ignition, combustion, and diagnostics. The absorption and scattering of laser radiation by liquid droplets in aerosols affects propagation of the laser beam in the atmosphere, [...] Read more.
Laser technology is being widely studied for controlled energy deposition for a range of applications, including flow control, ignition, combustion, and diagnostics. The absorption and scattering of laser radiation by liquid droplets in aerosols affects propagation of the laser beam in the atmosphere, while the ignition and combustion characteristics in combustion chambers are influenced by the evaporation rate of the sprayed fuel. In this work, we present a mathematical model built on OpenFOAM for laser heating and evaporation of a single droplet in the diffusion-dominated regime taking into account absorption of the laser radiation, evaporation process, and vapor flow dynamics. The developed solver is validated against available experimental and numerical data for heating and evaporation of ethanol and water droplets. The two main regimes—continuous and pulsed laser heating—are explored. For continuous laser heating, the peak temperature is higher for larger droplets. For pulsed laser heating, when the peak irradiance is close to transition to the boiling regime, the temporal dynamics of the droplet temperature does not depend on the droplet size. With the empirical normalization of time, the dynamics of the droplet shrinkage and cooling are found to be independent of droplet sizes and peak laser intensities. Full article
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<p>Schematic of laser heating of a droplet in ambient gas (not to scale).</p>
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<p>Efficiency factor of absorption, <math display="inline"><semantics> <msub> <mi>Q</mi> <mi>a</mi> </msub> </semantics></math>, against radius of the droplet, <span class="html-italic">R</span>.</p>
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<p>Block scheme of the numerical solver built in OpenFOAM.</p>
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<p>Normalized diameter squared against normalized time, lines represent simulation results and symbols represent experimental results from [<a href="#B32-energies-16-00388" class="html-bibr">32</a>].</p>
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<p>Computational domain specifications used during verification.</p>
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<p>Temporal evolution of the droplet temperature. Solid line, results from this work for a fine grid (8750 cells); dotted lines, results for a coarse grid (5600 cells); and dashed line, results from Armstrong’s model [<a href="#B22-energies-16-00388" class="html-bibr">22</a>]; water droplet of <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m at 300 K, <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>10.6</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>0</mn> </msub> <mo>=</mo> <msup> <mn>10</mn> <mn>7</mn> </msup> </mrow> </semantics></math> W/m<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>.</p>
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<p>Temporal evolution of the droplet temperature, left axis and black lines, and the normalized surface area, right axis and red lines, for water (solid lines), and ethanol (dashed lines). <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>D</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>0</mn> </mrow> </msub> <mo>=</mo> <msup> <mn>10</mn> <mn>10</mn> </msup> </mrow> </semantics></math> W/m<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, the continuous laser heating source.</p>
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<p>Temporal evolution of ethanol droplet heating and evaporation during continuous heating; (<b>a</b>) normalized radius, (<b>b</b>) droplet temperature, (<b>c</b>) evaporation mass flux, and (<b>d</b>) conduction and convective heat flux. Solid lines represent droplet of size <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>D</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, and dashed lines represent droplet of size <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>D</mi> </msub> <mo>=</mo> <mn>40</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m; <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>0</mn> </mrow> </msub> <mo>=</mo> <msup> <mn>10</mn> <mn>10</mn> </msup> </mrow> </semantics></math> W/m<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>.</p>
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<p>Time required to reach boiling temperature normalized by FWHM against the peak laser intensity. Analytical results are normalized for <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>15</mn> </mrow> </semantics></math> ns. Symbols show results for two cases with same integrated intensity but different FWHM.</p>
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<p>Velocity vectors overlaid on the contours of mass fraction of ethanol at 1 ms for pulsed laser heating. <math display="inline"><semantics> <mrow> <msub> <mi>t</mi> <mi>p</mi> </msub> <mo>=</mo> <mn>10</mn> </mrow> </semantics></math> ns, <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>1.0</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>14</mn> </msup> </mrow> </semantics></math> W/m<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mrow> <mi>D</mi> <mn>0</mn> </mrow> </msub> <mo>=</mo> <mn>25</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m.</p>
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<p>Temporal evolution of ethanol droplet parameters; (<b>a</b>) normalized radius, (<b>b</b>) droplet temperature, (<b>c</b>) evaporation mass flux, and (<b>d</b>) conduction and convective heat flux normalized by <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>0</mn> </mrow> </msub> <msubsup> <mi>R</mi> <mi>D</mi> <mn>2</mn> </msubsup> </mrow> </semantics></math> for cases from <a href="#energies-16-00388-t001" class="html-table">Table 1</a>.</p>
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<p>Temporal evolution of ethanol droplet parameters for cases shown in <a href="#energies-16-00388-t001" class="html-table">Table 1</a>; (<b>a</b>) normalized surface area, (<b>b</b>) droplet temperature normalized by maximum temperature. The normalized time <math display="inline"><semantics> <mrow> <mover accent="true"> <mi>t</mi> <mo>^</mo> </mover> <mo>=</mo> <mi>t</mi> <msup> <mrow> <mo>(</mo> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>0</mn> </mrow> </msub> <mo>/</mo> <msup> <mn>10</mn> <mn>14</mn> </msup> <mo>)</mo> </mrow> <mrow> <mn>2.11</mn> </mrow> </msup> <mo>/</mo> <msubsup> <mi>R</mi> <mrow> <mi>D</mi> <mn>0</mn> </mrow> <mn>2</mn> </msubsup> </mrow> </semantics></math>.</p>
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<p>Temporal evolution of (<b>a</b>) normalized surface area and (<b>b</b>) droplet temperature for pulsed laser heating of ethanol droplets <math display="inline"><semantics> <msub> <mi>R</mi> <mn>0</mn> </msub> </semantics></math> = 10 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, 25 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, 40 <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m with laser of <math display="inline"><semantics> <mrow> <mi>λ</mi> <mo>=</mo> <mn>1064</mn> </mrow> </semantics></math> nm, <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mn>0</mn> </msub> <mo>=</mo> <mn>0.5</mn> <mo>×</mo> <msup> <mn>10</mn> <mn>14</mn> </msup> </mrow> </semantics></math> W/m<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math>, 100 KHz repetition rate.</p>
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<p>Temporal evolution of the droplet temperature, left axis, and the normalized surface area, right axis, for the detailed model (solid lines), and semi-empirical model (dashed lines). Results are shown for <math display="inline"><semantics> <mrow> <msub> <mi>R</mi> <mi>D</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> <math display="inline"><semantics> <mi mathvariant="sans-serif">μ</mi> </semantics></math>m, <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mrow> <mi>L</mi> <mn>0</mn> </mrow> </msub> <mo>=</mo> <msup> <mn>10</mn> <mn>10</mn> </msup> </mrow> </semantics></math> W/m<math display="inline"><semantics> <msup> <mrow/> <mn>2</mn> </msup> </semantics></math> for the continuous laser heating case.</p>
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<p>Schematic showing the laser bleaching effect.</p>
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17 pages, 9299 KiB  
Article
Analysis of Ball Check Valves with Conical and Spherical Seat Designs from Common-Rail Pumps
by Narcis-Daniel Petrea, Razvan-Constantin Iordache and Carmen Bujoreanu
Machines 2022, 10(10), 959; https://doi.org/10.3390/machines10100959 - 20 Oct 2022
Cited by 4 | Viewed by 4910
Abstract
Common-rail fuel injection systems are still a good option for equipping new car models. The technology is well known, systems of this type are reliable and can be used on a wide variety of diesel and petrol engines. However, there is still room [...] Read more.
Common-rail fuel injection systems are still a good option for equipping new car models. The technology is well known, systems of this type are reliable and can be used on a wide variety of diesel and petrol engines. However, there is still room for improvement. The ball check valve, which is part of the common-rail pump, is designed to open and allow the compressed fluid to be sent to the high-pressure accumulator and close to not allow fuel to return to the compression chamber. The valves’ design directly influences the volumetric efficiency of the outlet flow and the robustness against high pressures that lead to low performance and short service life of the fuel injection systems. This paper aims to compare two ball check valves with conical and spherical seat designs. The analysis is based on theoretical calculations and CFD simulations, which will give more confidence in the results. Considering the comparative analysis results, the ball check valve with a spherical seat shows better flow dynamics than the ball check valve with a conical seat. In addition to the improved flow dynamics, the ball check valve with spherical seat seems to have a uniformly distributed fluid pressure inside the valve. In contrast, the conical seat ball check valve has high local fluid pressures, leading to fatigue. Full article
(This article belongs to the Special Issue Design and Manufacture of Advanced Machines)
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<p>Hydraulic head assembly with valves and plunger [<a href="#B22-machines-10-00959" class="html-bibr">22</a>]. 1—Outlet valve seat; 2—Outlet valve spring; 3—Outlet valve ball; 4—Inlet valve stem; 5—Hydraulic head body; 6—Inlet valve seat; 7—Compression chamber; 8—Plunger bore; 9—Plunger.</p>
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<p>(<b>a</b>) Ball check valve with conical seat; 1—Connection to the high-pressure accumulator; 2—Hydraulic head body; 3—Conical seat; 4—Spring; (<b>b</b>) Ball check valve with spherical seat; 1—Connection to the high-pressure accumulator; 2—Hydraulic head body; 3—Spherical seat; 4—Spring.</p>
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<p>Geometry of the ball check valve [<a href="#B30-machines-10-00959" class="html-bibr">30</a>]. D<sub>S</sub>—diameter of the flow section from the compression chamber; D<sub>b</sub>—ball diameter; A<sub>h</sub>—fluid passage area between the ball and the seat depending on x; x—height of the valve opening (lift); θ—seat angle relative to the axis of symmetry.</p>
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<p>Valve opening according to the pump speed and the pressure supplied.</p>
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<p>Opening area, flow and velocity of the fluid through the valve −50 °C.</p>
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<p>Drag of the ball −50 °C.</p>
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<p>Pressure CFD: Conical seat valve x = 0.201 [mm], p<sub>dn</sub> = 300 [bar] and p<sub>up</sub> = 330 [bar].</p>
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<p>Pressure CFD: Spherical seat valve x = 0.201 [mm], p<sub>dn</sub> = 300 [bar] and p<sub>up</sub> = 330 [bar].</p>
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<p>Pressure CFD: Conical Seat Valve − x = 1.005 [mm], p<sub>dn</sub> = 2000 [bar] and p<sub>up</sub> = 2100 [bar].</p>
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<p>Pressure CFD: Spherical Seat Valve − x = 1.005 [mm], p<sub>dn</sub> = 2000 [bar] and p<sub>up</sub> = 2100 [bar].</p>
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<p>Pressure CFD: Conical Seat Valve − x = 1.257 [mm], p<sub>dn</sub> = 2000 [bar] and p<sub>up</sub> = 2100 [bar].</p>
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<p>Pressure CFD: Spherical Seat Valve − x = 1.257 [mm], p<sub>dn</sub> = 2000 [bar] and p<sub>up</sub> = 2100 [bar].</p>
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<p>Velocity CFD: Conical Seat Valve: x = 0.201 [mm], p<sub>dn</sub> = 300 [bar] and p<sub>up</sub> = 330 [bar].</p>
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<p>Velocity CFD: Spherical Seat Valve: x = 0.201 [mm], p<sub>dn</sub> = 300 [bar] and p<sub>up</sub> = 330 [bar].</p>
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<p>Velocity CFD: Conical Seat Valve: x = 1.005 [mm], p<sub>dn</sub> = 2000 [bar] and p<sub>up</sub> = 2100 [bar].</p>
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<p>Velocity CFD: Spherical Seat Valve − x = 1.005 [mm], p<sub>dn</sub> = 2000 [bar] and p<sub>up</sub> = 2100 [bar].</p>
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<p>Velocity CFD: Conical Seat Valve: x = 1.257 [mm], p<sub>dn</sub> = 2000 [bar] and p<sub>up</sub> = 2100 [bar].</p>
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<p>Velocity CFD: Spherical Seat Valve: x = 1.257 [mm], p<sub>dn</sub> = 2000 [bar] and p<sub>up</sub> = 2100 [bar].</p>
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54 pages, 4328 KiB  
Review
Natural Source Zone Depletion (NSZD) Quantification Techniques: Innovations and Future Directions
by Roya Pishgar, Joseph Patrick Hettiaratchi and Angus Chu
Sustainability 2022, 14(12), 7027; https://doi.org/10.3390/su14127027 - 8 Jun 2022
Cited by 12 | Viewed by 6547
Abstract
Natural source zone depletion (NSZD) is an emerging technique for sustainable and cost-effective bioremediation of light non-aqueous phase liquid (LNAPL) in oil spill sites. Depending on regulatory objectives, NSZD has the potential to be used as either the primary or sole LNAPL management [...] Read more.
Natural source zone depletion (NSZD) is an emerging technique for sustainable and cost-effective bioremediation of light non-aqueous phase liquid (LNAPL) in oil spill sites. Depending on regulatory objectives, NSZD has the potential to be used as either the primary or sole LNAPL management technique. To achieve this goal, NSZD rate (i.e., rate of bulk LNAPL mass depletion) should be quantified accurately and precisely. NSZD has certain characteristic features that have been used as surrogates to quantify the NSZD rates. This review highlights the most recent trends in technology development for NSZD data collection and rate estimation, with a focus on the operational and technical advantages and limitations of the associated techniques. So far, four principal techniques are developed, including concentration gradient (CG), dynamic closed chamber (DCC), CO2 trap and thermal monitoring. Discussions revolving around two techniques, “CO2 trap” and “thermal monitoring”, are expanded due to the particular attention to them in the current industry. The gaps of knowledge relevant to the NSZD monitoring techniques are identified and the issues which merit further research are outlined. It is hoped that this review can provide researchers and practitioners with sufficient information to opt the best practice for the research and application of NSZD for the management of LNAPL impacted sites. Full article
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<p>Natural source zone depletion (NSZD) conceptual model, showing all the possible microbial processes at light non-aqueous phase liquid (LNAPL) contaminated sites. This image is recreated based on DiMarzio and Zimbron [<a href="#B23-sustainability-14-07027" class="html-bibr">23</a>]; Gieg et al. [<a href="#B36-sustainability-14-07027" class="html-bibr">36</a>]; Irianni-Renno et al. [<a href="#B41-sustainability-14-07027" class="html-bibr">41</a>]; Karimi Askarani et al. [<a href="#B46-sustainability-14-07027" class="html-bibr">46</a>].</p>
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<p>A summary of factors regulating NSZD rates. <a href="#app1-sustainability-14-07027" class="html-app">Appendix A</a> provides a thorough literature summary and discusses each factor individually (see refs [<a href="#B13-sustainability-14-07027" class="html-bibr">13</a>,<a href="#B25-sustainability-14-07027" class="html-bibr">25</a>,<a href="#B57-sustainability-14-07027" class="html-bibr">57</a>,<a href="#B58-sustainability-14-07027" class="html-bibr">58</a>,<a href="#B59-sustainability-14-07027" class="html-bibr">59</a>,<a href="#B60-sustainability-14-07027" class="html-bibr">60</a>,<a href="#B61-sustainability-14-07027" class="html-bibr">61</a>]).</p>
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<p>A summary of factors regulating NSZD rates. <a href="#app1-sustainability-14-07027" class="html-app">Appendix A</a> provides a thorough literature summary and discusses each factor individually (see refs [<a href="#B13-sustainability-14-07027" class="html-bibr">13</a>,<a href="#B25-sustainability-14-07027" class="html-bibr">25</a>,<a href="#B57-sustainability-14-07027" class="html-bibr">57</a>,<a href="#B58-sustainability-14-07027" class="html-bibr">58</a>,<a href="#B59-sustainability-14-07027" class="html-bibr">59</a>,<a href="#B60-sustainability-14-07027" class="html-bibr">60</a>,<a href="#B61-sustainability-14-07027" class="html-bibr">61</a>]).</p>
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<p>Factors affecting (<b>a</b>) actual and (<b>b</b>) apparent NSZD rate.</p>
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<p>Different alkali CO<sub>2</sub> trap configurations used for NSZD rate resolution: (<b>a</b>) McCoy model developed in 2012 [<a href="#B56-sustainability-14-07027" class="html-bibr">56</a>]; (<b>b</b>) Keith and Wong model developed in 2006 [<a href="#B30-sustainability-14-07027" class="html-bibr">30</a>]. The images are recreated. For additional details about each model, refer to [<a href="#B37-sustainability-14-07027" class="html-bibr">37</a>,<a href="#B56-sustainability-14-07027" class="html-bibr">56</a>,<a href="#B108-sustainability-14-07027" class="html-bibr">108</a>] and [<a href="#B30-sustainability-14-07027" class="html-bibr">30</a>], respectively.</p>
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<p>Precision of NSZD quantification techniques: (<b>a</b>) trap vs. dynamic closed chamber (DCC); (<b>b</b>) trap vs. concentration gradient (CG); (<b>c</b>) trap vs. thermal monitoring. Data were collected from Tracy [<a href="#B47-sustainability-14-07027" class="html-bibr">47</a>], Keith and Wong [<a href="#B30-sustainability-14-07027" class="html-bibr">30</a>], Rochette et al. [<a href="#B111-sustainability-14-07027" class="html-bibr">111</a>], Kulkarni et al. [<a href="#B11-sustainability-14-07027" class="html-bibr">11</a>] and Karimi Askarani et al. [<a href="#B46-sustainability-14-07027" class="html-bibr">46</a>].</p>
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<p>Precision of NSZD quantification techniques: (<b>a</b>) trap vs. dynamic closed chamber (DCC); (<b>b</b>) trap vs. concentration gradient (CG); (<b>c</b>) trap vs. thermal monitoring. Data were collected from Tracy [<a href="#B47-sustainability-14-07027" class="html-bibr">47</a>], Keith and Wong [<a href="#B30-sustainability-14-07027" class="html-bibr">30</a>], Rochette et al. [<a href="#B111-sustainability-14-07027" class="html-bibr">111</a>], Kulkarni et al. [<a href="#B11-sustainability-14-07027" class="html-bibr">11</a>] and Karimi Askarani et al. [<a href="#B46-sustainability-14-07027" class="html-bibr">46</a>].</p>
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<p>Bias introduced into CO<sub>2</sub> efflux measurements by different quantification techniques (CO<sub>2</sub> trap; dynamic closed chamber, DCC; and gradient concentration, CG). The graph is recreated based on lab-scale study of Tracy [<a href="#B47-sustainability-14-07027" class="html-bibr">47</a>].</p>
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9 pages, 3229 KiB  
Article
Microfluidics Integration into Low-Noise Multi-Electrode Arrays
by Mafalda Ribeiro, Pamela Ali, Benjamin Metcalfe, Despina Moschou and Paulo R. F. Rocha
Micromachines 2021, 12(6), 727; https://doi.org/10.3390/mi12060727 - 20 Jun 2021
Cited by 6 | Viewed by 3677
Abstract
Organ-on-Chip technology is commonly used as a tool to replace animal testing in drug development. Cells or tissues are cultured on a microchip to replicate organ-level functions, where measurements of the electrical activity can be taken to understand how the cell populations react [...] Read more.
Organ-on-Chip technology is commonly used as a tool to replace animal testing in drug development. Cells or tissues are cultured on a microchip to replicate organ-level functions, where measurements of the electrical activity can be taken to understand how the cell populations react to different drugs. Microfluidic structures are integrated in these devices to replicate more closely an in vivo microenvironment. Research has provided proof of principle that more accurate replications of the microenvironment result in better micro-physiological behaviour, which in turn results in a higher predictive power. This work shows a transition from a no-flow (static) multi-electrode array (MEA) to a continuous-flow (dynamic) MEA, assuring a continuous and homogeneous transfer of an electrolyte solution across the measurement chamber. The process through which the microfluidic system was designed, simulated, and fabricated is described, and electrical characterisation of the whole structure under static solution and a continuous flow rate of 80 µL/min was performed. The latter reveals minimal background disturbance, with a background noise below 30 µVpp for all flow rates and areas. This microfluidic MEA, therefore, opens new avenues for more accurate and long-term recordings in Organ-on-Chip systems. Full article
(This article belongs to the Special Issue Lab-on-PCB Devices)
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<p>Concentration profile (mol/m<sup>3</sup>) of square continuous-flow chamber featuring (<b>a</b>) one inlet and one outlet, and (<b>b</b>) three inlets and one outlet at steady state. (<b>c</b>) Concentration range for (<b>a</b>,<b>b</b>).</p>
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<p>Concentration profile (mol/m<sup>3</sup>) of trapezium continuous-flow chamber featuring three inlets and one outlet (<b>a</b>) with a curved profile following electrode geometry and (<b>b</b>) straight edges (<b>c</b>) featuring three inlets and three outlets. (<b>d</b>) Concentration range for (<b>a</b>,<b>b</b>,<b>c</b>).</p>
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<p>Three-dimensional model of the prototyped continuous flow microfluidic MEA platform. The sensing part of the device consists of four electrode pairs with the areas 12, 7, 2, and 1 mm<sup>2</sup>, and electrode spacing of 9, 7, 3, and 2 mm, respectively (measured from the centres of each electrode). The inset shows a magnified view of the highlighted cross-section, in dashed red lines, comprising the following material layers: FR4 (green), gold-plated contacts and electrodes (yellow), acrylic adhesive (orange), 50 µm PMMA (blue), and 1 mm PMMA for the lid (gray).</p>
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<p>(<b>a</b>) Microfluidic network design. (<b>b</b>–<b>d</b>) Assembled microfluidic MEA tested for fluidic tightness under continuous flow using a dye (<b>b</b>) when the device chamber was empty, (<b>c</b>) when the device chamber filled through the inlet ports, and (<b>d</b>) when the dye moved through the centre and into the outlet ports.</p>
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<p>(<b>a</b>) Microfluidics setup within Faraday cage, which includes the sample reservoir, microfluidic flow sensor (MFS3; Elveflow), voltage amplifier (Brookdeal Preamplifier 5006; Ortec), and a custom-built container for the MEA and microfluidic tubing. The chip container was covered with aluminium foil for experiments and the Faraday cage was closed. (<b>b</b>,<b>c</b>) Power spectra of all electrode areas (<b>b</b>) under a static solution, and (<b>c</b>) at a continuous flow rate of 80 µL/min. Inset: Change in noise at 20 Hz across all electrode areas under continuous flow.</p>
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16 pages, 35494 KiB  
Article
Structural Characteristics of Moho Surface Based on Time Series Function of Natural Earthquakes
by Xuelei Li, Zhuo Jia, Nanqiao Du, Yi Xu and Gongbo Zhang
Remote Sens. 2021, 13(4), 763; https://doi.org/10.3390/rs13040763 - 19 Feb 2021
Cited by 3 | Viewed by 2768
Abstract
Remote sensing is a non-contact, long-distance detection technology. The reflection characteristics of a seismic wave can be used to detect remote and non-contact targets. Based on the reflection characteristics of a seismic wave, the underground structure in Tengchong Volcanic Area is explored. In [...] Read more.
Remote sensing is a non-contact, long-distance detection technology. The reflection characteristics of a seismic wave can be used to detect remote and non-contact targets. Based on the reflection characteristics of a seismic wave, the underground structure in Tengchong Volcanic Area is explored. In order to further study the deep structure and magmatic activity of the crust in the volcanic area, we carried out a one-year mobile seismic observation. In this paper, nine broadband seismic stations were set up in the Tengchong Volcanic Area, and 3350 receiver function waveforms were collected. The crustal thickness, average wave velocity ratio, and Poisson’s ratio below these stations were calculated by the receiver function method, and the velocity structure near the Moho below these stations was evaluated. Combined with topographic data from SRTM3 (Shuttle Radar Topography Mission 3), this study reveals the dynamic relationship among crustal structure, crustal magmatism, and regional tectonic movement. Mantle upwelling plays an important role on the Moho uplift in the northern Tengchong Volcanic Area, and there are interconnected intracrustal magma chambers in the upper platform. The evaluation results of the Moho transition zone also indicate that the Dayingjiang fault is closely related to the tectonic activity of the Tengchong Volcanic fault. Full article
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
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<p>Ray path diagram of P-wave receiving function in single-layer homogeneous medium.</p>
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<p>Waveform shape rules of receiver function.</p>
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<p>Geological background and distribution of temporary seismic stations in the Tengchong area.</p>
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<p>Epicenter locations of Ms ≥ 5.0 earthquakes.</p>
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<p>Poor-quality receiver function; (<b>a</b>) and (<b>b</b>) the cross-correlation between tangential component and convolution of receiving function with vertical component is less than 80%; (<b>c</b>) the lack of P<sub>S</sub>, P<sub>P</sub>P<sub>S</sub> and P<sub>P</sub>S<sub>S</sub>+P<sub>S</sub>P<sub>S</sub> phase; (<b>d</b>) transition phase is too strong; (<b>e</b>) direct P-wave is minus.</p>
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<p>Stacking receiver function from the station MZT.</p>
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<p>P-wave receiver functions from the station MZT.</p>
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<p>Receiver function for Gaussian factor 2.0 from 9 seismic stations along this profile line.</p>
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<p>Results from stacking receiver functions of MZT. (<b>a</b>) Gauss factor 1.0 (<b>b</b>) Gauss factor 2.0 (<b>c</b>) Gauss factor 3.0 (<b>d</b>) Gauss factor 4.0.</p>
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<p>Common conversion point (CCP) stacking profile line.</p>
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<p>PS phase of receiver function with the changing of Gaussian factor.</p>
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<p>Comparison of <span class="html-italic">H-k</span> stack results obtained from 9 stations using 4 frequency band receiver functions. (<b>a</b>) Moho depth; (<b>b</b>) <span class="html-italic">V<sub>p</sub></span>/<span class="html-italic">V<sub>s</sub></span>; (<b>c</b>) Poisson ratio; (<b>d</b>) Poisson’s ratio data of depth.</p>
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<p>Schematic diagram of underground tectonic movement in the Tengchong area.</p>
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13 pages, 5078 KiB  
Letter
Developing an Automated Gas Sampling Chamber for Measuring Variations in CO2 Exchange in a Maize Ecosystem at Night
by Chaoqun Li, Wenting Han, Manman Peng and Mengfei Zhang
Sensors 2020, 20(21), 6117; https://doi.org/10.3390/s20216117 - 27 Oct 2020
Cited by 10 | Viewed by 4432
Abstract
The measurement of net ecosystem exchange (NEE) of field maize at a plot-sized scale is of great significance for assessing carbon emissions. Chamber methods remain the sole approach for measuring NEE at a plot-sized scale. However, traditional chamber methods are disadvantaged by their [...] Read more.
The measurement of net ecosystem exchange (NEE) of field maize at a plot-sized scale is of great significance for assessing carbon emissions. Chamber methods remain the sole approach for measuring NEE at a plot-sized scale. However, traditional chamber methods are disadvantaged by their high labor intensity, significant resultant changes in microclimate, and significant impact on the physiology of crops. Therefore, an automated portable chamber with an air humidity control system to determinate the nighttime variation of NEE in field maize was developed. The chamber system can automatically open and close the chamber, and regularly collect gas in the chamber for laboratory analysis. Furthermore, a humidity control system was created to control the air humidity of the chamber. Chamber performance test results show that the maximum difference between the temperature and humidity outside and inside the chamber was 0.457 °C and 5.6%, respectively, during the NEE measuring period. Inside the chamber, the leaf temperature fluctuation range and the maximum relative change of the maize leaf respiration rate were 0.3 to 0.3 °C and 23.2015%, respectively. We verified a series of measurements of NEE using the dynamic and static closed chamber methods. The results show a good common point between the two measurement methods (N = 10, R2 = 0.986; and mean difference: ΔCO2 = 0.079 μmol m2s1). This automated chamber was found to be useful for reducing the labor requirement and improving the time resolution of NEE monitoring. In the future, the relationship between the humidity control system and chamber volume can be studied to control the microclimate change more accurately. Full article
(This article belongs to the Section Remote Sensors)
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<p>Schematic design of the static chamber system.</p>
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<p>Photos of leaf temperature, air humidity, and temperature sensors in the chamber: (<b>a</b>) location of the sensors within the chamber and (<b>b</b>) location of the leaf temperature sensor.</p>
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<p>Experimental configuration used to measure the respiratory rate of the leaf using the Li6400 and net ecosystem exchange (NEE) of the maize field was measured by the developed static closed chamber.</p>
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<p>The temperature and humidity variations of the ambient temperature and humidity inside the chamber using the humidity controller and inside the chamber without the humidity controller. On 1/7/2019, one 110 cm high chamber was used to cover the maize plants in the vegetative stages. On 21/7/2019, two 110 cm high chambers were used for the taller maize plants in the reproductive stages. (<b>a</b>,<b>b</b>): Air temperature variation outside the chamber (T<sub>out</sub>), inside the chamber with the humidity controller (T<sub>in-hc</sub>) and inside the chamber without the humidity controller (T<sub>in-whc</sub>). (<b>c</b>,<b>d</b>): Air humidity variation outside the chamber(H<sub>out</sub>), inside the chamber with the humidity controller (H<sub>in-hc</sub>) and inside the chamber without the humidity controller (H<sub>in-whc</sub>). (<b>e</b>,<b>f</b>): The variation of T<sub>in-hc</sub> − T<sub>out</sub> and T<sub>in-whc</sub> − T<sub>out</sub>. (<b>g</b>,<b>h</b>): The variation of H<sub>in-hc</sub> − H<sub>out</sub> and H<sub>in-whc</sub> − H<sub>out</sub>.</p>
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<p>Leaf temperature change measured using two different height chambers: (<b>a</b>) on 3/7/2019, one 110 cm height chamber was used to cover a maize plant in the vegetative stages; and (<b>b</b>) on 24/7/2019, two 110 cm high chambers were used to cover a taller maize plant in the reproductive stages, as shown in <a href="#sensors-20-06117-f001" class="html-fig">Figure 1</a>.</p>
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<p>In experiments 1 and 2, a single 110 cm height chamber was used to cover the maize in the vegetative stages. In experiments 3 and 4, two 110 cm high chambers were used to cover the taller maize plants in the reproductive stages, as shown in <a href="#sensors-20-06117-f001" class="html-fig">Figure 1</a>. (<b>a</b>) Graph showing the changes in the respiration of leaves before and after the maize was covered. (<b>b</b>) Graph showing the violin plot with a box plot for each experiment (IQR = interquartile range).</p>
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<p>(<b>a</b>) Graph showing the maize field CO<sub>2</sub> flux measured using static and dynamic technology. (<b>b</b>) Graph showing the linear regression analysis of the CO<sub>2</sub> flux data measured using static and dynamic technology.</p>
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10402 KiB  
Article
Fluid-Mediated Stochastic Self-Assembly at Centimetric and Sub-Millimetric Scales: Design, Modeling, and Control
by Bahar Haghighat, Massimo Mastrangeli, Grégory Mermoud, Felix Schill and Alcherio Martinoli
Micromachines 2016, 7(8), 138; https://doi.org/10.3390/mi7080138 - 6 Aug 2016
Cited by 13 | Viewed by 6558
Abstract
Stochastic self-assembly provides promising means for building micro-/nano-structures with a variety of properties and functionalities. Numerous studies have been conducted on the control and modeling of the process in engineered self-assembling systems constituted of modules with varied capabilities ranging from completely reactive nano-/micro-particles [...] Read more.
Stochastic self-assembly provides promising means for building micro-/nano-structures with a variety of properties and functionalities. Numerous studies have been conducted on the control and modeling of the process in engineered self-assembling systems constituted of modules with varied capabilities ranging from completely reactive nano-/micro-particles to intelligent miniaturized robots. Depending on the capabilities of the constituting modules, different approaches have been utilized for controlling and modeling these systems. In the quest of a unifying control and modeling framework and within the broader perspective of investigating how stochastic control strategies can be adapted from the centimeter-scale down to the (sub-)millimeter-scale, as well as from mechatronic to MEMS-based technology, this work presents the outcomes of our research on self-assembly during the past few years. As the first step, we leverage an experimental platform to study self-assembly of water-floating passive modules at the centimeter scale. A dedicated computational framework is developed for real-time tracking, modeling and control of the formation of specific structures. Using a similar approach, we then demonstrate controlled self-assembly of microparticles into clusters of a preset dimension in a microfluidic chamber, where the control loop is closed again through real-time tracking customized for a much faster system dynamics. Finally, with the aim of distributing the intelligence and realizing programmable self-assembly, we present a novel experimental system for fluid-mediated programmable stochastic self-assembly of active modules at the centimeter scale. The system is built around the water-floating 3-cm-sized Lily robots specifically designed to be operative in large swarms and allows for exploring the whole range of fully-centralized to fully-distributed control strategies. The outcomes of our research efforts extend the state-of-the-art methodologies for designing, modeling and controlling massively-distributed, stochastic self-assembling systems at different length scales, constituted of modules from centimetric down to sub-millimetric size. As a result, our work provides a solid milestone in structure formation through controlled self-assembly. Full article
(This article belongs to the Special Issue Building by Self-Assembly)
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Graphical abstract

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<p>The experimental setup: (<b>a</b>) water-filled tank with six inlets (four orthogonal and two tangential to the wall); (<b>b</b>) details of a floating passive Lily module, including the latching mechanism composed of four permanent magnets with different pole orientation North-South (NS) and South-North (SN), respectively; (<b>c</b>) real-time visual tracking of four modules during an experiment (the blue lines show a short history of the trajectory of each module) .</p>
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<p>Experimental system, composed of water tank (<b>A</b>); overhead camera (<b>B</b>); base station desktop (<b>C</b>); diaphragm pumps agitating the fluidic environment (<b>D</b>); and control and driver board for pumps (<b>E</b>).</p>
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<p>Graphical representation of all assemblies that can be formed out of four modules and the forward reactions giving rise to them. Chiral copies of assemblies <math display="inline"> <semantics> <msub><mi mathvariant="sans-serif">F</mi> <mn>1</mn> </msub> </semantics> </math> and <math display="inline"> <semantics> <msub> <mi mathvariant="sans-serif">F</mi><mn>3</mn></msub> </semantics> </math> are not included. The shaded rectangles indicate assemblies with the same connection topology (using a four-neighbor topology). Black arrows denote the reactions that lead to the target structure <math display="inline"> <semantics> <mi mathvariant="sans-serif">E</mi> </semantics> </math>, whereas gray arrows other forward reactions in the system [<a href="#B9-micromachines-07-00138" class="html-bibr">9</a>].</p>
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<p>Overview of the <math display="inline"> <semantics> <msup> <mi>M</mi> <mn>3</mn> </msup> </semantics> </math> framework as deployed in this study and the different types of information flowing between its constitutive modules [<a href="#B9-micromachines-07-00138" class="html-bibr">9</a>]. Gray-shaded nodes are computational entities, whereas other nodes are physical entities. Dashed arrows denote flows that are not automated, but need to be performed only once prior to the experiment. Note that the closed-loop control is completely automated.</p>
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<p>Box plot of the first-passage time to the target structure <math display="inline"> <semantics> <mi mathvariant="sans-serif">E</mi> </semantics> </math> obtained over 40 runs of 30 min each for Experiments I to IV. On each box, the central mark is the median; the edges of the boxes are the first and third quartile; the whiskers extend to the most extreme data points not considered outliers; and outliers are plotted individually. Both Experiments I (Mode 0 only) and II (Mode 1 only) exhibit a poor performance due to the unfavorable exploration vs. exploitation balance when using a unique mode of agitation. The mean/median first-passage time of the optimized experiment (IV) is 524/205 s versus 930/612 s for the randomized experiment (III). A Mann–Whitney test rejects the null hypothesis of the two distributions of first-passage times being from the same distribution with equal medians with a <span class="html-italic">p</span>-value of <math display="inline"> <semantics> <mrow> <mn>5.8</mn> <mo>×</mo> <msup> <mn>10</mn> <mrow> <mo>-</mo> <mn>3</mn> </mrow> </msup> </mrow> </semantics> </math> [<a href="#B9-micromachines-07-00138" class="html-bibr">9</a>].</p>
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<p>The automated, closed control loop of the SelfSys platform [<a href="#B10-micromachines-07-00138" class="html-bibr">10</a>]. (<b>a</b>) The 1 cm long, centro-symmetric chamber molded in a 400 <math display="inline"> <semantics> <mi mathvariant="sans-serif">μ</mi> </semantics> </math>m-thick layer of polydimethylsiloxane (PDMS) and sealed between two glass slides; (<b>b</b>) The high-speed GigE camera (Fastec HighSpec 1) with a microscope lens assembly mounted above the chamber for tracking purpose.</p>
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<p>(<b>a</b>) 3D design of the microparticles for fluidic self-assembly (SA); (<b>b</b>) microfabricated SU-8 microparticles in water [<a href="#B10-micromachines-07-00138" class="html-bibr">10</a>].</p>
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<p>(<b>a</b>) Diagram of the particle tracking system in the SelfSys. The generation of the filter pattern is run only once, thus it does not contribute to the total processing time during operation [<a href="#B10-micromachines-07-00138" class="html-bibr">10</a>]; (<b>b</b>) Image processing steps in the SelfSys: (<b>1</b>) captured input image; (<b>2</b>) bandpass-filtered cross-correlation (Fourier domain); (<b>3</b>) matching result after inverse Fourier transform and probabilistic filtering; (<b>4</b>) final tracking displayed on the input image [<a href="#B10-micromachines-07-00138" class="html-bibr">10</a>].</p>
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<p>Self-assembly sequence for target cluster of size: three [<a href="#B10-micromachines-07-00138" class="html-bibr">10</a>]. (<b>a</b>) 2.00 s, assembly mode; (<b>b</b>) 2.80 s, disassembly mode; (<b>c</b>) 3.92 s, target structure achieved.</p>
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<p>Self-assembly sequence for a large microparticle set. Dimensions of the target cluster: five [<a href="#B10-micromachines-07-00138" class="html-bibr">10</a>]. (<b>a</b>) 7.04 s, assembly mode; (<b>b</b>) 7.24 s, disassembly mode; (<b>c</b>) 7.68 s, target structure achieved.</p>
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<p>(<b>a</b>) The experimental setup consisting of a water-filled tank with peripheral pumps agitating the fluidic environment, an overhead camera and a projector, a wireless node for establishing the radio link between the workstation and the Lily robots; (<b>b</b>) Visual tracking of ten Lily robots during an experiment (the blue lines show a short trajectory history for each robot); (<b>c</b>) The Lily robot [<a href="#B61-micromachines-07-00138" class="html-bibr">61</a>]; some key features visible in the picture are: charging contacts (A), chip antenna (B), two LEDs signaling board status (C), ambient light sensor (D), sealing gap filled with silicone paste (E) and two of the four trimming holes (F).</p>
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<p>The Lily robot comprises a flexible circuit board with four Electro-Permanent Magnets (EPMs) soldered on it, a 240 mAh LiPo battery, a 3D printed shell, a 3D printed transparent cap and a 3D printed frame for holding the EPMs in place [<a href="#B61-micromachines-07-00138" class="html-bibr">61</a>].</p>
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<p>(<b>A</b>) An EPM is composed of two magnet rods of different coercivities, but similar remnant fields (a), sandwiched between two iron pole pieces (b), and wrapped with 32 turns of grade 26 American wire gauge (AWG) wire (c); (<b>B</b>) The pieces are hold together using glue; (<b>C</b>) The assembly is then put in a polyurethane mold for protection against rusting [<a href="#B61-micromachines-07-00138" class="html-bibr">61</a>].</p>
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<p>Macroscopic kinetics of the system. (<b>a</b>) Autocorrelation of the speed (i.e., magnitude of velocity) for Lily robots. The speed is uncorrelated to its initial value after approximately 5 s. The shaded area signifies one standard deviation interval; (<b>b</b>) Distribution of the robots’ speeds acquired based on the tracking data with normalized occurrences (total sum of one).</p>
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<p>Reactions in the system. (<b>a</b>) Distribution of collision and binding events across the collision energy spectrum; (<b>b</b>) reaction probability distribution across the collision energy spectrum.</p>
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<p>(<b>a</b>) Interaction time distribution: synchronized unlatching can be seen to allow for more efficient breaking of a bond; (<b>b</b>) dynamic behavior changes upon perceiving environmental light.</p>
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<p>The progress of the SA process for the chain and cross shape target structures.</p>
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2926 KiB  
Review
Exploiting Laboratory and Heliophysics Plasma Synergies
by Jill Dahlburg, William Amatucci, Michael Brown, Vincent Chan, James Chen, Christopher Cothran, Damien Chua, Russell Dahlburg, George Doschek, Jan Egedal, Cary Forest, Russell Howard, Joseph Huba, Yuan-Kuen Ko, Jonathan Krall, J. Martin Laming, Robert Lin, Mark Linton, Vyacheslav Lukin, Ronald Murphy, Cara Rakowski, Dennis Socker, Allan Tylka, Angelos Vourlidas, Harry Warren and Brian Woodadd Show full author list remove Hide full author list
Energies 2010, 3(5), 1014-1048; https://doi.org/10.3390/en30501014 - 25 May 2010
Cited by 2 | Viewed by 14332
Abstract
Recent advances in space-based heliospheric observations, laboratory experimentation, and plasma simulation codes are creating an exciting new cross-disciplinary opportunity for understanding fast energy release and transport mechanisms in heliophysics and laboratory plasma dynamics, which had not been previously accessible. This article provides an [...] Read more.
Recent advances in space-based heliospheric observations, laboratory experimentation, and plasma simulation codes are creating an exciting new cross-disciplinary opportunity for understanding fast energy release and transport mechanisms in heliophysics and laboratory plasma dynamics, which had not been previously accessible. This article provides an overview of some new observational, experimental, and computational assets, and discusses current and near-term activities towards exploitation of synergies involving those assets. This overview does not claim to be comprehensive, but instead covers mainly activities closely associated with the authors’ interests and reearch. Heliospheric observations reviewed include the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) on the National Aeronautics and Space Administration (NASA) Solar Terrestrial Relations Observatory (STEREO) mission, the first instrument to provide remote sensing imagery observations with spatial continuity extending from the Sun to the Earth, and the Extreme-ultraviolet Imaging Spectrometer (EIS) on the Japanese Hinode spacecraft that is measuring spectroscopically physical parameters of the solar atmosphere towards obtaining plasma temperatures, densities, and mass motions. The Solar Dynamics Observatory (SDO) and the upcoming Solar Orbiter with the Heliospheric Imager (SoloHI) on-board will also be discussed. Laboratory plasma experiments surveyed include the line-tied magnetic reconnection experiments at University of Wisconsin (relevant to coronal heating magnetic flux tube observations and simulations), and a dynamo facility under construction there; the Space Plasma Simulation Chamber at the Naval Research Laboratory that currently produces plasmas scalable to ionospheric and magnetospheric conditions and in the future also will be suited to study the physics of the solar corona; the Versatile Toroidal Facility at the Massachusetts Institute of Technology that provides direct experimental observation of reconnection dynamics; and the Swarthmore Spheromak Experiment, which provides well-diagnosed data on three-dimensional (3D) null-point magnetic reconnection that is also applicable to solar active regions embedded in pre-existing coronal fields. New computer capabilities highlighted include: HYPERION, a fully compressible 3D magnetohydrodynamics (MHD) code with radiation transport and thermal conduction; ORBIT-RF, a 4D Monte-Carlo code for the study of wave interactions with fast ions embedded in background MHD plasmas; the 3D implicit multi-fluid MHD spectral element code, HiFi; and, the 3D Hall MHD code VooDoo. Research synergies for these new tools are primarily in the areas of magnetic reconnection, plasma charged particle acceleration, plasma wave propagation and turbulence in a diverging magnetic field, plasma atomic processes, and magnetic dynamo behavior. Full article
(This article belongs to the Special Issue Nuclear Fusion)
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Figure 1
<p>The electron density and temperature operating regimes of an illustrative group of current laboratory confined plasma experiments, plotted together with the observed parameter space that is accessed by elemental heliospheric phenomena.</p>
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<p>Left: TRACE image (171 Å) of magnetic loops. Right: <span class="html-italic">Yohkoh</span> image of soft X-ray loop and hard X-ray footpoint and loop top emission [<a href="#B25-energies-03-01014" class="html-bibr">25</a>].</p>
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<p>A laboratory spontaneous magnetic reconnection event in the Massachusetts Institute of Technology VTF [<a href="#B28-energies-03-01014" class="html-bibr">28</a>], depicted by measured contours of the plasma density, floating potential, current density, and the reconnection rate. The overlaid lines represent contours that coincide with the potential projection of magnetic field lines; see also <a href="#sec4dot4-energies-03-01014" class="html-sec">Section 4.4</a>, following.</p>
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<p>Left: An X-10 flare on 29 October 2003. The green image is a TRACE 195 Å filter image [<a href="#B29-energies-03-01014" class="html-bibr">29</a>] that shows the flare arcade loops at high spatial resolution. Two <span class="html-italic">RHESSI</span> energy channels are also shown. The lower energy channel corresponds to the loop tops near the reconnection region while the higher energy is probably thick target Bremsstrahlung from the footpoints. Right: An X-ray/gamma-ray spectrum from a gamma-ray flare.</p>
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<p>Schematic illustrating the First Ionization Potential (FIP) effect model of Laming (2009) [<a href="#B30-energies-03-01014" class="html-bibr">30</a>]. Alfvén waves are incident on the coronal loop from below on the right hand side. Waves are either transmitted into the loop or reflected back down again. Waves in the coronal loop bounce back and forth, with some leakage at each footpoint, giving rise to a ponderomotive force in the steep density gradients of the chromosphere. This force preferentially accelerates chromospheric ions into the coronal thereby enhancing the coronal abundance of low FIP ions [<a href="#B31-energies-03-01014" class="html-bibr">31</a>].</p>
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<p>Left: Iron line spectrum of a solar flare recorded by X-ray spectrometers on the US Department of Defense (DoD) Space Test Program (STP) P78-1 spacecraft compared with a tokamak spectrum from the Princeton Large Torus. Right: The extreme-ultraviolet spectrum of solar quiet and active regions obtained by the EIS instrument on the <span class="html-italic">Hinode</span> spacecraft.</p>
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<p>SECCHI imagery captures a solar storm propagating through the heliosphere.</p>
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<p>NRL-led space-based solar corona experiments (yellow diamonds).</p>
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<p>An NRL space scientist aligns optics on the Extreme-ultraviolet Imaging Spectrometer (EIS) at Rutherford Appleton Laboratory (RAL) in the United Kingdom. EIS component testing was carried out at NRL and on the NRL SSD beamline facility at Brookhaven National Laboratory. End-to-end calibration was performed at RAL prior to shipment to Japan for integration onto the Solar-B (<span class="html-italic">Hinode)</span> spacecraft.</p>
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<p>SoloHI field-of-view (FOV) from the Sun (red circle to the right) through the heliosphere, as compared with SECCHI’s Heliospheric Imagers HI-1 and HI-2.</p>
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<p>A schematic of the UW-Madison line-tied pinch experiment. The solenoidal magnets produce a nearly uniform magnetic field between the two ends. The anode plate is on the left and drawn to highlight its electrical conductivity. The hexagonally packed cathode array of plasma guns (shown on the right) breaks into three independently controllable regions of plasma represented by shadings in gray.</p>
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<p>a. The Madison Plasma Dynamo Experiment (MPDX) under construction at the UW-Madison. b. The flux contours for the MPDX; the ring cusp magnetic field is generated by axisymmetric rows of 1.2 Tesla dipole magnets with alternating polarity. c. Alternating positive and negative electrostatic bias is applied to electrodes between cusp rings; the resulting torque from the plasma current crossed into the magnetic field causes the plasma to rotate about the symmetry axis and can be used for generating a variety of velocity fields.</p>
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<p>a. The main NRL Space Physics Simulation Chamber (SPSC). b. Finite length Taylor double helix plasma (B<sub>z</sub> isosurfaces) for a length over radius of 10.</p>
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<p>The MIT Versatile Toroidal Facility (VTF).</p>
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<p>The Swarthmore Spheromak Experiment (SSX).</p>
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<p>HYPERION results for a coronal loop whose line-tied magnetic field is being convected by random footpoint motions. Left: Loop energies <span class="html-italic">vs</span>. time in Alfvén units (left), where e<sub>V</sub> is the kinetic energy, e<sub>B</sub> is the magnetic energy, and E<sub>int</sub> is the internal energy. Right: Dissipation <span class="html-italic">vs.</span> time in Alfvén units, where is the coronal loop enstrophy, J is mean-squared current, and D denotes the loop radiation losses.</p>
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<p>Energetic ion distribution accelerated by high harmonic on ion cyclotron RF (ICRF) waves.</p>
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<p>HiFi simulation of two interacting 3D magnetic structures, called spheromaks. Shown are streamlines of magnetic field as the two spheromaks tilt and reconnect through a localized region containing a magnetic null (left panel), merging into a single helical structure (right panel). Magnetic field vectors in the φ = ±π/2 plane are shown in the left panel. Successful comparison of the simulation with the SSX experimental data has been performed [<a href="#B6-energies-03-01014" class="html-bibr">6</a>].</p>
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<p>VooDoo simulation of Hall MHD reconnection. Left: Magnetic field streamlines at z = 0. Right: Flow velocity streamlines, at the same time and position.</p>
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<p>ARMS MHD simulation of solar flare reconnection in a Y-type current sheet, with time increasing from (a) through (f). Magnetic reconnection is initiated by a localized sphere of enhanced resistivity on the current sheet, high in the solar corona. Reconnected loops retract down to the low corona, coming to rest on the pre-existing arcade of loops. This arcade builds up as more magnetic flux reconnects above it, in agreement with observations. The simulation also shows the propagation of reconnection to the left and right of the initial reconnection site, as is observed in the corona, though here the propagation is due to artificial numerical effects [<a href="#B68-energies-03-01014" class="html-bibr">68</a>].</p>
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<p>CRUNCH 3D compressible MHD simulation of fluxtube tunneling. Note that fluxtubes pass through each other as multiple field line reconnections occur.</p>
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<p>Left: MHDCHAN 3D MHD simulation of the UT-Austin Helimak evolution. Vorticity isosurfaces and some streamlines are shown. Right: MHDFSL 3D simulation of the secondary instability that may provide explanation for the fast observed solar magnetic reconnection timescales. A late stage plasma current configuration is shown.</p>
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<p>PHAETHON 3D MHD simulation. Left: Vertical current isosurfaces and sample magnetic field lines in a random force coronal heating simulation. Right: Current isosurfaces and representative magnetic field lines during a line-tied explosive instability calculation.</p>
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