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19 pages, 8506 KiB  
Article
Rapid Intensification of Typhoon Rammasun (2014) with Strong Vertical Wind Shear
by Weiyu Lu and X. San Liang
Atmosphere 2025, 16(3), 297; https://doi.org/10.3390/atmos16030297 (registering DOI) - 2 Mar 2025
Abstract
From a traditional point of view, the growth of a tropical cyclone (TC) requires that the vertical wind shear (VWS) should be weak. However, Typhoon Rammasun (2014) underwent a rapid intensification (RI) even in the presence of a strong VWS background. This study [...] Read more.
From a traditional point of view, the growth of a tropical cyclone (TC) requires that the vertical wind shear (VWS) should be weak. However, Typhoon Rammasun (2014) underwent a rapid intensification (RI) even in the presence of a strong VWS background. This study investigates the counterintuive phenomenon, using the multiscale window transform (MWT) and the theory of canonical transfer. For the first time, the diagnostic results show that the strong VWS provided additional available potential energy (APE) to the mid-to-upper troposphere through baroclinic instability. This APE was converted into kinetic energy (KE) via buoyancy conversion and transported to the lower troposphere by pressure gradient, increasing the lower-troposphere wind speed. The strong VWS facilitated the RI in two main ways. First, it was via baroclinic instability. Strong VWS facilitated the transfer of APE from the background flow window to the typhoon scale window, supplying additional APE to the mid-to-upper troposphere, hence enhancing the warm-core structure. Second, the VWS direction shifted from an east-west orientation to a north-south orientation. This directional change put the typhoon’s vertical alignment from a westward tilt back to a straighter one. This effectively suppressed the destructive effects of the asymmetric circulation, and promoted the conversion of APE into KE via buoyancy conversion, hence contributed to the RI. Full article
(This article belongs to the Section Meteorology)
Show Figures

Figure 1

Figure 1
<p>The track of Typhoon Rammasun. Each point represents the typhoon’s position at different times, with different colors indicating its varying intensities: TD—tropical depression, TS—tropical storm, STS—super tropical storm, TY—typhoon, STY—severe typhoon, SuperTY—super typhoon.</p>
Full article ">Figure 2
<p>Intensity variations of Typhoon Rammasun over time. The black line denotes the minimum central atmospheric pressure, the blue line represents the maximum sustained wind speed, and the grey shaded area delineates the period from the onset of the second RI.</p>
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<p>The wavelet power spectrum (m<sup>2</sup>/s<sup>2</sup>) and global wavelet spectrum (m<sup>2</sup>/s<sup>2</sup>) of the 925 hPa geopotential field time series at the selected point (19° N, 113.25° E). <span class="html-italic">X</span>-axis in the left panel: the time steps, with each step corresponding to a 3 h interval. <span class="html-italic">X</span>-axis in the right panel: energy/variance summed over time steps. <span class="html-italic">Y</span>-axis in both panels: the period (days). The dotted regions mark areas with statistically significant wavelet power at the 95% confidence level.</p>
Full article ">Figure 4
<p>The geopotential anomaly fields of the original field, the TC-scale window, and the background flow window (from top to bottom) at different altitudes at 0000 UTC July 18 (from left to right: 925 hPa, 850 hPa, 500 hPa, 200 hPa; units: <math display="inline"><semantics> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>/</mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>). The black line represents the typhoon’s track, with black dots indicating the typhoon’s position at the corresponding time; the same applies to other figures below. The colorbar used in this figure is such that negative values are in blue and positive values are in red; the same applies to all figures hereafter.</p>
Full article ">Figure 5
<p>(<b>a</b>) Temporal evolution of the VWS magnitude for different fields, with the RI period indicated between the two black dashed lines. (<b>b</b>–<b>e</b>) The specific magnitude and direction of the VWS (m/s) for the three fields at different time points (black for the original field; blue for the background flow window; red for the TC-scale window). The time period between the two black dashed lines represents the RI period. This convention is applied in all subsequent figures.</p>
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<p>Spatial distribution of VWS at different times: 0000 UTC 13 July, 0000 UTC 15 July, 0000 UTC 17 July, and 1800 UTC 18 July (represented by arrows; the same in the figures hereafter).</p>
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<p>The time evolution of available potential energy (APE), kinetic energy (KE), and their rates of change at different vertical layers (specific values are indicated in the upper-left corner of the figure). The units for the energy terms are <math display="inline"><semantics> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>/</mo> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>; for the rates of change, the units are <math display="inline"><semantics> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>/</mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>.</p>
Full article ">Figure 8
<p>The time evolution of energy components, where positive values indicate energy gained in the TC-scale window, and negative values represent energy loss (averaged over grid points within a 500 km radius of the center; units: <math display="inline"><semantics> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>/</mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>).</p>
Full article ">Figure 9
<p>Energy terms related to available potential energy (APE) and kinetic energy (KE), and their transfers within the TC-scale window in the lower, middle, and upper troposphere. (<b>a</b>) Energy transfer pathways before RI. (<b>b</b>) Energy transfer pathways during RI and their changes compared to those before RI. The arrows indicate the direction of energy path.</p>
Full article ">Figure 10
<p>Distribution of <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="sans-serif">Γ</mi> </mrow> <mrow> <mi mathvariant="normal">A</mi> </mrow> <mrow> <mn>0</mn> <mo>→</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> at the 200 hPa pressure level at six different time points, corresponding to the following stages of Rammasun: the initial appearance over the Western Pacific, the first RI, the time prior to the onset of the second RI, the onset of the second RI, the period during the second RI, and the moment of its peak intensity.</p>
Full article ">Figure 11
<p>The temporal evolution of different components of <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="sans-serif">Γ</mi> </mrow> <mrow> <mi mathvariant="normal">A</mi> </mrow> <mrow> <mn>0</mn> <mo>→</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> (as shown in Equation (5)) within a 500 km radius of the typhoon center as it moved. (The terms ①–⑥ are expressed as the respective terms of the right hand side of Equation (5)).</p>
Full article ">Figure 12
<p>The horizontal distribution of the eddy heat flux terms in <math display="inline"><semantics> <mrow> <msubsup> <mrow> <mi mathvariant="sans-serif">Γ</mi> </mrow> <mrow> <mi mathvariant="normal">A</mi> </mrow> <mrow> <mn>0</mn> <mo>→</mo> <mn>1</mn> </mrow> </msubsup> </mrow> </semantics></math> during RI. (<b>a</b>–<b>c</b>) Term ③; (<b>d</b>–<b>f</b>) term ⑤; (<b>g</b>–<b>i</b>) term ④ (units: <math display="inline"><semantics> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>/</mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>).</p>
Full article ">Figure 13
<p>(<b>a</b>–<b>d</b>): Horizontal distribution of the physical quantities involved in Equation (5) at 200 hPa at 1200 UTC 18 July. (<b>a</b>) <math display="inline"><semantics> <mrow> <mo>∂</mo> <mover accent="true"> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mo>¯</mo> </mover> <mo>/</mo> <mo>∂</mo> <mi mathvariant="normal">x</mi> </mrow> </semantics></math>; (<b>b</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">u</mi> <mo>′</mo> <mi mathvariant="normal">T</mi> <mo>′</mo> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>; (<b>c</b>) <math display="inline"><semantics> <mrow> <mo>∂</mo> <mover accent="true"> <mrow> <mi mathvariant="normal">T</mi> </mrow> <mo>¯</mo> </mover> <mo>/</mo> <mo>∂</mo> <mi mathvariant="normal">y</mi> </mrow> </semantics></math>; (<b>d</b>) <math display="inline"><semantics> <mrow> <mover accent="true"> <mrow> <mi mathvariant="normal">v</mi> <mo>′</mo> <mi mathvariant="normal">T</mi> <mo>′</mo> </mrow> <mo>¯</mo> </mover> </mrow> </semantics></math>. (<b>e</b>): Horizontal distribution of VWS at the same time.</p>
Full article ">Figure 14
<p>The vertical structure of the potential and the vertical distribution of buoyancy before and during the RI phase. (<b>a</b>–<b>c</b>) The state before RI, and (<b>d</b>) the state during RI. In the figure, the contour lines represent the potential, with the color-filled areas representing the buoyancy (units: <math display="inline"><semantics> <mrow> <msup> <mrow> <mi mathvariant="normal">m</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> <mo>/</mo> <msup> <mrow> <mi mathvariant="normal">s</mi> </mrow> <mrow> <mn>2</mn> </mrow> </msup> </mrow> </semantics></math>). The black horizontal lines mark the 200 hPa height level, and the dashed lines are the cyclone’s center reference line.</p>
Full article ">
26 pages, 1149 KiB  
Review
Photothermal and Hydrophobic Surfaces with Nano-Micro Structure: Fabrication and Their Anti-Icing Properties
by Meng Li, Renping Ma, Chaokun Yang, Lebin Wang, Shuangqi Lv, Xin Zhao, Mengyao Pan, Jianjian Zhu and Hongbo Xu
Nanomaterials 2025, 15(5), 378; https://doi.org/10.3390/nano15050378 - 28 Feb 2025
Viewed by 136
Abstract
The formation of ice due to global climate change poses challenges across multiple industries. Traditional anti-icing technologies often suffer from low efficiency, high energy consumption, and environmental pollution. Photothermal and hydrophobic surfaces with nano-micro structures (PHS-NMSs) offer innovative solutions to these challenges due [...] Read more.
The formation of ice due to global climate change poses challenges across multiple industries. Traditional anti-icing technologies often suffer from low efficiency, high energy consumption, and environmental pollution. Photothermal and hydrophobic surfaces with nano-micro structures (PHS-NMSs) offer innovative solutions to these challenges due to their exceptional optical absorption, heat conversion capabilities, and unique surface water hydrophobic characteristics. This paper reviews the research progress of PHS-NMSs in their anti-icing applications. It introduces the mechanisms of ice prevention, fabrication methods, and pathways for performance optimization of PHS-NMSs. The anti-icing performance of PHS-NMSs in different application scenarios is also discussed. Additionally, the paper provides insights into the challenges and future development directions in this field. Full article
(This article belongs to the Special Issue Photofunctional Nanomaterials and Nanostructure, Second Edition)
34 pages, 1078 KiB  
Review
Review of Molten Salt Corrosion in Stainless Steels and Superalloys
by Ying Wei, Peiqing La, Yuehong Zheng, Faqi Zhan, Haicun Yu, Penghui Yang, Min Zhu, Zemin Bai and Yunteng Gao
Crystals 2025, 15(3), 237; https://doi.org/10.3390/cryst15030237 - 28 Feb 2025
Viewed by 64
Abstract
In the context of the global energy structure transformation, concentrated solar power (CSP) technology has gained significant attention. Its future trajectory is oriented towards the construction of ultra-high temperature (700–1000 °C) power plants, aiming to enhance thermoelectric conversion efficiency and economic competitiveness. Chloride [...] Read more.
In the context of the global energy structure transformation, concentrated solar power (CSP) technology has gained significant attention. Its future trajectory is oriented towards the construction of ultra-high temperature (700–1000 °C) power plants, aiming to enhance thermoelectric conversion efficiency and economic competitiveness. Chloride molten salts, serving as a crucial heat transfer and storage medium in the third-generation CSP system, offer numerous advantages. However, they are highly corrosive to metal materials. This paper provides a comprehensive review of the corrosion behaviors of stainless steels and high-temperature alloys in molten salts. It analyzes the impacts of factors such as temperature and oxygen, and it summarizes various corrosion types, including intergranular corrosion and hot corrosion, along with their underlying mechanisms. Simultaneously, it presents an overview of the types, characteristics, impurity effects, and purification methods of molten salts used for high-temperature heat storage and heat transfer. Moreover, it explores novel technologies such as alternative molten salts, solid particles, gases, liquid metals, and the carbon dioxide Brayton cycle, as well as research directions for improving material performance, like the application of nanoparticles and surface coatings. At present, the corrosion of metal materials in high-temperature molten salts poses a significant bottleneck in the development of CSP. Future research should prioritize the development of commercial alloy materials resistant to chloride molten salt corrosion and conduct in-depth investigations into related influencing factors. This will provide essential support for the advancement of CSP technology. Full article
(This article belongs to the Section Crystalline Metals and Alloys)
36 pages, 7735 KiB  
Article
Systematic Security Analysis of Sensors and Controls in PV Inverters: Threat Validation and Countermeasures
by Fengchen Yang, Kaikai Pan, Chen Yan, Xiaoyu Ji and Wenyuan Xu
Sensors 2025, 25(5), 1493; https://doi.org/10.3390/s25051493 - 28 Feb 2025
Viewed by 92
Abstract
As renewable energy sources (RES) continue to expand and the use of power inverters has surged, inverters have become crucial for converting direct current (DC) from RES into alternating current (AC) for the grid, and their security is vital for maintaining stable grid [...] Read more.
As renewable energy sources (RES) continue to expand and the use of power inverters has surged, inverters have become crucial for converting direct current (DC) from RES into alternating current (AC) for the grid, and their security is vital for maintaining stable grid operations. This paper investigates the security vulnerabilities of photovoltaic (PV) inverters, specifically focusing on their internal sensors, which are critical for reliable power conversion. It is found that both current and voltage sensors are susceptible to intentional electromagnetic interference (IEMI) at frequencies of 1 GHz or higher, even with electromagnetic compatibility (EMC) protections in place. These vulnerabilities can lead to incorrect sensor readings, disrupting control algorithms. We propose an IEMI attack that results in three potential outcomes: Denial of Service (DoS), physical damage to the inverter, and power output reduction. These effects were demonstrated on six commercial single-phase and three-phase PV inverters, as well as in a real-world microgrid, by emitting IEMI signals from 100 to 150 cm away with up to 20 W of power. This study highlights the growing security risks of power electronics in RES, which represent an emerging target for cyber-physical attacks in future RES-dominated grids. Finally, to cope with such threats, three detection methods that are adaptable to diverse threat scenarios are proposed and their advantages and disadvantages are discussed. Full article
(This article belongs to the Section Electronic Sensors)
Show Figures

Figure 1

Figure 1
<p>An illustration of the IEMI threat: IEMI can affect PV inverters and cause DoS or physical damage, or damping the power output.</p>
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<p>A typical PV inverter can be modeled as a three-layer structure: Power conversion unit-Sensor-Control algorithms.</p>
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<p>The schematic of voltage and current sensors in the PV inverter.</p>
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<p>The principle of IEMI impact on voltage sensors. The IEMI signal is coupled into the sensor circuit, and then rectified, amplified by the op−amp, and ultimately turned into an offset on the output. (<b>a</b>) Transmission process of IEMI signals in the voltage sensor. (<b>b</b>) The parasitic capacitance of sensor’s PCB.</p>
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<p>The structure of the OPA2171 used in voltage and current sensors.</p>
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<p>Simulation of IEMI injection on different inputs of the op−amp chip.</p>
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<p>The principle of IEMI impact on Hall current sensors. The IEMI signal is injected into the Hall chip and generates a noise <math display="inline"><semantics> <msub> <mi>V</mi> <mi>H</mi> </msub> </semantics></math>. Then the noise will be rectified, amplified by the op−amp, and result in a deviation on the output.</p>
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<p>Setup of feasibility test on sensors.</p>
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<p>The voltage and current sensors’ PCB we designed for the initial feasibility test.</p>
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<p>The result of the IEMI frequency test on the voltage and current sensors. The IEMI power and distance are set to <math display="inline"><semantics> <mrow> <mn>10</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">W</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>50</mn> <mspace width="0.166667em"/> <mi>cm</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 11
<p>The experiment result of manipulation with a single-frequency signal and an AM signal on the sensor. ①: Without EMI; ②: Single-frequency EMI; ③: AM-modulated EMI.</p>
Full article ">Figure 12
<p>The simulation of the DC bus voltage manipulation. We add a fake <math display="inline"><semantics> <msub> <mi>V</mi> <mi>a</mi> </msub> </semantics></math> on the measured DC bus voltage and record the real DC bus voltage under control. For <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>&lt;</mo> <mn>0</mn> </mrow> </semantics></math>, ①: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> V, ②: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>50</mn> </mrow> </semantics></math> V, ③: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>100</mn> </mrow> </semantics></math> V, ④: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>200</mn> </mrow> </semantics></math> V, ⑤: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>=</mo> <mo>−</mo> <mn>300</mn> </mrow> </semantics></math> V; for <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>&gt;</mo> <mn>0</mn> </mrow> </semantics></math>, ①: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> V, ②: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>20</mn> </mrow> </semantics></math> V, ③: <math display="inline"><semantics> <mrow> <msub> <mi>V</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>100</mn> </mrow> </semantics></math> V.</p>
Full article ">Figure 13
<p>Design of IEMI signals <math display="inline"><semantics> <mrow> <mi>s</mi> <mo>(</mo> <mi>t</mi> <mo>)</mo> </mrow> </semantics></math> of <tt>DoS</tt> and <tt>Damage</tt>.</p>
Full article ">Figure 14
<p>The simulations of grid current sensors spoofing. It gives the simulated waveform of the real current value and the sensor output value when the single-phase and three-phase grid current measurement is manipulated. (<b>a</b>) Single-phase PV inverter. ①: <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> A, ②: <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>50</mn> <mo form="prefix">sin</mo> <mi>ω</mi> <mi>t</mi> <mspace width="0.166667em"/> </mrow> </semantics></math> A, ③: <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>200</mn> <mo form="prefix">sin</mo> <mi>ω</mi> <mi>t</mi> <mspace width="0.166667em"/> </mrow> </semantics></math> A. (<b>b</b>) Three-phase PV inverter. ①: <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>0</mn> </mrow> </semantics></math> A, ②: <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>50</mn> </mrow> </semantics></math> A, ③: <math display="inline"><semantics> <mrow> <msub> <mi>I</mi> <mi>a</mi> </msub> <mo>=</mo> <mn>200</mn> </mrow> </semantics></math> A.</p>
Full article ">Figure 15
<p>Simulation of injecting perturbations of different frequencies into the MPPT control system. The red dots represent the positions at which the perturbations are injected.</p>
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<p>Experiment setup of evaluation on PV inverters.</p>
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<p>The tested single-phase solar inverters and three-phase solar inverters under laboratory conditions.</p>
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<p>The experiment results of <tt>DoS</tt> and <tt>Damage</tt>. (<b>a</b>) Result of <tt>DoS</tt>. ①: Before EMI, ②: IEMI begins, ③: After EMI. (<b>b</b>) Result of <tt>Damage</tt>. ①: Before EMI, ②: IEMI begins, ③: Burning out, ④: After EMI.</p>
Full article ">Figure 19
<p>The impact of <tt>DoS</tt> on a real-world PV microgrid’s frequency. Stage ①: real-world experiment, Stage ②: simulation. (<b>a</b>) Experiment setup in the real-world microgrid. (<b>b</b>) Impact of <tt>DoS</tt> on microgrid frequency.</p>
Full article ">Figure 20
<p>The influence of distance and power to manipulate inverter sensors and <tt>DoS</tt> a commercial inverter. The nonmonotonicity in (<b>c</b>) is mainly because the power will affect the electromagnetic field distribution of the antenna, which is not linear.</p>
Full article ">Figure 21
<p>The simulation result of Switching Attack with <tt>Damping</tt>.</p>
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<p>The detection method based on the distributed effect of IEMI. IEMI coupled before the transducer is converted to DC bias, while IEMI coupled behind the transducer remains AC noise, which can be regarded as a detection feature.</p>
Full article ">Figure 23
<p>The voltage sensor’s output under different IEMI attack frequencies. (<b>a</b>) is under normal state, (<b>b</b>–<b>f</b>) are under IEMI attack with the attack power of <math display="inline"><semantics> <mrow> <mn>7</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">W</mi> </mrow> </semantics></math> and frequency of <math display="inline"><semantics> <mrow> <mn>1604</mn> <mspace width="0.166667em"/> <mi>MHz</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>1236</mn> <mspace width="0.166667em"/> <mi>MHz</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>1560</mn> <mspace width="0.166667em"/> <mi>MHz</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>1740</mn> <mspace width="0.166667em"/> <mi>MHz</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>1726</mn> <mspace width="0.166667em"/> <mi>MHz</mi> </mrow> </semantics></math>.</p>
Full article ">Figure 24
<p>Sensor output’s STD under different IEMI frequencies.</p>
Full article ">Figure 25
<p>The sensor’s output under different IEMI attack power. (<b>a</b>) is under normal state, while (<b>b</b>–<b>f</b>) are under IEMI attack at a frequency of <math display="inline"><semantics> <mrow> <mn>1560</mn> <mspace width="0.166667em"/> <mi>MHz</mi> </mrow> </semantics></math> and power levels of <math display="inline"><semantics> <mrow> <mn>4.47</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">W</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>5.01</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">W</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>5.62</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">W</mi> </mrow> </semantics></math>, <math display="inline"><semantics> <mrow> <mn>6.31</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">W</mi> </mrow> </semantics></math>, and <math display="inline"><semantics> <mrow> <mn>7.08</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">W</mi> </mrow> </semantics></math>, respectively.</p>
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<p>Sensor output’s STD under different IEMI power levels.</p>
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<p>The experiment result of the detection of <tt>Damping</tt> attack on the TI C2000 solar inverter. <math display="inline"><semantics> <mrow> <mn>0</mn> <mo>∼</mo> <mn>0.8</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>: Initialization, <math display="inline"><semantics> <mrow> <mn>0.8</mn> <mo>∼</mo> <mn>3</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math> and <math display="inline"><semantics> <mrow> <mn>3.2</mn> <mo>∼</mo> <mn>4</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>: Normal operation, <math display="inline"><semantics> <mrow> <mn>3</mn> <mo>∼</mo> <mn>3.2</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>: <tt>Damping</tt> attack, <math display="inline"><semantics> <mrow> <mn>4</mn> <mo>∼</mo> <mn>5</mn> <mspace width="0.166667em"/> <mi mathvariant="normal">s</mi> </mrow> </semantics></math>: Manual reduce power by half.</p>
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<p>The impact of inverter working power and sensor’s deviation under attack on <math display="inline"><semantics> <mi>σ</mi> </semantics></math>. (<b>a</b>) The efficiency <math display="inline"><semantics> <mi>σ</mi> </semantics></math> under different working power. (<b>b</b>) The maximum deviation of efficiency <math display="inline"><semantics> <mi>σ</mi> </semantics></math> under different sensor’s deviation caused by IEMI attack.</p>
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<p>The structure of the lightweight CNN model. Including 2 convolution layers, a flattened layer and a fully connected layer.</p>
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<p>The comparison of the three methods. Method 1 is based on the distribution of IEMI, method 2 is based on the conservation of energy, and method 3 is based on neural networks. The “3” means excellent, “2” means good, “1” means fair.</p>
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22 pages, 7361 KiB  
Article
Exploring Performance Degradation of Proton Exchange Membrane Fuel Cells Based on Diffusion Transformer Model
by Lingling Lv, Pucheng Pei, Peng Ren, He Wang and Geng Wang
Energies 2025, 18(5), 1191; https://doi.org/10.3390/en18051191 - 28 Feb 2025
Viewed by 118
Abstract
Proton exchange membrane fuel cells (PEMFCs) stand at the forefront of energy conversion technology, efficiently converting the chemical energy of hydrogen and oxygen directly into electricity. Research on predicting the remaining useful life of PEMFCs has long been a focus, as it plays [...] Read more.
Proton exchange membrane fuel cells (PEMFCs) stand at the forefront of energy conversion technology, efficiently converting the chemical energy of hydrogen and oxygen directly into electricity. Research on predicting the remaining useful life of PEMFCs has long been a focus, as it plays a crucial role in preventing failures and mitigating safety risks. This paper introduces a robust diffusion transformer (DiT) model, which is a novel approach leveraging generative artificial intelligence (GAI) technology to innovate the existing methods for predicting the performance degradation of PEMFCs. This model employs random Gaussian noise to generate stable performance degradation data of PEMFCs under specified conditions. The predictive accuracy is then assessed by benchmarking against a bi-directional long short-term memory recurrent neural network (Bi-LSTM) using two distinct experimental datasets. The evaluation shows that the DiT model achieves higher predictive accuracy than the reference model. Specifically, the mean absolute prediction error is reduced by 72.7% under steady-state conditions and 59.3% under dynamic conditions. Correspondingly, the remaining useful life error (RE) is diminished by 80% and 88%, respectively. These findings indicate that the DiT model has significant potential in PEMFCs performance degradation research. Full article
(This article belongs to the Special Issue Trends and Prospects in Fuel Cell Towards Industrialization)
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<p>Framework of the proposed diffusion transformer (DiT) model.</p>
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<p>The schematic diagram of the PEMFC experimental setups.</p>
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<p>Smoothed voltage data: (<b>a</b>) FC1 and (<b>b</b>) FC2.</p>
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<p>Diagram of diffusion model.</p>
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<p>Diagram of transformer encoder.</p>
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<p>Diagram of DiT model training and inference process.</p>
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<p>Division of datasets on (<b>a</b>) FC1 and (<b>b</b>) FC2.</p>
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<p>The prediction results of two models for FC1: (<b>a</b>) DiT and (<b>b</b>) Bi-LSTM.</p>
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<p>The comparison of predictive evaluation indicators of two models for FC1: (<b>a</b>) MAE; (<b>b</b>) RMSE.</p>
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<p>The prediction results of two models for FC2: (<b>a</b>) DiT and (<b>b</b>) Bi-LSTM.</p>
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<p>The comparison of predictive evaluation indicators of two models for FC2: (<b>a</b>) MAE; (<b>b</b>) RMSE.</p>
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<p>The comparison of remaining useful life error under different models for FC1 and FC2.</p>
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<p>The prediction results of the FC2 dataset using the model trained on FC1: (<b>a</b>) DiT and (<b>b</b>) Bi-LSTM.</p>
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<p>The comparison of predictive evaluation indicators of the prediction results for the FC2 dataset using the FC1-trained model: (<b>a</b>) MAE; (<b>b</b>) RMSE.</p>
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<p>The prediction results of the FC1 dataset using the model trained on FC2: (<b>a</b>) DiT and (<b>b</b>) Bi-LSTM.</p>
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<p>The comparison of predictive evaluation indicators of the prediction results for the FC1 dataset using the FC2-trained model (<b>a</b>) MAE; (<b>b</b>) RMSE.</p>
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15 pages, 2296 KiB  
Article
Plasma Gasification of Medical Plastic Waste to Syngas in a Greenhouse Gas (CO2) Environment
by Andrius Tamošiūnas, Mindaugas Milieška, Dovilė Gimžauskaitė, Mindaugas Aikas, Rolandas Uscila, Kęstutis Zakarauskas, Sebastian Fendt, Sebastian Bastek and Hartmut Spliethoff
Sustainability 2025, 17(5), 2040; https://doi.org/10.3390/su17052040 - 27 Feb 2025
Viewed by 149
Abstract
The global coronavirus (COVID-19) pandemic in early 2020 caused the amount of medical waste, especially plastic waste, to increase. The pandemic exacerbated the plastic waste management problem, including the need to find more sustainable treatment methods. This study investigated the sustainable conversion of [...] Read more.
The global coronavirus (COVID-19) pandemic in early 2020 caused the amount of medical waste, especially plastic waste, to increase. The pandemic exacerbated the plastic waste management problem, including the need to find more sustainable treatment methods. This study investigated the sustainable conversion of plastic waste (FFP2-type face masks) to syngas via pure CO2 plasma gasification to recover energy and reduce environmental pollution. A direct current (DC) thermal arc plasma torch of 40.6–68.4 kW power generated the plasma stream. Carbon dioxide (CO2), as a greenhouse gas (GHG), was used as the main plasma-forming gas and gasifying agent. The 140thermal feedstock input plasma gasification system was used in the study. The effect of the CO2-to-C ratio on the gasification performance efficiency was investigated. The best CO2 plasma gasification process performance was obtained at a CO2-to-C ratio of 2.34. In these conditions, the main syngas components (H2 + CO) comprised 80.46 vol.% (H2: 24.62 vol.% and CO: 55.84 vol.%) and the following values were seen for the heating value of the syngas (LHVsyngas: 13.88 MJ/Nm3), the syngas yield (3.13 Nm3/kgFFP2), the tar content in the syngas (23.0 g/Nm3), the carbon conversion efficiency (CCE: 70.6%), and the cold gas efficiency (CGE: 47.8%). Additionally, the plasma gasification process mass and energy balance were evaluated. It was demonstrated that CO2 plasma gasification could be a promising thermochemical treatment technology for sustainable plastic waste disposal and the simultaneous utilization of greenhouse gases, such as carbon dioxide. Full article
(This article belongs to the Section Energy Sustainability)
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<p>Plasma gasification system.</p>
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<p>Gas concentration changes during the plasma gasifier preheating step with no feeding of the FFP2 pellets, and later during gasification with feeding of the FFP2 pellets.</p>
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<p>Effect of the CO<sub>2</sub>-to-C ratio on producer gas content.</p>
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<p>Effect of the CO<sub>2</sub>-to-C ratio on the LHV of syngas and the H<sub>2</sub>/CO ratio.</p>
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<p>Effect of the CO<sub>2</sub>-to-C ratio on the yield of syngas, H<sub>2</sub>, and CO.</p>
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<p>Effect of the CO<sub>2</sub>-to-C ratio on the CCE and CGE.</p>
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<p>Mass balance of the FFP2 plastic pellets gasification in the CO<sub>2</sub> thermal plasma at a CO<sub>2</sub>-to-C ratio of 2.34.</p>
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<p>Sankey diagram for the energy flow of the FFP2 plastic pellets gasification in the CO<sub>2</sub> thermal plasma at a CO<sub>2</sub>-to-C ratio of 2.34.</p>
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25 pages, 3450 KiB  
Article
Extending Power Electronic Converter Lifetime in Marine Hydrokinetic Turbines with Reinforcement Learning
by Samuel Barton, Ted K. A. Brekken and Yue Cao
Appl. Sci. 2025, 15(5), 2512; https://doi.org/10.3390/app15052512 - 26 Feb 2025
Viewed by 171
Abstract
Hydrokinetic turbines (HKTs) are a promising renewable energy source due to the consistency and high energy density in river and tidal resources. One of the primary barriers to the widespread adoption of HKT technologies is a high levelized cost of energy (LCOE). Considering [...] Read more.
Hydrokinetic turbines (HKTs) are a promising renewable energy source due to the consistency and high energy density in river and tidal resources. One of the primary barriers to the widespread adoption of HKT technologies is a high levelized cost of energy (LCOE). Considering the marine operating environment, the operation and maintenance costs are substantial. The power electronic converter, a key element in the electrical energy conversion system, is a common point of failure in direct-drive turbine applications—leading to increased maintenance efforts. This work presents a reinforcement learning (RL) method built within a quadratic feedback torque control framework to balance energy generation with power electronic device lifetime. The effectiveness of the RL-based control scheme is compared against a static baseline controller through two year-long tidal case studies. The results showed that the proposed method reduced cumulative damage on the device by upwards of 75% but reduced energy generation by up to 25.2%. Using a custom real-time cost estimation function that considers the sale of energy and an estimate of the costs associated with operating a device at a given temperature, it was found that the RL method can increase net income by up to 45.4% depending on the energy market conditions. Full article
(This article belongs to the Special Issue Dynamics and Control with Applications to Ocean Renewables)
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<p>Hydrokinetic turbine system overview highlighting the turbine, PMSG, and generator-side converter subsystems.</p>
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<p>Plots of normalized turbine power (<math display="inline"><semantics> <msub> <mi>P</mi> <mrow> <mi>t</mi> <mi>u</mi> <mi>r</mi> <mi>b</mi> <mi>i</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> </semantics></math>), torque (<math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>t</mi> <mi>u</mi> <mi>r</mi> <mi>b</mi> <mi>i</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> </semantics></math>), and rotational speed (<math display="inline"><semantics> <mi>ω</mi> </semantics></math>) compared to flow velocity (<math display="inline"><semantics> <msub> <mi>u</mi> <mn>0</mn> </msub> </semantics></math>), highlighting the operation of the turbine in each of the four regions of operation.</p>
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<p>Schematic of a two-level voltage source converter.</p>
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<p>The simplified Cauer thermal network used to estimate junction temperature for each device. This figure only represents two of the six devices that comprise the two-level voltage source converter. The dots are representative of the other four devices feeding into the same node.</p>
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<p>A block diagram representing the agent–environment interaction defined by the RL framework, derived from Sutton and Barto [<a href="#B37-applsci-15-02512" class="html-bibr">37</a>].</p>
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<p>A block diagram highlighting the interaction between the RL agent and the HKT system. Descriptions of the environment block (blue), reward function block (pink), and RL agent block (green) can be found in <a href="#sec2-applsci-15-02512" class="html-sec">Section 2</a> and <a href="#sec3dot2-applsci-15-02512" class="html-sec">Section 3.2</a>.</p>
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<p><math display="inline"><semantics> <msub> <mi>C</mi> <mi>p</mi> </msub> </semantics></math> versus <math display="inline"><semantics> <mi>λ</mi> </semantics></math> curve derived from the results presented in [<a href="#B42-applsci-15-02512" class="html-bibr">42</a>].</p>
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<p>Data representative of the training environment in terms of locational marginal price (LMP), ambient temperature (<math display="inline"><semantics> <mrow> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>m</mi> <mi>b</mi> </mrow> </msub> <mrow> <mo>)</mo> </mrow> </mrow> </semantics></math>, and flow velocity (<math display="inline"><semantics> <msub> <mi>u</mi> <mn>0</mn> </msub> </semantics></math>) for (<b>a</b>) 2020, (<b>b</b>) 2021, (<b>c</b>) 2022, and (<b>d</b>) 2023.</p>
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<p>Normalized reward profiles for each training year in the training set. The reward signals for each year are normalized by the initial reward received under the highly exploratory policy in the first training epoch.</p>
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<p>Plots of ambient temperature <math display="inline"><semantics> <msub> <mi>T</mi> <mrow> <mi>a</mi> <mi>m</mi> <mi>b</mi> </mrow> </msub> </semantics></math>, LMP data, and flow velocity <math display="inline"><semantics> <msub> <mi>u</mi> <mn>0</mn> </msub> </semantics></math> for the (<b>a</b>) 2019 and (<b>b</b>) 2024 case studies, respectively.</p>
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<p>Profiles of feedback gain values <span class="html-italic">k</span> applied by the baseline and RL-based controllers for (<b>a</b>) 2019 and (<b>b</b>) 2024.</p>
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<p>Junction temperature (<math display="inline"><semantics> <msub> <mi>T</mi> <mi>j</mi> </msub> </semantics></math>) profiles for both the baseline and RL-based control case studies completed for (<b>a</b>) 2019 and (<b>b</b>) 2024. The dashed and dotted lines are rolling mean profiles of <math display="inline"><semantics> <msub> <mi>T</mi> <mi>j</mi> </msub> </semantics></math> with an averaging window of three months for the baseline and RL-based control cases, respectively.</p>
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<p>Extracted thermal cycle amplitude data from the output of the rainflow counting algorithm highlighting the number of thermal cycles experienced at each stress level during the case studies completed for (<b>a</b>) 2019 and (<b>b</b>) 2024.</p>
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<p>Energy harvested by the turbine (<math display="inline"><semantics> <msub> <mi>E</mi> <mrow> <mi>t</mi> <mi>u</mi> <mi>r</mi> <mi>b</mi> <mi>i</mi> <mi>n</mi> <mi>e</mi> </mrow> </msub> </semantics></math>) using both the baseline and RL-based control schemes for the (<b>a</b>) 2019 case study and (<b>b</b>) 2024 case study.</p>
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21 pages, 2535 KiB  
Article
A Bidirectional Resonant Converter Based on Partial Power Processing
by Junfeng Liu, Zhouzhou Wu and Qinglin Zhao
Electronics 2025, 14(5), 910; https://doi.org/10.3390/electronics14050910 - 25 Feb 2025
Viewed by 158
Abstract
This article proposes a bidirectional half-bridge resonant converter based on partial power regulation. The converter adopts an LLC converter as a DC-DC transformer (LLC-DCX) in the main power circuit and works in the open loop at the resonant frequency to give full play [...] Read more.
This article proposes a bidirectional half-bridge resonant converter based on partial power regulation. The converter adopts an LLC converter as a DC-DC transformer (LLC-DCX) in the main power circuit and works in the open loop at the resonant frequency to give full play to the performance advantages of the LLC resonant converter. The partial power regulation circuit incorporates a synchronous Buck converter, enabling forward and backward power transmission by controlling the power flow direction. The converter achieves soft switching in both forward and backward directions, thereby reducing switching losses and enhancing conversion efficiency. Compared with the LLC-DCX converter, this converter can achieve wide voltage gain regulation while having high efficiency, which makes it suitable for charge–discharge applications between energy storage systems and DC Buses. In order to verify the performance of the proposed converter, a 1 kW prototype was constructed, maintaining a constant primary voltage of 400 V and a secondary voltage range of 350 V to 450 V. Experimental results indicate that the prototype achieves peak efficiencies of 97.74% in forward operation and 96.92% in backward operation, thoroughly demonstrating the feasibility and effectiveness of the proposed converter. Full article
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<p>Partial power regulation architecture.</p>
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<p>Voltage and power distribution of the converter.</p>
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<p>Bidirectional half-bridge LLC resonant circuit (<b>a</b>) Main circuit topology. (<b>b</b>) LLC fundamental equivalent circuit.</p>
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<p>The main waveform of the split-capacitor LLC volt-doubling resonant converter. (<b>a</b>) Key forward waveform. (<b>b</b>) Key backward waveform.</p>
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<p>Partial power-processing bidirectional resonant converter architecture.</p>
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<p>Key waveform of Buck converter in FCCM mode.</p>
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<p>Mode of synchronous Buck converter in FCCM mode. (<b>a</b>) Mode 1 [<math display="inline"><semantics> <msub> <mi>t</mi> <mn>0</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>t</mi> <mn>1</mn> </msub> </semantics></math>]. (<b>b</b>) Mode 2 [<math display="inline"><semantics> <msub> <mi>t</mi> <mn>1</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>t</mi> <mn>2</mn> </msub> </semantics></math>]. (<b>c</b>) Mode 3 [<math display="inline"><semantics> <msub> <mi>t</mi> <mn>2</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>t</mi> <mn>3</mn> </msub> </semantics></math>]. (<b>d</b>) Mode 4 [<math display="inline"><semantics> <msub> <mi>t</mi> <mn>3</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>t</mi> <mn>4</mn> </msub> </semantics></math>]. (<b>e</b>) Mode 5 [<math display="inline"><semantics> <msub> <mi>t</mi> <mn>4</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>t</mi> <mn>5</mn> </msub> </semantics></math>]. (<b>f</b>) Mode 6 [<math display="inline"><semantics> <msub> <mi>t</mi> <mn>5</mn> </msub> </semantics></math>, <math display="inline"><semantics> <msub> <mi>t</mi> <mn>6</mn> </msub> </semantics></math>].</p>
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<p>Partial power regulation circuit gain characteristic diagram.</p>
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<p>Photograph of the designed converter prototype.</p>
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<p>The key waveforms of the LLC-DCX circuit under the output condition of 400 V/2.22 A. (<b>a</b>) Key waveforms of <math display="inline"><semantics> <msub> <mi>S</mi> <mn>2</mn> </msub> </semantics></math> and resonant current. (<b>b</b>) Key waveforms of <math display="inline"><semantics> <msub> <mi>S</mi> <mn>4</mn> </msub> </semantics></math> and resonant current. (<b>c</b>) Key waveforms of <math display="inline"><semantics> <msub> <mi>S</mi> <mn>6</mn> </msub> </semantics></math> and resonant current.</p>
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<p>The key waveforms of synchronous Buck circuit under different output voltages (<math display="inline"><semantics> <msub> <mi>I</mi> <mrow> <mi>o</mi> <mn>2</mn> </mrow> </msub> </semantics></math> = 2.22 A). (<b>a</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mn>7</mn> </msub> </semantics></math> and inductor current waveforms (<math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> = 350 V). (<b>b</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mn>7</mn> </msub> </semantics></math> and inductor current waveforms (<math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> = 400 V). (<b>c</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mn>7</mn> </msub> </semantics></math> and inductor current waveforms (<math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> = 450 V).</p>
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<p>The key waveforms of the LLC-DCX circuit under the output condition of 400V/2.22A. (<math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> = 400 V). (<b>a</b>) Key waveforms of <math display="inline"><semantics> <msub> <mi>S</mi> <mn>2</mn> </msub> </semantics></math> and resonant current. (<b>b</b>) Key waveforms of <math display="inline"><semantics> <msub> <mi>S</mi> <mn>4</mn> </msub> </semantics></math> and resonant current. (<b>c</b>) Key waveforms of <math display="inline"><semantics> <msub> <mi>S</mi> <mn>6</mn> </msub> </semantics></math> and resonant current.</p>
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<p>The key waveforms of the synchronous Boost circuit with different input parameters on the secondary side. (<math display="inline"><semantics> <msub> <mi>U</mi> <mn>1</mn> </msub> </semantics></math> = 400 V). (<b>a</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mn>8</mn> </msub> </semantics></math> and inductor current waveforms (<math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> = 350 V). (<b>b</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mn>8</mn> </msub> </semantics></math> and inductor current waveforms (<math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> = 400 V). (<b>c</b>) <math display="inline"><semantics> <msub> <mi>S</mi> <mn>8</mn> </msub> </semantics></math> and inductor current waveforms (<math display="inline"><semantics> <msub> <mi>U</mi> <mn>2</mn> </msub> </semantics></math> = 450 V).</p>
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<p>The distribution of power in the forward mode.</p>
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<p>Constant-current–constant-voltage charging efficiency curve in forward mode.</p>
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<p>Power distribution in the backward mode.</p>
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<p>Efficiency graph of the converter during backward operation.</p>
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30 pages, 5107 KiB  
Article
Experimental Study and Reaction Pathway Analysis of Solvothermal Directional Conversion of Pyrolysis Crude Oil to Liquid Fuel
by Qi Wei, Zhongyang Luo, Qian Qian, Jingkang Shi and Feiting Miao
Energies 2025, 18(4), 981; https://doi.org/10.3390/en18040981 - 18 Feb 2025
Viewed by 223
Abstract
The high viscosity and oxygen content of pyrolysis crude oil hinder the advancement of pyrolysis technology. To address the issue, this study conducted hydrodeoxygenation upgrading experiments on pyrolysis crude oil using hydrothermal directional conversion. A variable analysis was performed to assess the differences [...] Read more.
The high viscosity and oxygen content of pyrolysis crude oil hinder the advancement of pyrolysis technology. To address the issue, this study conducted hydrodeoxygenation upgrading experiments on pyrolysis crude oil using hydrothermal directional conversion. A variable analysis was performed to assess the differences in upgrading effects based on the active metal (Ru, Pt) and the supports (activated carbon, Nb2O5, MgO) of the supported catalyst, and further investigations were conducted on the catalyst with bimetallic doping modification. Optimal reaction conditions were determined by adjusting the reaction temperature. Additionally, directional conversion studies of model compounds were carried out to elucidate the reaction pathway. The results indicated that the Pt/MgO catalyst achieved the highest yield of stable and combustible compounds (hydrocarbons, alcohols, ethers, esters, and ketones), with a yield of 17.8 wt%. Upon modification with Ni doping, the yield increased by 49.5%. The upgrading effect improved with an increase in reaction temperature, and the yield of target compounds was 26.7 wt% at 290 °C, with an energy conversion rate of 72.6% and a selectivity of 75.8%. Moreover, the physicochemical properties of the upgraded oil were similar to those of ethanol. All three model compounds underwent 100% conversion. This study provides both experimental support and a theoretical foundation for the further development of biomass conversion technology. Full article
(This article belongs to the Special Issue Biomass to Liquid Fuels)
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<p>Selectivity of pyrolysis crude oil and upgraded oils from various catalyst supports.</p>
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<p>Product selectivity and yield of upgraded oils from various catalyst loading types.</p>
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<p>Partial reaction mechanism diagram on Pt–Ni/MgO.</p>
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<p>N<sub>2</sub>-TPD curve and BJH pore size distribution of Pt–Ni/MgO catalyst.</p>
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<p>XRD spectrum of Pt–Ni/MgO catalyst.</p>
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<p>XPS spectrum of Pt–Ni/MgO catalyst: (<b>a</b>) Pt; (<b>b</b>) Ni; (<b>c</b>) Mg.</p>
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<p>SEM images of Pt–Ni/MgO catalysts: (<b>a</b>) 2 µm scale; (<b>b</b>) 1 µm scale; (<b>c</b>) 400 nm scale.</p>
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<p>Variation in the quality of gas phase substances with reaction temperature.</p>
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<p>Liquid phase product selectivity and yield of various temperatures.</p>
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<p>Generation pathway diagram of butyl acetate.</p>
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<p>Main conversion pathways of furfural.</p>
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<p>FT-IR spectrum of pyrolysis crude oil and upgraded oil.</p>
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<p>Molecular weight distribution of pyrolysis crude oil.</p>
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30 pages, 4824 KiB  
Review
Advancements in Lignin Valorization for Energy Storage Applications: Sustainable Technologies for Lignin Extraction and Hydrothermal Carbonization
by Haoyu Wang, Haozheng Meng, Joshua O. Olowoyo, Yimin Zeng and Ying Zheng
Nanomaterials 2025, 15(4), 309; https://doi.org/10.3390/nano15040309 - 18 Feb 2025
Viewed by 357
Abstract
The conversion of industrial waste lignin into sustainable carbon materials is an essential step towards reducing dependency on fossil fuels and mitigating environmental impacts. This review explores various aspects of lignin utilization, with particular focus on the extraction of lignin and the application [...] Read more.
The conversion of industrial waste lignin into sustainable carbon materials is an essential step towards reducing dependency on fossil fuels and mitigating environmental impacts. This review explores various aspects of lignin utilization, with particular focus on the extraction of lignin and the application of lignin-derived carbon materials in energy storge applications. The review explores advanced chemical methods to improve the efficiency of biomass conversion, detailing emerging technologies for lignin extraction from various biomasses using innovative solvents and techniques, such as Ionic Liquids and Deep Eutectic Solvents (DESs). Additionally, it discusses the parameters that impact the hydrothermal carbonization (HTC) process. The produced hydrochar shows potential for use as optimized precursors for energy storage applications. This review also considers the implications of these technologies for environmental sustainability and the circular economy, suggesting future research directions to enhance and scale these processes for global impact. This comprehensive analysis highlights the critical role of advanced biomass conversion technologies in achieving sustainability and outlines pathways for future lignin-based carbon materials innovations. Full article
(This article belongs to the Section Energy and Catalysis)
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<p>Three common monolignols: (<b>a</b>) paracoumaryl alcohol, (<b>b</b>) coniferyl alcohol and (<b>c</b>) sinapyl alcohol [<a href="#B39-nanomaterials-15-00309" class="html-bibr">39</a>].</p>
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<p>Diagram illustrating the HTC process flow [<a href="#B27-nanomaterials-15-00309" class="html-bibr">27</a>].</p>
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<p>The mechanism and reaction route for hydrochar from lignin [<a href="#B128-nanomaterials-15-00309" class="html-bibr">128</a>].</p>
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<p>Phase diagram of subcritical and supercritical water [<a href="#B134-nanomaterials-15-00309" class="html-bibr">134</a>].</p>
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<p>Schematic diagram for conversion approaches to prepare lignin-based porous carbon. (<b>a</b>) Direct carbonization with chemical activation [<a href="#B164-nanomaterials-15-00309" class="html-bibr">164</a>]; (<b>b</b>) hydrothermal carbonization with chemical activation [<a href="#B165-nanomaterials-15-00309" class="html-bibr">165</a>]; (<b>c</b>) template method [<a href="#B161-nanomaterials-15-00309" class="html-bibr">161</a>].</p>
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<p>Schematic diagram for conversion approaches to prepare (<b>a</b>) lignin-based CNFs with an electrospinning technique [<a href="#B170-nanomaterials-15-00309" class="html-bibr">170</a>]; lignin-based carbon composites; (<b>b</b>) solvothermal method to prepare Ni<sub>4−x</sub>Co<sub>x</sub>WO<sub>4</sub>/HPC composites [<a href="#B173-nanomaterials-15-00309" class="html-bibr">173</a>]; (<b>c</b>) carbonization of lignin–metal precursor to prepare LDC/ZnO [<a href="#B174-nanomaterials-15-00309" class="html-bibr">174</a>].</p>
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<p>Electrochemical performance of lignin-derived porous carbons in supercapacitors (<b>a</b>) hierarchical porous carbon from direct carbonization with KOH activation [<a href="#B185-nanomaterials-15-00309" class="html-bibr">185</a>]; (<b>b</b>) 3D porous carbon material from HTC followed by KOH activation [<a href="#B165-nanomaterials-15-00309" class="html-bibr">165</a>]; (<b>c</b>) 3D porous carbon material from soft template method coupled with KOH activation [<a href="#B186-nanomaterials-15-00309" class="html-bibr">186</a>].</p>
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<p>Fabrication of heteroatom-doped lignin-based carbon materials and the electrochemical performance in supercapacitors (<b>a</b>) N-doped hierarchical porous carbon [<a href="#B191-nanomaterials-15-00309" class="html-bibr">191</a>]; (<b>b</b>) N/S co-doped hierarchical porous carbon [<a href="#B193-nanomaterials-15-00309" class="html-bibr">193</a>]; (<b>c</b>) electrochemical performance of HPC/WO<sub>3</sub> carbon composite in supercapacitors [<a href="#B177-nanomaterials-15-00309" class="html-bibr">177</a>].</p>
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<p>Lignin-derived hard carbons and the electrochemical performance as the anode materials in SIBs (<b>a</b>) hard carbon via one-step carbonization [<a href="#B179-nanomaterials-15-00309" class="html-bibr">179</a>]; (<b>b</b>) P-doped hard carbon [<a href="#B214-nanomaterials-15-00309" class="html-bibr">214</a>]; (<b>c</b>) hard carbon with pre-oxidation treatment [<a href="#B215-nanomaterials-15-00309" class="html-bibr">215</a>].</p>
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19 pages, 23112 KiB  
Review
Review of Conductive Reciprocating Liquid Metal Magnetohydrodynamic Generators
by Lingzhi Zhao and Aiwu Peng
Energies 2025, 18(4), 959; https://doi.org/10.3390/en18040959 - 17 Feb 2025
Viewed by 233
Abstract
Reciprocating liquid metal magnetohydrodynamic (MHD) power generation is a new MHD power generation method in which the working fluid is a single-phase liquid metal with a low melting point and high conductivity. The internal combustion stroke of automobiles, ocean waves, sound waves and [...] Read more.
Reciprocating liquid metal magnetohydrodynamic (MHD) power generation is a new MHD power generation method in which the working fluid is a single-phase liquid metal with a low melting point and high conductivity. The internal combustion stroke of automobiles, ocean waves, sound waves and other reciprocating external forces drive the liquid metal to flow back and forth in an applied magnetic field, generating single-phase alternating current (AC) energy. Reciprocating liquid metal MHD (LMMHD) power generation has the advantages of a high power density, high efficiency, a fast start and good stability, and it provides a new solution for space static nuclear power conversion, variable-stroke automobile engines, distributed power supply and ocean energy utilization. According to the mode of action of an electromagnetic field, reciprocating LMMHD generators can be divided into the inductive type and conductive type. Compared with the inductive type, the conductive type has a simple structure and is the current research hot spot. Firstly, the classification and characteristics of reciprocating LMMHD power generation are introduced. Then, the working characteristics of conductive reciprocating LMMHD (CRLMMHD) generators are analyzed. On this basis, technical key points and issues in the current research of CRLMMHD generators are elaborated. Finally, conclusions and the future research direction of CRLMMHD generators are pointed out. Full article
(This article belongs to the Section F: Electrical Engineering)
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<p>Classification of LMMHD power generation.</p>
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<p>Diagram of conductive reciprocating LMMHD generator.</p>
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<p>Coupling of electric–magnetic–flow thermal field and field circuit. <b><span class="html-italic">j</span></b> is the current density vector, and <b><span class="html-italic">E</span></b> is the electric field strength vector. <b><span class="html-italic">B</span></b>, <b><span class="html-italic">B</span><sub>0</sub></b>, <b><span class="html-italic">B<sub>l</sub></span></b> are the total, applied and induced magnetic flux density vectors, respectively. <span class="html-italic">σ</span> is the conductivity, and <span class="html-italic">µ</span> is the relative permeability. <span class="html-italic"><b>A</b></span> is the vector potential and <span class="html-italic">Φ</span> is the scalar potential. <b><span class="html-italic">u</span></b> is the velocity vector of liquid metal, <span class="html-italic">p</span> is the static pressure and <b><span class="html-italic">τ</span></b> is the viscous stress tensor; <span class="html-italic">ρ</span> is the density of the liquid metal.</p>
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<p>Configuration of generation channel. (<b>a</b>) Hartmann duct; (<b>b</b>) Generation channel with variable cross-sections and insulating vanes [<a href="#B33-energies-18-00959" class="html-bibr">33</a>]; (<b>c</b>) Generation channel with variable cross-sections [<a href="#B36-energies-18-00959" class="html-bibr">36</a>]; (<b>d</b>) Generation channel with transitions of circles to squares [<a href="#B39-energies-18-00959" class="html-bibr">39</a>,<a href="#B41-energies-18-00959" class="html-bibr">41</a>]; (<b>e</b>) Double-duct generation channel [<a href="#B14-energies-18-00959" class="html-bibr">14</a>,<a href="#B15-energies-18-00959" class="html-bibr">15</a>,<a href="#B31-energies-18-00959" class="html-bibr">31</a>].</p>
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<p>Power conversion overall circuit structure of a CRLMMHD generator in wave energy conversion.</p>
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<p>LMMHD generator assembly of Los Alamos National Lab.</p>
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<p>100 kW lab prototype of SARA.</p>
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<p>Measured generator efficiency varying with input mechanical power.</p>
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<p>Proof-of-principle experimental facility of AIST. (<b>a</b>) Experimental facility without the electromagnet; (<b>b</b>) Layout of the experimental facility.</p>
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<p>Experimental facility of Uni. of Texas at Dallas. (<b>a</b>) Experimental setup in the thermal chamber; (<b>b</b>) Generation channel and magnetic flux density measurements in it.</p>
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<p>Experimental device of the National Autonomous University of Mexico [<a href="#B62-energies-18-00959" class="html-bibr">62</a>].</p>
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<p>Proof-of-principle LMMHD generator of IEECAS [<a href="#B44-energies-18-00959" class="html-bibr">44</a>,<a href="#B55-energies-18-00959" class="html-bibr">55</a>,<a href="#B63-energies-18-00959" class="html-bibr">63</a>].</p>
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<p>KW-class LMMHD generator lab prototype of IEECAS [<a href="#B52-energies-18-00959" class="html-bibr">52</a>]. (<b>a</b>) Experimental device; (<b>b</b>) Generation channel; (<b>c</b>) Open air-gap, combinable dipole permanent magnet.</p>
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<p>LMMHD generator set for 10 kW MHD wave energy converter of IEECAS. (<b>a</b>) Onshore test platform; (<b>b</b>) Sea trial.</p>
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<p>A 25 kW LMMHD generator prototype for MHD wave energy conversion of IEECAS. (<b>a</b>) A 25 kW LMMHD generator; (<b>b</b>) A 2.2 T permanent magnet.</p>
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29 pages, 1445 KiB  
Review
Algal-Based Carbonaceous Materials for Environmental Remediation: Advances in Wastewater Treatment, Carbon Sequestration, and Biofuel Applications
by Lázaro Adrián González Fernández, Nahum Andrés Medellín Castillo, Manuel Sánchez Polo, Amado Enrique Navarro Frómeta and Javier Ernesto Vilasó Cadre
Processes 2025, 13(2), 556; https://doi.org/10.3390/pr13020556 - 16 Feb 2025
Viewed by 273
Abstract
Water pollution from industrial, municipal, and agricultural sources is a pressing global concern, necessitating the development of sustainable and efficient treatment solutions. Algal biomass has emerged as a promising feedstock for the production of carbonaceous adsorbents due to its rapid growth, high photosynthetic [...] Read more.
Water pollution from industrial, municipal, and agricultural sources is a pressing global concern, necessitating the development of sustainable and efficient treatment solutions. Algal biomass has emerged as a promising feedstock for the production of carbonaceous adsorbents due to its rapid growth, high photosynthetic efficiency, and ability to thrive in wastewater. This review examines the conversion of algal biomass into biochar and hydrochar through pyrolysis and hydrothermal processes, respectively, and evaluates their potential applications in wastewater treatment, carbon sequestration, and biofuel production. Pyrolyzed algal biochars typically exhibit a moderate to high carbon content and a porous structure but require activation treatments (e.g., KOH or ZnCl2) to enhance their surface area and adsorption capabilities. Hydrothermal carbonization, conducted at lower temperatures (180–260 °C), produces hydrochars rich in oxygenated functional groups with enhanced cation exchange capacities, making them effective for pollutant removal. Algal-derived biochars and hydrochars have been successfully applied for the adsorption of heavy metals, dyes, and pharmaceutical contaminants, with adsorption capacities significantly increasing through post-treatment modifications. Beyond wastewater treatment, algal biochars serve as effective carbon sequestration materials due to their stable structure and high carbon retention. Their application as soil amendments enhances long-term carbon storage and improves soil fertility. Additionally, algal biomass plays a key role in biofuel production, particularly for biodiesel synthesis, where microalgae’s high lipid content facilitates bio-oil generation. Hydrochars, with energy values in the range of 20–26 MJ/kg, are viable solid fuels for combustion and co-firing, supporting renewable energy generation. Furthermore, the integration of these materials into bioenergy systems allows for waste valorization, pollution control, and energy recovery, contributing to a sustainable circular economy. This review provides a comprehensive analysis of algal-derived biochars and hydrochars, emphasizing their physicochemical properties, adsorption performance, and post-treatment modifications. It explores their feasibility for large-scale wastewater remediation, carbon capture, and bioenergy applications, addressing current challenges and future research directions. By advancing the understanding of algal biomass as a multifunctional resource, this study highlights its potential for environmental sustainability and energy innovation. Full article
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<p>Diagram of algal structures: (<b>a</b>) <span class="html-italic">Porphyra umbilicalis</span> (macroalgae); (<b>b</b>) <span class="html-italic">Scenedesmus</span> (microalgae). Reproduced from Pereira, 2021 [<a href="#B21-processes-13-00556" class="html-bibr">21</a>], under terms of Creative Commons Attribution (CC BY) license.</p>
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<p>Possible mechanisms of interaction between hydrochar surfaces and (<b>a</b>) heavy metals, (<b>b</b>) dyes, and (<b>c</b>) pharmaceuticals. Images reused from Petrović et al., 2024 [<a href="#B42-processes-13-00556" class="html-bibr">42</a>], in accordance with Creative Commons Attribution license (CC BY).</p>
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<p>Photosynthetic carbon assimilation process in algae. Image reused from Li and Yao, 2024 [<a href="#B93-processes-13-00556" class="html-bibr">93</a>], in accordance with Creative Commons Attribution license (CC BY).</p>
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<p>Biofuel generation process from algae. Image reused from Li and Yao, 2024 [<a href="#B93-processes-13-00556" class="html-bibr">93</a>], in accordance with Creative Commons Attribution license (CC BY).</p>
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20 pages, 9964 KiB  
Article
Damage Behaviour and Fractal Characteristics of Underground Openings Under True Triaxial Loading
by Yunfeng Wu, Peng Li, Xiaolou Chi, Baokun Zhou, Erhui Zhang, Youdong Zhu and Changhong Li
Fractal Fract. 2025, 9(2), 121; https://doi.org/10.3390/fractalfract9020121 - 15 Feb 2025
Viewed by 320
Abstract
In the context of advancements in deep resource development and underground space utilisation, deep underground engineering faces the challenge of investigating the mechanical behaviour of rocks under high-stress conditions. The present study is based on a gold mine, and the bulk ore taken [...] Read more.
In the context of advancements in deep resource development and underground space utilisation, deep underground engineering faces the challenge of investigating the mechanical behaviour of rocks under high-stress conditions. The present study is based on a gold mine, and the bulk ore taken from the mine perimeter rock was processed into two sets of specimens containing semicircular arched roadways with half and full penetrations. The tests were carried out using a true triaxial rock test system. The results indicate that the true triaxial stress–strain curve included stages such as compression density, linear elasticity, yielding, and destructive destabilisation following the peak; the yield point was more pronounced than that in uniaxial and conventional triaxial tests; and the peak stress and strain of the semi-excavation were higher than those of the full excavation. Furthermore, full excavation led to greater deformation along the σ3 direction. The acoustic emission energy showed a sudden increase during the unloading stage, then fluctuated and increased with increasing stress until significant destabilisation occurred. Additionally, increased burial stress in the half-excavation decreased the proportion of tension cracks and shear cracks. Conversely, in semi-excavation, the proportion of tensile cracks decreased, while that of shear cracks increased. However, the opposite was observed in full excavation. In terms of fractal dimension, semi-excavation fragmentation due to stress concentration followed a power distribution, while the mass fragmentation in full excavation followed a random distribution due to uniform stress release. Furthermore, the specimen strength was positively correlated with fragmentation degree, and primary defects also influenced this degree. This study provides a crucial foundation for predicting and preventing rock explosions in deep underground engineering. Full article
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<p>Testing of physical parameters of specimens: (<b>a</b>) sample preparation, (<b>b</b>) specimen quality testing, (<b>c</b>) specimen P-wave velocity test.</p>
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<p>Microstructural characterisation of the materials: (<b>a</b>) polarising microscope micrographs Pl (plagioclase), K (K-feldspar), Bi (black mica), and Q (quartz); (<b>b</b>) results of XRD test analyses.</p>
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<p>True triaxial test systems: (<b>a</b>) overview of the test loading system, (<b>b</b>) three-way loading unit, (<b>c</b>) control interface of the operating system, (<b>d</b>,<b>e</b>) acoustic emission, (<b>f</b>) equipment hydraulics.</p>
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<p>Experimental loading path designed at different burial depths: (<b>a</b>) <span class="html-italic">H</span> = 600 m, (<b>b</b>) <span class="html-italic">H</span> = 800 m, (<b>c</b>) <span class="html-italic">H</span> = 1000 m.</p>
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<p>Stress–strain curves: (<b>a</b>) specimen B1, (<b>b</b>) specimen B2, (<b>c</b>) specimen B3, (<b>d</b>) specimen Q1, (<b>e</b>) specimen Q2, (<b>f</b>) specimen Q3.</p>
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<p>Trends in peak strain and strength of two specimens: (<b>a</b>) peak stress variation of <span class="html-italic">σ</span><sub>1</sub>, (<b>b</b>) peak strain variation of <span class="html-italic">σ</span><sub>1</sub>.</p>
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<p>Acoustic emission characterisation: (<b>a</b>) specimen B1, (<b>b</b>) specimen B2, (<b>c</b>) specimen B3, (<b>d</b>) specimen Q1, (<b>e</b>) specimen Q2, (<b>f</b>) specimen Q3.</p>
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<p>Acoustic emission characterisation: (<b>a</b>) specimen B1, (<b>b</b>) specimen B2, (<b>c</b>) specimen B3, (<b>d</b>) specimen Q1, (<b>e</b>) specimen Q2, (<b>f</b>) specimen Q3.</p>
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<p>Schematic of an average frequency (AF) vs. rise angle (RA) plot showing rock microcrack types.</p>
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<p>Classification of acoustic emission crack characteristics: (<b>a</b>) specimen B1, (<b>b</b>) specimen B2, (<b>c</b>) specimen B3, (<b>d</b>) specimen Q1, (<b>e</b>) specimen Q2, (<b>f</b>) specimen Q3.</p>
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<p>Classification of acoustic emission crack characteristics: (<b>a</b>) specimen B1, (<b>b</b>) specimen B2, (<b>c</b>) specimen B3, (<b>d</b>) specimen Q1, (<b>e</b>) specimen Q2, (<b>f</b>) specimen Q3.</p>
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<p>Specimen destruction modes: (<b>a</b>) specimen B1, (<b>b</b>) specimen B2, (<b>c</b>) specimen B3, (<b>d</b>) specimen Q1, (<b>e</b>) specimen Q2, (<b>f</b>) specimen Q3.</p>
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<p>Granite destructive debris screening results: (<b>a</b>) specimen B1, (<b>b</b>) specimen B2, (<b>c</b>) specimen B3, (<b>d</b>) specimen Q1, (<b>e</b>) specimen Q2, (<b>f</b>) specimen Q3.</p>
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<p>Mass distribution of rockburst fragments in granite specimens: (<b>a</b>) semi-excavated, (<b>b</b>) fully-excavated.</p>
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<p>The ln[<span class="html-italic">M</span>(<span class="html-italic">r</span>)/<span class="html-italic">M</span>]-ln[<span class="html-italic">r</span>/<span class="html-italic">R</span>] curves for granite specimens with different excavation schedules: (<b>a</b>) semi-excavated, (<b>b</b>) fully-excavated.</p>
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22 pages, 2349 KiB  
Article
Digital Real-Time Simulation and Power Quality Analysis of a Hydrogen-Generating Nuclear-Renewable Integrated Energy System
by Sushanta Gautam, Austin Szczublewski, Aidan Fox, Sadab Mahmud, Ahmad Javaid, Temitayo O. Olowu, Tyler Westover and Raghav Khanna
Energies 2025, 18(4), 937; https://doi.org/10.3390/en18040937 - 15 Feb 2025
Viewed by 549
Abstract
This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain [...] Read more.
This paper investigates the challenges and solutions associated with integrating a hydrogen-generating nuclear-renewable integrated energy system (NR-IES) under a transactive energy framework. The proposed system directs excess nuclear power to hydrogen production during periods of low grid demand while utilizing renewables to maintain grid stability. Using digital real-time simulation (DRTS) in the Typhoon HIL 404 model, the dynamic interactions between nuclear power plants, electrolyzers, and power grids are analyzed to mitigate issues such as harmonic distortion, power quality degradation, and low power factor caused by large non-linear loads. A three-phase power conversion system is modeled using the Typhoon HIL 404 model and includes a generator, a variable load, an electrolyzer, and power filters. Active harmonic filters (AHFs) and hybrid active power filters (HAPFs) are implemented to address harmonic mitigation and reactive power compensation. The results reveal that the HAPF topology effectively balances cost efficiency and performance and significantly reduces active filter current requirements compared to AHF-only systems. During maximum electrolyzer operation at 4 MW, the grid frequency dropped below 59.3 Hz without filtering; however, the implementation of power filters successfully restored the frequency to 59.9 Hz, demonstrating its effectiveness in maintaining grid stability. Future work will focus on integrating a deep reinforcement learning (DRL) framework with real-time simulation and optimizing real-time power dispatch, thus enabling a scalable, efficient NR-IES for sustainable energy markets. Full article
(This article belongs to the Section B4: Nuclear Energy)
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<p>Transactive energy framework [<a href="#B8-energies-18-00937" class="html-bibr">8</a>].</p>
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<p>Simplified plot illustrating the increase in profitability of a nuclear power plant with hydrogen production [<a href="#B8-energies-18-00937" class="html-bibr">8</a>].</p>
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<p>Tightly coupled NR-IES [<a href="#B10-energies-18-00937" class="html-bibr">10</a>].</p>
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<p>DRL framework based on OpenAI Gym and Ray-RLlib [<a href="#B10-energies-18-00937" class="html-bibr">10</a>].</p>
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<p>Cumulative revenue with and without hydrogen IES [<a href="#B10-energies-18-00937" class="html-bibr">10</a>].</p>
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<p>Three-phase power conversion system with a hybrid active power filter.</p>
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<p>SCADA panel.</p>
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<p>Energy price data and load profile.</p>
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<p>Ramping factor.</p>
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<p>Circuit configuration of an active harmonic filter (AHF).</p>
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<p>(<b>a</b>) Reference current generator. (<b>b</b>) Current control.</p>
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<p>Hybrid active power filter—shunt active + shunt passive topology.</p>
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<p>Grid frequencies with and without an AHF.</p>
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<p>(<b>a</b>) Source current without a filter. (<b>b</b>) Source current with an AHF.</p>
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<p>Grid harmonic mitigation with an AHF.</p>
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<p>(<b>a</b>) Zoomed-in view of the electrolyzer profile for the first 48 h. (<b>b</b>) Power factor before and after HAPF implementation.</p>
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<p>Comparison of active filter currents.</p>
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17 pages, 12293 KiB  
Article
Investigations on the Aerodynamic Interactions Between Turbine and Diffuser System by Employing the Kriging Method
by Bin Qiu, Jinglun Fu, Xiangling Kong, Hongwu Zhang and Qiang Yu
Energies 2025, 18(4), 921; https://doi.org/10.3390/en18040921 - 14 Feb 2025
Viewed by 280
Abstract
An exhaust diffuser determines the turbine outlet pressure by recovering kinetic energy. Conversely, the distributions of the total pressure and flow directions at the turbine exit affect the aerodynamic performance of the exhaust diffuser. As the output power increases gradually, the structure of [...] Read more.
An exhaust diffuser determines the turbine outlet pressure by recovering kinetic energy. Conversely, the distributions of the total pressure and flow directions at the turbine exit affect the aerodynamic performance of the exhaust diffuser. As the output power increases gradually, the structure of the modern gas turbine becomes more compact. Consequently, the coupled effect of the flow in the last-stage turbine and the exhaust diffuser becomes increasingly obvious. Understanding the correlation between the flow field and the performance of the coupled system is of great significance. As a predictive regression algorithm, the Kriging method is widely used due to its high efficiency and unique mathematical characteristics. In this paper, computational fluid dynamics (CFD) numerical simulation is employed to investigate the interactions between the flow fields of the coupled system, and the corresponding datasets are obtained. Accordingly, the Kriging method is successfully employed to reconstruct the complex flow field, and a quantitative model describing the interaction between the two parts is established. This paper provides a detailed summary of the interaction between the flow field in the exhaust diffuser and the flow field at the outlet of the last-stage turbine. Through the prediction of the flow field, the conditions that induce the separation vortex on the casing of the diffuser are determined. Specifically, the slope of the total pressure change along the blade height near the casing is found to be k = −4.37. Full article
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<p>Computational domain model of the full-scale gas turbine model.</p>
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<p>Computational mesh.</p>
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<p>Independence verification of computational mesh.</p>
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<p>Test facility.</p>
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<p>Pressure recovery coefficient axial distributions.</p>
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<p>Total pressure coefficient and streamlines at turbine outlet (<a href="#sec2-energies-18-00921" class="html-sec">Section 2</a>): (<b>a</b>) Case 1, (<b>b</b>) Case 2, (<b>c</b>) Case 3.</p>
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<p>Flow field at the turbine stage outlet: (<b>a</b>) axial Mach number, (<b>b</b>) absolute tangential flow angle, (<b>c</b>) total pressure coefficient.</p>
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<p>Comparison of diffuser performance under different operational conditions: (<b>a</b>) static pressure recovery loss coefficient, (<b>b</b>) total pressure loss coefficient.</p>
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<p>Total pressure coefficient contour at section <span class="html-italic">θ</span> = 0° in the diffuser under different operational conditions: (<b>a</b>) Case 1, (<b>b</b>) Case 2, (<b>c</b>) Case 3.</p>
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<p>Pressure coefficient contour and 2D streamline at section <span class="html-italic">θ</span> = 0° in the diffuser under different operational conditions: (a) Case 1, (<b>b</b>) Case 2, (<b>c</b>) Case 3.</p>
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<p>Radial distributions of circumferential averaged Mach number at different axial positions in the diffuser: (<b>a</b>) Case 1, (<b>b</b>) Case 2, (<b>c</b>) Case 3.</p>
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<p>The process of coupling model based on the Kriging model.</p>
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<p>Comparison of the error of the leave-one-out method.</p>
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<p>Prediction results of swirl angle and total pressure of the turbine outlet: (<b>a</b>) swirl angle, (<b>b</b>) total pressure.</p>
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<p>Comparison of axial Ma number contours calculated by Kriging surrogate model and CFD solver: (<b>a</b>) Calculated by CFD solver (<b>b</b>) Calculated by surrogate model.</p>
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<p>Comparison of total pressure contours calculated by Kriging surrogate model and CFD solver: (<b>a</b>) Calculated using a CFD solver. (<b>b</b>) Calculated using a surrogate model.</p>
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<p>The circumferential position of static pressure points of the casing.</p>
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<p>Sample point: (<b>a</b>) inlet total pressure distribution, (<b>b</b>) wall velocity distribution at the casing along the flow direction.</p>
Full article ">Figure 19
<p>Initial condition of separation vortex: (<b>a</b>) inlet total pressure distribution, (<b>b</b>) wall velocity distribution at the casing.</p>
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