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Optimizing heat transfer in phase change thermal energy storage systems: A bionic method using alveolar vessel fins and nanofluids

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APPLIED THERMAL ENGINEERING 2025年 266卷

作者： Ren, Fan Li, Qibin Wang, Penglai Chongqing Univ Sch Energy & Power Engn Key Lab Low grade Energy Utilizat Technol & Syst Minist Educ Chongqing 400044 Peoples R China Natl Univ Singapore Dept Mech Engn 9 Engn Dr 1 Singapore 117575 Singapore

This paper investigates the enhanced heat transfer performance of a phase change thermal energy storage system (TES) using alveolar vessel-inspired fins and nano-fluid. Compared with traditional rectangular fins, alveolar vascular fins have better heat dissipation ability. The study utilizes computational fluid dynamics (CFD) to simulate and optimize the heat storage process. The results demonstrate that the 5 % Cu nanoparticles and water mixed nanofluid exhibits 27.63 % improvement in heat storage density compared to pure water. Furthermore, the heat transfer performance of nanofluid follows the trend Cu > CuO > Al2O3 > TiO2. The study also investigates the impact of heat transfer fluid (HTF) operating conditions, finding that an initial velocity of 0.25 m/s results in a 17 K increase in the average phase change material (PCM) temperature compared to 0.05 m/s. Finally, multi-objective optimization is conducted using response surface method and non-dominated sorting genetic algorithm II to determine the optimal parameters, heat storage capacity, PCM average temperature and kinetic energy values of 10.33 kJ, 492.19 K, and 2.25 mJ, respectively, corresponding to initial velocity 0.15 m/s of HTF, initial temperature 373.00 K of HTF, and volume fraction 4.41 % of nanoparticles in HTF. This simulation and multi-objective optimization method highlights the bionic inspiration design, and at the same time has certain reference significance for the multi-objective competitive design of nano-fluids in TES.

关键词： Alveolar vessel Nano-fluid Phase change thermal energy storage non-dominated sorting genetic algorithm

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Leveraging BIM and time-cost-safety trade-off analysis for optimized decisions in construction projects

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ENGINEERING CONSTRUCTION AND ARCHITECTURAL MANAGEMENT 2025年

作者： Shams, Alia Marzouk, Mohamed Cairo Univ Giza Egypt Badr Univ Cairo Badr Egypt

PurposeConstruction projects are frequently plagued by fatal accidents, emphasizing the necessity for proactive safety measures. Therefore, it is important to evaluate safety leading indicators to prevent construction project accidents proactively. This study aims to evaluate safety-leading indicators in construction projects and then conducts a time-cost-safety trade-off to enhance the performance of construction ***/methodology/approachThis research introduces a comprehensive framework designed to enhance construction project safety through safety visualization to illustrate safety risk scores throughout the project implementation. Safety is evaluated through two-stage structured interviews and analytical hierarchy process. Then, an optimization process takes place using a nondominated sorting genetic algorithm to get the Pareto front solutions in terms of time, cost and safety. Multi-criteria decision-making model is then applied to get a feasible solution which is fed to a safety visualization model that employs a 3D heat map to visualize safety on project elements over *** proposed framework is capable to evaluate safety leading indicators, optimize the time-cost-safety trade-off and visualize the selected feasible solution on a 3D-colored heat map. A case study of a residential building located in Cairo, Egypt has been applied to demonstrate the practicality of the ***/valueThis research focuses on proactive safety measures by assessing leading indicators rather than reactive measures like incident reports. Integrating BIM with decision-making techniques allows stakeholders to see how the safety risk levels change to assess and respond to safety risks proactively.

关键词： Construction safety assessment Multi-objective optimization non-dominated sorting genetic algorithm Multi-criteria decision-making Analytical hierarchy process BIM-based safety management 3D heat map

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Multi-objective optimization research on nonlinear energy sink system of finite-length beam on elastic medium

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nonLINEAR DYNAMICS 2025年第2期113卷 1007-1024页

作者： Ma, Jianjun Liu, Zongtong Wang, Chaosheng Guo, Ying Liu, Chenlu Han, Yanwei Wang, Lianhua Henan Univ Sci & Technol Sch Civil Engn & Architecture Luoyang 471023 Henan Peoples R China Engn Technol Res Ctr Safety & Protect Bldg Henan Luoyang 471023 Henan Peoples R China Hunan Univ Sch Civil Engn Changsha 410082 Hunan Peoples R China

Based on the nonlinear dynamic model of the elastic medium-beam coupling system under a semi-sinusoidal pulse, this paper analyzes the influence of variations in nonlinear stiffness, damping coefficient, and mass ratio on the energy dissipation capacity and vibration suppression effect of the beam system. Since the frequency domain response function of nonlinear system often lacks an exact analytical expression, a second-order polynomial expression between the objective function and the various system parameters is established using the response surface method. Furthermore, with the aim of improving the vibration suppression effect and energy dissipation capacity of the nonlinear energy sink, a multi-objective optimization design of the system parameters is conducted using the non-dominated sorting genetic algorithm. By combining the non-dominated sorting genetic algorithm with the response surface method, the Pareto front with the objectives of maximum amplitude reduction percentage and maximum energy dissipation ratio are determined, obtaining a more accurate set of parameter solutions, which enables the nonlinear energy sinks to have better vibration suppression effect and energy dissipation capacity.

关键词： Energy dissipation capacity Vibration suppression effect Response surface method non-dominated sorting genetic algorithm Multi-objective optimization

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Multi-objective optimization on combined heat pump and mechanical vapor recompression system for waste heat recovery of offshore wind power converter

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DESALINATION 2025年 601卷

作者： Hu, Xiaowei Shi, Chenyang Dong, Shengming Fu, Xingyu Ma, Tianya Chen, Qijing Tianjin Univ Commerce Sch Mech Engn Tianjin 300134 Peoples R China

Offshore wind power converter station generates a significant amount of low-temperature waste heat, requiring tremendous freshwater for discharge. Conventional waste heat utilization solutions are no longer effective because their products are unable to be reused in situ. Therefore, two types of seawater desalination systems combining heat pump and mechanical vapor recompression were proposed and investigated by multi-objective optimization and sensitivity analysis. From a view of thermodynamics and economics, the total heat transfer area and total power consumption were adopted as objective functions. An improved non-dominated sorting genetic algorithm was adopted to determine the key parameters, including the condensation temperature of the heat pump, the temperature of seawater after the electronic expansion valve, and the condensation temperature of water vapor in the seawater and water vapor heat exchanger. The Pareto front and approximate ideal solution sorting methods determined the optimal operational states of the two systems as [70.5, 20, 28.57] and [61.42, 40, 73.48], respectively. From the sensitivity analysis, the condensation temperature of the condenser is the most critical parameter. Moreover, HP-MVR-1 and HP-MVR-2 need 139.75 kW and 158.17 kW electric power if freshwater is fully utilized in situ. It may be an effective solution for waste heat in offshore wind power converters.

关键词： Mechanical vapor recompression Heat pump Seawater desalination Multi-objective optimization non-dominated sorting genetic algorithm Offshore power wind converter

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Managing equitable contagious disease testing: A mathematical model for resource optimization

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OMEGA-INTERNATIONAL JOURNAL OF MANAGEMENT SCIENCE 2025年 135卷

作者： Ghasemi, Peiman Ehmke, Jan Fabian Bicher, Martin Univ Vienna Dept Business Decis & Analyt Kolingasse 14-16 A-1090 Vienna Austria Univ Vienna Dept Business Decis & Analyt Vienna Austria TU Wien Inst Informat Syst Engn Favoritenstr 11 A-1040 Vienna Austria

All nations in the world were under tremendous economic and logistical strain as a result of the advent of COVID19. Early in the epidemic, getting COVID-19 diagnostic tests was a significant difficulty. Furthermore, logistical challenges arose from the restricted transportation infrastructure and disruptions in international supply chains in the distribution of these testing kits. In the face of such obstacles, it is critical to give patients' needs top priority in order to provide fair access to testing. In order to manage contagious disease testing, this work proposes a bi-objective and multi-period mathematical model with an emphasis on mobile tester route plans and testing resource allocation. In order to optimize patient scores and reduce the likelihood of patients going untreated, the suggested team orienteering model takes into account issues like resource limitations, geographic clustering, and testing capacity limitations. To this aim, we present a comparison between quarantine and non- quarantine scenarios, introduce an equitable categorization based on disease backgrounds into "standard" and "risky" groups, and cluster geographical locations according to average age and contact rate. We use a Multi- Objective Variable Neighborhood Search (MOVNS) and a non-dominated sorting genetic algorithm II (NSGA-II) to solve our problem. Due to the superiority of MOVNS, it is applied to a case study in Vienna, Austria. The results demonstrate that, over the course of several weeks, the average number of unserved risky patients in the prioritizing scenario is consistently lower than the usual number of patients. In the absence of prioritization, the average number of high-risk patients who remain untreated rises sharply and exceeds that of regular patients, though. Furthermore, it is clear that waiting times are greatly impacted by demand volume when comparing scenarios with and without quarantine.

关键词： Contagious disease testing Team orienteering problem Multi objective variable neighborhood search non-dominated sorting genetic algorithm Equitable testing

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Multi-sensor system deployment planning method for underwater surveillance based on formation characteristics

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AD HOC NETWORKS 2025年 170卷

作者： Yan, Zheping Cai, Sijia Hou, Shuping Zhang, Mingyao Harbin Engn Univ Coll Intelligent Syst Sci & Engn Harbin 150001 Peoples R China Harbin Engn Univ Qingdao Innovat & Dev Ctr Qingdao 266000 Peoples R China Harbin Engn Univ Coll Mech & Elect Engn Harbin 150001 Peoples R China

The deployment planning issue for a multi-sensor system comprising a limited number of sensors designed to detect underwater intrusion targets is defined as a multi-objective NP-hard problem. This problem is constituted by two competing and incommensurable optimization objectives: "larger sensor coverage" and "higher probability of detecting intrusion targets". The map of the mission area is transformed into a topological map through the application of polygon fitting and segmentation based on Delaunay triangulation. This study employs a characteristics-based non-dominated sorting genetic algorithm (CBNSGA) to address the deployment planning issue of the multi-sensor system. In this algorithm, Mean-Shift clustering is employed to yield characteristics information through the clustering of the multi-sensor system formation. Subsequently, this information is employed to enhance the crossover, mutation, and selection strategies. Adaptive parameters are designed to accelerate convergence and avoid local optima. Additionally, the Cauchy inverse cumulative distribution operator is employed to enhance the mutation step. The feasibility and effectiveness of the CBNSGA in multi-sensor system deployment planning are demonstrated through simulation and comparison with other algorithms.

关键词： Multi-sensor system Multi-objective optimization Underwater surveillance non-dominated sorting genetic algorithm Mean-Shift clustering Barrier coverage

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Multi-objective optimization of Tesla valve channel battery cold plate with nanofluid by RSM and NSGA-II

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JOURNAL OF ENERGY STORAGE 2025年 115卷

Tesla valve cold plate is a promising technology in battery thermal management system, but its temperature is uneven, the pressure loss is large, and the heat transfer effect needs to be further improved. Therefore, computational fluid dynamics simulation is employed to analysis the three-dimensional Tesla valve cold plate with different flow conditions. The effects of flow direction combinations of Tesla valve pipes, discharge powers, types of nanofluids, volume fractions of nanofluids, initial temperature and initial velocity of nanofluids on the fluid heat transfer characteristics of cold plate are discussed. The results show that the cooling effect of cold plate is the best in the way of outer tube downstream and inner tube countercurrent. At the same time, the Nusselt number and average heat flux increased by 8.68 % and 2.73 % respectively compared with pure downstream. By comparing different kinds of nanofluids, it is found that the 5 % Cu nanoparticles and water mixed nanofluid has the best heat transfer characteristics. Finally, the response surface method (RSM) and non-dominated genetic algorithm (NSGA-II) are used to optimize the working conditions of nanofluids. The optimal comprehensive heat transfer effect can be obtained when the initial velocity, temperature and volume fraction are 0.13 m/s, 288 K and 4.38 %, respectively.

关键词： Nanofluids Tesla valve Battery cold plate non-dominated sorting genetic algorithm Response surface method

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Multi-Objective Optimization Design of PCS Box Girder Bridges with Small and Medium Spans Using genetic algorithms

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BUILDINGS 2025年第3期15卷 361-361页

作者： Li, Zhijie Qi, Jianan Wang, Jingquan Southeast Univ Sch Civil Engn Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 211189 Peoples R China Southeast Univ Natl Key Lab Safety Durabil & Hlth Operat Long Spa Nanjing 211189 Peoples R China Southeast Univ Bridge Engn Res Ctr Southeast Univ Nanjing 211189 Peoples R China Jiangsu Univ Fac Civil Engn & Mech Zhenjiang 212013 Peoples R China

With the development of algorithms for autonomous decision-making in the field of structural engineering, the design of precast concrete segment (PCS) box girder bridges faces new challenges. This paper proposes using a multi-objective optimization method based on genetic algorithms for the rapid design of PCS box girder bridges with small and medium spans. By considering 20 design parameters such as the physical dimensions of the box girder cross-section, material properties, and prestressing parameters, the paper formulates and quantifies three objective functions: cost, safety, and structural performance. The multi-objective optimization was conducted using four optimization algorithms (NSGA-II, NSGA-III, GDE3, and PSO). An optimization evaluation index (phi[F(x)]) was established and weights were assigned to different optimization objectives. A specific design case based on the general diagram of a 3 x 25 m-long continuous PCS box girder bridge was carried out. The results indicate that genetic algorithms performed exceptionally well on this problem, with the NSGA-III algorithm achieving the best phi[F(x)] value of 0.2789 among all algorithms. A performance analysis was conducted on various optimization models using box plots and sensitivity studies. Scatter plots and surface plots of the Pareto front of the optimized solutions were generated, and corresponding cross-sectional design drawings were created based on the two proposed solutions. Compared with the general graph, the design cases provided by the NSGA-III algorithm model have a change rate of 8.03%, -0.29%, and 75.49% in the three optimization objectives, respectively, indicating a significant improvement effect. The research content of this paper provides a reasonable direction for future studies on intelligent bridge design methodologies.

关键词： multi-objective optimization precast concrete segmental bridge box girder non-dominated sorting genetic algorithm

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Multi-Objective Optimization-Based GA in PLS of IRS-Assisted PDNOMA Communication

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IEEE ACCESS 2024年 12卷 87361-87383页

作者： Thuc, Kieu-Xuan Hong, Nguyen-Thi Anh, Le-Thi Hanoi Univ Ind Fac Informat Technol Hanoi Vietnam Posts & Telecommun Inst Technol Hanoi Vietnam

The intelligent reflecting surface (IRS) supports communication systems well, especially in physical layer security for the cooperative power domain non-orthogonal multiple access (PDNOMA). In this work, we investigate the secrecy performance of PDNOMA with the assistance of the IRS and a multiple-relaying network in the presence of an eavesdropper. Three selection strategies are considered at the relaying network to boost the system's performance: the first method is based on the best relay selection, the second on the max-min concept, and the third on harmonious characteristics. Moreover, the phase shift of IRS element and power allocation for each NOMA user can be controlled to improve the secrecy quality and reduce the influence of an eavesdropper (E). Besides, applying the technique of transmitting artificial noise (AN) from the source is also considered in this paper to interfere with the signal at E. Furthermore, in this paper, we determine two primary metrics to evaluate the secrecy performance of our proposed system: the worst secrecy capacity and secrecy energy efficiency. The balance of these two metrics needs to be assured to improve the secrecy performance. Thus, in this paper, we consider the multi-object problem and propose the genetic algorithm-based approach, a non-dominated sorting genetic algorithm with three procedures (NSGA-II), to solve this problem. Then, to highlight the proposed algorithm's outstanding performance, we compare it with other algorithms, Reference point based NSGA-II (R-NSGA-II) and the exhaustive search (ES). Additionally, the impacts of critical system parameters are investigated for both cases as IRS and none-IRS assistance comprises three relaying selection techniques, the number of IRS elements, the strength of AN signal, the distances of source-relay link, relay-IRS link, and IRS-Eavesdropper link. Finally, the summaries of these archived results show the benefits of our proposed model in different cases of the deployment

关键词： NOMA genetic algorithms Wireless communication 6G mobile communication Security Relays Optimization Energy efficiency IRS-assisted PDNOMA non-dominated sorting genetic algorithm multi-objective optimization worst secrecy capacity secrecy energy efficiency

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Clusters of floor locations-allocation of stores to cross-docking warehouse considering satisfaction and space using MOGWO and NSGA-II algorithms

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FLEXIBLE SERVICES AND MANUFACTURING JOURNAL 2024年第1期36卷 315-342页

作者： Shoaee, Maryam Samouei, Parvaneh Bu Ali Sina Univ Fac Engn Dept Ind Engn Hamadan Iran

Optimal warehouse design is the most substantial issue for many companies, because of increasing their efficiency and productivity. Nowadays, a warehouse is just undefined as a site for goods storage and delivery but is taken into account as a unit interacting with other sectors. Recently, cross-docking warehouses have been widely used since they differ from traditional warehouses in terms of good deposition volume, storage and delivery times, mitigating procurement, exploitation of resources, and accelerating service pace. This study proposes a mathematical model for transit warehouse layout and design to minimize travel distances, downsize the free space of the warehouse and make retailers satisfied. Small-sized problems are solved by the augmented epsilon-constraint and the weighted sum methods, while NSGA-II and MOGWO algorithms are proposed for large-sized problems. Comparison between algorithms indicates higher efficiency of NSGA-II rather than MOGWO algorithm to solve cross-docking warehouse design for allocating stores to floor locations.

关键词： Cross-docking warehouse Layout non-dominated sorting genetic algorithm Multi-objective grey wolf optimizer Augmented epsilon-constraint Weighted sum method

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