Energy Science & Engineering最新文献

{"title":"Study on the Influence of Slope Static Load on Shallow Lining Support Structure","authors":"Hongwei Wang,&nbsp;Chaofeng Yuan,&nbsp;Jianqiang Jiao,&nbsp;Jinyuan Bai","doi":"10.1002/ese3.70502","DOIUrl":"https://doi.org/10.1002/ese3.70502","url":null,"abstract":"<p>On the basis of the background of the shallow-buried main adit supported by stone lining, the mechanical model of the supporting structure is constructed and the stability of the supporting structure is analyzed by using the methods of field investigation, theoretical analysis, and numerical simulation. The results show that (1) the range from 38 to 82 m away from the entrance of the main adit belongs to the slope influence zone. (2) The bending moment and axial force of the supporting structure are asymmetrically distributed under the static load of the slope, and the maximum value of the bending moment is located on the side of the noncovered slope, while the maximum value of the axial force is located on the side of the covered slope, and the maximum displacement is biased to the side of the slope, which corresponds to the position of the cracks on the inner surface of the supporting structure measured in the field, thus verifying the correctness of the conclusions. (3) From the angle of stone bearing and dynamic load transfer, the supporting structure will be in a stable state. This study can provide a reference for the stability analysis of similar supporting structures.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 6","pages":"2721-2735"},"PeriodicalIF":3.4,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70502","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208877","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":"Multiobjective Optimization of Hydrogen-Assisted Calophyllum inophyllum Biodiesel Operation in a Compression Ignition Engine","authors":"C. Naga Kumar,&nbsp;Nageswara Rao Dayadi,&nbsp;J. M. Babu,&nbsp;Srinivasa Reddy Yarram,&nbsp;Ravikiran Chintalapudi,&nbsp;Bridjesh Pappula","doi":"10.1002/ese3.70511","DOIUrl":"https://doi.org/10.1002/ese3.70511","url":null,"abstract":"<p>This study investigates operating conditions for a diesel engine running in tri-fuel mode with diesel, <i>Calophyllum inophyllum</i> biodiesel (0–30 vol%), and inducted hydrogen (0–15 L·min⁻¹) over 0%–100% load (20% steps), yielding 96 steady-state points. Brake thermal efficiency (BTE), brake-specific fuel consumption, exhaust gas temperature, and regulated emissions (NO_<i>x</i>, HC, CO, CO₂, and smoke) were measured. To avoid dependence on a single, fixed objective weighting, Bootstrapped Empirical Multiobjective Ranking and Selection (BEMORS) was applied using percentile scaling, Pareto screening, and Dirichlet preference weights under baseline, NO_<i>x</i>-biased, and NO_<i>x</i>, along with smoke-biased scenarios. Stochastic Multicriteria Acceptability Analysis benchmarking was used to check ranking consistency. Hydrogen enrichment increased BTE and decreased CO, HC, and smoke across loads, while tending to increase NO_<i>x</i>. Increasing biodiesel fraction has also decreased smoke, CO, and HC, but increased fuel consumption due to lower heating value. BEMORS identified 40% load with high hydrogen induction and low biodiesel fraction as the compromise across preference scenarios, whereas high-load hydrogen addition scenarios were attractive when smoke scenarios dominate priorities. Combining tri-fuel experiments with preference-uncertain selection provides practical and implementable operating corridors that are competitive when regulatory emphasis shifts and quantifies the suitability of recommendations within the tested steady-state conditions.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 6","pages":"2897-2929"},"PeriodicalIF":3.4,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70511","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148209075","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":"Real-Time Data-Driven Fault Diagnosis of Photovoltaic Arrays Using an Edge-Server Machine-Learning Framework","authors":"Premkumar Manoharan,&nbsp;Sowmya Ravichandran,&nbsp;Deepak Kumar Sinha,&nbsp;Chandran Ramakrishnan,&nbsp;Omar Almomani","doi":"10.1002/ese3.70514","DOIUrl":"https://doi.org/10.1002/ese3.70514","url":null,"abstract":"<p>With the rapid global expansion of photovoltaic (PV) systems, reliable fault detection and classification have become crucial to ensuring system efficiency, safety, and longevity. This paper presents a comprehensive approach to diagnosing common PV array faults, such as line-to-line faults, open-circuit faults, short-circuit faults, and partial shading using machine-learning (ML) techniques. A 3 × 2 PV array is modeled in MATLAB/Simulink to simulate various fault scenarios, and corresponding data sets are generated. The Raspberry Pi Zero 2W is employed for real-time acquisition of voltage, current, and temperature signals. Raspberry Pi 4 serves as an edge intelligence node that performs online fault classification and notification. Sensor data are transmitted wirelessly using a lightweight communication protocol, enabling low-latency decision-making and real-time fault alerts. Five supervised ML classifiers, such as decision tree (DT), <i>k</i>-nearest neighbors, support vector machine, artificial neural network, and bagged DT, are systematically evaluated based on their accuracy, robustness, and generalization capabilities under varying operating conditions. Furthermore, an ensemble model is implemented to enhance classification performance by aggregating multiple learners. The results from both simulation and hardware experiments demonstrate that the proposed ML-based approach can detect and classify PV array faults with high precision and low computational overhead. This integrated data-driven framework offers a scalable, adaptive, and cost-effective solution for intelligent PV monitoring systems.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 6","pages":"2961-2982"},"PeriodicalIF":3.4,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70514","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208950","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 Hybrid EV Charging Architecture Integrating DC Fast Charging and Wireless Power Transfer","authors":"Ali Almaktoof,&nbsp;Mohamad Taha,&nbsp;Marco Adonis,&nbsp;Kareem Halablab","doi":"10.1002/ese3.70517","DOIUrl":"https://doi.org/10.1002/ese3.70517","url":null,"abstract":"<p>As electric vehicles (EVs) continue to gain popularity worldwide, the demand for faster, smarter, and more flexible charging infrastructures is becoming critical to meet both grid requirements and user expectations. This study proposes a hybrid dual-mode charging system that combines the high power of DC fast charging with the safety and convenience of wireless power transfer (WPT), thereby addressing the limitations of conventional plug-in chargers while enabling seamless vehicle-to-grid (V2G) integration. At the system's core, a three-phase boost-type active rectifier serves as a multipurpose interface, providing high-efficiency AC–DC conversion, power factor correction, DC bus voltage stabilization and smooth bidirectional power exchange. The rectifier is controlled using direct–quadrature (DQ) vector transformation and closed-loop current regulation, implemented with proportional–integral (PI) controllers, while spatial vector modulation (SVM) is employed to enhance switching efficiency. The wireless charging path relies on resonant inductive coupling, driven by a high-frequency inverter, to achieve contactless power transfer with improved durability and user convenience. Engineering modelling and validation were carried out using the PSIM simulation platform. The results show that the proposed system achieves near-unity power factor, low total harmonic distortion (THD), and high-power transfer efficiency. The DC fast charger demonstrated robust dynamic response to load changes (50–60 kW and 50–40 kW), maintaining voltage deviations within the ±5% tolerance of ISO 15118-20. The wireless charger consistently delivered 11 kW across a 10 cm air gap, complying with SAE J2954 standards. The study concludes that integrating DC fast charging with WPT in a unified architecture offers a practical pathway toward scalable, grid-friendly, and user-centric EV charging ecosystems, highlighting its potential to support the next generation of smart mobility solutions.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 6","pages":"3013-3027"},"PeriodicalIF":3.4,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70517","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148208943","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":"Low-Carbon Optimal Scheduling of Multiple Virtual Power Plants Based on Asymmetric Nash Bargaining","authors":"Junjie Qiu,&nbsp;Min Liu,&nbsp;Yang Peng,&nbsp;Haiqiang Zhao,&nbsp;Yuanda Wu,&nbsp;Yongyuan Tian","doi":"10.1002/ese3.70510","DOIUrl":"https://doi.org/10.1002/ese3.70510","url":null,"abstract":"<p>To effectively investigate the structural discrepancies and complementary energy characteristics among multiple virtual power plants (VPPs), and to improve the economic efficiency, low-carbon performance, and operational reliability of the multi-agent system, this paper proposes a low-carbon collaborative optimal operation strategy for multiple VPPs based on the asymmetric Nash bargaining solution. First, considering the interests of various VPP operators and users, a cooperative operation framework for multiple VPPs under the green certificate and carbon trading mechanism is constructed using Nash bargaining theory, which facilitates electric–thermal energy transactions among different VPPs. Second, the Latin hypercube scenario generation method and a fast backward reduction technique are employed to handle the uncertainties associated with renewable generation and load power. Third, accounting for the multidimensional contributions of each VPP, an asymmetric Nash bargaining mechanism based on energy contribution rates is designed to allocate the cooperative gains among VPPs. Finally, the alternating direction method of multipliers (ADMM) is adopted to protect the information privacy of each participant. Case study results demonstrate that the proposed strategy can enhance the overall operational benefits and carbon reduction performance of the system, while promoting stronger collaboration and fairness-oriented benefit allocation among VPPs.</p>","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 6","pages":"2874-2896"},"PeriodicalIF":3.4,"publicationDate":"2026-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/ese3.70510","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148209096","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":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 5","pages":"2345-2347"},"PeriodicalIF":3.4,"publicationDate":"2026-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148089191","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 4","pages":"1683-1685"},"PeriodicalIF":3.4,"publicationDate":"2026-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148090435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 5","pages":"2393-2413"},"PeriodicalIF":3.4,"publicationDate":"2026-04-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148086965","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 5","pages":"2626-2643"},"PeriodicalIF":3.4,"publicationDate":"2026-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148080756","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"","authors":"","doi":"","DOIUrl":"","url":null,"abstract":"","PeriodicalId":11673,"journal":{"name":"Energy Science & Engineering","volume":"14 5","pages":"2380-2392"},"PeriodicalIF":3.4,"publicationDate":"2026-04-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148063129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}