Liquid-gas phase change cooling technology has emerged as an effective strategy for addressing the thermal management challenges of high-power chip arrays within confined spaces. However, conventional two-phase cooling systems, which rely on hardware design and structural optimization, are often constrained by limited flexibility, complex integration, and high costs. To overcome these limitations, this study adopts the concept of “software cooling” and investigates a 4×4 high-power chip array cooled by a two-phase parallel-channel cooling plate. A non-time-varying simulation method enabling rapid and coupled evaluation of the thermal-hydraulic performance of the two-phase cooling plate and the temperature of the mounted chips is employed to analyse the effect of chip power allocation on the overall system performance. Steady-state multi-objective optimization is conducted using the NSGA-II genetic algorithm, targeting the total power of the chip array, the variance of power among chips, and the pressure drop across the cooling plate. By adjusting the power distribution strategy among chips at the software level without altering the hardware configuration, substantial improvements in cooling performance are achieved. The results demonstrate that, compared with the conventional core-based fixed scheduling strategy and under identical cooling conditions with the average chip temperature constrained to below 373.15 K, the optimized solution yields an approximately 12.3

To enhance the performance of hypersonic vehicles in cross-domain and wide-speed regimes, the efficient and compact cooling technologies for engines are required. Due to superior thermal-hydraulic performance and stability, Ga-In-Sn alloy and supercritical N2 emerge as promising working fluids for cooling systems. To address the complex heat transfer fluctuations and unclear mechanisms caused by dramatic thermophysical-property changes of transcritical fluids, an investigation on thermo-hydraulic characteristics of Ga-In-Sn alloy/supercritical N2 in a zigzag printed circuit heat exchanger (PCHE) is conducted. First, an adaptive method for modeling the thermophysical properties of transcritical fluids is proposed. Next, a numerical model of zigzag PCHE is established to analyze the overall flow and heat transfer characteristics. Finally, the evolution of N2-side heat transfer performance is examined, and the mechanisms of heat transfer deterioration and recovery across the critical region are elucidated based on the pseudo-boiling theory. The results show that the mean absolute percentage error in thermophysical-property fitting is below 2.2%, providing reliable thermophysical-property data support for simulations. The overall heat transfer performance is dominated by N2-side convection, contributing over 90% of the total thermal resistance. As the N2 mainstream temperature approaches the critical point, a low-density, low-conductivity vapor-like layer forms near the wall, causing heat transfer deterioration and a reduction of approximately 13.6% in the heat transfer coefficient. As the mainstream temperature enters the vapor-like state, weakened thermophysical property gradients, increased flow velocity due to density reduction and enhanced turbulence intensity synergistically promote heat transfer recovery.

Although dual-tower solar power tower systems may offer potential optical performance advantages, their practical performance is affected by many coupled engineering factors, including shadowing and blocking losses, finite heliostat size, optical errors, receiver flux constraints, land occupation, and operational constraints, among others. Therefore, the actual performance of a practical plant does not directly reveal the intrinsic geometric-optical potential of the dual-tower configuration itself. Motivated by this, this study develops an idealized geometric-optical upper-bound model for the optical efficiency limit of dual-tower systems. The objective is not to provide directly deployable engineering layouts or quantitative design recommendations, but to establish a comparable upper-bound benchmark for revealing the intrinsic optical potential of the configuration and supporting rapid upper-bound assessment at the conceptual stage. The model systematically examines the effects of latitude, tower height, receiver radius, total heliostat area, and tower spacing on the dual-tower optical efficiency limit. The results show that, as tower spacing increases, the optical efficiency limit exhibits a transition from enhancement to degradation, indicating the existence of an upper-bound optical optimum tower spacing under the prescribed assumptions. Furthermore, based on 26,472,600 simulated samples, an explicit symbolic-regression predictive equation is developed, achieving an R2 of 0.9339 and a mean APE of 1.10% in repeated random evaluations; the P90 and P95 APEs over the full parameter space are 2.19% and 2.87%, respectively. These results provide an idealized geometric-optical upper-bound reference for understanding the dual-tower configuration.

To further improve the condensation heat transfer, the electric field is actively imposed to accelerate the detachment of the condensation droplets from the cold wall surface. In this paper, the research is conducted through the coupling of the pseudopotential model and the leaky dielectric model. By comparing the condensation heat transfer with and without the action of electric field, the detachment enhancement of the electric field on the condensed droplets is confirmed. The electric field with intensity of 1.6E0 (E0 is the reference intensity for CaE=1) saves the droplet growth and departure period by 48.48 % compared with case without electric field. Subsequently, the influence of the liquid conductivity, permittivity and deformation numbers on the enhancement of detachment by electric field was studied. The results show that the greater the electric conductivity of the liquid, the smaller the permittivity of the liquid, or the greater the deformation number, the stronger strengthening effect of the electric field on condensation detachment. Subsequently, this paper reveals the relation among the departure diameter, the gravitational acceleration and the electric field intensity through force balance analysis. Lastly a correlation formula of the departure diameter was proposed for the first time, with an average relative deviation of 1.48 % and coefficient of determination R2 is 0.998.

In the fuel assembly design of pressurized water reactors, helical cruciform fuel (HCF) rods have garnered significant attention due to their substantial potential for enhancing thermal-hydraulic performance and achieving higher safety margins. However, the complex geometry of HCFassemblies leads to prohibitively high computational costs for full-scale CFD simulations, severely restricting their application in design optimization. This paper develops a reduced-order model named HyPOD-AdTransformer to achieve efficient prediction of flow and heat transfer characteristics in HCF assemblies. The model incorporates a hybrid reduced-order algorithm (HyPOD), which employs traditional proper orthogonal decomposition (POD) for velocity field processing and proposes explicit boundary POD (EbPOD) for the temperature field to retain critical boundary layer information, thereby improving the applicability of traditional POD for temperature field analysis. Additionally, the AdTransformer is integrated to enhance the model's capability in capturing nonlinear features and global fullfield information, improving its ability to represent the complex flow characteristics induced by the HCF helical structure. This reduces prediction errors in the temperature field boundary layer and the mainstream region of the velocity field. Case analysis demonstrates that compared to POD-BPNN (with average errors of 0.29%, 10%, and 1.1% for u, w, and T respectively), the prediction errors of HyPOD-AdTransformer are reduced to 0.00095%, 0.25%, and 0.013%. This model takes boundary conditions as input and directly outputs the full-field distribution without requiring CFD iterations, providing a reliable method for efficient thermal-hydraulic analysis and flow field prediction in complex structures.

Solid-liquid phase change materials (PCMs) have obvious advantages in thermal management for temperaturesensitive electronic devices benefiting from their high latent heat storage density and excellent properties of absorbing heat at stable temperatures. Ultrasonic assistance is an available means to enhance heat transfer efficiency of PCMs. In present study, a numerical model for predicting thermal performance of ultrasonic-assisted PCM thermal management unit (TMU) was proposed and validated by experimental results. The influence mechanisms of different ultrasonic loading strategies, including different powers and frequencies, on thermal characteristics of heat sources and PCMs domains were studied through qualitative and quantitative comparisons, including melting, velocity and temperature characteristics. Dimensionless numbers of ultrasonic were defined. Data-driven dimensionless correlation formulas for certain and different heat fluxes were proposed for rapid prediction of steady-state and transient thermal characteristics in thermal management applications of ultrasonic-assisted PCM TMUs. Results indicated that the higher ultrasonic power led to lower and more uniform temperature of heat source. The increase in ultrasonic frequency resulted in two opposing effects. Compared with the non-ultrasonic case, the preferred ultrasonic loading strategy (Pu = 120 W and fu = 100 kHz) could achieve a 50.99 % decrease in average temperatures of heat sources, a 237.39 % increase in average velocities of PCMs domains, and a 75.92 % reduction in temperature nonuniformities of heat sources. The deviation between predicted and actual values of the performance prediction model was mostly less than 10 %, which was of great engineering application significance for design and operational control of ultrasonic-assisted PCMs in the thermal management of temperature-sensitive equipment.

Hydrocarbon fuel regenerative/transpiration combined cooling integrates the advantages of convective cooling and film cooling in the thermal protection system of air-breathing vehicles, representing a promising active cooling approach. This study propose a coupled computational method based on regenerative/transpiration combined cooling to simulate the heat transfer between the mainstream flow in the nozzle and the regenerative/transpiration combined cooling outside. This method captures the key features across the transonic region and analyzes the factors influencing the thermal-protection performance of regenerative/transpiration combined cooling by coupling regenerative cooling pyrolysis with oxidative cracking in the mainstream flow. The results show that combined cooling reduces wall heat-flux and achieves higher cooling efficiency at the same mass flow rate. As the allocation ratio of transpiration cooling increases, the peak heat-flux and coking rate decrease by 54.36% and 90%, respectively, while the average cooling efficiency improves by 9.78%. Reducing the angle between the transpiration holes and the wall enhances the coverage of the boundary-layer film and improves local cooling efficiency. The oxidative cracking effect of hydrocarbon fuel increases heat-transfer capability near the wall, achieving local heat-transfer enhancement, with a peak temperature reduction of 90 K (corresponding to an 11% increase in cooling efficiency).

Phase change transpiration cooling is an effective thermal protection method for its utilization of latent heat of the coolant. Aimed at studying the pore-scale convective evaporation mechanism in transpiration cooling, a nonisothermal phase change pseudopotential lattice Boltzmann model is established. A ceramic thermal protection tile's porous structure obtained via micro-CT serves as the transpiration cooling material. Driven by the heating of a high-temperature external mainstream, the phase change and heat-mass transfer processes in the porous structure with a reservoir are investigated. Results suggest that the phase patterns exhibit 3 distinct modes of evolution at different coolant injection ratios: liquid rewetting, periodic drying-rewetting, and liquid overflow at low, moderate, and high injection ratios, respectively. Varying the contact angle of the porous skeleton from 119 degrees to 22 degrees, the evaporation rate shows a non-monotonic trend of first increasing and then decreasing as the dominant mechanism shifts from capillary pumping to the Kelvin effect. A porous structure with hydrophilic surface/lateral sides and hydrophobic interior is designed to improve the cooling performance. The heterogeneous wettability structure can enhance the capillary pumping effect and transport liquid coolant to the top surface, achieving a 26% enhancement of the time-averaged cooling effectiveness compared with the uniform hydrophilic structure. Pore-scale convective evaporation analysis can deepen the understanding of transpiration cooling and facilitate the design of more efficient cooling materials.

Determining the upper limit of concentrated solar power (CSP) systems' total efficiency is fundamental to guiding system design and optimization. The energy transfer and conversion processes in such systems primarily include solar concentration, optical-thermal conversion, and thermal-power conversion. While the mutual constraint between the optical-thermal conversion efficiency (eta rec) and the thermal-power conversion efficiency (eta t) has been well recognized, the relationship between the optical efficiency of the concentration process (eta opt) and the other two efficiencies remains unclear. Consequently, the upper limit of systems' total efficiency (eta total) and its key influencing parameters have yet to be fully elucidated. In assessments of systems' total efficiency, the optical efficiency is often treated as a constant-either idealized to 1 without specifying the type of concentration system, or assigned an empirical value. These simplifications fail to account for its actual variation with the geometric configuration of the concentration system. The aforementioned problems make it difficult to further guide the design of the system. To address this issue, this study focuses on solar power tower (SPT) systems and develops a theoretical full-chain efficiency limit model for an ideal SPT system, followed by an investigation of the upper limit of systems' total efficiency. The results reveal that, in addition to the receiver operating temperature (T), which is widely acknowledged, the concentration ratio (C) is also a key parameter affecting the upper limit of systems' total efficiency. The relationship between eta total and key parameters can be expressed as: eta total(C,T)=eta opt(C)& centerdot;eta rec (C,T)& centerdot;eta t(T). Quantitative analysis indicates that as C increases, the heliostat field optical efficiency limit eta opt(C) gradually decreases, whereas the optical-thermal-power conversion efficiency limit eta rec(C,T)& centerdot;eta t(T) increases, revealing a clear tradeoff. It can be found that an optimal combination of C and T exists that maximizes eta total(C,T). Based on this insight, a multistage optimization strategy-"temperature by thermal performance, ratio by optical efficiency"- is proposed for the practical design of SPT plants. This strategy operates on two distinct principles. First, it identifies the optimal operating temperature for each concentration ratio by maximizing the system's optical-thermal-power conversion efficiency-a principle termed "temperature by thermal performance." Second, it determines the best concentration ratio by maximizing the total system efficiency, which incorporates coupled optical performance, referred to as "ratio by optical efficiency". Taking the Hami 50 MW SPT plant as an example, the highest total efficiency under the constraints of the real heliostat field and cycle configuration is presented, along with the corresponding optimal concentration ratio and the optimal operating temperature of the receiver. This highest total efficiency (27.01%) represents a 12% increase compared to the current total efficiency of the Hami 50 MW SPT plant (approximately 15%). The proposed analysis of the upper limit of systems' total efficiency and multi-stage optimization approach can provide valuable guidance for the design and performance enhancement of next-generation solar power tower plants.

The thermal management of ultra-high heat flux chips has become an urgent problem that needs to be addressed. For the manifold microchannels, previous studies have generally treated the hot manifold side and cold manifold side as a whole, considering that when the structure of one side changes, the other side should maintain the same symmetrical structural configuration. In fact, due to the different flow patterns on the hot and cold manifold sides, they should not be simply treated as a whole. Instead, the most suitable heat transfer enhancement strategies for each side should be individually employed to achieve the best cooling performance. This paper focuses on this novel design concept and proposes a new manifold microchannel (termed PFMMC). The major differences of PFMMC from that reported in Nature 2020(585) are in three aspects. First, the hot manifold side have the same spanwise width, rather than the gradually expanding structure corresponding to the cold manifold side. Second, microfins are added only to the hot-side microchannels. Third, the microchannels have a narrow inlet and wider outlet to complement the fins. A nearly optimum construction is obtained. Numerical results show that it can reach an ultra-high heat flux up to 1425 W/cm(2) by using water coolant with Delta P being only 9762.9 Pa. This demonstrates that the hot manifold side has greater enhancement potential and that the enhancement strategies for the hot and cold sides should be considered separately in future structural designs.

To mitigate the intermittency of solar energy and maximize utilization efficiency, this study introduces a novel hybrid thermal management system that synergizes active rotation with passive non-uniform fins. Another central to this work is the development of a multi-strategy improved sparrow search algorithm (MSISSA), which incorporates sine chaotic mapping, elite reverse learning, fitness guidance, and a greedy selection mechanism. Validation against 12 benchmark functions confirms the superior convergence accuracy and robustness of the developed algorithm. Leveraging this advanced optimizer, we conduct a multi-variable optimization of fin geometry (spacing, height, and angle). The results demonstrate significant performance gains: the optimized configuration reduces the heat storage time by 27.83% and boosts the average heat storage efficiency by 26.83% relative to the baseline design. Additionally, the system elevates temperature uniformity by 8.60%, offering a robust solution for high-efficiency latent heat thermal energy storage.

Helical Cruciform Fuel (HCF) rods exhibit outstanding heat exchange performance and a compact geometric structure. These advantageous features have made them promising candidates for application in Lead‑Bismuth Fast Reactors (LFR). However, the geometric configuration of the HCF assembly is complex, resulting in extremely high costs for high-fidelity Computational Fluid Dynamics (CFD) simulation calculations. This paper proposes a reduction-order model that integrates partition eigen orthogonal decomposition and physical information-Mamba to achieve high-precision prediction of the flow heat transfer characteristics of HCF assemblies under few sample conditions. This method first divides the flow field into the boundary layer and mainstream regions based on their distinct flow and heat transfer characteristics. Separate Proper Orthogonal Decomposition (POD) basis functions are then established for each region. This approach ensures that the key flow features of different regions are preserved during the dimensionality reduction process. Furthermore, the Mamba model is introduced to replace the traditional Back Propagation Neural Network (BPNN). Finally, by embedding the residuals of the control equation as physical information constraints, the prediction results are forced to satisfy the conservation law. Research shows that this method achieves high-fidelity reconstruction in both the boundary layer and the mainstream region. Compared with neural network models without physical information, the prediction accuracy is improved by several orders of magnitude under the same sample. The proposed model can achieve the prediction accuracy that conventional data-driven methods require approximately 47 samples with only 10 training samples.

Wind load induces deformation of heliostats in solar power tower (SPT) plants, altering the direction of reflected sunrays. This leads to an increase in the heliostat slope error, thereby reducing concentrating efficiency. To accurately characterize the relationship between wind load and heliostat slope error, a triangular-facet analysis method is proposed in this study. This method accurately calculates heliostat deformation and effectively predicts slope error. Furthermore, an artificial neural network model has been developed for rapid prediction of slope error under varying load conditions. The results indicate that for the SPT plant studied, within a wind speed range of 0-20 m/s, the root mean square slope error of the heliostat varies between 0.10 and 2.30 mrad. At low wind speeds, wind load is the primary contributor to slope error, whereas at high wind speeds, gravitational load has a greater influence. Under strong wind conditions, the concentrating efficiencies decline significantly. Compared with wind-free conditions, a wind speed of 20 m/s increases the average slope error of the heliostat field by 0.70 mrad, reduces optical efficiency by 8 percentage points, and lowers the receiver's intercept energy by 12.6 %. Real-time simulations show that wind loading from the rear partially offsets gravitational effects, improving annual optical efficiency by 0.4 percentage points. The methodology proposed in this study offers an effective tool for predicting heliostat slope errors, analyzing wind load effects on concentrating efficiency, and guiding SPT plant design and operation under windy conditions.

ABSTRACT Single‐atom catalysts (SACs) play a critical role in diverse catalytic applications, but their efficient synthesis remains a significant challenge. Herein, we develop an ultrafast magnetic‐field‐enabled quench (MFEQ) strategy to synthesize a series of M 1 /G‐FeO x (M═Ni, Fe, Co, Ir, Ru, and Pt) SACs within a few seconds. Using Ni 1 /G‐FeO x as a proof of concept, this method leverages the rapid quenching of thermally incandescent Fe foam into an Ni‐containing ethanol solution, triggering simultaneous graphene formation and Ni anchoring. The Ni 1 /G‐FeO x catalyst shows exceptional alkaline oxygen evolution reaction (OER) performance, operating at 200 mV for 10 mA cm −2 and sustaining 105 mA cm −2 for 330 h without degradation. Notably, the Ni 1 /G‐FeO x ‐catalyzed anion exchange membrane water electrolysis (AEMWE) device exhibits a low voltage of 1.86 V at 1.0 A cm −2 and 600 h long‐term stability. Density functional theory (DFT) calculations and experiments reveal that the strong electronic interactions between Ni 1 /G and FeO x contribute to the optimized electronic structure and reduced energy barrier. Techno‐economic analysis (TEA) highlights the superior energy efficiency of the MFEQ method, which requires only US$19.2 in energy expenditure to synthesize 1 kg of SACs. This work provides new insights into the ultrafast fabrication of SACs.

Metal foams (MF) are widely used in phase change materials (PCMs) due to their high thermal conductivity, high porosity and large specific surface area. These characteristics jointly improve the thermal performance of PCMs. This study investigates the influence of porosity variation (ranging from 0.85 to 0.96) on the thermal behavior of a phase change thermal storage (PCTS) unit. The research finds that while the reduction of porosity significantly improves the heat storage efficiency, it concurrently reduces the overall storage capacity. Specifically, compared to a porosity of 0.96, a porosity of 0.85 leads to a 71.06% increase in efficiency but is accompanied by a 10.51% decrease in capacity. To further optimize the prediction performance, an improved Tactical Unit Algorithm (ITUA), incorporating elite retention, Levy flight, and Gaussian mutation strategies, is proposed. Compared to conventional algorithms, ITUA exhibits markedly enhanced optimization performance. Furthermore, ITUA is integrated with a backpropagation artificial neural network (BP-ANN) to develop a model of liquid phase distribution during the melting process. To maintain heat storage capacity while enhancing efficiency, both linear and nonlinear porosity distributions are investigated. At an average porosity of 0.95, energy storage efficiency is increased by 58.46% and 68.95% for linear and nonlinear arrangements, respectively, relative to uniform porosity distribution. The proposed model provides valuable guidance for optimizing MF porosity configuration in PCTS systems.

To meet the cross-domain and wide-speed operational requirements of hypersonic vehicles, efficient and compact cooling technologies are urgently required. Zigzag printed circuit heat exchangers (PCHE) using Ga-InSn alloy and supercritical N2 as working fluids exhibit superior heat transfer performance, compactness and operational safety, demonstrating strong potential for the propulsion system. However, the dramatic thermophysical-property variations of transcritical fluids coupled with periodic geometric disturbances induced by zigzag channels, result in highly nonlinear thermo-hydraulic behaviors, posing significant challenges to reliable prediction. To overcome these limitations, a data-mechanism-integrated framework is proposed for zigzag PCHEs simulation and design, which systematically unifies physical mechanism analysis with data-driven modeling. A three-dimensional numerical model is first developed, and a micro-element-based analysis is conducted to elucidate local thermo-hydraulic characteristics and evolution mechanisms, from which a physically informed input feature system is established. Artificial neural network (ANN) models are subsequently trained to rapidly and accurately predict the heat transfer coefficients, pressure drops and wall conductive thermal resistance, with explainable analysis employed to achieve data-physics mutual validation. Finally, the ANN models are embedded into a one-dimensional energy conservation solver to establish an AI-driven rapid simulation and intelligent design method, and is demonstrated through a design case. The results indicate that the proposed approach enables compact zigzag PCHE designs under specific thermo-hydraulic constraints, accurately capturing N2 transcritical effects and geometric disturbances. Compared to three-dimensional simulations, the average streamwise temperature and pressure errors are below 1%, while the design efficiency improves by about 6 orders of magnitude, demonstrating strong engineering applicability.

Integrating metal foam with phase change materials (PCMs) improves heat transfer in thermal energy storage systems. The thermal performance of these composites, however, varies with several geometric and operational factors. Although computational fluid dynamics (CFD) can provide detailed insights into phase-change behavior, its high computational cost makes it impractical for large-scale design and optimization. Therefore, efficient predictive models are necessary for engineering applications of copper foam-paraffin composite materials (CFPCMs) and for supporting their role in sustainable energy systems. This work systematically investigated how six key parameters affected the transient liquid fraction during melting in a shell-and-tube CFPCM accumulator, including metal foam porosity, heat transfer fluid temperature, initial PCM temperature, fluid flow velocity, outer-to-inner diameter ratio, and height-to-inner diameter ratio. After normalizing the parameters and introducing a modified Fourier number that accounts for the effective thermal conductivity of metal foam, we developed an empirical correlation between the resulting dimensionless groups and the liquid fraction using stepwise regression. The analysis reveals that the logarithm of the total melting time is linearly related to the logarithms of the influencing factors. The proposed correlation predicts the transient liquid fraction to within about 15% deviation for liquid fractions from 0.2 to 1.0. For liquid fractions above 0.4, the deviation drops below 10%. The model reliability was confirmed through validation with three independent cases, all showing prediction errors generally within 10% for liquid fractions above 0.4.

Alkaline zinc-based flow batteries (AZFBs) have attracted huge attention due to their advantages of high safety, high voltage, and low cost. Nevertheless, the zinc dendrites and side reactions attributed to the contradiction between slow zinc ion transport and rapid electrochemical reaction significantly restrict their development as promising long-duration energy storage devices. Herein, a strategy for constructing organic molecular differential locks using L-serine (Ser) additives is reported to balance transport-reaction kinetics and mitigate side reactions. In the bulk electrolyte, Ser reshapes the solvation structure of zinc ions, effectively shielding coulombic repulsion to enhance transport rate. Meanwhile, the increased solvation energy and steric hindrance confine the rapid reduction kinetics. At the electrode/electrolyte interface, Ser preferentially adsorbs on the electrode surface, thereby homogenizing interfacial ion flux and promoting stable 3D diffusion. Concurrently, the elevated nucleation overpotential decelerates zinc deposition kinetics. In addition, Ser can anchor onto the metallic zinc surface to establish an interface protective layer depleted of H2O and OH-, suppressing hydrogen evolution reactions and corrosion. Remarkably, the alkaline zinc-iron flow batteries with Ser can operate stably for over 230 h at 50 mA cm-2 (30 mAh cm-2). This work provides a unique strategy for developing high-stability and long-life AZFBs.