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Optimizing thermal energy storage in rectangular unit: Exploring the impact of moving fin with phase change material

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JOURNAL OF ENERGY STORAGE 2024年 99卷

作者： Izgi, Burak Demirag, Hueseyin Zahit Yozgat Bozok Univ Dept Mech Engn TR-66200 Yozgat Turkiye

This research presents an innovative moving fin design for a latent heat storage unit, with the objective of improving thermal performance. The methodology integrates numerical simulation, a machine learning approach, and a multi-objective genetic optimization algorithm (NSGA-II). The study optimizes fin parameters, such as fin velocity (Vf), V f ), fin thickness (tf), t f ), fin length (Lf) L f ) and initial fin position (yf). y f ). The optimization process aims to maximize both power (P) P ) and stored energy per mass (Em), E m ), assigning equal importance to both criteria. The outcomes reveal a considerable rise in power with minor compromises in stored energy per mass. The incorporation of moving fins in rectangular enclosure with PCM leads to a significant power boost while preserving stored latent energy with minimal compromise. In comparison to the no-fin condition, fixed 1-fin and fixed 3-fin configurations lead to stored energy losses of 4.16 % and 11.73 %, respectively, accompanied by power increases of 20.48 % and 51.17 %, respectively. Conversely, the optimized condition for maximum power with moving fins incurs a mere 2.73 % stored energy loss but achieves a remarkable 173.92 % increase in power. The optimized design parameters for maximizing power (P) P ) include Vf f = 22.5 x 10-3 mm/s, yf f = 60 mm, tf= f = 2 mm, and Lf f = 40 mm.

关键词： Thermal energy storage genetic algorithm optimization Dynamic thermal systems

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Thermal performance optimization of a novel integrated photovoltaic thermal collector system

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RENEWABLE ENERGY 2025年 238卷

作者： Xiao, Yuling Fei, Fan Zou, Junlong Tian, Lei Liang, Qian Ma, Qingsong Fukuda, Hiroatsu Gao, Weijun Qingdao Univ Technol Innovat Inst Sustainable Maritime Architecture Res Qingdao 266033 Peoples R China Univ Kitakyushu Fac Environm Engn Kitakyushu 8080135 Japan

The construction industry is actively seeking energy-efficient solutions to reduce reliance on fossil fuels. Building-integrated photovoltaic thermal (PV-T) systems, combining electricity generation with solar heat utilization, have emerged as a promising technology. However, conventional PV-T systems often face limitations in achieving optimal PV panel angles for maximum solar gain while maintaining architectural aesthetics, especially when integrated into building facades. This study investigates the thermal performance of a novel photovoltaic thermal collector (PV-TC) system specifically designed to address these challenges. This system, intended for installation on south-facing building facades, allows for flexible PV panel angling design to optimize solar energy capture without compromising the building's aesthetic appeal. We employ a simulation-based optimization approach using EnergyPlus, Radiance, and a genetic algorithm to determine the optimal design parameters for minimizing annual energy use intensity (EUI) in Nanjing, China's climate. Our results demonstrate that a fan rate of 288 m3/h, a winter fan setpoint of 21 degrees C, a summer fan setpoint of 25 degrees C, and a PV panel angle of 41 degrees achieve the lowest EUI of 92.33 kWh/m2. This optimized configuration effectively balances electricity generation with solar heat utilization, minimizing reliance on conventional HVAC systems. We further analyzed individual parameter influences, demonstrating that increasing fan rate consistently improves heat transfer efficiency, while optimal PV angle and fan setpoint temperature are crucial for maximizing solar heat utilization and nighttime cooling, respectively. By comparison it was found that the installation of the PV-TC system reduces the annual EUI by 22.7 %. These findings provide valuable insights for designing and operating high-performance PV-TC systems that address both energy efficiency and architectural integration concerns in buildings.

关键词： Integrated photovoltaic thermal collector Energy use intensity EnergyPlus simulation genetic algorithm optimization

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Scattering Reduction and Axial Resolution Enhancement in Light-Sheet Fluorescence Microscopy

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JOURNAL OF BIOPHOTONICS 2025年第5期18卷 e202400556页

作者： Zhang, Yang Li, Runze Yu, Xianghua Miao, Hao Yang, Ruiwen Li, Xing Min, Junwei Yang, Yanlong Dan, Dan Dai, Taiqiang Kong, Liang Yao, Baoli Chinese Acad Sci Xian Inst Opt & Precis Mech State Key Lab Ultrafast Opt Sci & Technol Xian Shaanxi Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Fourth Mil Med Univ Natl Clin Res Ctr Oral DisSch Stomatol State Key Lab Oral & Maxillofacial Reconstruct & R Shaanxi Clin Res Ctr Oral DisDept Oral & Maxillof Xian Peoples R China

Light-sheet fluorescence microscopy (LSFM) provides an ideal tool for long-term observation of live specimens due to its low photodamage and fast volumetric imaging speed. The wavefront distortions in the illumination path of LSFM will reduce the intensity and broaden the light-sheet thickness, thereby degrading the image quality. We propose to use the wavefront shaping technique to reduce the scattering effect and shrink the light-sheet thickness. Scanning the refocused laser beam to generate LS improves both the fluorescence intensity and the axial resolution. The axial resolution can be further enhanced by subtracting the two images captured via double scanning the samples with the refocused beam and the uncorrected scattered beam for each slice. The axial resolution is improved from 2.2 +/- 0.3 to 1.5 +/- 0.2 mu m across the field of view of 270 mu m x 270 mu m. The effectiveness of the wavefront shaping subtraction method is demonstrated by imaging fluorescent beads and Aspergillus conidiophores behind a scattering medium.

关键词： genetic algorithm optimization light-sheet fluorescence microscopy wavefront shaping

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An AI-ISM Methodology for Structural Modeling of Sustainable Supply Chain Management Drivers

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IEEE ACCESS 2025年 13卷 76481-76496页

作者： Roshanpour, Reza Parsanejad, Mohammadreza Pishvaee, Mir Saman Iran Univ Sci & Technol Sch Management Econ & Progress Engn Tehran *** Iran Iran Univ Sci & Technol Sch Ind Engn Tehran *** Iran

Achieving Sustainable Supply Chain Management (SSCM) has become a strategic priority for organizations integrating environmental, social, and economic objectives. Interpretive Structural Modeling (ISM) is a powerful methodology for analyzing interdependencies among SSCM drivers by structuring them into a multilevel hierarchical framework. However, traditional ISM relies on subjective expert judgments, making it prone to inconsistencies and biases. To address these challenges, this study proposes a novel Hybrid AI-ISM methodology, integrating genetic algorithms (GA) to optimize the reachability matrix by minimizing transitivity violations and reciprocal inconsistencies. The research adopts a structured, expert-driven approach, engaging 25 domain specialists from executive, production, and supply chain management sectors within the steel industry. By leveraging AI-driven optimization, the proposed framework enhances accuracy, objectivity, and scalability, refining hierarchical structuring for effective SSCM decision-making. This study contributes to the SSCM literature by introducing a bias-resistant, computationally enhanced ISM framework, facilitating granular decision-making across organizational levels. Findings reveal notable differences in expert perspectives across professional roles. Executives emphasize strategic sustainability initiatives, particularly the Adoption of Renewable Energy, while production managers prioritize operational aspects, including Waste Minimization and Supply Chain Flexibility. Conversely, supply chain managers focus on stakeholder engagement and risk mitigation, highlighting Community Engagement and Supply Chain Disruption Management as critical drivers. The study provides actionable insights for policymakers, industry leaders, and supply chain professionals seeking to drive sustainable transformations in global supply chains.

关键词： Artificial intelligence Supply chains Decision making Sustainable development Vehicles Organizations Green products Economics Accuracy Natural language processing decision-making genetic algorithm optimization interpretive structural modeling sustainable supply chain management

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Aerodynamic optimization of the tangential stacking line of a transonic axial flow compressor rotor using genetic algorithm

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2019年第1期41卷 1-12页

作者： Asghari, M. Mirzabozorg, M. Agha Seyed Adami, M. Malek Ashtar Univ Technol Dept Mech & Aerosp Engn Esfahan Iran

In this paper, aerodynamic optimization of the tangential stacking line of the NASA Rotor 37 as a transonic axial flow compressor rotor is carried out using computational fluid dynamics and genetic algorithm. To cover a wide range of curves with a minimum number of design parameters, a B-spline curve with three control points at 33, 66 and 100% of the blade span is used to define the blade stacking lines. Firstly, by rotating the tangential position of the control points, different rotors have been created and are simulated using the Navier-Stokes governing equations. Then, using genetic algorithm operators, based on the adiabatic efficiency as an objective function, new blades are created and numerically simulated. This process is repeated to achieve maximum adiabatic efficiency. The comparison of the optimum blade and the original blade indicates that optimal tangential stacking line causes the shock wave to move downstream and reduce the secondary flow which has led to an improvement of about 1.7% of the adiabatic efficiency.

关键词： Axial flow compressor Transonic rotor genetic algorithm optimization Tangential stacking line CFD

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Techno-economic optimization and social costs assessment of microgrid-conventional grid integration using genetic algorithm and Artificial Neural Networks: A case study for two US cities

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JOURNAL OF CLEANER PRODUCTION 2019年第Aug.20期229卷 552-569页

作者： Nagapurkar, Prashant Smith, Joseph D. Missouri Univ Sci & Technol Chem & Biochem Engn Dept Rolla MO 65401 USA

Through two case studies, a methodology is presented that assessed the techno-economic and environmental performance of microgrid-conventional grid integration scenarios for fifty homes located in US cities of Fargo and Phoenix. The microgrid was composed of seven components - solar photovoltaics, wind-turbines, lead acid batteries, biodiesel generators, fuel cells, electrolyzers and H-2 tanks. Firstly, mathematical models that predicted the hourly power generation were developed for every microgrid component. Secondly, Artificial Neural Networks were utilized to predict hourly electricity demand and its results were validated with actual available data. Thirdly, through an electricity dispatch strategy and a genetic algorithm optimization technique, microgrid configurations were determined that had lowest levelized cost of energy, $/kWh. From peak power standpoint, four microgrid-conventional grid integration scenarios were examined, namely, microgrid possessing penetration level of 25%, 50%, 75%, 100%. Based on the environmental life cycle assessment of power generation, three carbon taxes were imposed -$12, $48, $72/tonne carbon dioxide emitted. Microgrid's electricity cost was found to be $0.43-0.86/kWh. Imposing carbon taxes barely showed any effect on microgrid's electricity cost nor its optimum configuration, but conventional grid's electricity cost was found to increase by 7-33% as its carbon emissions were five times as that of microgrid. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Microgrid Techno-economic assessment Environmental assessment (LCA) genetic algorithm optimization Artificial neural network Carbon tax

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DTester: Diversity-Driven Test Case Generation for Web Applications

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INTERNATIONAL JOURNAL OF SOFTWARE ENGINEERING AND KNOWLEDGE ENGINEERING 2024年第2期34卷 357-390页

作者： Wu, Shumei Chang, Zexing Zhang, Zhanwen Li, Zheng Liu, Yong Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing 100000 Peoples R China

Search-based Test Case Generation (TCG) for web applications suffers from unstable performance and suboptimal test suite problems due to diversity loss. However, previous diversity metrics mainly only focus on client-side models or server-side code, which are prone to low robustness and poor generalization in practical applications. We propose a diversity-driven TCG method DTester, which can maximize behavior exploration and minimize the test suite size while covering more server-side vulnerable paths. Three diversity metrics (i.e. phenotypic coupling, intent coupling and competitiveness) are proposed to measure the underlying relationship between test cases from user behavior, code logic and test execution history. Moreover, a 3-dimensional weight graph is designed to model association among metrics, which provides fine-grained guidance for the genetic algorithm to generate diverse test cases from the client-side behavior model. Our empirical evaluation on five web applications shows that DTester can efficiently and robustly generate better test suites than the state-of-the-art TCG method. The maximum improvement is 85%, 60%, 716% and 88% in efficiency, test suite size, diversity and robustness.

关键词： Web application test case generation genetic algorithm optimization diversity metric vulnerable path

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On efficient modeling of drain current for designing high-power GaN HEMT-based circuits

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JOURNAL OF COMPUTATIONAL ELECTRONICS 2024年第6期23卷 1355-1367页

作者： Jarndal, Anwar Rakib, Famin Rahman Alim, Mohammad Abdul Univ Sharjah Elect Engn Dept Sharjah U Arab Emirates Univ Chittagong Elect & Elect Engn Dept Chittagong Bangladesh

In this paper, different modeling approaches to the drain current, including analytical and artificial neural network (ANN) modeling, are investigated. The adopted models address the inherent self-heating and kink effects, especially in high-power GaN-based high electron mobility transistors (HEMTs). Different optimization algorithms were demonstrated for extracting the model parameters, including genetic algorithm optimization (GAO), gray wolf optimization (GWO), growth optimization (GO), and particle swarm optimization (PSO). The modeling approaches are applied to DC IV measurements of 1-mm, 4-mm, and 2-mm GaN HEMTs on SiC and Si substrates. An improved optimization procedure was applied to the analytical models to find the main parameters responsible for fitting the general nonlinear behavior of the device. Then, the thermal or self-heating parameters are tuned for best fitting in the high-power dissipation region. The kink effect has been counted by adding another factor to the analytical formula to characterize the voltage dependency of this effect. The ANN modeling provides an efficient and cost-effective solution to accurately simulate the IV characteristics with less effort. In this technique, there is no need for a predefined closed formula or a complicated fitting parameter extraction process. Also, the model training was enhanced by using a genetic algorithm augmented backpropagation technique. The investigated analytical and ANN techniques were demonstrated by modeling the IV characteristics of the considered GaN HEMTs. The results obtained confirm the advantages of using ANN modeling for solving such problems and large-signal modeling applications.

关键词： GaN HEMT Large-signal modeling Artificial neural networks genetic algorithm optimization Gray wolf optimization Growth optimization Particle swarm optimization Kink effect Self-heating effect

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Design optimization of Water Distribution Networks with Dynamic Search Space Reduction GA

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WATER RESOURCES MANAGEMENT 2024年第1期38卷 63-79页

作者： Gangwani, Laxmi Dongre, Shilpa Gupta, Rajesh Abdy Sayyed, Mohd Abbas H. Tanyimboh, Tiku Shri Ramdeobaba Coll Engn & Management Dept Civil Engn Nagpur 440013 India Visvesvaraya Natl Inst Technol VNIT Dept Civil Engn Nagpur 440010 India Indian Inst Technol Bombay IITB Ctr Urban Sci & Engn Mumbai 400076 India Univ Witwatersrand Sch Civil & Environm Engn WITS Private Bag 3 ZA-2050 Johannesburg South Africa

Evolutionary algorithms (EAs) have been used extensively for the optimal design of water distribution networks (WDNs). There is evidence in the literature that search space reduction is highly effective. However, practical methods that do not introduce extra computational requirements are lacking. A dynamic search space reduction methodology is proposed to search the entire solution space without eliminating any part of the search space beforehand. The proposed methodology works on the information explored during the execution of the algorithm. Further, a self-adaptive penalty is suggested which is based on both flow and pressure deficits instead of only pressure deficit and is obtained using pressure dependent analysis. In this study, the methodology is demonstrated using a genetic algorithm (GA). The effectiveness of the methodology is demonstrated on the Ramnagar Network of Nagpur City, India and two benchmark problems from the literature. The proposed methodology resulted in a substantial reduction in the computational efforts and provided nine improved solutions as compared to the best solution available in the literature for one of the networks. The techniques proposed are generic and can be incorporated in other EAs.

关键词： Combined flow and pressure deficit penalty genetic algorithm optimization Pressure dependent analysis Water distribution network Dynamic search space reduction (DSSR) Self-adaptive penalty function

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Prediction surface roughness of 3D printed parts using genetic algorithm optimized hybrid learning model

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JOURNAL OF THERMOPLASTIC COMPOSITE MATERIALS 2024年第7期37卷 2225-2245页

作者： Akgun, Gazi Ulkir, Osman Marmara Univ Dept Mechatron Engn RTE Maltepe CampusIdealtepe Yolu 15 Istanbul Turkiye Mus Alparslan Univ Dept Elect & Energy Mus Turkiye

The final product of additive manufacturing (AM) or 3D printing critically depends on the surface quality. An experimental study on the 3D printed intake manifold flange using acrylonitrile butadiene styrene (ABS) material was executed by varying the four process parameters. A fused deposition modeling (FDM) based 3D printer was used to fabricate the flanges. The association between the parameters and the surface roughness of printed ABS flanges was investigated. A feed forward neural network (FFNN) model trained on particle swarm optimization (PSO) optimized with a genetic algorithm (GA) was used to estimate the surface roughness. A Box-Behnken design (BBD) with printing parameters at three levels was used, and 25 parts were fabricated. The suggested model demonstrated a coefficient of determination (R2) of 0.9865 on test values, mean of root-mean-square-error (RMSE) of 0.1231 after 500 times training for generalization. And also mean of overfitting factor is 0.7110. This means that the suggested system could generalize. Comparing the results from the suggested model and ANN, the suggested hybrid model outperformed ANN in predicting the surface roughness values with no overfitting. This suggests that GA optimized PSO based FFNN may be a more suitable method for estimating product quality in terms of surface roughness.

关键词： Additive manufacturing surface roughness fused deposition modeling particle swarm optimization learning artificial neural network genetic algorithm optimization

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