Energy Science & Engineering最新文献

{"title":"Performance Evaluation of Self-Degradable Gel Temporary Plugging Agents for Pressurized Workover","authors":"Deji Liu,&nbsp;Chao Chen,&nbsp;Xiaohui Li,&nbsp;Ying Wang,&nbsp;Li Cheng,&nbsp;Shoumin Sun,&nbsp;Jiayi Tan","doi":"10.1002/ese3.2031","DOIUrl":"https://doi.org/10.1002/ese3.2031","url":null,"abstract":"<p>Band pressure operation has become the main way of oil and gas well workover in the world, to solve the gel breaking problem in the gel plugging pressure technology, acrylamide and ester-based cross-linking agent UCL-1 were used to synthesize a self-degradable gel that can be used at 40°C–60°C by the one-pot method. The cross-linking reaction principle of the gel was analyzed by infrared spectroscopy; in addition, the degradation performance of the gel and the effects of acrylamide, UCL-1, initiator and metal ions on the degradation performance of the gel as well as the influence law were investigated; finally, sand-filled tubing and casing were used to simulate the stratigraphy and the wellbore, respectively, thus evaluating the sealing performance of the gel. The results showed that the cross-linking reaction of the gel was a double-bond copolymerization reaction; the viscosity of the gel after complete degradation in the range of 40°C–60°C was 51–450 mPa-s, and the degradation time was 115–220 h, and the degradation time of the gel could be adjusted by changing the formulation components and the mineralization degree; moreover, the pressure-bearing capacity of the gel in the formation at 40°C–60°C was 8.5–14.9 MPa, and the pressure-bearing capacity of gel in wellbore is 52–73 kPa, and the blocking time is 3–6 d, which meets the construction time requirement of pressurized operation. This study extends the breaking method of gel plugging pressure technology and further promotes the development and application of pressure work technology.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 4","pages":"1555-1566"},"PeriodicalIF":3.5,"publicationDate":"2025-04-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.2031","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Techno-Economic Comparison of Battery–Flywheel With Battery–Hydrogen Storage System in the Vicinity of Off-Grid HRES for Four Climates: MCDM Method","authors":"Alireza Ahmadi,&nbsp;Ahmad Hajinezhad,&nbsp;Reza Fattahi,&nbsp;Seyed Farhan Moosavian","doi":"10.1002/ese3.70048","DOIUrl":"https://doi.org/10.1002/ese3.70048","url":null,"abstract":"<p>Ensuring sustainable power and heating in remote rural areas presents a considerable challenge. Renewable hybrid systems are typically recommended for this purpose; however, maintaining stability necessitates either a connection to the grid requiring electricity purchases from power plants, which are significant sources of pollution, or the deployment of extensive equipment to ensure system stability. This study examines four climatic regions in Iran, evaluating the selection between two storage systems, battery-hydrogen and battery–flywheel, through simulation and two-stage optimization. HOMER PRO software was utilized for both simulation and optimization, while the method based on the removal effects of criteria (MEREC) was employed for criteria weighting in decision-making, in conjunction with the technique for order of preference by similarity to ideal solution (TOPSIS) method. The findings indicate that the battery-hydrogen system is significantly more cost-effective, achieving savings of up to $211,327 in net present cost (NPC) and $0.738 in cost of energy (COE). Furthermore, the battery–hydrogen system demonstrates a greater reliance on renewable energy sources, increasing by up to 23.6% compared to the battery–flywheel system.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 5","pages":"2512-2529"},"PeriodicalIF":3.5,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Agglomeration Level and Its Influencing Factors of the Power Industry: A Spatial Econometric Analysis Based on Interprovincial Panel in China","authors":"Tanbo Zhu,&nbsp;Wenxing Li,&nbsp;Wei Bu","doi":"10.1002/ese3.70020","DOIUrl":"https://doi.org/10.1002/ese3.70020","url":null,"abstract":"<p>The agglomeration of the power industry can not only improve industrial production efficiency but also promote energy structure adjustment, which is of great significance for improving national energy security and environmental protection levels. This paper is based on panel data from 30 provinces in China from 2001 to 2021, using the improved location entropy method to measure the agglomeration level of the power industry. The spatial Durbin model (SDM) is used to empirically test the influencing factors and spatial effects of the agglomeration level of the power industry. Research has found that (1) there is a significant spatial correlation in the agglomeration level of China's power industry, and the agglomeration level of the power industry in a region is influenced by neighboring regions; (2) the industrial structure, economies of scale, and power consumption of this region have a significant positive spatial effect on the level of power industry agglomeration, while the population of this region and factors such as the industrial structure, economies of scale, and power consumption of adjacent regions have a significant negative spatial effect on power industry agglomeration. Based on empirical results, relevant suggestions have been proposed to improve the agglomeration level of China's power industry. Based on empirical results, relevant suggestions have been proposed to improve the agglomeration level of China's power industry.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 4","pages":"2153-2163"},"PeriodicalIF":3.5,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852676","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Coordinated Optimal Dispatch of Distribution Grids and P2P Energy Trading Markets","authors":"Jing Deng,&nbsp;Fawu He,&nbsp;Qingbin Zeng,&nbsp;Jie Yan,&nbsp;Rangxiong Liu,&nbsp;Dongsheng He,&nbsp;Song Zhou","doi":"10.1002/ese3.70046","DOIUrl":"https://doi.org/10.1002/ese3.70046","url":null,"abstract":"<p>With the increasing integration of distributed renewable energy, traditional power users are evolving into prosumers capable of both generation and consumption. However, their decentralized nature poses challenges in resource coordination. This study proposes a bi-level optimization framework for distribution networks integrating peer-to-peer (P2P) energy trading and shared energy storage. The upper-level model minimizes distribution system operator (DSO) operational costs, including network losses and storage management, while ensuring voltage stability. The lower-level model enables prosumers to maximize P2P market profits through adaptive load adjustments and shared storage utilization. To address the nonlinear, high-dimensional optimization challenges, an improved Convex-Soft Actor-Critic (C-SAC) algorithm is developed, combining deep reinforcement learning with convex optimization to achieve privacy-preserving distributed coordination. Case studies on an IEEE 33-node system demonstrate that the framework increases prosumer profits by 56.9%, reduces DSO costs by 23.6%, and lowers network losses by 21.5% compared to non-cooperative scenarios. The shared storage system reduces capacity and power requirements by 20% and 14.1%, respectively. The C-SAC algorithm outperforms traditional methods (DDPG, SAC) in convergence speed and economic metrics, showing scalability across larger systems (IEEE 69/118 nodes). This work provides a model-free solution for renewable-rich distribution networks, balancing efficiency and operational security.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 5","pages":"2206-2219"},"PeriodicalIF":3.5,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70046","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919934","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Multi-Perspective Behavioural Investigations on Coolant of Battery Thermal Management Systems in Electrical Vehicles Using Computational Fluid Dynamics","authors":"Laxana Sourirajan,&nbsp;Mohankumar Subramanian,&nbsp;Beena Stanislaus Arputharaj,&nbsp;Parvathy Rajendran,&nbsp;Pradesh Sakthivel,&nbsp;Vijayanandh Raja,&nbsp;Arunkumar Karuppasamy,&nbsp;C. Ahamed Saleel,&nbsp;Nasim Hasan","doi":"10.1002/ese3.70044","DOIUrl":"https://doi.org/10.1002/ese3.70044","url":null,"abstract":"<p>Battery thermal management system (BTMS) is a very important field that is currently being focused on by the thermal and energy departments all around the world. This work primarily emphasizes channel design for BTMS and the utilization of modern computational fluid dynamics (CFD) investigations in BTMS. Enhancing the fluid-battery heat transfer interaction is the aim of the proposed channel design. A reliable CFD study and better wall treatment confirmed the thermal performance of the identical channel design. Secondly, this study focuses on finding a suitable velocity at which a coolant can perform its best efficiently, that is, by absorbing most of the heat present in the battery system. Six coolant fluids were chosen to achieve the goal of finding the best velocity at three different heat generation rates (HGR). These HGRs include 5318, 19,452 and 42,400 W/m³ describing the C Ratings 1C, 2C and 3C, respectively. Six coolants were Ethylene Glycol, Propylene Glycol, Glycerine, Ethyl Alcohol, Water liquid and Water Glycol. It is concerning that even after choosing the required coolant for heat absorption, it becomes necessary that the velocity at which it can be allowed to flow through the battery system determines the effectiveness of the coolants. It was concluded that the coolant fluids better perform at 1 m/s. This lets us know that, when the flow of the coolant is at its lowest velocity, it can efficiently absorb the heat while it stays at that particular instant. The coolant's temperature was measured to be higher at the outlet (after it has flowed through the entire battery system) compared to the intake temperature. This indicates that the coolant has absorbed heat through molecular interaction. The input temperature was recorded at 29.85°C. It was also noted that Ethyl Alcohol and Propylene Glycol work the best at the HGR of 5318 W/m³, and the other coolants work the best at 19,452 W/m³ at 1 m/s. Using low-velocity fluids in liquid BTMS has been found to enhance thermal management by improving heat transfer efficiency, ensuring structural integrity, extending the duration of heat exchange, enhancing temperature uniformity and reducing energy consumption. These factors collectively contribute to making lithium-ion batteries safer and more effective for a range of applications.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 5","pages":"2455-2479"},"PeriodicalIF":3.5,"publicationDate":"2025-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143920006","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Experimental Study on Instability Mechanism of Red Shale Roadway Under Dynamic Disturbance","authors":"Xuewu Wu,&nbsp;Zhenqian Ma,&nbsp;Jinlian Zhou,&nbsp;Chunhng Mao,&nbsp;Jimin Zhang","doi":"10.1002/ese3.70043","DOIUrl":"https://doi.org/10.1002/ese3.70043","url":null,"abstract":"<p>To delve into the instability mechanism of the surrounding rock in red shale roadways, a bespoke device was chosen to fabricate a physical model, and a similar experiment was conducted with a blasting-induced disturbance. A meticulous examination was performed on the evolution of surface fractures and the macroscopic failure patterns of the surrounding rock in conjunction with the temperature data gathered via infrared thermal imaging. In accordance with the similarity principle, five perturbation sources were strategically positioned on either side of the roadway, at the haunches, and at a location three times the roadway diameter away from the roof, aiming to comprehensively investigate the root causes of instability under dynamic loading conditions. Simultaneously, a 30° inclined rock layer model was developed using numerical simulation techniques to contrast the alterations in stress, displacement, and other relevant aspects of the surrounding rock under both static and dynamic loads. External dynamic disturbances were then applied to probe the deformation behavior. The experimental results revealed that, subsequent to applying a dynamic load at the midpoint of the left rib of the model, the horizontal and vertical displacements of the surrounding rock augmented, whereas the displacement distribution pattern exhibited minimal alteration. Under static load conditions, the displacement of the left rib surged by 22.5%, that of the right rib climbed by 20.6%, the roof displacement expanded by 33%, and the floor displacement grew by 12.2%, with the peak acceleration at the left rib being the most prominent.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 5","pages":"2440-2454"},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70043","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Blended Ensemble Learning for Robust Normal Behavior Modeling of Wind Turbines","authors":"Jianghao Zhu,&nbsp;Tingting Pei,&nbsp;Le Su,&nbsp;Bin Lan,&nbsp;Wei Chen","doi":"10.1002/ese3.70055","DOIUrl":"https://doi.org/10.1002/ese3.70055","url":null,"abstract":"<p>The increasing scale of wind farms demands more efficient approaches to turbine monitoring and maintenance. Here, we present an innovative framework that combines enhanced kernel principal component analysis (KPCA) with ensemble learning to revolutionize normal behavior modeling (NBM) of wind turbines. By integrating random kitchen sinks (RKS) algorithm with KPCA, we achieved a 25.21% reduction in computational time while maintaining model accuracy. Our mixed ensemble approach, synthesizing LightGBM, random forest, and decision tree algorithms, demonstrated exceptional performance across diverse operational conditions, achieving <i>R</i>² values of 0.9995 in primary testing. The framework reduced mean absolute error by 25.1% and mean absolute percentage error by 33.4% compared to conventional methods. Notably, when tested across three distinct operational environments, the model maintained robust performance (<i>R</i>² &gt; 0.97), demonstrating strong generalization capability. The system automatically detects anomalies using a 0.1% threshold, enabling real-time monitoring of 78 variables across 136,000+ operational records. This scalable approach integrates seamlessly with existing SCADA infrastructure, offering a practical solution for large-scale wind farm management. Our findings establish a new paradigm for wind turbine monitoring, combining computational efficiency with unprecedented accuracy in normal behavior prediction.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 5","pages":"2565-2584"},"PeriodicalIF":3.5,"publicationDate":"2025-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70055","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143919943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Estimation of Daily Photovoltaic Power One Day Ahead With Hybrid Deep Learning and Machine Learning Models","authors":"Tuba T. Ağır","doi":"10.1002/ese3.1994","DOIUrl":"https://doi.org/10.1002/ese3.1994","url":null,"abstract":"<p>In this study, hybrid LSTM-SVM and hybrid LSTM-KNN models were developed to predict hourly PV power one day ahead. The performances of these hybrid models were compared with K-nearest neighbors (KNN), long short-term memory (LSTM), and support vector machine (SVM) models. The input data of these models were pressure, cloudiness, humidity, temperature, and solar intensity, while the output data was the daily photovoltaic (PV) power one day ahead. The performances of the models were evaluated using mean square error (MSE), root mean square error (RMSE), normalized root mean square error (NRMSE), and peak signal-to-noise ratio (PSNR). The prediction accuracies of hybrid LSTM-KNN, LSTM, KNN, hybrid LSTM-SVM, and SVM were 98.72%, 95.8%, 90.25%, 76.3%, and 48.87%, respectively. Hybrid LSTM-KNN predicted the daily PV power of the day ahead with higher accuracy than LSTM, KNN, SVM, and hybrid LSTM-SVM. The effect of input variables on output variables was examined with sensitivity analysis. Sensitivity analyses showed that the most important meteorological data affecting the daily PV power one day ahead was solar intensity with a rate of 95%.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 4","pages":"1478-1491"},"PeriodicalIF":3.5,"publicationDate":"2025-03-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.1994","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852830","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Method for Predicting Different Types of Natural Fractures in Tight Sandstone Based on the Secondary Rescaled Range Analysis of Logging Curves: A Case Study From the Chang 7 Member in Huaqing Oilfield, Ordos Basin, China","authors":"Zikang Xiao,&nbsp;Wenlong Ding,&nbsp;Arash Dahi Taleghani,&nbsp;Liu Jingshou,&nbsp;Chong Xu,&nbsp;Huiran Gao,&nbsp;Wenwen Qi,&nbsp;Xiangli He","doi":"10.1002/ese3.70034","DOIUrl":"https://doi.org/10.1002/ese3.70034","url":null,"abstract":"<p>Currently, there are various methods for predicting natural fractures using logging data, however these methods are primarily for predicting the number and location of fractures. This is making it difficult to determine fracture types. This paper introduces the R/S-FD method, and combined with the natural fracture development pattern in the study area, secondary R/S analysis was introduced to construct the Secondary R/S-FD method. This method overcomes the limitations of traditional R/S-FD methods that can only predict the location of fractures and cannot predict the type of fractures. After eliminating systematic errors, the prediction accuracy of the Secondary R/S-FD method for bedding fractures and high-angle fractures reaches 73% and 74%, respectively. By analyzing the fracture development characteristics of 23 wells in the study area, the research provided insights into the development characteristics of bedding fractures and high-angle fractures in oil layers within the region. The secondary R/S-FD method is a precise, fast, and cost-effective approach for predicting the development characteristics of different types of natural fractures. The next step involves leveraging a large number of fracture prediction cases as the data foundation, based on big data analysis and machine learning techniques, to establish a correlation between the F value and fracture type and number to enabling more accurate predictions of the types and quantities of natural fractures.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 4","pages":"2045-2062"},"PeriodicalIF":3.5,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70034","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of Geological Characteristics and Reservoir Potential Formation Damage Factors of Shallow Low-Temperature Low-Pressure Low-Permeability Sandstone Reservoir","authors":"Yanfei Li,&nbsp;Lizhi Yuan,&nbsp;Tao Wang,&nbsp;Wei Liu,&nbsp;Xingbin Zhao,&nbsp;Lanling Shi,&nbsp;Wei Huang,&nbsp;Yu Wang","doi":"10.1002/ese3.2027","DOIUrl":"https://doi.org/10.1002/ese3.2027","url":null,"abstract":"<p>The C-S reservoir in the YQ district of Ordos basin, China, is located at a relatively shallow depth (240–720 m), with an original pressure coefficient of approximately 0.85 for the oil layer. Calculations indicate that the initial pressure of the oil layer ranges from 4.1 to 6.0 MPa, averaging 4.75 MPa, with an average temperature of 30°C. The reservoir is classified as shallow, low-pressure, low-temperature sandstone. This research examines the C-S tight sandstone oil reservoir located in the Ordos Basin, providing an in-depth analysis of its mineral and rock composition along with its porosity and permeability characteristics. Through the analysis of the microscopic geological features of the reservoir, significant geological factors that may contribute to reservoir degradation are identified. Research shows that the C-S reservoir has an average porosity of 8.39%, average permeability of 0.54 × 10⁻³ μm², a micro-thin-necked pore type, and a median pore radius of 1.9060 μm. The reservoir exhibits strong heterogeneity, characterized by low porosity and permeability. Laboratory experiments revealed sensitivity characteristics including weak sensitivity to velocity and water, as well as moderate sensitivity to acid and salt. Water-phase seal test results show that the self-absorption rate decreases to less than 0.1 g/h within about 12 h, leading to significant water-phase seal formation damage due to high water saturation (above 45%) within a short time. The research suggests that limited fluid passageways in the reservoir result in insufficient in situ energy for fluid migration and increased viscosity, which complicates the process of returning fractured reservoirs to their original state after digitalization.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"13 4","pages":"1544-1554"},"PeriodicalIF":3.5,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.2027","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852998","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}