China, People’s Republic

The thermal protection of rocket engine combustion chambers presents a critical challenge in supersonic flight applications. This study numerically investigates the enhancement of heat transfer and coolant flow characteristics in regenerative cooling channels through cylindrical rib integration employing ANSYS Fluent with SST k-ω turbulence modeling to evaluate single- and double-row configurations (0.75–1.25 mm diameter) under supercritical hydrogen conditions (3 MPa 300 K inlet). Results demonstrate that rib-induced turbulence disrupts thermal boundary layers with a 1.25 mm single-row design achieving a 13.67 % reduction in peak wall temperature compared to smooth channels while double-row arrangements show diminishing returns due to increased flow resistance. The thermal performance factor (η = (Nu/Nu₀)/(f/f₀) 1/3) reveals Case 3′s superiority (21.88 % improvement over the smooth channel configuration) in balancing heat transfer enhancement against pressure drop penalties (9.23–20.93 % for single-row 8.26–18.7 % for double-row). Notably density-driven flow acceleration near heated walls mitigates pressure losses through localized viscosity reduction. Furthermore cylindrical ribs reduce thermal stratification by up to 30 % in single-row configurations with double-row designs providing additional temperature homogenization at the cost of increased flow resistance. These findings offer critical insights for optimizing rib-enhanced cooling systems in high-performance rocket engines achieving simultaneous thermal efficiency and hydraulic performance improvements.

This work investigates a biomass-driven multi-generation system designed for simultaneous power freshwater and hydrogen production addressing the interlinked energy-waterenvironment nexus. The configuration integrates Brayton supercritical carbon dioxide (SCO2) organic Rankine cycle (ORC) and thermoelectric generator (TEG) subsystems to maximize utilization of biomass-derived syngas. The recovered energy drives a reverse osmosis (RO) desalination unit for freshwater production and an alkaline electrolyzer for hydrogen generation followed by two-stage compression for storage. Under baseline conditions the system generates 1.99 MW of electricity 9.38 kg/h of hydrogen and 88.6 m3 /h of freshwater with an overall exergetic efficiency of 20.25 % emissions intensity of 0.85 kg/kWh and a payback period of 5.87 years. The Brayton cycle accounts for 49.3 % of the total cost rate while the gasifier exhibits the highest exergy destruction at 46 %. Sensitivity analyses show that varying biomass moisture content (10–30 %) and operating temperatures (700–900 ◦C) significantly influence system performance. Using a data-driven optimization framework that combines artificial neural networks (ANN) and a genetic algorithm (GA) the system’s exergetic efficiency improves to 21.76 % freshwater output rises to 90.96 m3 /h and emissions intensity decreases to 0.877 kg/kWh. Additionally optimization reduces the total cost rate by 2.71 % leading to a payback period of 5.4 years and enhances the system’s overall performance by 12.64 %.

Mobile emergency power supply vehicles (MEPSVs) powered by diesel engines or lithiumion batteries (LIBs) have become a viable tool for emergency power supply. However diesel-powered MEPSVs generate noise and environmental pollution while LIB-powered vehicles suffer from limited power supply duration. To overcome these limitations a hydrogen-powered MEPSV incorporating a soft open point (SOP) was developed in this study. We analyzed widely used operating scenarios for the SOP-equipped MEPSV and determined important parameters including vehicle body structure load capacity driving speed and power generation capability for the driving motor hydrogen fuel cell (FC) module auxiliary LIB module and SOP equipment. Subsequently we constructed an energy management strategy for the model for MEPSV which uses multiple energy sources of hydrogen fuel cells and lithium-ion batteries. Through simulations an optimal hydrogen consumption rate in various control strategies was validated using a predefined load curve to optimize the energy consumption minimization strategy and achieve the highest efficiency.

To address the economic and reliability challenges of high-penetration renewable energy integration in electricity-heat-hydrogen integrated energy systems and support the dualcarbon strategy this paper proposes an optimal dispatch method integrating Information Gap Decision Theory (IGDT) and Fuzzy Chance-Constrained Programming (FCCP). An IES model coupling multiple energy components was constructed to exploit multi-energy complementarity. A stepped carbon trading mechanism was introduced to quantify emission costs. For interval uncertainties in renewable generation IGDT-based robust and opportunistic dispatch models were established; for fuzzy load uncertainties FCCP transformed them into deterministic equivalents forming a dual-layer “IGDT-FCCP” uncertainty handling framework. Simulation using CPLEX demonstrated that the proposed model dynamically adjusts uncertainty tolerance and confidence levels effectively balancing economy robustness and low-carbon performance under complex uncertainties: reducing total costs by 12.7% cutting carbon emissions by 28.1% and lowering renewable curtailment to 1.8%. This study provides an advanced decision-making paradigm for low-carbon resilient IES.

Reliable operation of proton exchange membrane fuel cells (PEMFCs) is crucial for their widespread commercialization and accurate fault diagnosis is the key to ensuring their long-term stable operation. However traditional fault diagnosis methods not only lack sufficient interpretability making it difficult for users to trust their diagnostic decisions but also one-dimensional (1D) feature extraction methods highly rely on manual experience to design and extract features which are easily affected by noise. This paper proposes a new interpretable fault diagnosis algorithm that integrates Gramian angular field (GAF) transform convolutional neural network (CNN) and gradient-weighted class activation mapping (Grad-CAM) for enhanced fault diagnosis and analysis of proton exchange membrane fuel cells. The algorithm is systematically validated using experimental data to classify three critical health states: normal operation membrane drying and hydrogen leakage. The method first converts the 1D sensor signal into a two-dimensional GAF image to capture the temporal dependency and converts the diagnostic problem into an image recognition task. Then the customized CNN architecture extracts hierarchical spatiotemporal features for fault classification while Grad-CAM provides visual explanations by highlighting the most influential regions in the input signal. The results show that the diagnostic accuracy of the proposed model reaches 99.8% which is 4.18% 9.43% and 2.46% higher than other baseline models (SVM LSTM and CNN) respectively. Furthermore the explainability analysis using Grad-CAM effectively mitigates the “black box” problem by generating visual heatmaps that pinpoint the key feature regions the model relies on to distinguish different health states. This validates the model’s decision-making rationality and significantly enhances the transparency and trustworthiness of the diagnostic process.

As a zero-carbon energy carrier hydrogen is playing an increasingly vital role in the decarbonization of maritime transportation. The hydrogen pressure reducing valve (PRV) is a core component of ship-borne hydrogen storage systems directly influencing the safety efficiency and reliability of hydrogen-powered vessels. However the marine environment— characterized by persistent vibrations salt spray corrosion and temperature fluctuations— poses significant challenges to PRV performance including material degradation flow instability and reduced operational lifespan. This review comprehensively summarizes and analyzes recent advances in the study of high-pressure hydrogen PRVs for marine applications with a focus on transient flow dynamics turbulence and compressible flow characteristics multi-stage throttling strategies and valve core geometric optimization. Through a systematic review of theoretical modeling numerical simulations and experimental studies we identify key bottlenecks such as multi-physics coupling effects under extreme conditions and the lack of marine-adapted validation frameworks. Finally we conducted a preliminary discussion on future research directions covering aspects such as the construction of coupled multi-physics field models the development of marine environment simulation experimental platforms the research on new materials resistant to vibration and corrosion and the establishment of a standardized testing system. This review aims to provide fundamental references and technical development ideas for the research and development of high-performance marine hydrogen pressure reducing valves with the expectation of facilitating the safe and efficient application and promotion of hydrogen-powered shipping technology worldwide.

The urgent global transition toward low-carbon energy systems has highlighted the need for systematic planning of hydrogen refueling stations (HRS) to facilitate clean energy adoption. This study develops an integrated framework for regional HRS layout optimization and carbon emission assessment considering population distribution land area and hydrogen demand. Using Hainan Province as a case study the model estimates regional hydrogen demand determines optimal HRS deployment evaluates spatial coverage and refueling distances and quantifies potential carbon emission reductions under various renewable energy scenarios. Model validation with Haikou demonstrates its reliability and applicability at the regional scale. Results indicate pronounced spatial disparities in hydrogen demand and infrastructure requirements emphasizing that prioritizing station deployment in densely populated urban areas can enhance accessibility and maximize emission reduction. The framework offers a practical data-efficient tool for policymakers and planners to guide early-stage hydrogen infrastructure development and supports strategies for regional decarbonization and sustainable energy transitions.

Titanium dioxide (TiO2) is widely used in solar cells and photocatalysts given its excellent photoactivity low cost and high structural electronic and optical stability. Here a novel TiO2 composite was prepared by coating TiO2 inverse opal (IO) with TiO2 nanorods (NRs). With a porous three-dimensional network structure the composite exhibited higher light absorption; enhanced the separation of the electron–hole pairs; deepened the infiltration of the electrolyte; better transported and collected charge carriers; and greatly improved the power conversion efficiency (PCE) of the quantum-dot sensitized solar cells (QDSSCs) based on it while also boosting its own photocatalytic hydrogen generation efficiency. A very high PCE of 12.24% was achieved by QDSSCs utilizing CdS/CdSe sensitizer. Furthermore the TiO2 composite exhibited high photocatalytic activity with a H2 release rate of 1080.2 µ mol h−1 g −1 several times that of bare TiO2 IO or TiO2 NRs.

Driven by carbon neutrality goals electric–hydrogen coupled virtual power plants (EHCVPPs) integrate renewable hydrogen production with power system flexibility resources emerging as a critical technology for large-scale renewable integration. As distributed energy resources (DERs) within EHCVPPs diversify heterogeneous resources generate diversified market values. However inadequate benefit allocation mechanisms risk reducing participation incentives destabilizing cooperation and impairing operational efficiency. To address this benefit allocation must balance fairness and efficiency by incorporating DERs’ regulatory capabilities risk tolerance and revenue contributions. This study proposes a multi-stage benefit allocation framework incorporating risk–reward tradeoffs and an enhanced optimization model to ensure sustainable EHCVPP operations and scalability. The framework elucidates bidirectional risk–reward relationships between DERs and EHCVPPs. An individualized risk-adjusted allocation method and correction mechanism are introduced to address economic-centric inequities while a hierarchical scheme reduces computational complexity from diverse DERs. The results demonstrate that the optimized scheme moderately reduces high-risk participants’ shares increasing operator revenue by 0.69% demand-side gains by 3.56% and reducing generation-side losses by 1.32%. Environmental factors show measurable yet statistically insignificant impacts. The framework meets stakeholders’ satisfaction and minimizes deviation from reference allocations.

The cooling effect from the para-ortho hydrogen conversion (POC) combined with a vaporcooled shield (VCS) and multi-layer insulation (MLI) can effectively extend the storage duration of liquid hydrogen in cryogenic tanks. However there is currently no effective and straightforward empirical correlation available for predicting the catalytic POC efficiency in VCS pipelines. This study focuses on the development of correlations for the catalytic conversion of para-hydrogen to ortho-hydrogen in pipelines particularly in the context of cryogenic hydrogen storage systems. A model that incorporates the Langmuir adsorption characteristics of catalysts and introduces the concept of conversion efficiency to quantify the catalytic process’s performance is introduced. Experimental data were obtained in the temperature range of 141.9~229.9 K from a cryogenic hydrogen catalytic conversion facility where the effects of temperature pressure and flow rate on the catalytic conversion efficiency were analyzed. Based on a validation against the experimental data the proposed model offers a reliable method for predicting the cooling effects and optimizing the catalytic conversion process in VCS pipelines which may contribute to the improvement of liquid hydrogen storage systems enhancing both the efficiency and duration of storage.