Applied Energy最新文献

{"title":"Forming multi-transmission-node distributed energy resource aggregations in wholesale energy market: An optimal node aggregation approach and admissible capacity expansion regions","authors":"Weilun Wang ,&nbsp;Xianbang Chen ,&nbsp;Yikui Liu ,&nbsp;Lei Wu","doi":"10.1016/j.apenergy.2026.127536","DOIUrl":"10.1016/j.apenergy.2026.127536","url":null,"abstract":"<div><div>To manage a large fleet of distributed energy resources (DERs) over wide geographic areas and facilitate their wholesale energy market integration, multi-transmission-node DER aggregation (M-DERA) covering DERs across multiple transmission nodes has emerged in industrial practice. However, integrating the concept of M-DERAs into wholesale market operations presents new challenges for regional transmission organizations (RTOs), particularly in evaluating transmission line flows, as accurately determining the sensitivity of an M-DERA to the power flow of a transmission line remains difficult. To address the challenge, this paper explores an optimal node aggregation approach to derive the effective M-DERA formation scheme, by balancing the DER aggregation intensity (measured by the average capacity of M-DERAs) and the maximum line flow calculation error (evaluated by the difference between line flows calculated via aggregated sensitivities of M-DERAs and exact sensitivities of individual transmission nodes). Moreover, after M-DERAs are optimally formed, their continued capacity expansions could further deteriorate line flow calculation errors. To this end, a multi-parametric optimization-based approach is proposed to calculate admissible capacity expansion regions (ACERs), which concisely describe the acceptable nodal capacity expansion ranges with respect to the RTO’s preference on line flow calculation errors to assist M-DERAs’ complex expansion planning process. Results on a modified IEEE 24-bus system validate that the proposed optimal M-DERA formation scheme outperforms all other aggregation options with reduced line flow calculation errors as well as alleviated transmission line overloading in real-time operations, and that ACERs can effectively guide nodal DER capacity expansions to meet the required level of line flow calculation errors.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127536"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385975","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":"Spatially resolved diagnostic in solid oxide cell short stack – multisampling analysis under pure hydrogen operation","authors":"Luca Riccio ,&nbsp;Francesca Santoni ,&nbsp;Alfredo Zingone ,&nbsp;Viviana Cigolotti ,&nbsp;Giuseppina Roviello ,&nbsp;Stefan Megel ,&nbsp;Mihails Kusnezoff","doi":"10.1016/j.apenergy.2026.127522","DOIUrl":"10.1016/j.apenergy.2026.127522","url":null,"abstract":"<div><div>This study presents a fully integrated diagnostic platform for a 10-cell electrolyte-supported solid oxide fuel cell short stack, enabling spatially resolved investigation of internal physicochemical phenomena under realistic operating conditions. The platform combines multisampling gas analysis, distributed temperature sensing, and per-cell electrochemical characterization within a single experimental framework. Eleven gas sampling ports along the fuel-electrode manifold and eleven thermocouples on the air side allow mapping of local hydrogen and water concentrations together with temperature distributions, while polarization curves and multichannel electrochemical impedance spectroscopy provide cell-resolved performance information. Statistical analyses based on correlation, partial correlation, and confidence interval assessment are employed to quantitatively link local gas composition, thermal behavior, and electrochemical response. The results show that compositional gradients are primarily governed by the fuel flow direction, with progressive hydrogen depletion and water accumulation along the anode channel, whereas temperature remains highly uniform across the stack, with minor gradients aligned with the air crossflow direction. Electrochemical analysis identifies an operating temperature of 835 °C as thermally and electrochemically optimal, minimizing activation and ohmic losses without significant performance gains at higher temperatures. While high cell-to-cell homogeneity is observed under reference conditions, localized performance effects emerge at extreme operating parameters such as high fuel utilization or elevated temperature. Overall, the proposed multi-domain diagnostic approach provides a robust experimental methodology for assessing chemical, thermal, and electrochemical non-uniformities in solid oxide fuel cell stacks, supporting improved stack design, control strategies, and durability assessment.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127522"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385976","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 fair and low-carbon V2G scheduling framework based on carbon-economic contracts and fulfillment-aware dispatch","authors":"Haiwen Chen ,&nbsp;Xuelian Li ,&nbsp;Xiaoxue Wang ,&nbsp;Zhen Xin ,&nbsp;Leijiao Ge ,&nbsp;Weida Zhao ,&nbsp;Yuanlin Zhang","doi":"10.1016/j.apenergy.2026.127470","DOIUrl":"10.1016/j.apenergy.2026.127470","url":null,"abstract":"<div><div>The large-scale integration of electric vehicles (EVs) offers significant flexibility for the low-carbon transition of power systems. However, effectively incentivizing heterogeneous users with private characteristics to participate in vehicle-to-grid (V2G), while ensuring efficient and fair scheduling remains a key challenge for virtual power plants (VPPs). To address this, we propose a V2G scheduling framework based on contract theory, targeting both carbon reduction and economic objectives. First, we integrate the carbon inclusion (CI) concept into contract theory to design a differentiated incentive mechanism. This mechanism not only leverages both economic rewards and environmental value to motivate heterogeneous users, but also resolves information asymmetry. Second, to achieve efficient and fair scheduling of large-scale EV fleets, we design a two-stage aggregate-disaggregate framework. In the aggregation stage, we propose a real-time optimization method based on a Beta soft Actor-Critic algorithm, specifically adapted for the bounded action space of contract-based V2G. In the disaggregation stage, we design a fair dispatch policy, based on a fulfillment progress deviation index to dynamically balance contract fulfillment progress among EVs. Simulation results demonstrate that the proposed incentive mechanism significantly enhances the VPP's economic and environmental benefits, and the fair dispatch policy substantially improves fairness while maintaining high system performance.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127470"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147385979","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":"Grid physics-informed and time-adaptive stacked learning for real-time electricity price forecasting: a short-term to mid-term approach","authors":"Yawen Yi ,&nbsp;Xinyu Chen ,&nbsp;Zhiyong Tian ,&nbsp;Yuxin Zhang","doi":"10.1016/j.apenergy.2026.127502","DOIUrl":"10.1016/j.apenergy.2026.127502","url":null,"abstract":"<div><div>Driven by the rapid transition towards high-proportion renewable energy and market-oriented reforms, China's real-time electricity markets face unprecedented price volatility. Accurate forecasting of these critical prices remains challenging as existing models often neglect physical grid constraints and robust anomaly handling, exposing market participants to substantial financial risks. To address these gaps, we propose a novel grid physics-informed time-adaptive stacked learning model for short- to mid-term real-time electricity price forecasting. Considering physical constraints, it incorporates a security-constrained unit commitment simulation to emulate the real-time market clearing process, enhancing spike prediction by capturing real-time supply-demand balance through accurate commitment unit capacity forecasting. A dual-stage time-segment robust outlier correction factor with outlier labeling is introduced to accurately distinguish and label true price anomalies, which promotes precise trend extraction by variational mode decomposition and substantially improves both stable and extreme-period forecasts. Furthermore, a time-adaptive stacked learning framework accounting for distinct fluctuation patterns across different time periods is developed, dynamically weighting base models via feedback mechanisms to generate targeted predictions, ensuring robust performance from 24-h to 168-h horizons. Validated by Hubei provincial data, the proposed model achieves mean absolute errors of 37.42 CNY/MWh (5.24 USD/MWh) and 92.08 CNY/MWh (12.89 USD/MWh) for 24-h and 168-h predictions, respectively, outperforming all state-of-the-art benchmarks by at least 34.25% and 9.24%. For extreme price forecasting, the model achieves root mean square error of 43.09 CNY/MWh (6.03 USD/MWh) for spikes and 53.21 CNY/MWh (7.45 USD/MWh) for troughs, reducing errors by up to 71.91% and 57.45%. Overall, the proposed model provides a robust and accurate solution for multi-horizon real-time price forecasting, offering valuable insights for highly volatile electricity markets and practical guidance for market participants in risk management and bidding strategy optimization.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127502"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386061","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":"Superstructure optimization reveals novel cost-optimal power-to-methanol pathways using hybrid solid oxide electrolysis cells","authors":"Meng Qi ,&nbsp;Seyed Mojtaba Alirahmi ,&nbsp;Chengtian Cui ,&nbsp;Zhiwei Zhang ,&nbsp;Xiaodong Zhang ,&nbsp;Lina Wang ,&nbsp;Ming Chen ,&nbsp;Guillermo Díaz-Sainz ,&nbsp;Haoshui Yu","doi":"10.1016/j.apenergy.2026.127579","DOIUrl":"10.1016/j.apenergy.2026.127579","url":null,"abstract":"<div><div>Power-to-methanol offers a promising pathway for large-scale, long-term energy storage, yet hydrogen (H₂)/syngas production remains the key techno-economic bottleneck. Solid oxide electrolysis cells (SOECs) can efficiently produce H₂ and carbon monoxide, but with multiple options available (such as steam electrolysis, co-electrolysis, and CO₂ electrolysis) and possible coupling with (reverse) water–gas shift reactions, it is still unclear which option and process pathway, whether alone or in hybrid, most effectively minimizes methanol production costs. This study addresses this gap with a two-stage framework that combines surrogate-based superstructure optimization with detailed process integration and assessment. The optimization reveals two cost-optimal, previously unexplored pathways featuring hybrid SOEC configurations: (i) production of CO-based feed gas using steam and CO₂ electrolysis, and (ii) production of CO₂-based feed gas with an additional water–gas shift step to generate additional H₂ while producing CO₂. Advanced process design is then conducted and shows that CO-based methanol synthesis outperforms the CO₂-based route in terms of methanol yield and energy consumption. The combined steam and CO₂ electrolysis configuration achieves the highest energy efficiency (74%), compared with co-electrolysis and steam electrolysis alone. The levelized cost of methanol is estimated at 1050–1155 USD/t, with the potential to decrease to bio- or fossil-methanol cost levels under projected reductions in electricity price and SOEC stack cost.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127579"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386164","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-based and stability-aware analytical modeling of wind turbine wakes","authors":"Marwa Souaiby,&nbsp;Fernando Porté-Agel","doi":"10.1016/j.apenergy.2026.127586","DOIUrl":"10.1016/j.apenergy.2026.127586","url":null,"abstract":"<div><div>Wind turbine wake behavior is strongly affected by atmospheric stability, yet most analytical wake models have been developed and validated under neutral boundary-layer conditions. As a result, their predictive reliability under stable and convective stratification remains uncertain. In this study, we address this gap by evaluating the physics-based wake-expansion model of Vahidi and Porté-Agel (2022) (VPA model) using large-eddy simulation (LES) data spanning stable, neutral, and convective atmospheric boundary layers. The model shows consistently strong agreement with LES across all stability regimes, with root-mean-square wake-deficit errors below <span><math><mn>5</mn><mi>%</mi></math></span>. A key contribution of this work is the assessment of the VPA model robustness when Lagrangian integral time scales—quantities rarely available in field measurements—are replaced by empirical estimates. The results demonstrate low sensitivity to uncertainties in these inputs, indicating that accurate wake predictions can be achieved using turbulence quantities obtainable, in principle, from high-frequency velocity measurements. Importantly, the VPA model links wake expansion to cross-stream turbulence intensities and integral time scales, which control lateral and vertical wake meandering and mixing, unlike classical Gaussian formulations that rely solely on the streamwise turbulence intensity. Guided by physical insights from the validated model, we propose a simple stability-aware refinement of the classical Gaussian wake formulation, linking the total wake-growth rate to the radial turbulence intensity. This refinement improves wake predictions under non-neutral conditions while preserving analytical simplicity, making it suitable for wind-energy engineering applications.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127586"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386222","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":"Characterizing peak electricity demand for U.S. households: an assessment of end-use loads and demand factors","authors":"Brennan D. Less,&nbsp;Iain S. Walker","doi":"10.1016/j.apenergy.2026.127422","DOIUrl":"10.1016/j.apenergy.2026.127422","url":null,"abstract":"<div><div>Understanding household peak electricity demand is critical to evaluate the technical need for electrical infrastructure upgrades. This study characterizes peak loads for existing and new equipment using metered data from a convenience sample of 11,940 U.S. dwellings from four sources, including 911 from two sources with end-use metering. After standardized data cleaning and labeling, we derived descriptive statistics for key metrics, such as maximum demand and demand factors, and developed predictive models relating 60- to 15-min demand for the National Electrical Code (NEC). Mean 15-min maximum demand was 9.7 kW (median 9.0 kW; IQR 7.0–11.5 kW, 95% CI 9.6–9.8 kW), indicating spare capacity in 98% of homes with hypothetical 100 A panels. Maximum demand increased with floor area and number of high-demand loads. Dwelling maximum demand was driven by higher-power, longer-duration heating appliances and vehicle charging, while most user-operated appliances contributed little. Demand factors are used to account for how most devices contribute less than their rated power to maximum demand. Existing load mean demand factors (28%; median 10%; IQR 0–58%; CI 28–29%) were higher than those for new loads (21%; median 7%; IQR 0–35%; CI 20–21%), because new loads changed the timing and magnitude of maximum demand. New high-demand loads had higher than average demand factors (40–60%). Whole dwelling demand factors support the NEC's 40% assumption, but they challenge its conservative 100% treatment of new HVAC. We propose a data-driven 50% demand factor for new equipment, which would align with metered data, improve affordability, and modernize electrical codes.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127422"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154246","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":"Optimal energy portfolio investment strategies for data centers under deep market uncertainty","authors":"Mohamed Abdelhady ,&nbsp;Eleftherios Iakovou ,&nbsp;Efstratios N. Pistikopoulos","doi":"10.1016/j.apenergy.2026.127510","DOIUrl":"10.1016/j.apenergy.2026.127510","url":null,"abstract":"<div><div>Artificial intelligence is driving soaring electricity demand, with data centers projected to account for 44% of U.S. load growth through 2028. Energy planning for data centers requires robust investment strategies amid volatile electricity prices and grid interconnection delays. This study introduces a regret minimization framework, inspired by decision theory and game theory, to optimize energy portfolios for data centers, with potential relevance to other energy-intensive sectors. Using hindsight benchmarks and historical back-testing, the framework employs deterministic optimization and full cross-validation to evaluate portfolio robustness through regret-based metrics. In a hyperscale data center case study, diversified portfolios combining solar photovoltaic (PV), wind energy, and gas turbines demonstrate lower regret than grid-only strategies and provide resilience to evolving regulatory requirements. Reducing grid reliance from 100% to 20% mitigates cost volatility and interconnection risks. Low-carbon technologies, like nuclear small modular reactors (SMR), become viable with reduced capital costs and limited land availability. This work offers a robust planning framework for optimal strategic investments not only for data centers but also for energy-intensive industries navigating the modern, volatile energy landscape.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127510"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146154248","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":"Electrolyte design strategies, failure mechanisms, and future perspectives for wide-temperature-range aqueous zinc-ion batteries","authors":"Wentao Li ,&nbsp;Yixin Zhou ,&nbsp;Jingjing Du ,&nbsp;Lijian Xu ,&nbsp;Yi Chen ,&nbsp;Aokui Sun","doi":"10.1016/j.apenergy.2026.127580","DOIUrl":"10.1016/j.apenergy.2026.127580","url":null,"abstract":"<div><div>Aqueous zinc-ion batteries (AZIBs) have emerged as a promising alternative to lithium-ion batteries (LIBs) due to their high safety, low cost, and environmental friendliness. However, aqueous electrolytes face inherent challenges such as freezing at low temperatures and water loss at high temperatures. Achieving reliable operation of AZIBs under extreme conditions remains a significant challenge and has attracted considerable research attention. Furthermore, extreme temperatures can exacerbate parasitic side reactions, including hydrogen evolution reaction (HER) and zinc dendrite formation. This review begins by outlining the failure mechanisms of AZIBs under various temperature conditions, followed by a comprehensive discussion of design strategies to mitigate these issues. Various electrolyte optimization approaches are examined, including solute optimization, functional additives, cosolvents, ionic liquids, deep eutectic electrolytes, and hydrogel-based systems. The article provides a detailed overview of the basic components, operating principles, and unique features of wide-temperature-range AZIBs. Additionally, persistent challenges and bottlenecks regarding AZIBs are summarized, together with constructive perspectives and future research directions. This review aims to offer valuable insights and practical guidelines for developing next-generation electrolyte systems for high-quality, wide-temperature-range AZIBs.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127580"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386149","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":"SAGE: Sustainable and green energy-aware client selection for federated learning in IoT","authors":"Martina Savoia ,&nbsp;Ciro Della Bruna ,&nbsp;Ciro D’Addio ,&nbsp;Francesco Piccialli","doi":"10.1016/j.apenergy.2026.127568","DOIUrl":"10.1016/j.apenergy.2026.127568","url":null,"abstract":"<div><div>The Internet of Things (IoT) is widely adopted in industrial applications and smart infrastructures, enabling large-scale data collection and intelligent services. However, the distributed nature of IoT networks introduces significant energy challenges, as devices often operate under heterogeneous conditions and with highly variable energy availability. Ensuring the sustainability of such systems requires adaptive energy management strategies and localized processing techniques that reduce unnecessary energy usage while preserving reliable performance. Federated Learning (FL) has emerged as a promising paradigm for IoT, allowing the collaborative training of a global model on a server aggregating parameters from different devices, the clients, without exposing private data. Nevertheless, the repetitive communication and computation inherent to FL can exacerbate energy constraints. To minimize energy consumption, communication strategies such as model compression can be used to transmit updates, or client selection (CS), to select clients that can effectively improve the global model with their updates.</div><div>Our work introduces <em>SAGE</em>, a novel energy efficient CS strategy tailored for distributed IoT scenarios. Unlike conventional methods, the proposed approach jointly considers the residual energy available at each client device, the Jensen–Shannon divergence (JSD) as a statistical measure of data dissimilarity, and the proportion of renewable energy in the client’s power supply. By combining these factors, the method not only improves energy balancing across the network but also promotes the prioritization of renewable energy usage, enhancing both system efficiency and environmental sustainability while maintaining competitive model accuracy. The code is available at <span><span>https://github.com/MODAL-UNINA/SAGE</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":246,"journal":{"name":"Applied Energy","volume":"410 ","pages":"Article 127568"},"PeriodicalIF":11.0,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147386218","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}