Applied Energy最新文献

{"title":"Insights into energy efficiency for vanadium redox flow battery (VRFB) using the artificial intelligence technique","authors":"Rasoul Talebian,&nbsp;Ali Pourian,&nbsp;Pouya Zakerabbasi,&nbsp;Sina Maghsoudy,&nbsp;Sajjad Habibzadeh","doi":"10.1016/j.apenergy.2025.126485","DOIUrl":"10.1016/j.apenergy.2025.126485","url":null,"abstract":"<div><div>Vanadium redox flow battery (VRFB) offers a sustainable and reliable solution for large-scale energy storage applications. This study represents the first investigation into the comprehensive data-driven analysis of inter-parameter correlation and prediction of the energy efficiency of VRFBs utilizing the Gaussian Process Regression (GPR) model. Namely, 420 VRFB datasets were collected from the literature, whereas 10 structural and 2 operational features are considered input parameters. Indeed, in the VRFB cells with the greater active area, i.e., pilot-to-commercial-scale applications, the Serpentine flow field configuration, higher electrolyte concentration, thicker electrodes, and higher felt compression are more prevalent. The outcomes reveal that the current density, membrane type, and electrode treatment with the respective Pearson correlation coefficient values of −0.4167, 0.2862, and 0.1546 significantly affect the VRFBs' energy efficiency. Besides, the developed ML models can accurately result in the associated energy efficiency in the VRFBs, with the highest accuracy of the GPR- Matern5/2. The training and testing R² values are 0.9933 and 0.9565, respectively, indicating near-perfect accuracy, making it a reliable model. This research paves the way for improving VRFB performance, advancing its practical application, and providing key insights into AI-driven battery design.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126485"},"PeriodicalIF":10.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670949","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring sustainable solutions in PV-integrated indoor farming: Energy, economic, and environmental insights from major U.S. cities","authors":"Guoqing Hu ,&nbsp;Fengqi You","doi":"10.1016/j.apenergy.2025.126469","DOIUrl":"10.1016/j.apenergy.2025.126469","url":null,"abstract":"<div><div>As urban populations grow, sustainable local food production becomes essential. Indoor farming with integrated photovoltaic systems offers consistent yields under optimal conditions. This study evaluates photovoltaic-based controlled environment agriculture system in the ten most populous U.S. cities, organized by region—North Central, South Central, Northeast, and Southwest—focusing on energy savings, costs, and environmental impacts. A simulation framework resolves control optimization problems at 15-min intervals, where control outcomes and greenhouse states are analyzed for energy efficiency and environmental effects. The study introduces novel aspects: (1) comprehensive environmental impact assessments, targeting light pollution, carbon footprint reduction, and nitrification; (2) a multi-city evaluation for diverse climate insights; and (3) crop growth modeling within a model predictive control framework, offering a scalable, climate-sensitive solution that optimizes energy efficiency and crop yield. Results show that photovoltaic-based greenhouse can cut annual energy consumption by 25.7 %, reducing reliance on non-renewable sources. Geographic factors influence costs: East and Southwest cities, such as New York and Los Angeles, face increased operational expenses (18 %–26 %) due to land and energy constraints, whereas South Central cities like Houston and Phoenix benefit from lower costs due to ample sunlight. Environmental impacts vary; Northeast photovoltaic-based greenhouse reduces carbon emission emissions by 0.658 kg CO₂-eq/m² annually but increases light pollution by 5 % in dense urban areas. North Central and South cities experience less light pollution but face nitrification issues, averaging 0.77 N₂O eq-kg/m².</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126469"},"PeriodicalIF":10.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The physical-encoded Photovoltaic forecasting method combined with continuous learning and multi-digital twins mechanisms","authors":"Shuwei Liu ,&nbsp;Jianyan Tian ,&nbsp;Yuanyuan Dai ,&nbsp;Zhengxiong Ji ,&nbsp;Amit Banerjee","doi":"10.1016/j.apenergy.2025.126390","DOIUrl":"10.1016/j.apenergy.2025.126390","url":null,"abstract":"<div><div>End-to-end neural network models, often seen as black boxes, have been widely used in photovoltaic (PV) power forecasting. However, they face challenges regarding poor model adaptability, transferability, and interpretability. To address these issues, this paper proposes a physical-encoded PV forecasting model, which decomposes the end-to-end network into a data-driven external parameter forecasting model and a physics-driven power calculation model. The power calculation model, with explicit physical meanings, enhances the model's interpretability. A continual learning mechanism is designed to enable the model to quickly adapt to environmental changes, mitigating the impact of model drift and improving adaptability and transferability. A multi-digital twins synergistic operation mechanism is introduced to incorporate the strengths of other models, further enhancing forecasting accuracy. Model drift can be categorized into concept drift and data drift. This paper designs two scenario experiments to test these drifts. Scenario 1 focuses on concept drift, and the experimental results show that the proposed method in this paper achieves improvements of 30.5 %, 16.5 %, and 1.9 % in the nMAE, nRMSE, and R² metrics, respectively, compared to the best results of the comparison models. In Scenario 2, the model is transferred to other power plants for data drift tests. Results show that when transferred to Plant 4, its accuracy improves by 45.8 %, 21 %, and 2.1 % compared to the best comparison method; for Plant 5, the improvements are 34.1 %, 18.3 %, and 2.5 %.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126390"},"PeriodicalIF":10.1,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144670946","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental study on expansion force characteristics of LiFePO4 battery under overcharge cycles","authors":"Yueyang Yu ,&nbsp;Ping Ping ,&nbsp;Gongquan Wang ,&nbsp;Jiaxin Guo ,&nbsp;Zhenkai Feng ,&nbsp;Wei Gao ,&nbsp;Kailong Liu ,&nbsp;Depeng Kong","doi":"10.1016/j.apenergy.2025.126498","DOIUrl":"10.1016/j.apenergy.2025.126498","url":null,"abstract":"<div><div>Addressing early stage of overcharge cycling through reliable detection methods is crucial to enhancing battery reliability and lifespan. This study examines the characteristics of expansion force evolution in lithium iron phosphate (LiFePO₄) batteries during overcharge cycles under different cut-off voltages, with a view to elucidating the impact of cut-off voltage on expansion force. The results demonstrate that the expansion force and its derivative increase with the number of cycles and cut-off voltages, with the expansion during discharge being more significant than during the charging process. Furthermore, a correlation between irreversible expansion force and capacity loss has been identified, with post-mortem analysis and theoretical studies shedding light on the underlying mechanisms of expansion force evolution during overcharge cycles. Based on these findings, an early warning method based on expansion force is proposed, which can also assess the severity of failure by analyzing the expansion force derivative. This work reveals characterization of the expansion force evolution of batteries under overcharge cycling, and provides a reliable approach for the early warning strategy of slight failure due to overcharge cycling, helping to prevent the escalation of accidents.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126498"},"PeriodicalIF":10.1,"publicationDate":"2025-07-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144662802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hybrid multi-agent deep reinforcement learning for multi-type mobile resources dispatching under transportation and power network recovery","authors":"Shaohua Sun ,&nbsp;Gengfeng Li ,&nbsp;Zhaohong Bie ,&nbsp;Dingmao Zhang ,&nbsp;Yuxiong Huang","doi":"10.1016/j.apenergy.2025.126423","DOIUrl":"10.1016/j.apenergy.2025.126423","url":null,"abstract":"<div><div>Rainstorm waterlogging or typhoon can not only cause seriously failure of power network (PN), but also damage the normal traffic of transportation network (TN). Equipment fault of PN affects normal power supply of critical loads, and the interruption of TN severely limits the flexible transfer of mobile resources for recovery of transportation and power network (TPN). Previous work only addresses dispatching of multi-type mobile resources (MMRs) for power network recovery on the assumption of healthy TN, which makes dispatching strategy impractical. To fill this gap, this paper proposes a dispatching model of MMRs for collaborative recovery of TPN, embedding road repair crews (RRCs) dispatching behaviors into road repair constraints. To solve the above model, firstly road island and various topology update strategies are introduced to simplify shortest path searching for MMRs routing. Then, the dispatching model of MMRs is described as a parameterized action Markov decision process, in which MMRs are modeled as different types of intelligent agents considering various discrete-continuous dispatching characteristics. And, a hybrid multi-agent deep reinforcement learning (HMADRL) method characterizing master-slave architecture is developed to improve the solving efficiency and convergence speed of model, where the master module describes the recovery process of TN with dispatching of RRCs, and the slave module is constructed to recovery PN based on the path update strategies. The case analysis based on 15-node PN (18-node TN), 33-node PN (45-node TN) and practical example demonstrates that this approach can elevate the practicality of dispatching strategies and the recovery efficiency of TPN.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126423"},"PeriodicalIF":10.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656638","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The impact of temporal clustering on long-term energy system models","authors":"Matteo Catania ,&nbsp;Giuseppe Muliere ,&nbsp;Fabrizio Fattori ,&nbsp;Paolo Colbertaldo","doi":"10.1016/j.apenergy.2025.126354","DOIUrl":"10.1016/j.apenergy.2025.126354","url":null,"abstract":"<div><div>The field of energy system modelling is experiencing significant development, driven by the urgent need to redesign the national energy systems to achieve carbon neutrality. A growing interest regards long-term energy system models, which enable tracking the pathway and not only the final need for installations. The increase in complexity may easily lead them to face computational limits. Therefore, modelling approaches are required that cluster data to reduce the size of the problem while limiting errors and inaccuracies. This article studies the impact of temporal clustering on the performances of a sector-integrated energy system model, considering the double-layer clustering scheme operating on two distinct temporal scales: intra-year and inter-year. The former is addressed through typical-day clustering (k-means and k-medoids), while the latter introduces multi-year gaps between representative years. This methodology is implemented in the open-source framework <em>oemof</em>, which is customized to the dual clustering approach. The study addresses a sector-integrated energy system, built on the Italian structure, with a multi-vector and multi-sector perspective along the 2020–2050 horizon. The impact is investigated by comparing multiple options with varying number of typical days and multi-year gap, comparing each configuration with a benchmark without clustering. The approach yields coherent representations of the energy system evolution, simultaneously reducing the memory usage down to 4 %. The outcomes show the benefits of balancing the number of representative years with the number of representative days, suggesting that such a trade-off leads to significant computational advantages. Although literature shows that time-series reduction is case-dependent, the double-layer clustering scheme appears promising for application on even more complex models, where a full-hour resolution would be computationally intractable.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126354"},"PeriodicalIF":10.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review","authors":"Arash J. Khabbazi ,&nbsp;Elias N. Pergantis ,&nbsp;Levi D. Reyes Premer ,&nbsp;Panagiotis Papageorgiou ,&nbsp;Alex H. Lee ,&nbsp;James E. Braun ,&nbsp;Gregor P. Henze ,&nbsp;Kevin J. Kircher","doi":"10.1016/j.apenergy.2025.126459","DOIUrl":"10.1016/j.apenergy.2025.126459","url":null,"abstract":"<div><div>A large body of simulation research suggests that model predictive control (MPC) and reinforcement learning (RL) for heating, ventilation, and air-conditioning (HVAC) in residential and commercial buildings could reduce energy costs, pollutant emissions, and strain on power grids. Despite this potential, neither MPC nor RL has seen widespread industry adoption. Field demonstrations could accelerate MPC and RL adoption by providing real-world data that support the business case for deployment. Here we review 24 papers that document field demonstrations of MPC and RL in residential buildings and 80 in commercial buildings. After presenting demographic information – such as experiment scopes, locations, and durations – this paper analyzes experiment protocols and their influence on performance estimates. We find that 71 % of the reviewed field demonstrations use experiment protocols that may lead to unreliable performance estimates. Over the remaining 29 % that we view as reliable, the weighted-average cost savings, weighted by experiment duration, are 16 % in residential buildings and 13 % in commercial buildings. While these savings are potentially attractive, making the business case for MPC and RL also requires characterizing the costs of deployment, operation, and maintenance. Only 13 of the 104 reviewed papers report these costs or discuss related challenges. Based on these observations, we recommend directions for future field research, including: Improving experiment protocols; reporting deployment, operation, and maintenance costs; designing algorithms and instrumentation to reduce these costs; controlling HVAC equipment alongside other distributed energy resources; and pursuing emerging objectives such as peak shaving, arbitraging wholesale energy prices, and providing power grid reliability services.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126459"},"PeriodicalIF":10.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallax and cloud shadow correction in satellite-based solar irradiance estimation: A study in tropical environments","authors":"Arindam Roy ,&nbsp;Annette Hammer ,&nbsp;Detlev Heinemann ,&nbsp;Marion Schroedter-Homscheidt ,&nbsp;Ontje Lünsdorf ,&nbsp;Jorge Lezaca","doi":"10.1016/j.apenergy.2025.126457","DOIUrl":"10.1016/j.apenergy.2025.126457","url":null,"abstract":"<div><div>Accurate estimation of Global horizontal solar irradiance (GHI) from geostationary satellite imagery is essential for intraday solar PV power forecasting. Tropical regions show an even more challenging situation: A typically much higher tropopause results in higher cloud tops and correspondingly larger parallax errors in satellite imagery with significantly larger cloud shadow displacements compared to mid-latitudes. This study improves GHI estimates from Meteosat-8 by correcting cloud parallax and shadow displacement using gridded cloud top height (CTH) data. Fractional or sub-pixel displacement of individual cloudy pixels is enabled by bilinear interpolation in contrast to prior methods that allowed only integer shifts or assigned a single CTH value to a grouping of adjacent cloud pixels. Validation against one year of 15-min resolution ground-based measurements at five sites in South and Southeast Asia shows a reduction in relative root mean square error (rel. RMSE) from 23.8 % to 22.1 %. Improvements are more pronounced at higher satellite viewing zenith angles (<span><math><msub><mi>θ</mi><mi>sza</mi></msub></math></span>) and in the presence of high-altitude clouds. The corrected satellite-based GHI exhibits 4–7 percentage points lower rel. RMSE than National Solar Radiation Database (NSRDB) and 2.5 points lower than CAMS solar radiation service for similar <span><math><msub><mi>θ</mi><mi>sza</mi></msub></math></span>. Greatest error reductions occur during partly cloudy conditions for sites within 61° <span><math><msub><mi>θ</mi><mi>sza</mi></msub></math></span>, and under overcast skies for sites close to the edge of Meteosat-8's field of view. Improvements also depend on the co-scattering angle between sun and satellite with respect to the site, and the availability of sufficient upstream cloud information along the path of solar irradiance falling on the site. Ramp detection accuracy improves, particularly at lower detection thresholds, as measured using the Swinging Door Algorithm.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"399 ","pages":"Article 126457"},"PeriodicalIF":10.1,"publicationDate":"2025-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656641","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluating advanced nuclear fission technologies for future decarbonized power grids","authors":"Emilio Cano Renteria ,&nbsp;Jacob A. Schwartz ,&nbsp;Jesse Jenkins","doi":"10.1016/j.apenergy.2025.126395","DOIUrl":"10.1016/j.apenergy.2025.126395","url":null,"abstract":"<div><div>Advanced nuclear fission, which encompasses various innovative nuclear reactor designs, could contribute to the decarbonization of the United States electricity sector. However, little is known about how cost-competitive these reactors would be compared to other technologies, or about which aspects of their designs offer the most value to a decarbonized power grid. We employ an electricity system optimization model and a case study of a decarbonized U.S. Eastern Interconnection circa 2050 to generate initial indicators of future economic value for advanced reactors and the sensitivity of future value to various design parameters, the availability of competing technologies, and the underlying policy environment. These results can inform long-term cost targets and guide near-term innovation priorities, investments, and reactor design decisions. We find that advanced reactors should cost $5.7–$7.3/W to gain an initial market share (assuming 30 year asset life and 3.5 %–6.5 % real weighted average cost of capital), while those that include thermal storage in their designs can cost up to $6.0/W–$7.7/W (not including cost of storage). Since the marginal value of advanced fission reactors declines as market penetration increases, break-even costs fall <span><math><mo>∼</mo></math></span>32 % at 100 GW of cumulative capacity and <span><math><mo>∼</mo></math></span>51 % at 300 GW. Additionally, policies that provide investment tax credits for nuclear energy create the most favorable environment for advanced nuclear fission. These findings can inform near-term resource allocation decisions by stakeholders, innovators and investors working in the energy technology sector.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126395"},"PeriodicalIF":10.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656176","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A self-optimizing defrost initiation controller for air-source heat pumps: Experimental validation of deep reinforcement learning","authors":"Jonas Klingebiel,&nbsp;Christoph Höges,&nbsp;Janik Horst,&nbsp;Oliver Nießen,&nbsp;Valerius Venzik,&nbsp;Christian Vering,&nbsp;Dirk Müller","doi":"10.1016/j.apenergy.2025.126400","DOIUrl":"10.1016/j.apenergy.2025.126400","url":null,"abstract":"<div><div>Air-source heat pumps (ASHPs) play a key role in sustainable heating, but their efficiency is significantly reduced by frost formation on the evaporator. The timing of defrost initiation is crucial to minimize energy losses, yet conventional demand-based defrosting (DBD) controllers rely on specialized sensors for frost detection and heuristic thresholds for defrost initiation, leading to increased system costs and suboptimal performance. This paper presents an experimental validation of a self-optimizing deep reinforcement learning (RL) controller. With our proposed implementation, RL determines defrost timing using standard temperature measurements and autonomously generates tailored control rules, overcoming the limitations of conventional DBD methods. The study consists of three case studies conducted on a hardware-in-the-loop test bench with a variable-speed ASHP. First, RL’s defrost timing accuracy is evaluated against experimentally pre-determined optima. Across five stationary test conditions, RL achieves near-optimal defrost initiations with maximum efficiency losses of at most 1.9 %. Second, RL is benchmarked against time-based (TBD) and demand-based defrost controllers for three typical days with varying ambient conditions. RL outperforms TBD by up to 7.1 % in <span><math><mi>S</mi><mi>C</mi><mi>O</mi><mi>P</mi></math></span> and 3.6 % in heat output. Compared to DBD, RL improves <span><math><mi>S</mi><mi>C</mi><mi>O</mi><mi>P</mi></math></span> by up to 9.1 % and heat output by 4.9 %. Finally, we assess RL’s ability to adapt its strategy through online learning. We emulate airflow blockage, a common soft-fault condition, caused by obstructions on the evaporator fins (e.g., leaves). RL adjusts its strategy to the changed environment and improves efficiency by 16.6 %. While the results are promising, limitations remain, requiring further research to validate RL in real-world ASHPs.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"398 ","pages":"Article 126400"},"PeriodicalIF":10.1,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144656178","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}