Search Results (294)

The continuously improving power density of Li-ion batteries and the widespread application of fast charging and discharging have rendered thermal management an increasingly critical task. Cold plates are among the most important means for such a task, and their channel structure significantly affects battery performance. Aiming to further improve the thermohydraulic performance of cold plate, this study proposes a cold plate with sinusoidal wave-shaped channel. Using channel quantity, amplitude, wavelength, diameter, and coolant mass flow rate as variables, the orthogonal experimental scheme is employed to design combinations of different variables for numerical simulation. The numerical simulation results are used to train a deep neural network for cold plate performance prediction. The trained neural network can accurately predict the maximum temperature, comprehensive performance indicators, and entropy generation rate with errors below 5.0%, 5.0%, and 10.0%, respectively. Multi-objective optimization design (MOOD) is implemented by combining a deep neural network with the NSGA-II genetic optimization, yielding two sets of Pareto fronts as follows: one for maximizing comprehensive performance indicator and minimizing entropy generation rate, and the other for minimizing maximum temperature and entropy generation rate, and TOPSIS decision points are provided. This study provides a new method and valuable MOOD results for the thermal management of Li-ion batteries and cold plate engineering while offering theoretical guidance for practical applications. Full article

The rapid increase in the penetration of renewable energy has imposed more stringent requirements on the regulation capacity and response speed of Francis turbines in modern power grids. Vortex-induced energy loss significantly constrains the energy performance and hydraulic stability of giant Francis turbines. However, the formation mechanisms of vortex-induced hydraulic loss near the operating boundary remain insufficiently understood. Based on numerical simulations and parameter validation under 30 representative operating conditions, three 50% rated load conditions located near the operating boundary were strategically selected for detailed investigation. By integrating rigid vorticity analysis with entropy production theory, the vortex dynamics and hydraulic loss characteristics were systematically quantified and visualized. The results indicate that entropy production rates caused by turbulent dissipation and wall shear constitute the primary components of hydraulic loss, among which entropy production rate caused by turbulent dissipation (EPRT) is more sensitive to variations in external operating conditions and dominates both the magnitude and spatial distribution of energy dissipation. Distinct loss evolution patterns are observed in the runner and the draft tube. Recirculation and separation flows along the blade surfaces alter the normal blade loading distribution in the runner. In the draft tube, hydraulic loss is mainly governed by the energy dissipation associated with the interaction between the main flow region and the reverse flow region, while the intensity of hydraulic loss is not directly related to the specific vortex morphology. Overall, shear vorticity remains the key mechanism responsible for the increase in EPRT. This study provides theoretical insights and practical evidence for understanding the mechanisms of vortex-induced energy loss in giant Francis turbines and for quantitatively evaluating the distribution and evolution of hydraulic loss. Full article

The increasing penetration of wind generation requires performance evaluation methods that extend beyond average annual energy production. Temporal delivery characteristics, such as monthly dispersion and exposure to low-production periods, can influence both technical robustness and economic sensitivity. Building upon a previously developed probabilistic and entropy-based assessment framework, this study evaluates the robustness of delivery-oriented performance metrics for onshore and offshore wind units under parametric and economic uncertainty. Using high-resolution operational data from four wind units (three onshore and one offshore), the analysis incorporates percentile sensitivity, threshold variation in low-production exposure, bootstrap-based uncertainty intervals, and Monte Carlo simulation of economic inputs including CAPEX, operation and maintenance costs, and discount rate. The results indicate that variations in percentile definitions and stochastic economic assumptions modify absolute performance values but do not substantially alter the relative positioning between offshore and onshore units. Averaged over 2022–2024, the analyzed offshore unit exhibited a lower monthly energy dispersion coefficient ($C{V}_E=0.255$) than the analyzed onshore units ($C{V}_E=0.368$), corresponding to an approximate 30% reduction in relative variability. The offshore unit also showed lower mean low-production exposure ($LPE=0.526$ versus $0.581$ for onshore units) and consistently lower amplification of robustness-adjusted LCOE under conservative delivery assumptions. These results indicate that the analyzed offshore unit retains stronger delivery robustness and lower economic sensitivity across the tested parameter ranges. The proposed robustness-validation framework complements conventional yield-based assessments and provides additional insight for risk-aware evaluation of wind generation assets in renewable-dominated power systems. Full article

This study numerically investigates the influence of different fluid temperatures on the cavitation characteristics of a space-use micropump under microgravity conditions. A homogeneous multiphase model coupled with a thermal modified Zwart–Gerber–Belamri cavitation model is employed, and the SST turbulence model is applied to resolve the cavitating flow under rated and off-design flow rates. Results indicate that cavitation behavior is strongly dependent on both temperature and flow rate. At low temperatures, cavitation intensity increases, leading to reductions in head and efficiency and a slight increase in shaft power. In contrast, elevated temperatures suppress cavitation development, resulting in milder performance degradation and, in some cases, slight improvements in head and shaft power. Internal flow analysis reveals that lower temperatures promote more extensive vapor fraction distributions and greater flow distortion, while entropy production analysis shows that cavitation contributes limited additional loss overall, though entropy generation rises markedly under combined low temperature and high flow rate conditions. The findings highlight that cavitation effects are more pronounced at low temperatures and are further amplified at higher flow rates, providing insights for the design and reliable operation of space micropumps in on-orbit thermal management systems. Full article

To address the issues of suboptimal recovery performance, low timeliness, and poor economic efficiency associated with traditional fault recovery methods following large-scale power outages in active distribution networks (ADNs) caused by extreme weather, this paper proposes a dynamic fault recovery strategy for ADNs based on a two-layer hybrid algorithm under extreme ice and snow conditions. First, a line fault rate model considering the thermal effect of current under extreme ice and snow conditions is constructed, and an information entropy-based typical scenario screening method is introduced to filter the fault scenarios. Second, a photovoltaic (PV) output model and a time-varying load model under the influence of extreme ice and snow conditions are established. Subsequently, a multi-objective dynamic fault recovery model is formulated, incorporating island partitioning and integration constraints based on the concept of single-commodity flow, alongside tightened relaxation constraints. To achieve an accurate and rapid solution for the fault recovery model, a two-layer hybrid algorithm is proposed. This algorithm combines an outer-layer improved binary grey wolf optimizer (IBGWO) and an inner-layer second-order cone relaxation (SOCR) algorithm to solve the discrete and continuous decision variables within the model, respectively. Finally, the effectiveness and superiority of the proposed method are verified using the PG&E 69-bus and IEEE 123-bus systems. Full article

Aqueous supercapacitors are a class of crucial high-power, long-life, safe and reliable energy storage devices, with their performance fundamentally dependent on electrode materials. Two-dimensional (2D) vanadium-based MXenes, possessing rich multivalent redox activity and tunable layered structures, have emerged as one of highly promising electrode candidates, exhibiting significantly superior specific capacitance and pseudocapacitive properties compared to conventional Ti₃C₂T_z. To overcome inherent limitations in conductivity and structural stability, this review summarizes strategies for regulating composition and microstructure through transition metal solid solution and medium-/high-entropy design. These approaches synergistically optimize electron conduction, expand ion migration pathways, and suppress electrode degradation, thereby comprehensively enhancing rate performance, cycle life, and energy density. This review systematically reveals the composition–structure–performance relationships, providing critical design insights and theoretical foundations for developing next-generation high-performance, long-life aqueous MXene-based supercapacitors. Full article

As a critical hydraulic component of pumped storage power stations, the side inlet/outlet directly affects unit efficiency, flow stability, and system safety. This study investigates the side inlet/outlet of a pumped storage power station using three-dimensional numerical simulations, focusing on the influence of the diffuser length L on hydraulic performance, and further analyzes the underlying mechanisms of energy loss based on entropy production theory. The results indicate that, with increasing diffuser length L, the flow rates in individual channels gradually deviate from the design values, leading to an aggravated imbalance in flow distribution. In contrast, the velocity non-uniformity coefficient C_V at the trash rack decreases, accompanied by a pronounced attenuation of recirculation and local flow separation, resulting in a more uniform and stable flow field. Moreover, increasing L improves the streamwise velocity uniformity within each channel, while the extent and intensity of the top recirculation zone are reduced, suppressing local flow separation. Quantitative analysis shows that when L increases from 65 m to 85 m, the total turbulent dissipation entropy production rate in the diffuser section increases linearly from 2732.32 W/K to 2842.32 W/K, whereas the direct dissipation entropy production rate increases from 0.41 W/K to 0.59 W/K. This indicates that turbulent dissipation entropy production plays a dominant role in the overall energy loss. Shorter diffusers tend to induce high-intensity local dissipation, whereas longer diffusers reduce local peak dissipation but increase the overall entropy production within the diffuser, reflecting a trade-off between local optimization and global energy loss. This study reveals the sensitivity and governing effects of diffuser length on the hydraulic characteristics of side inlet/outlets, providing a reference for geometry optimization and engineering design of similar hydraulic components. Full article

The prediction of material lifetime is central to nanomaterial engineering and reliability analysis. We propose the Marshall–Olkin Power Half-Logistic (MOPHL) distribution, obtained by applying a Marshall–Olkin transform to the Power Half-Logistic baseline. We derive core properties—including moments, hazard rate characterization, and Rényi entropy—and develop inference under generalized progressive hybrid censoring. Estimation is carried out via maximum likelihood and Bayesian methods using a Metropolis–Hastings sampler. Asymptotic results, Fisher information, and corresponding confidence/credible intervals are provided. A Monte Carlo study assesses bias, the mean squared error, and coverage across censoring scenarios and hazard regimes. In a case study on hydroxylated fullerene degradation, MOPHL outperforms nine competing models in goodness-of-fit and predictive reliability. We also report the mean time to failure and mean residual life to support engineering decision-making. The proposed framework offers a tractable and robust tool for degradation analysis under censored data, with applicability to materials, mechanical components, biomedical devices, and environmental monitoring. Full article

(WMoTaNb)SiN refractory high-entropy nitride films were deposited via magnetron cosputtering, and the Si content was systematically regulated by varying the Si target power to investigate its influence on the microstructure, mechanical properties, oxidation resistance, and oxyhydrogen-flame ablation behavior. All the films exhibited dense columnar architectures with a distinct FCC + BCC dual-phase structure, whereas increasing the Si target power led to a gradual increase in the deposition rate and Si incorporation. The mechanical properties displayed a non-monotonic relationship with the Si target power, with film applied at an intermediate level of Si target power showing the highest hardness, approximately 28.5 GPa, and improved elastic recovery. Tribological evaluations using a GCr15 steel ball revealed that this film exhibited the lowest wear rate of 4.1 × 10⁻⁶ mm³·N⁻¹·m⁻¹ and a narrower wear track, which was attributed to reduced plastic deformation and the development of an oxygen-enriched tribofilm during sliding. High-temperature oxidation at 1000 °C in air revealed that Si incorporation significantly modified oxide-scale evolution by refining the oxidation products and altering the scale architecture, while the protection of the scale was governed by its continuity and compactness rather than its thickness alone. Oxyhydrogen-flame ablation tests revealed that the degradation behavior was primarily driven by the competition between oxidation-induced mass increase and ablation-induced material loss, with localized film disruption and substrate exposure playing a decisive role. In summary, the findings illustrate that an optimal Si target power establishes a favorable equilibrium between mechanical strength, tribological efficiency, oxidation resistance, and ablation performance, underscoring the potential of (WMoTaNb)SiN films for protective applications in complex mechanical and extreme thermal environments. Full article

Developing new quality productive forces represents a core strategy for steering China’s path to modernization and shaping new competitive advantages for the nation. As a leading technology in the new round of technological revolution and industrial transformation, artificial intelligence (AI) serves as a key engine for fostering new quality productive forces. Utilizing panel data from China’s A-share listed manufacturing firms (2012–2024), this study employs the penetration rate of industrial robots to proxy for AI development levels and the entropy method to measure new quality productive forces. From the perspective of supply chain resilience, ordinary least squares (OLS) and instrumental variable (IV) methods are employed to examine the impact of AI on enterprise new quality productive forces and its underlying mechanisms. The findings indicate that AI significantly enhances corporate new quality productive forces, a conclusion that remains robust after addressing potential endogeneity and conducting robustness checks. Mediation analysis reveals that AI reinforces corporate supply chain resilience by improving supply chain efficiency and strengthening supply chain discourse power, which in turn drives the enhancement of corporate new quality productive forces. Heterogeneity analysis indicates that the impact of AI on corporate new quality productive forces is heterogeneous, with particularly pronounced effects observed in firms with higher innovation levels, state-owned enterprises, and firms located in western China. This study contributes new evidence from a supply chain resilience perspective to understand the micro-level pathways through which AI empowers new quality productive forces, and offers targeted policy and managerial recommendations to foster the sustainable development of the manufacturing sector. Full article

In the field of modern power systems, power supply reliability has become a core indicator for measuring distribution network performance. It serves not only as a fundamental criterion for judging the continuous power supply capacity of distribution networks but also as a key benchmark for evaluating their power quality. Considering the current status of reliability assessment for distribution network power supply, this study conducts an in-depth analysis of a series of key indicators, namely outage duration, outage frequency, the number of affected customers, power supply reliability rate, and the proportion of affected customers. Through a detailed deconstruction of these indicators, an evaluation model for distribution network power supply reliability is established. In the process of model construction, this study innovatively combines the hierarchical recursive weighting method with the entropy measurement weight determination method to accurately define the weights of each evaluation dimension. On this basis, a fuzzy membership quantification strategy is introduced to precisely determine the classification level of distribution networks, and Monte Carlo simulation combined with triangular fuzzy number is used to carry out uncertainty modeling on the reliability score, realizing the transformation from deterministic evaluation to probabilistic evaluation. This strategy is developed to transform qualitative issues into quantitative analysis, effectively clarify the fuzzy and complex interrelationships among multiple influencing factors, and thereby realize a comprehensive evaluation of power supply reliability for distribution networks. To verify the effectiveness and practicality of the proposed method, a distribution network in a specific region is selected as the research object. The aforementioned model and method are applied to assess its power supply reliability, and the precise classification of distribution network levels in this region is successfully realized. This combined model significantly improves the accuracy of evaluation while ensuring the scientific rigor and fairness of the evaluation process. It provides an innovative and practical method for the field of distribution network power supply reliability assessment, and offers substantive reference and support for relevant decision-making and practical operations. Full article

This study introduces a novel four-parameter lifetime distribution constructed within the Topp–Leone Power Gompertz framework. Owing to its flexible structure, the proposed model accommodates a wide range of density shapes and hazard-rate patterns, including increasing, decreasing, bathtub-shaped, unimodal, and other non-monotone behaviors. Key distributional properties, including moments, entropy-based measures, quantile-based measures, and order statistics, are derived. Parameter inference is conducted using both likelihood-based and Bayesian approaches, and the finite-sample performance of the resulting estimators is assessed via Monte Carlo simulations. The practical relevance of the proposed distribution is illustrated using two real datasets and benchmarked against several competing lifetime models, including the Gompertz, Power Gompertz, Weibull, Topp–Leone Gompertz, Marshall–Olkin Gompertz, and Exponentiated Gompertz distributions. Overall, the comparative analyses demonstrate the superior fitting performance of the proposed model, highlighting its effectiveness for complex reliability, survival, and pharmacokinetic data. Full article

Unmanned Aerial Vehicle (UAV)-assisted edge computing supports latency-sensitive applications by offloading computational tasks to ground-based servers. However, determining optimal resource allocation under strict latency constraints and stochastic channel conditions remains challenging. This paper addresses the joint computation partitioning and power allocation problem for UAV-assisted edge computing systems. We formulate the problem as a Constrained Markov Decision Process (CMDP) that explicitly models latency constraints, rather than relying on implicit reward shaping. To solve this CMDP, we propose Constrained Soft Actor–Critic (C-SAC), a deep reinforcement learning algorithm that combines maximum-entropy policy optimization with Lagrangian dual methods. C-SAC employs a dedicated constraint critic network to estimate long-term constraint violations and an adaptive Lagrange multiplier that automatically balances energy efficiency against latency satisfaction without manual tuning. Extensive experiments demonstrate that C-SAC achieves an $18.9\%$ constraint violation rate. This represents a 60.6-percentage-point improvement compared to unconstrained Soft Actor–Critic, with $79.5\%$, and a 22.4-percentage-point improvement over deterministic TD3-Lagrangian, achieving $41.3\%$. The learned policies exhibit strong channel-adaptive behavior with a correlation coefficient of $-0.894$ between the local computation ratio and channel quality, despite the absence of explicit channel modeling in the reward function. Ablation studies confirm that both adaptive mechanisms are essential, while sensitivity analyses show that C-SAC maintains robust performance with violation rates varying by less than 2 percentage points even as channel variability triples. These results establish constrained reinforcement learning as an effective approach for reliable UAV edge computing under stringent quality-of-service requirements. Full article

Generative diffusion models have emerged as a powerful class of models in machine learning, yet a unified theoretical understanding of their operation is still developing. This paper provides an integrated perspective on generative diffusion by connecting the information-theoretic, dynamical, and thermodynamic aspects. We demonstrate that the rate of conditional entropy production during generation (i.e., the generative bandwidth) is directly governed by the expected divergence of the score function’s vector field. This divergence, in turn, is linked to the branching of trajectories and generative bifurcations, which we characterize as symmetry-breaking phase transitions in the energy landscape. Beyond ensemble averages, we demonstrate that symmetry-breaking decisions are revealed by peaks in the variance of pathwise conditional entropy, capturing heterogeneity in how individual trajectories resolve uncertainty. Together, these results establish generative diffusion as a process of controlled, noise-induced symmetry breaking, in which the score function acts as a dynamic nonlinear filter that regulates both the rate and variability of information flow from noise to data. Full article

Future wireless networks require efficient device-to-device (D2D) communication to meet the demands of increasing connectivity; however, practical challenges such as limited coverage and severe interference persist. This paper addresses these issues by employing simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) equipped with low-resolution phase shifters, thereby enabling full-space coverage while conforming to hardware constraints. To further improve system performance, we propose an irregular STAR-RIS configuration, in which only a subset of elements is activated to enhance spatial diversity without increasing power consumption. Additionally, we introduce a group scheduling strategy that assigns users to different time slots, effectively mitigating interference and improving the overall sum rate. To solve the resulting high-dimensional and non-convex optimization problem, we develop a cross-entropy optimization framework that jointly optimizes element selection, amplitude and phase configurations, and user scheduling. Simulation results demonstrate that the proposed design significantly outperforms existing benchmarks in terms of both the sum rate and scalability, thus providing a practical and efficient solution for STAR-RIS-assisted D2D communication systems. Full article