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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 357
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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Figure 1

Figure 1
<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>
Full article ">Figure 17 Cont.
<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>
Full article ">Figure 19 Cont.
<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 437
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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Figure 1
<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>
Full article ">Figure 8 Cont.
<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>
Full article ">Figure 9 Cont.
<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
Viewed by 490
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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Graphical abstract

Graphical abstract
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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>
Full article ">Figure 9 Cont.
<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>
Full article ">Figure 10 Cont.
<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 710
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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Figure 1

Figure 1
<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>
Full article ">Figure 9
<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>
Full article ">Figure 10
<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>
Full article ">Figure 11
<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 734
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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Figure 1

Figure 1
<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 459
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 502
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 1114
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
Viewed by 1088
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)
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<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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16 pages, 1229 KiB  
Article
Northern Hemisphere Glaciation: Its Tectonic Origin in the Neogene Uplift
by Hsien-Wang Ou
Glacies 2024, 1(1), 19-34; https://doi.org/10.3390/glacies1010003 - 21 Jul 2024
Cited by 1 | Viewed by 740
Abstract
The Earth has cooled since the early Pliocene, which was punctuated by accelerated cooling indicative of thresholds. I posit that the cooling was initiated when the Neogene uplift of the Tibetan highland caused it to ice over, augmenting the albedo. I formulate a [...] Read more.
The Earth has cooled since the early Pliocene, which was punctuated by accelerated cooling indicative of thresholds. I posit that the cooling was initiated when the Neogene uplift of the Tibetan highland caused it to ice over, augmenting the albedo. I formulate a minimal warm/cold/Arctic box model to test this hypothesis and prognose the Pliocene climate. In particular, based on model physics, I discern three thermal thresholds as Pliocene cools: (1) when the Arctic temperature falls below the marking temperature of the ice front, the East Greenland ice sheet would descend to the sea level and calve into the Nordic Seas; (2) when the Arctic temperature cools to the freezing point, the ice sheet would form and expand over circum-Arctic lowlands to cause a massive deposition of ice-rafted debris marking Northern Hemisphere glaciation (NHG); (3) when glacial state persists through low eccentricity, it would cause a transition from obliquity- to eccentricity-dominated glacial cycles. Aligning these thresholds with the observed ones around 3.5, 2.7, and 1 million years ago, the model produces a temporal evolution of the Pliocene temperature as well as its driving albedo change. Since the latter can be accommodated by the observed one, it supports the Neogene uplift as the tectonic origin of NHG. Full article
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<p>A model of warm/cold/Arctic oceanic boxes designated by numerals 1/2/3 separated by the subtropical front (30° N) and the Fram Strait (80° N). The SST (thick solid line) is anchored by the Arctic temperature to retain a linear profile in the cold box. The differential incoming SW flux (<math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi>q</mi> </mrow> <mrow> <mi>i</mi> </mrow> <mrow> <mo>*</mo> </mrow> </msubsup> </mrow> </semantics></math>), after atmospheric absorption and reflection by planetary albedo, is absorbed by the ocean (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>i</mi> </mrow> </msub> </mrow> </semantics></math>) to differentiate the SST, which in turn differentiates the SAT by the convective flux <math display="inline"><semantics> <mrow> <mo>(</mo> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>c</mi> </mrow> </msub> </mrow> </semantics></math>). The latitudinal coordinate is defined as <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mrow> <mrow> <mi mathvariant="normal">sin</mi> </mrow> <mo>⁡</mo> <mrow> <mo>(</mo> <mi mathvariant="normal">l</mi> <mi mathvariant="normal">a</mi> <mi mathvariant="normal">t</mi> <mi mathvariant="normal">i</mi> <mi mathvariant="normal">t</mi> <mi mathvariant="normal">u</mi> <mi mathvariant="normal">d</mi> <mi mathvariant="normal">e</mi> <mo>)</mo> </mrow> </mrow> </mrow> </semantics></math>, so ocean-box boundaries lie at <math display="inline"><semantics> <mrow> <mi>y</mi> <mo>=</mo> <mn>0.5</mn> </mrow> </semantics></math> and 0.98.</p>
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<p>Profiles of cold/Arctic-box summer SAT (doubling as the snowline, both tick-marked on the ordinate) to illustrate thermal thresholds as the Pliocene cools. The initial condition (labeled EP for the early Pliocene) is when the Tibet plateau rises above the snowline to initiate the Pliocene cooling. The first threshold (labeled GIS for Greenland ice sheet) is when the Arctic temperature falls below the glacial marking temperature (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>, dashed) to fully glaciate Greenland. The second threshold (labeled NHG for Northern Hemisphere glaciation) is when the Arctic Ocean is cooled to the freezing point, so the polar cap (light-shaded) would form on the circum-Arctic lowlands and advance toward <math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mi>g</mi> </mrow> </msub> </mrow> </semantics></math>.</p>
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<p>Time evolution of the modeled global (<math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi>T</mi> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>), averaged cold-box (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msub> </mrow> </semantics></math>), and Arctic (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>T</mi> </mrow> <mrow> <mn>3</mn> </mrow> </msub> </mrow> </semantics></math>) temperatures and uplift-induced ice cover (<span class="html-italic">i</span>) when thermal thresholds (dashed vertical lines) are aligned with abrupt cooling of the observed subpolar SST (thick solid line, adapted from [<a href="#B4-glacies-01-00003" class="html-bibr">4</a>]). The shaded portions are solution spans for global sensitivity ranging from 0.5 (thin solid line) to 1.5 (thin dashed line). The solid square is the estimated ice cover by uplift, which is seen sufficient to cause NHG.</p>
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<p>North–south section of a plateau (of meridional span <span class="html-italic">l</span>) when the snowline (SL, thick solid) is lower than the plateau, so its overhead moisture transport would cause accumulation (with annual rate <math display="inline"><semantics> <mrow> <mi>p</mi> </mrow> </semantics></math>), and the southward ice flux would be depleted by ablation at the glacial line (GL, thick dashed). The moisture transport crossing the snowline is linked to energy transport (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>F</mi> </mrow> <mrow> <mi>a</mi> </mrow> </msub> </mrow> </semantics></math>) by the Clausius–Clapeyron relation, which in turn balances the radiative cooling through the tropopause (<math display="inline"><semantics> <mrow> <msub> <mrow> <mi>q</mi> </mrow> <mrow> <mi>r</mi> </mrow> </msub> </mrow> </semantics></math>). The aim is to derive the marking temperature of GL.</p>
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24 pages, 11382 KiB  
Article
Instrument Design and In-Flight Performance of an Airborne Terahertz Ice Cloud Imager
by Rongchuan Lv, Wenyu Gao, Feng Luo, Yinan Li, Zheng He, Congcong Wang, Yan Zhang, Chengzhen Zhang, Daozhong Sun, Jian Shang, Fangli Dou and Xiaodong Wang
Remote Sens. 2024, 16(14), 2602; https://doi.org/10.3390/rs16142602 - 16 Jul 2024
Viewed by 593
Abstract
The Airborne Terahertz Ice Cloud Imager (ATICI) is an airborne demonstration prototype of an ice cloud imager (ICI), which will be launched on the next generation of Fengyun satellites and plays an important role in heavy precipitation detection, typhoon, and medium-to-short-term meteorological/ocean forecasting. [...] Read more.
The Airborne Terahertz Ice Cloud Imager (ATICI) is an airborne demonstration prototype of an ice cloud imager (ICI), which will be launched on the next generation of Fengyun satellites and plays an important role in heavy precipitation detection, typhoon, and medium-to-short-term meteorological/ocean forecasting. At present, it has 13 frequency channels covering 183–664 GHz, which are sensitive to scattering by cloud ice. This paper provides an overview of ATICI and proposes a receiving front-end design scheme using a planar mirror and a quasi-optical feed network which improves the main beam efficiency of each frequency band, with measured values better than 95.5%. It can detect factors such as ice particle size, ice water path, and ice water content in clouds by rotating the circular scanning of the antenna feed system. A high-sensitivity receiver system has been developed and tested for verification. The flight verification results show that the quasi-optical feed network subsystem works well and performs stably under vibration and temperature changes. The system sensitivity is better than 1.5 K, and the domestically produced high-frequency receiver has stable performance, which can meet the conditions of satellite applications. The ATICI performs well and meets expectations, verifying the feasibility of the Fengyun-5 ICI payload. Full article
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<p>Cessna 550 aircraft.</p>
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<p>Diagram of scanning method.</p>
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<p>ATICI interconnections schematic.</p>
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<p>Schematic of ATICI.</p>
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<p>Standard radiation black load.</p>
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<p>Flight calibration schematic.</p>
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<p>Quasi-optical feed network and receiver physical diagram and schematic diagram.</p>
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<p>Frequency selection surface.</p>
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<p>Simulation diagram of TM wave (H polarization) and TE wave (V polarization) for “C” type unit.</p>
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<p>Substrate free metal fine wire grid structure.</p>
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<p>Substrate free metal fine wire grid structure.</p>
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<p>Reflection and transmission characteristics of vertically polarized waves by polarization line gratings (<b>a</b>,<b>b</b>).</p>
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<p>Reflection and transmission characteristics of horizontally polarized waves by polarization line gratings (<b>a</b>,<b>b</b>).</p>
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<p>Reflection and transmission characteristics of horizontally polarized waves by polarization line gratings (<b>a</b>,<b>b</b>).</p>
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<p>The physical and model schematic diagram of the corrugated horn.</p>
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<p>E-plane (<b>a</b>) and H-plane (<b>b</b>) radiation pattern of 664 GHz horn.</p>
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<p>Schematic of the 183 GHz (<b>a</b>) and 664 GHz (<b>b</b>) receivers on ATICI.</p>
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<p>Structure diagram of 664 GHz receiver.</p>
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<p>The 664 GHz harmonic mixer.</p>
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<p>The 664 GHz harmonic mixer.</p>
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<p>Structural design diagram of a 330 GHz band triple frequency converter.</p>
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<p>Test Results of 330 GHz Band Tripler.</p>
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<p>664 GHz receiving front-end block diagram.</p>
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<p>Receiver back-end schematic diagram.</p>
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<p>Principle Composition Block Diagram of 18.444 GHz Digital Loop Circuit.</p>
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<p>Cessna 550 aircraft with ATICI.</p>
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<p>Flight trajectory.</p>
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<p>Observe the fluctuation trend between brightness temperature and simulated brightness temperature.</p>
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<p>Observing brightness temperature and simulating brightness temperature.</p>
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18 pages, 9471 KiB  
Article
Design and Test of Offset Quadrature Phase-Shift Keying Modulator with GF180MCU Open Source Process Design Kit
by Emma Mascorro-Guardado, Susana Ortega-Cisneros, Emilio Isaac Baungarten-Leon, Luis A. Luna-Rodriguez, Uriel Jaramillo-Toral, Manuel Hernández-Aramburo and Emanuel Murillo-García
Electronics 2024, 13(9), 1705; https://doi.org/10.3390/electronics13091705 - 28 Apr 2024
Viewed by 2730
Abstract
This article explores the evolution of integrated circuits (ICs), highlighting the fundamental role of open source Electronic Design Automation (EDA) tools in their development. It describes the IC’s design flow, differentiating between Front-end and Back-end design stages, and [...] Read more.
This article explores the evolution of integrated circuits (ICs), highlighting the fundamental role of open source Electronic Design Automation (EDA) tools in their development. It describes the IC’s design flow, differentiating between Front-end and Back-end design stages, and details the process of implementing the digital stage in offset quadrature phase-shift keying (OQPSK) modulation in an IC, including its hardware description language (HDL), the implementation test in the field-programmable gate array (FPGA), and the physical layout using the first manufactured open source process design kits (PDKs) in Global Foundries’ 180 nm, as well as the use of OpenLane and Caravel. To conclude, the results of the physical tests obtained from the digital modulation are presented, as well as the performance of the raised cosine shaping filter. Full article
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<p><math display="inline"><semantics> <mrow> <mi>O</mi> <mi>Q</mi> <mi>P</mi> <mi>S</mi> <mi>K</mi> </mrow> </semantics></math> modulator.</p>
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<p><span class="html-italic">OQPSK</span> modulation for FPGA and <span class="html-italic">IC</span>.</p>
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<p>Block diagram of <span class="html-italic">OQPSK</span> modulator implemented (<b>a</b>). Structure of the pulse shaping (<b>b</b>).</p>
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<p>The OpenLane tool executes the RTL to GDSII flow, commencing with synthesis facilitated by the <span class="html-italic">Yosys</span> tool, and culminating in physical verification utilizing the Magic layout tool [<a href="#B8-electronics-13-01705" class="html-bibr">8</a>].</p>
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<p>Caravel block diagram, divided into three sections: harness frame, management area, and user area, which instantiate the <span class="html-italic">OQPSK</span> modulator.</p>
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<p>Simulation result of modulator with raised cosine shaping filter in phase branch.</p>
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<p>Simulation result of modulator with raised cosine shaping filter in quadrature branch.</p>
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<p>View of the <span class="html-italic">GDSII</span> file depicting the <span class="html-italic">OQPSK</span> modulation (<b>a</b>), and the <span class="html-italic">Caravel SoC</span> (<b>b</b>), <span class="html-italic">OQPSK IC</span> (<b>c</b>), breakout board (<b>d</b>), <span class="html-italic">Caravel</span> M.2 developed board (<b>e</b>).</p>
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<p>Physical connection for chip testing.</p>
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<p>Waveform obtained with the shaping filter in the time domain (<b>a</b>); waveform in the frequency domain of the raised cosine filter (<b>b</b>).</p>
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<p>Comparison between the expected signal and the post-processed measured signal.</p>
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<p>Comparison between the expected signal and the post-processed measured signal. Comparison between the expected signal and the post-processed measured signal in the frequency domain.</p>
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<p><span class="html-italic">RTL</span> diagram of <span class="html-italic">OQPSK</span> modulator with raised cosine shaping filter implemented.</p>
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12 pages, 4884 KiB  
Article
Effect of Freeze–Thaw and Wetting–Drying Cycles on the Hydraulic Conductivity of Modified Tailings
by Longlong Meng, Liangxiong Xia, Min Xia, Shaokai Nie, Jiakai Chen, Wenyuan Wang, Aifang Du, Haowen Guo and Bate Bate
Geosciences 2024, 14(4), 93; https://doi.org/10.3390/geosciences14040093 - 25 Mar 2024
Viewed by 1670
Abstract
Mine tailings have shown viability as the fine–grained layer in a capillary barrier structure for controlling acid mine drainage in a circular economy. Their saturated hydraulic conductivities (ksat) under wetting–drying cycles and freeze–thaw cycles remain unexplored. In this study, modified [...] Read more.
Mine tailings have shown viability as the fine–grained layer in a capillary barrier structure for controlling acid mine drainage in a circular economy. Their saturated hydraulic conductivities (ksat) under wetting–drying cycles and freeze–thaw cycles remain unexplored. In this study, modified tailings with a weight ratio of 95:5 (tailings/hydrodesulfurization (HDS) clay from waste–water treatment) and an initial water content of 12% were used. The ksat of specimens was measured after up to 15 wetting–drying cycles, each lasting 24 h, with a drying temperature of 105 °C. The ksat for wetting–drying cycles decreased from 3.9 × 10−6 m/s to 9.5 × 10−7 m/s in the first three cycles and then stabilized in the subsequent wetting–drying cycles (i.e., 5.7 × 10−7 m/s–6.3 × 10−7 m/s). Increased fine particles due to particle breakage are the primary mechanism for the ksat trend. In addition, the migration of fines and their preferential deposition near the pore throat area may also promote this decreasing trend through the shrinking and potentially clogging–up of pore throats. This could be explained by the movement of the meniscus, increased salinity, and, subsequently, the shrinkage of the electrical diffuse layer during the drying cycle. Similar specimens were tested to measure ksat under up to 15 freeze–thaw cycles with temperatures circling between −20 °C and 20 °C at 12 h intervals. Compared to the untreated specimen (i.e., 3.8 × 10−6 m/s), the ksat after three freeze–thaw cycles decreased by 77.6% (i.e., 8.5 × 10−7 m/s) and then remained almost unchanged (i.e., 5.6 × 10−7 m/s–8.9 × 10−7 m/s) in subsequent freeze–thaw cycles. The increased fine grain content (i.e., 3.1%) can be used to explain the decreased ksat trend. Moreover, the migration of fines toward the pore throat area, driven by the advancing and receding of ice lens fronts and subsequent deposition at the pore throat, may also contribute to this trend. Full article
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<p>Schematic of the inclined two–layer mining capillary barrier system.</p>
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<p>Copper mine waste: (<b>a</b>) waste rock; (<b>b</b>) tailings; (<b>c</b>) HDS clay (photo courtesy of Dr. Qiong Wang).</p>
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<p>Particle size distributions of mining materials.</p>
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<p>Schematic of the test apparatuses.</p>
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<p>Test paths: (<b>a</b>) Freeze–thaw cycles; (<b>b</b>) wetting–drying cycles.</p>
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<p>X–ray diffraction patterns of tailings and HDS clay (K: kaolinite; M: muscovite; C: clinochlore; Q: quartz; A: albite; Ca: calcite; and MI: microcline).</p>
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<p>Percentage change in particle groups of modified tailings under freeze–thaw cycles.</p>
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<p>The <span class="html-italic">k</span><sub>sat</sub> of modified tailings under freeze–thaw cycles.</p>
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<p>Conceptual illustration of the micro–particles of modified tailings under freeze–thaw cycles.</p>
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<p>Percentage change in particle groups of modified tailings under wetting–drying cycles.</p>
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<p>The <span class="html-italic">k</span><sub>sat</sub> of modified tailings under wetting–drying cycles.</p>
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<p>Conceptual illustration of the micro–particles of modified tailings under wetting–drying cycles.</p>
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17 pages, 18373 KiB  
Article
Meteorological Characteristics of a Continuous Ice-Covered Event on Ultra-High Voltage Transmission Lines in Yunnan Region in 2021
by Sen He, Yunhai Song, Heyan Huang, Yuhao He, Shaohui Zhou and Zhiqiu Gao
Atmosphere 2024, 15(4), 389; https://doi.org/10.3390/atmos15040389 - 22 Mar 2024
Cited by 1 | Viewed by 1036
Abstract
Yunnan plays a pivotal role in transmitting electricity from west to east within China’s Southern Power Grid. During 7–13 January 2021, a large-scale continuous ice-covering event of ultra-high voltage (UHV) transmission lines occurred in the Qujing area of eastern Yunnan Province. Based on [...] Read more.
Yunnan plays a pivotal role in transmitting electricity from west to east within China’s Southern Power Grid. During 7–13 January 2021, a large-scale continuous ice-covering event of ultra-high voltage (UHV) transmission lines occurred in the Qujing area of eastern Yunnan Province. Based on ERA5 reanalysis data and meteorological observation data of UHV transmission line icing in China’s Southern Power Grid, the synoptic causes of the icing are comprehensively analyzed from various perspectives, including weather situations, vertical stratification of temperature and humidity, local meteorological elements, and atmospheric circulation indices. The results indicate a strong East Asian trough and a blocking high directing northern airflow southward ahead of the ridge. Cold air enters the Qujing area and combines with warm and moist air from the subtropical high pressure of 50–110° E. As warm and cold air masses form a quasi-stationary front over the northern mountainous area of Qujing due to topographic uplift, the mechanism of “supercooling and warm rain” caused by the “warm–cold” temperature profile structure leads to freezing rain events. Large-scale circulation indices in the Siberian High, East Asian Trough, and 50–110° E Subtropical High regions provided clear precursor signals within 0–2 days before the icing events. Full article
(This article belongs to the Section Meteorology)
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<p>The probability distribution of the maximum ice thickness of transmission lines in Yunnan during the continuous icing process from 7 January to 13 January 2021.</p>
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<p>The rectangular region of the six atmospheric circulation indices was calculated. The green dot represents the areas where power transmission lines experienced icing (the colored plot is the 500 hPa potential height field at 8:00 a.m. on 14 January 2023).</p>
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<p>Time series plot of icing thickness, temperature, and humidity for pylons A-H from 7 January to 13 January 2021. (The vertical downward phase of the curve corresponds to the rapid melting of the icing caused by the high-voltage company’s direct current de-icing operation on the towers).</p>
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<p>The 500 hPa large-scale circulation pattern (daily 8:00 A.M. from 5 January to 14 January 2023). The green dots represent the areas where power transmission lines experienced icing, and the brown solid lines represent the locations of low-pressure troughs. “H” represents high-pressure centers, “L” represents low-pressure centers, and “T” represents troughs.</p>
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<p>The 750 hPa large-scale circulation situation field ((<b>a</b>–<b>j</b>) represent 8:00 A.M. each day from 5 January to 14 January 2023, respectively). Contours are potential height fields, filled colors are temperature fields, and vector fields are water vapor fluxes. The black filled areas represent the Tibetan Plateau.</p>
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<p>Time series of temperature vertical profiles.</p>
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<p>Time series of relative humidity vertical profiles.</p>
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<p>Transport of water vapor flux at 750 hPa.</p>
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<p>Daily total precipitation in the area (the latitude is from 25.7 to 26.0° N and the longitude is from 103.3 to 104.4° E) of the eight pylons.</p>
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<p>The latitudinal temperature (<b>a</b>) and humidity (<b>b</b>) vertical profiles on 9 January at 08:00 during the first stage (longitude is 104° E).</p>
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<p>The latitudinal temperature (<b>a</b>) and humidity (<b>b</b>) vertical profiles on 11 January at 03:00 during the second stage (longitude is 104° E).</p>
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<p>Standardization of daily average value of selected atmospheric circulation indices during the icing process (The time point on the horizontal axis represents 08:00 a.m. on one day to 07:00 a.m. the next day).</p>
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<p>Leading and lagging correlation coefficients of average daily variation and maximum daily ice cover thickness of each circulation index. The negative (positive) abscissa indicates the number of days leading (lagging), and the ordinate indicates the correlation coefficient. The negative (positive) delay on the horizontal axis represents the leading (lagging) correlation between the daily mean circulation index and the daily maximum ice cover thickness ((<b>a</b>) represents East Asia trough intensity index, (<b>b</b>) represents 850hPa subtropical high index, (<b>c</b>) represents subtropical high area, (<b>d</b>) represents subtropical high intensity, (<b>e</b>) represents Siberian High Pressure System, and (<b>f</b>) represents subtropical ridge point.).</p>
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12 pages, 5872 KiB  
Article
Numerical Study of Pore Water Pressure in Frozen Soils during Moisture Migration
by Bicheng Zhou, Anatoly V. Brouchkov and Jiabo Hu
Water 2024, 16(5), 776; https://doi.org/10.3390/w16050776 - 5 Mar 2024
Cited by 1 | Viewed by 1449
Abstract
Frost heaving in soils is a primary cause of engineering failures in cold regions. Although extensive experimental and numerical research has focused on the deformation caused by frost heaving, there is a notable lack of numerical investigations into the critical underlying factor: pore [...] Read more.
Frost heaving in soils is a primary cause of engineering failures in cold regions. Although extensive experimental and numerical research has focused on the deformation caused by frost heaving, there is a notable lack of numerical investigations into the critical underlying factor: pore water pressure. This study aimed to experimentally determine changes in soil water content over time at various depths during unidirectional freezing and to model this process using a coupled hydrothermal approach. The agreement between experimental water content outcomes and numerical predictions validates the numerical method’s applicability. Furthermore, by applying the Gibbs free energy equation, we derived a novel equation for calculating the pore water pressure in saturated frozen soil. Utilizing this equation, we developed a numerical model to simulate pore water pressure and water movement in frozen soil, accounting for scenarios with and without ice lens formation and quantifying unfrozen water migration from unfrozen to frozen zones over time. Our findings reveal that pore water pressure decreases as freezing depth increases, reaching near zero at the freezing front. Notably, the presence of an ice lens significantly amplifies pore water pressure—approximately tenfold—compared to scenarios without an ice lens, aligning with existing experimental data. The model also indicates that the cold-end temperature sets the maximum pore water pressure value in freezing soil, with superior performance to Konrad’s model at lower temperatures in the absence of ice lenses. Additionally, as freezing progresses, the rate of water flow from the unfrozen region to the freezing fringe exhibits a fluctuating decline. This study successfully establishes a numerical model for pore water pressure and water flow in frozen soil, confirms its validity through experimental comparison, and introduces an improved formula for pore water pressure calculation, offering a more accurate reflection of the real-world phenomena than previous formulations. Full article
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<p>Microscopic schematic of soil (<b>a</b>) particle–ice–water and (<b>b</b>) particle–lens–water at the freezing fringe.</p>
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<p>(<b>a</b>)The schematic of a freezing soil column and (<b>b</b>) the diagram of the physical principle for calculating water flow.</p>
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<p>Experimental and model depth variation in frost front with time.</p>
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<p>Experimental and model water content variation with time at different heights. The freezing times are (<b>a</b>) 36 h, (<b>b</b>) 72 h, (<b>c</b>) 96 h, and (<b>d</b>) 120 h, respectively.</p>
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<p>Pore water pressure variation with time at different depths (temperature of cold end <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math> °C). (<b>a</b>) Frozen soil with ice lens and (<b>b</b>) frozen soil without ice lens.</p>
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<p>Pore water pressure variation with time at different depths (temperature of cold end <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>10</mn> </mrow> </semantics></math> °C). (<b>left</b>) Frozen soil with ice lens and (<b>right</b>) frozen soil without ice lens.</p>
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<p>Comparison of the results of this paper’s model with Konrad’s model (cold-end temperature <math display="inline"><semantics> <mrow> <mo>−</mo> <mn>5</mn> </mrow> </semantics></math> °C and freezing time 20 h).</p>
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<p>Variation in water flow (<b>a</b>) per unit of time and (<b>b</b>) total water flow with time.</p>
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