Applied Energy最新文献

{"title":"Auxiliary energy consumption of electric vehicles: Modeling and prediction using real-world vehicle data","authors":"Dongmin Kim ,&nbsp;Jeongsik Yun ,&nbsp;Kitae Jang ,&nbsp;Soomin Woo","doi":"10.1016/j.apenergy.2025.126766","DOIUrl":"10.1016/j.apenergy.2025.126766","url":null,"abstract":"<div><div>This paper solves the problem of modeling and predicting the auxiliary energy consumption in battery electric vehicles (BEVs). Auxiliary energy in BEVs can contribute to the total energy consumption, affecting their driving range significantly. However, the current literature mostly focuses only on total or traction energy consumption and neglects modeling of the auxiliary energy consumption. Therefore, this study proposes and validates both statistical and machine learning models for trip-based auxiliary energy consumption, as well as machine learning models for seconds-based auxiliary energy consumption. The models are developed and tested using comprehensive datasets collected from 42 identical commercial BEVs operating under real-world driving conditions, ensuring robust performance and practical applicability. Through real-world dataset analysis, we find that auxiliary energy consumption can contribute up to 45% of the total energy usage, emphasizing its substantial impact. Using statistical modeling, we investigate key parameters influencing auxiliary energy consumption and achieve an <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> of 0.893 in prediction accuracy for trip-based auxiliary energy consumption. This is accomplished by leveraging the Multi-Layer Perceptron (MLP) model with the identified parameters, demonstrating the effectiveness of our approach. Furthermore, we identify that the trip duration <span><math><mi>t</mi></math></span>, the thermal management system parameters, including the heat pump (<span><math><msub><mi>l</mi><mrow><mi>H</mi><mi>P</mi></mrow></msub></math></span>), A/C compressor (<span><math><msub><mi>w</mi><mrow><mi>A</mi><mi>C</mi></mrow></msub></math></span>) and PTC (<span><math><msub><mi>r</mi><mrow><mi>P</mi><mi>T</mi><mi>C</mi></mrow></msub></math></span>) are the most significant variables on trip-based auxiliary energy consumption prediction. Also, we observe prediction accuracy of 0.883 in <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> at a 20-s interval, identified as a knee point, using the XGBoost-based algorithm, with accuracy further improving to 0.906 in <span><math><msup><mi>R</mi><mn>2</mn></msup></math></span> at a 120-s interval.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126766"},"PeriodicalIF":11.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118320","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":"Robust photovoltaic forecasting under severe data missingness via multi-domain collaboration and covariate interaction","authors":"Ke Yan ,&nbsp;Jian Liu ,&nbsp;Jiazhen Zhang ,&nbsp;Fan Yang ,&nbsp;Yuan Gao ,&nbsp;Yang Du","doi":"10.1016/j.apenergy.2025.126771","DOIUrl":"10.1016/j.apenergy.2025.126771","url":null,"abstract":"<div><div>High-quality photovoltaic (PV) power forecasting is essential for efficient energy management and reliable grid integration, yet real-world data are often plagued by extensive missingness in both target and auxiliary variables. To address this challenge, we propose MDCTL-MCI, a missingness-aware forecasting framework that jointly leverages signal decomposition, multi-scale covariate interaction, and multi-domain collaborative transfer learning. First, multivariate singular spectrum analysis (MSSA) denoises and reconstructs incomplete time series, enhancing underlying temporal structures without explicit imputation. Next, a lightweight multiscale covariate interaction (MCI) module models interactions among reconstructed PV power, global horizontal irradiance, direct normal irradiance, and total solar irradiance at varying temporal resolutions, capturing both local fluctuations and global trends. Finally, a multi-source domain collaborative transfer learning strategy aggregates knowledge from multiple PV sites to form a global model, which is then fine-tuned on a small set of high-quality, MSSA-processed samples at each site. By freezing all but the output layer during fine-tuning, MDCTL-MCI adapts efficiently to local data heterogeneity. Extensive experiments on four Chinese PV installations reveal that, compared to baseline methods, the proposed method improves average accuracy by 10.5 % under complete data conditions and by 15.3 % under various missing data scenarios.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126771"},"PeriodicalIF":11.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118442","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":"Co-optimized recovery framework for damaged coupled power-water systems with temporal-spatial coordination and MT-SOPs","authors":"Yesen Yang ,&nbsp;Zhengmao Li ,&nbsp;Yan Xu ,&nbsp;Gaoxi Xiao ,&nbsp;Edmond Y. Lo ,&nbsp;Peng Wang","doi":"10.1016/j.apenergy.2025.126781","DOIUrl":"10.1016/j.apenergy.2025.126781","url":null,"abstract":"<div><div>The resilience of modern coupled power-water (CPW) systems is challenged by various disruptions and risks. While the restoration and repair of individual systems are documented in the literature, their coordination in CPW recovery is rarely focused on. This work proposes a temporal-spatial coordinative method to improve CPW resilience by co-optimizing the recovery process comprising repair crew dispatch and adaptive service restoration. Firstly, a CPW model is developed based on physical mechanisms and component-level interdependencies. The model includes typical post-disruption features, like imbalanced three-phase power flows and pipe breakages. Secondly, a coordinated framework is designed for recovering damaged CPW, considering faulted components, available crew, and resources. The framework hierarchically comprises two stages. The first stage conducts absorption via components' operation and network topology adjustment. The second stage organizes the grouping and routing of repair crews, with further adjustments of absorption to exploit newly repaired components. In addition, multi-terminal soft open points (MT-SOPs) are applied to facilitate flexible power flow control and network reconfiguration of imbalanced power networks to augment the repair process. We also modeled the diverse correlated uncertainties and applied a sample-based optimization approach for timely and robust solutions. The proposed method is validated on a modified 36-node/33-bus CPW system, demonstrating a 32.51 % reduction in unsupplied loads compared to separate recovery. Additionally, incorporating MT-SOPs further reduces unsupplied loads by 16.94 % compared to traditional tie-lines. A large-scale evaluation on a 308-node/150-bus synthetic model further confirms the effectiveness of our framework in real-world CPW systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126781"},"PeriodicalIF":11.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118319","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":"Physics-informed dual-stage network for lithium-ion battery state-of-charge estimation under various aging and temperature conditions","authors":"Donghee Son ,&nbsp;Shina Park ,&nbsp;Junseok Oh ,&nbsp;Taehan Lee ,&nbsp;Sang Woo Kim","doi":"10.1016/j.apenergy.2025.126770","DOIUrl":"10.1016/j.apenergy.2025.126770","url":null,"abstract":"<div><div>Accurate state-of-charge (SOC) estimation is essential for ensuring the safe and efficient operation of lithium-ion battery-based applications. However, traditional SOC estimation methods exhibit limitations in generalizability across diverse aging and temperature conditions. To address this challenge, this study proposes a physics-informed dual-stage network (PIDN) that enables robust SOC estimation under various aging, temperature, and current conditions. The PIDN method extracts key parameters of the 1-RC equivalent circuit model using a forgetting factor recursive least-squares algorithm. These physics-informed parameters, along with terminal voltage, current, and temperature measurements, are used as inputs to a dual-stage network comprising an aging model and a temperature compensation model for SOC estimation. A Kalman filter is then employed to refine the estimated SOC by leveraging the recursive characteristics of SOC dynamics. The PIDN method is validated under various operating conditions, including different aging levels, temperatures, and dynamic current profiles, using the urban dynamometer driving schedule and US06 tests. The results demonstrate that the PIDN method achieves reliable estimation accuracy, with a root mean square error below 1.76 % and a maximum absolute error below 4.55 % under previously untrained conditions. Thus, the PIDN method effectively combines domain knowledge of lithium-ion batteries with deep learning techniques, offering generalizable performance for real-time SOC estimation in practical battery management systems.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126770"},"PeriodicalIF":11.0,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118444","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":"System dynamics modeling and copula-based risk evaluation of the water–energy–carbon nexus","authors":"Jiani Fang ,&nbsp;Tong Lv ,&nbsp;Delin Fang","doi":"10.1016/j.apenergy.2025.126776","DOIUrl":"10.1016/j.apenergy.2025.126776","url":null,"abstract":"<div><div>Rapid urbanization has significantly increased water and energy consumption, leading to escalated carbon emissions and concurrently posing challenges to the water–energy–carbon (WEC) nexus. There is a growing need for integrated approaches to capture the complex dynamics within the WEC nexus and evaluate the integrated risks in future scenarios, thereby synergistically mitigating the pressures on the WEC nexus. This study selected Beijing, heavily reliant on external resources and in urgent need of carbon emission reductions, as a case study. Incorporating socioeconomic impacts, a water–energy–carbon system dynamics model was constructed to simulate the supply and demand of resources and the corresponding carbon emissions. The water, energy, and carbon pressure indices were also predicted to quantify the pressure on each subsystem. Three-dimensional copula functions were subsequently established for integrated risk evaluation. The results reveal that the three pressure indices will exhibit an increasing trend without policy intervention in 2022–2035. With respect to different scenarios, improving utilization efficiency and augmenting the external supply can effectively alleviate water and energy shortages, whereas adjusting the energy consumption structure contributes to reducing carbon emissions. Compared with the baseline scenario, the combined scenario of all the policies performs best among the multiple scenarios, where the risks of water scarcity, energy insufficiency, and excessive carbon emissions decrease significantly to 0.19, 0.06, and 0.05, respectively, and the integrated risk experiences a substantial reduction of 71 %. This study presents a scientific framework for the systematic simulation and risk evaluation of the WEC nexus and provides theoretical support for future policymaking.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126776"},"PeriodicalIF":11.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118440","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 novel theoretical optical efficiency limit estimation model guiding parameter design of solar power tower heliostat fields with pattern-free layout","authors":"Jia−Chen Li,&nbsp;Sheng−Song Xia,&nbsp;Zhan−Bin Liu,&nbsp;Li−Dong Song,&nbsp;Bo−Wen Zeng,&nbsp;Xin−Yuan Tang,&nbsp;Wei−Wei Yang,&nbsp;Ya−Ling He","doi":"10.1016/j.apenergy.2025.126804","DOIUrl":"10.1016/j.apenergy.2025.126804","url":null,"abstract":"<div><div>The heliostat field, as the critical energy conversion component of Solar Power Tower (SPT) systems, requires precise modeling of its optical efficiency limit to guide system parameter optimization. Existing models tend to overestimate theoretical optical efficiency limits by neglecting significant shadowing and blocking losses, while most optimization methods lack universal guidance for heliostat field design. This study proposes a pattern-free layout model to estimate optical efficiency limits more realistically. By incorporating a mechanism that screens shadowing and blocking losses under ideal distance constraints, the model corrects these overestimations. Additionally, the high-freedom layout strategy, without preset symmetry, overcomes traditional performance limitations. Compared to the existing model, the proposed model reduces the overestimation of optical efficiency limits by 8.92 % for large heliostat field. The study reveals that macro parameters, such as tower height and heliostat total area, have a dominant effect on efficiency limits, while micro parameters have a negligible impact. Through response surface methodology, a second-order regression model was developed to quantify the interactions among macro parameters, achieving a high prediction accuracy (<em>R</em>^<em>2</em> = 0.888). The study also investigates the optimal levelized cost of optical parameter combinations and heliostat field layouts at 30°N, with the results showing that the parameter combinations are significantly lower than those for the optimal optical efficiency limit estimation. The optimal layouts exhibit a consistent umbrella-like shape, emphasizing the importance of layout in field performance. This study provides a more realistic optical efficiency limit estimation, offering valuable insights into parameter optimization for the design of next-generation SPT.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126804"},"PeriodicalIF":11.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118441","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 detailed simulation model for fifth generation district heating and cooling networks with seasonal latent storage evaluated on field data","authors":"Manuel Kollmar ,&nbsp;Adrian Bürger ,&nbsp;Markus Bohlayer ,&nbsp;Angelika Altmann-Dieses ,&nbsp;Marco Braun ,&nbsp;Moritz Diehl","doi":"10.1016/j.apenergy.2025.126757","DOIUrl":"10.1016/j.apenergy.2025.126757","url":null,"abstract":"<div><div>Fifth generation district heating and cooling (5GDHC) networks accelerate the use of renewable energies in the heating sector and enable flexible, efficient and future-proof heating and cooling supply via a single network. Due to their low temperature level and high integration of renewables, 5GDHC systems pose new challenges for the modeling of these networks in order to simulate and test operational strategies. A particular feature is the use of uninsulated pipes, which allow energy exchange with the surrounding ground. Accurate modeling of this interaction is essential for reliable simulation and optimization. This paper presents a thermo-physical model of the pipe connections, the surrounding soil, a latent heat storage in the form of an ice storage as a seasonal heat storage and the house transfer stations. The model is derived from mass and energy balances leading to ordinary differential equations (ODEs). Validation is performed using field data from the 5GDHC network in Gutach-Bleibach, Germany, which supplies heating and cooling to 30 modern buildings. With an average model deviation of 1.7 % in the normalized mean bias error (NMBE) and 13.1 % in the coefficient of the variation of the root mean square error (CVRMSE), the model’s accuracy is validated against the available temperature measurements. The realistic representation of the thermal-hydraulic interactions between soil and pipes, as well as the heat flow within the network, confirms the accuracy of the model and its applicability for the simulation of 5GDHC systems. The Modelica implementation of the model is made openly accessible under an open-source license.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126757"},"PeriodicalIF":11.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118438","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":"Large-scale model driven real-time economic generation control for integrated energy systems","authors":"Wenxuan Huang ,&nbsp;Linfei Yin","doi":"10.1016/j.apenergy.2025.126733","DOIUrl":"10.1016/j.apenergy.2025.126733","url":null,"abstract":"<div><div>As a result of the growing integration of renewable energy generation units into integrated energy systems (IESs), the coupling configurations of equipment within the IESs are constantly changing, and the fluctuations of renewable energy sources (RESs) are even more drastic. To mitigate frequency deviations and area control errors (ACEs) in IESs, this paper proposes a transformer-soft actor-critic (T-SAC) algorithm, which integrates the efficient feature extraction capability of the large-scale model transformer with the online learning capability of deep reinforcement learning, and enables the mining of rich feature information from frequency deviation and ACE signals to generate accurate control commands. Furthermore, this paper constructs the cyber-physical-social systems-centralized real-time economic intelligent generation control (CPSS-CREIGC) framework built upon the T-SAC algorithm, which employs virtual parallel systems to optimize the parameters of T-SAC and thereby enhances training efficiency. By issuing control commands every 4 s, the CPSS-CREIGC framework effectively mitigating the reverse regulation phenomenon. The T-SAC algorithm is simulated and compared with seven different comparison algorithms in two-area and four-area IESs under high RESs penetration. Compared to the comparison algorithms, the T-SAC algorithm reduces frequency deviations by at least 46.67 %. The numerical results confirm the effectiveness and feasibility of the CPSS-CREIGC framework</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126733"},"PeriodicalIF":11.0,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118439","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 demonstration of in-situ cracked premixed swirl NH3-air flames","authors":"B. Aravind,&nbsp;Sivachidambaram Sadasivam,&nbsp;Jordan Davies,&nbsp;Syed Mashruk,&nbsp;Agustin Valera-Medina","doi":"10.1016/j.apenergy.2025.126754","DOIUrl":"10.1016/j.apenergy.2025.126754","url":null,"abstract":"<div><div>This study investigates the in-situ thermo-catalytic cracking of ammonia (NH₃) and the combustion characteristics of the resulting cracked flame. A swirl-stabilized burner equipped with a novel multi-pass heat exchanger and a catalytic tube was employed to analyse NH₃ cracking and combustion. Five catalysts were evaluated, including three developed in-house‑ruthenium (Ru) and cobalt (Co) electroplated on stainless steel wire mesh, and Ru nanoparticles loaded onto sodium zeolite along with two commercially available alumina-based Ni and Ru pellets. The performance of thermal cracking is then compared to thermo-catalytic cracking. The cracking efficiency decreased inversely with NH₃ flow rate, from 70 % to 17 % at 773-813 K and 100 % to 60 % at 893–932 K, with Ru-based catalysts outperforming thermal cracking by 20 % at 35 SLPM of NH₃. At 773–813 K, both electroplated Ru and Ru<img>Co stainless steel mesh configurations performed similarly, indicating that catalyst contact time can be further optimised. The stability and emissions of the cracked flames were assessed at air flow rates of 100–200 SLPM. The cracking efficiencies of 54–58 %, 61–62 %, and 58–65 % were observed at 798–825 K, 836–857 K, and 878–901 K for cracker flow rates of 15, 20, and 25 SLPM respectively. Emissions analysis revealed increasing N₂O levels with higher air flow and NO peaks at an equivalence ratio between 0.74 and 0.83. Flame instabilities under lean conditions led to NH₃ slip. These findings highlight the need for catalyst optimisation to enhance NH₃ cracking efficiency, improve flame stability, and reduce emissions, advancing sustainable combustion technologies.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126754"},"PeriodicalIF":11.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105480","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 charging infrastructure for electric vehicles: A comprehensive review and avenues for future research","authors":"E. Sandhiya ,&nbsp;M.S. Gajanand","doi":"10.1016/j.apenergy.2025.126749","DOIUrl":"10.1016/j.apenergy.2025.126749","url":null,"abstract":"<div><div>Electric Vehicles offer significant environmental benefits, but their reliance on the existing power grid raises concerns about increased demand, potential strain on existing energy systems and carbon emissions, particularly if the grid is heavily reliant on fossil fuel-based sources. Hence, it is imperative to integrate grid-charging stations with renewable energy sources, which is termed as hybrid charging infrastructure. This combines the stability and reliability of the grid with the sustainability and environmental benefits of renewable energy, offering a promising solution to the challenges associated with EV adoption. To understand the current state of developments, challenges and opportunities in the planning and designing of HCI for EVs, we undertake a comprehensive review of existing studies on HCI. We investigate the nature of the problems addressed, their modelling approaches, objectives, constraints, decisions to be made, and the solution methodology adopted. Based on the review, we identify key opportunities and future directions for modelling HCI. Furthermore, we suggest ways for practical implementation with managerial relevance, deriving insights from case studies, and discussing pathways for strategic implementation of HCI. We also suggest that future research should focus on optimising charging strategies, data security, and addressing economic and policy challenges.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"401 ","pages":"Article 126749"},"PeriodicalIF":11.0,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145105481","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}