Precise characterization of anisotropic thermal conductivity (k) under dynamic conditions is essential for accurate thermal modeling and safe operation of lithium-ion batteries. Here, we established a transformable platform by combining an optimized localized symmetric heating method with the heat flow method. With only minor adjustments, this platform enables reliable measurement of in-plane thermal conductivity (k//) and through-plane thermal conductivity (k perpendicular to) of pouch cells under varied temperatures, states of charge (SOC), and charge-discharge rates (C-rates). The results show that both k// and k perpendicular to increase with temperature, with the in-plane enhancement being more pronounced. In contrast, their dependence on SOC and C-rates is non-monotonic and strongly coupled, leading to complex variations. These dynamic behaviors likely originate from electrode structural transitions, interfacial contact changes, and products of side reactions, which collectively regulate directional heat transport. Importantly, the anisotropy k ratio consistently remains between 20 and 60 for the 1 Ah NCM613 pouch cell, confirming the persistence of strong anisotropy. Moreover, the variations in k// and k perpendicular to can exceed a factor of two under dynamic operating conditions. These findings provide essential input for next-generation thermal models and highlight the critical role of anisotropic k and its dependence on dynamic operating conditions in accurately capturing directional heat transport during battery operation.

Latent thermal energy storage (LTES)-based thermal batteries are rapidly transcending theoretical research to become pivotal solutions for peak-load shifting, offering a promising pathway for enhancing energy efficiency and flexibility at the building end-use level. Existing studies mainly regulate LTES performance by adjusting external operational parameters (e.g., flow rate, inlet temperature), while the role of state of charge (SOC) as an indicator of real-time operating condition has received comparatively less attention. SOC provides an intuitive measure of stored energy and facilitates precise heat capacity management to optimize charge-discharge processes, thereby improving indoor thermal comfort and reducing building energy demands. In this study, a numerical approach based on a CFD model is employed to systematically investigate the charge-discharge behaviour and the quantitative impact of SOC on LTES thermal performance. The response surface method (RSM) is adopted to optimize the SOC operating range, implementing heat capacity management for charge-discharge processes to achieve synergistic enhancement of overall charge-discharge performance. Results show that sensible heat dominates thermal energy storage in 0-27% and 90-100% SOC intervals under the current operating condition, with a distinct decay in heat transfer rate. Efficient latent heat utilization occurs in 27-90% SOC, maintaining a high and stable heat transfer rate. The optimal SOC window for efficient operation is determined as 22-69%. Compared with full-load operation, the optimized strategy increases charging heat flux by 139.7%, discharging heat flux by 171.8%, and operating frequency by 113%. The proposed SOC-driven heat capacity management approach provides novel insights and practical strategies for the advanced control of LTES thermal batteries in building energy systems.

In the practical operation of advanced adiabatic compressed air energy storage (AA-CAES), the compression, thermal storage, cavern storage, and expansion processes are exposed to ambient air with varying ambient relative humidity (RH), whose system-level influence remains insufficiently quantified. Existing studies have mainly examined isolated components or fixed-boundary operating conditions, leaving the coupled humidity effects under dynamic off-design operation unclear. This work develops a moisture-coupled dynamic model of an AA-CAES system that integrates moisture-dependent air thermophysical properties, off-design characteristics of key components, and transient water-vapor condensation in both the packed-bed latent thermal energy storage (PBLTES) and the air storage cavern. The model is used to reveal how ambient RH affects the stabilized and cyclic performance of an AA-CAES system. Results show that operation with a high inlet RH produces a peak-and-decay trend because condensation continuously introduces additional exergy destruction and effective storage-volume loss. Quantitatively, the stabilized round-trip efficiency decreases from 77.13% at dry conditions to 74.69% at 30 °C and 100% RH condition. The additional efficiency loss is mainly attributable to the PBLTES and air cavern, which contribute 1.12% and 1.74%, respectively, owing to condensation-induced chemical exergy loss and increased heat dissipation. Meanwhile, the energy storage density decreases by 11.5% from 1.91 to 1.69 kWh/m3. This reduction is caused by moisture-induced compressor capacity degradation, reduced net air delivery after vapor condensation, and a comparatively small additional contribution from cumulative cavern-volume occupation. These results identify ambient moisture as a non-negligible design and operation factor for AA-CAES, and highlight air moisture management as an effective route for improving system performance.

The increasing energy density of battery storage systems highlights crucial thermal safety issues. Composite phase change materials (CPCMs) exhibit immense potential for temperature regulation. However, their widespread application is restricted by inherent flammability, low thermal conductivity, and susceptibility to leakage. We developed a multifunctional solid-solid flame-retardant CPCM via two-step chemical crosslinking, strong hydrogen bonding, and an intumescent flame-retardant system comprising an "acid-nitrogen-carbon" source. This innovatively resolves the trade-off between flame retardancy and the deterioration of phase-change/mechanical properties. While maintaining a high latent heat (100.8 J/g), high thermal conductivity (1.6 W/(m & centerdot;K)), and favorable mechanical flexibility, it concurrently exhibits exceptional flame retardancy. It achieves the highest V-0 rating in the UL-94 standard, with a total heat release (THR) and total smoke production (TSP) limited to only 35.43 MJ/m2 and 0.53 m2, respectively. In lithium-ion batteries under a 3C discharge rate, they maintain the maximum temperature and temperature difference below 45 degrees C and 3.7 degrees C. Crucially, leveraging synergistic flame retardancy and excellent heat dissipation, they address the critical challenge of mitigating the propagation of battery thermal runaway. This work provides a novel strategy for battery thermal management, integrating efficient heat dissipation and thermal runaway isolation.

Thermo-electrochemical cells (abbreviated as thermocells) are promising heat-to-power devices for low-grade heat harvesting. To enhance the output performance of thermocells, the effective temperature difference applied across the thermocell must be maximized. However, the thermal resistance between the thermocell electrodes and the heat/cold sources inevitably reduces the utilization efficiency of the applied temperature difference, leading to a substantial reduction in the power output of thermocells. To address this issue, this study identifies micron-scale polyamide microporous membranes as ideal selective filters for typical Fe(CN)(6)(3-/4-) thermocells, significantly enhancing intrinsic thermocell thermal resistance while maintaining low electric resistance. Systematic optimization of key parameters and material properties-including mesh count, operating temperature, and the incorporation of a hydrophilic polydopamine coating-has been performed on the polyamide microporous membranes to achieve superior performance enhancement. As a result, the temperature-difference utilization ratio increases from similar to 60% in the membraneless configuration to 87.5%, yielding a P-max of 0.398 W/m(2), a 91% improvement over the membraneless group. Furthermore, based on multiphysics simulations, this study elucidates the microscopic mechanism of the output performance improvement of the thermocell with PA membrane: the micron-scale polyamide microporous membrane suppresses convective heat transfer (vortex mixing) while preserving ion-migration pathways.

Understanding thermal transport within Lithium-ion batteries (LIBs) is critical for accurate temperature prediction toward thermal safety diagnosis. This study systematically investigates the temperature-dependent intrinsic thermal conductivity (k) and interfacial thermal resistance (R-c) of LIB components (namely the anode, cathode, and separator) via an offline, layer-resolved experimental platform over a temperature range of 0-80 degrees C. Our results reveal that the intrinsic k of all components generally increases with temperature, with the anode exhibiting the most pronounced rise, and that R-c exhibits no clear trends. A comparative analysis shows that the interfacial contributions become increasingly dominant with temperature, up to similar to 50 % at 80 degrees C, shifting the limiting mechanism of thermal transport from bulk to interface. The study also differentiates between testing temperature (T-test) and cycling temperature (T-cycle). Measurements on fresh cells under controlled conditions suggest that the thermal behavior is predominantly governed by T-test, with limited influence from prior T-cycle within the studied temperature range. Comparisons with literature data for full-cell k reveal inconsistencies in temperature trends, underscoring the need for structure-resolved and aging-aware thermal characterizations. This work provides a foundational dataset for improving the fidelity of LIB thermal modeling and offers insights into the layered thermal mechanisms governing heat dissipation in LIBs.

The efficient conversion and utilization of low-grade thermal energy is essential to enhancing the energy efficiency and reaching the carbon neutrality goals of modern industrial energy systems. As a cutting-edge heat-to-electricity technology, thermo-electrochemical cells (also known as thermocells) have great prospects in low-grade heat recovery due to its ultra-high thermopower, high scalability, and low cost. However, conventional aqueous thermocells, typically based on planar or rectangular modules, face substantial limitations when deployed in real-world environments characterized by geometric irregularity, spatial constraints, and distributed thermal sources. This study addresses the utilization of waste heat in complex circular tubular sources by proposing a machine learning-enabled optimization framework for fan-shaped aqueous thermocells. Firstly, a database is established using simulation models. By integrating machine learning methods, a surrogate model is developed to achieve high-accuracy performance prediction of fan-shaped thermocells under various geometric configurations and deployment positions. Secondly, by using the surrogate model to decouple the relationship between the geometric parameters and the deployment position, a two-step Gray Wolf optimization algorithm (GWO) is proposed to optimize the performance of single fan-shaped thermocell unit. Its maximum power density can reach 333.7 mW·m−2, which is 66.35% higher than that of the conventional square one. To further conquer the issue of full coverage deployment, a synergistic performance optimization for array-level fan-shaped thermocells modules (hereinafter referred to as the “module”) is proposed. Through a combinatorial arrangement, six fan-shaped thermocell units are aligned to conform to circular heat sources, resulting in a power density of 235.7 mW·m−2. These results highlight the potential of geometry-driven and machine learning-assisted design in advancing next-generation thermocells technologies toward scalable applications.

Thermal batteries based on phase change materials (PCMs) are a key technology for energy savings and carbon reduction in building heating since latent thermal energy storage by PCMs with high energy storage density indicates great potential to utilize renewable energies. However, the complicated structure of finned-tube heat exchangers and the nonlinear melting–solidification process of PCMs require a great deal of computation and time resources, highly restricting the engineering design and application of PCM-based thermal batteries. To address these issues, this study proposed a universal machine learning-enabled performance prediction framework for finned-tube PCM-based thermal batteries designed for residential domestic hot-water supply, in which thermal energy is stored in PCM during the charging process and released to cold water during the discharging process to provide usable hot water for end users. First, a simplified simulation method coupling a 1D tube model with a 3D computational fluid dynamics model is established for the rapid performance computation of thermal batteries. Second, the deep operator network framework is introduced to directly map static parameters to the time-based temperature response of outlet hot water validated by the simulation and previous experimental results. Based on the machine learning-enabled performance prediction framework, the effects of critical parameters such as tube outer diameter and thickness combination, fin distance, and flow rate on the heat storage capacity and heat release power are systematically analyzed, providing guidelines for the proposed flow rate feedback regulation strategy of thermal batteries to satisfy different usage temperatures Tuse and time constraints. The results show that the proposed framework can accurately predict the outlet temperature and available total volume of hot-water output by thermal batteries under various conditions.

This study investigates the dynamic behaviors of a single near-wall bubble collapse under various operating conditions, specifically focusing on the effects of wall vibration, vibration frequency, and stand-off distance. The primary objective is to reveal the underlying mechanisms of how vibration influences the collapse process and to provide insights for developing effective cavitation suppression technologies. To achieve this, a two-phase flow numerical simulation coupled with fluid-structure interaction was employed. The Volume of Fluid method was used to track the bubble interface, while the finite volume method was applied to solve the governing equations. The results demonstrate that bubble collapse near a vibrating wall exerts a more pronounced impact than near a static wall, characterized by an accelerated collapse process, a smaller minimum volume, and a higher instantaneous peak temperature. Furthermore, higher vibration frequencies lead to a more rapid contraction and a significantly more intense pressure impulse on the solid boundary. Additionally, reducing the initial stand-off distance continuously increases the severity of the peak impulsive pressure sustained by the wall. The findings provide a theoretical foundation for understanding cavitation erosion mechanisms and developing effective cavitation suppression technologies.

Addressing the severe challenges posed by internal short circuit (ISC) faults to lithium-ion battery safety, this paper proposes a diagnosis method based on impedance characteristics and deep learning. By triggering internal short circuits of varying severity through needle-penetration experiments, and employing electrochemical impedance spectroscopy (EIS) coupled with equivalent circuit model (ECM) fitting and Distribution of Relaxation Times (DRT) analysis, the study thoroughly investigates the changes in battery impedance characteristics after ISC occurrence. Furthermore, based on Pearson correlation analysis, feature parameters strongly correlated with the severity of internal short circuits are identified. A deep neural network (DNN) model is then constructed to achieve accurate detection and classification of ISC faults. Experimental validation demonstrates that the proposed method achieves an average accuracy and class-specific recall rates of over 95%.

Shear-thinning fluids, widely used in high-performance applications such as gelled propellants, inkjet printing, and biomedical sprays, exhibit complex atomization mechanisms that remain challenging to decipher due to their intricate rheology and limitations in traditional measurement techniques. This study investigates the breakup dynamics and droplet characteristics of a shear-thinning fluid (0.15 wt% Carbopol 934) during straightthrough jet atomization, emphasizing the role of injection pressure (0.7-1.5 MPa). Leveraging high-speed imaging and a machine learning model, the research enables accurate identification of in-focus droplets, substantially enhancing detection precision within the shallow depth of field over traditional approaches. A Gaussian fitting method in logarithmic space is proposed to model the wide-scale droplet size distribution. This method successfully captures the transition from unimodal to bimodal distributions with increasing Weber number, identifying a rheology-specific shoulder near 70 & micro;m and a secondary atomization peak around 60 & micro;m. Statistical analysis reveals a continuous reduction in D32 with increasing injection pressure. An empirical correlation for the core droplet diameter achieves high predictive accuracy (R2 = 0.983). Unlike Newtonian jets, shear-thinning jets exhibit a consistent radial decrease in droplet size due to the combined influence of velocity and viscosity distributions, with low viscosity at the jet edge playing a dominant role in enhancing peripheral breakup.

The thermal safety of lithium-ion batteries (LIBs) in confined spaces remains a critical challenge in power battery pack design. This study conducts a multidimensional evaluation into the effects of spatial scales on thermal runaway (TR) characteristics through integrated experimental and simulation approaches. Key findings reveal that reducing spatial volume from 8.0 x 108 mm3 to 2.88 x 105 mm3 significantly advances the TR trigger time of single cells by 973 s (from 1490s to 517 s), attributed to accelerated heat accumulation under degraded thermal dissipation. Furthermore, the TR propagation interval between adjacent batteries shortens by 64 s, revealing that spatial compression accelerates the chain reaction of TR through enhanced heat transfer. The simulation based on the Fire Dynamics Simulator (FDS) demonstrated the flame development dynamics in a confined environment, with a heat release rate simulation error within 4 %. Notably, vertical height reduction proves pivotal in flame suppression-spaces below 80 mm reduce heat flux to adjacent batteries by 52.3 % compared to 800 mm. These findings establish key spatial scale threshold parameters for thermal safety strategies in transportation and storage scenarios. And the innovative application of FDS provides advanced engineering solutions for battery pack design and TR fire prediction.

As the one of key components for electric vehicle and renewable energy systems, power battery is always confronted with the issue of thermal safety. It is therefore necessary to design an adequate thermal management solution. Two-phase cooling based on flow boiling has proved its unique advantage of strong heat dissipation capability in cooling applications of small-scale electronic devices due to the utilization of latent heat, and it is considered as one of the promising successors for conventional battery thermal management system (BTMS) based on single-phase liquid cooling. However, the popularization of two-phase direct cooling for power battery is limited due to the difficulty on the maintenance of thermal uniformity, resulted from the strict challenge in the organization of two-phase flow during boiling. In order to highlight the major concerns on two-phase cooling BTMS, and provide directions for future investigations, this review is organized based on the consideration of physical mechanisms of two-phase cooling. The flow patterns during flow boiling are reviewed to show their relationship with heat transfer. Studies on flow maldistribution during two-phase flow are then summarized to show the complexity of two-phase flow organization. Further introduction is presented on the current manipulation methods on flow pattern based on various principles. Based on the reviews on previous studies, future research directions on two-phase cooling BTMS are outlined, while methods to achieve uniform flow pattern distribution is suggested as the possible solution towards optimal thermal uniformity for two-phase cooling BTMS.

Gel-based propellants have gained significant attention in aerospace and propulsion applications, yet the atomization characteristics of gel-based propellants with shear-thinning behavior are still lacking understanding. In this study, the primary atomization behavior of shear-thinning fluid jets is investigated using a three-dimensional coupled Volume of Fluid and Lagrangian Particle Tracking model integrated with direct numerical simulation. A modified power-law model is employed to accurately capture the shear-dependent viscosity, and the numerical approach is rigorously validated. The results show that shear-thinning jets exhibit earlier onset and faster radial growth of surface instabilities compared with Newtonian jets. During the early stage, a viscosity–pressure positive-feedback mechanism accelerates wave amplification, while downstream axial curvature effects dominate and suppress further growth, consistent with the observed reduction in cumulative deviation beyond ≈11D. Interface steepening enhances aerodynamic shear and induces multilayer recirculation near surface waves, promoting the formation of internal voids within the liquid column. With increasing consistency index k and decreasing power-law index n, the maximum cumulative deviation rises from 2.77D to 4.14D, while the length of the positive-feedback region decreases from 11.0D to 9.46D, indicating that stronger shear-thinning effects accelerate the amplification of instability waves while confining their axial growth. These findings provide new insights into the nonlinear instability dynamics and interfacial evolution during the breakup of shear-thinning jets and establish a theoretical foundation for optimizing fuel injection systems utilizing rheologically complex or gel-based propellants.

The knowledge of anisotropic thermal conductivity of lithium-ion batteries (LIBs) is paramount for accurate battery thermal modeling and diagnosis because of its decisive influence on the internal temperature distribution and overall thermal behaviors of LIBs. However, due to the complex materials, geometries, and dynamic state variations of LIBs, obtaining accurate anisotropic thermal conductivity at the full-cell level remains challenging. Here, a comprehensive review is provided on the latest methodologies and data analytics of the anisotropic thermal conductivity of battery cells with different chemistries and geometries under dynamic operating conditions. The applicability, uncertainty, advantages, and limitations of current measurement methods are examined, providing guidance for method selection and future improvement. The anisotropic thermal conductivity data across various cell formats and electrode materials are evaluated, and their variations with temperature, state of charge, state of health, and charge/discharge rates  are analyzed. Additionally, future applications are highlighted for thermal conductivity to serve as a key input for thermal modeling and an indicator for battery diagnostics, with the development of in situ and real-time measurement methods and artificial-intelligence-driven models. These efforts collectively support an integrated framework for next-generation thermal management systems toward improving the performance, safety, and lifespan of LIBs and other emerging batteries.

Thermo-electrochemical cells (thermocells) with high Seebeck coefficients hold great potential for low-grade heat recovery and low-power electronic applications such as wearable devices and wireless sensors. Hydrogel-based thermocells, which eliminate leakage risks, offer greater convenience and flexibility compared to liquid thermocells in real-world applications. However, hydrogel thermocells still suffer from low power density and limited mechanical performance. Sodium polyacrylate (PANa) hydrogels, with their abundant carboxylate groups, reduce mass transfer resistance for [Fe(CN)6]3-/K4[Fe(CN)6]4- redox couples, while also outperforming widely studied polyacrylamide-based hydrogels in terms of absorption capacity and moisture retention. In light of this, we developed a polyanionic polymer hydrogel thermocell composed of sodium polyacrylate cross-linked with sodium alginate (PANa-SA), which demonstrates an extensibility of 314%, a swelling rate of 571% in 0.4 M equimolar K3[Fe(CN)6]/K4[Fe(CN)6] solutions, and a moisture retainability of 94% over 4 weeks. Furthermore, the PANa-SA hydrogel thermocell exhibits a 35.9% higher normalized power density P max/Delta T 2 compared to that of conventional polyacrylamide-based hydrogel thermocells. Additionally, since thermocells with higher electrolyte concentrations tend to exhibit superior performance, we successfully achieved a stable 0.8 M excessively concentrated PANa-SA hydrogel thermocell at room temperature, delivering a 20% increase in P max/Delta T 2. Moreover, we developed a quasi-stable 1.0 M hydrogel thermocell, which achieves a 134.5% increase in P max/Delta T 2 compared to that of the standard 0.4 M sample. These findings present a universal and effective strategy for enhancing the output performance of hydrogel thermocells by introducing excessive electrolyte concentrations via a simple heated-solution immersion method.

Phase change material (PCM) plays a vital role in thermal energy storage across a wide range of applications, thanks to their distinctive properties and benefits. To address the pressing need for improved thermal energy storage efficiency in waste heat recovery systems exposed to fluctuating heat sources, such as exhaust gas and hot water, this study investigates the charging performance of Gas-PCM latent heat storage units under variable flow conditions. Using the enthalpy-porous medium model, the phase change dynamics of PCM under different heat transfer fluid inlet conditions are examined, with experimental validation integrated into the simulations to ensure the robustness of the model and methodology. The findings reveal that flat heat storage designs perform optimally under prominent temperature variations, while corrugated designs excel when large flow fluctuations dominate. Notably, under fluctuating flow conditions, the PCM melting time was reduced by up to 11.7 % compared to constant flow conditions, while the heat storage capacity, over a fixed duration, was enhanced by as much as 11.0 % relative to constant flow conditions, highlighting the critical role of flow rate dynamics over temperature variations in improving heat transfer efficiency and storage performance. These results provide valuable insights into the optimization of thermal energy storage systems for renewable energy applications, contributing to advancements in energy utilization efficiency.

As a cutting-edge heat-to-electricity technology, thermogalvanic cells (thermocells) have great prospects in lowgrade heat recovery due to its high Seebeck coefficient (Se), high scalability and low cost. Most of previous studies about aqueous thermocells have been focused on the overall performance by experimentally exploring advanced electrode and electrolyte materials, while very few simulation studies were reported before, leading to the unclear mechanisms of heat and ion transport inside the thermocell. In view of these challenges, this study aims to reveal the heat and ion transport characteristics of aqueous thermocells under various critical operating parameters, providing theoretical guidelines for further design and optimization of aqueous thermocells with fixed electrode and electrolyte materials. Firstly, a multi-physical model considering the diffusion, migration and convection was established and validated. Then, the effects of hot electrode temperature, electrode spacing and electrode orientation were evaluated on the thermocell performance from the aspects of distributions of multi- physical fields, overpotentials and overall performance. Finally, a prototype aqueous thermocell was proposed based on the understandings of restrictions associated with these operating parameters. Results indicated that each operating parameter can attribute to the variation of natural convection from the intensity and forms, and then affected the ion transport flux and overpotentials, and thus determined the power density of thermocells. These findings prompted the design and optimization of new aqueous thermocells, and the proposed prototype thermocell delivered the maximum power density of 0.43 W/m2, which was 115 % higher than that of the basic rectangular thermocell.