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multi-objective optimization study of three-phase intrinsic self-sensing concrete conductive mix proportion

JOURNAL OF BUILDING ENGINEERING 2025年 103卷

作者： Tian, Xinru Hu, Shaowei Xue, Xiang Qi, Hao Zhengzhou Univ Coll Water Conservancy & Environm Engn Zhengzhou 450001 Peoples R China Chongqing Univ Sch Civil Engn Chongqing 400045 Peoples R China

In order to prepare a three-phase intrinsic self-sensing concrete with both compressive strength and sensing performance, the Box-Behnken experimental design method in response surface methodology (RSM) was adopted. The macroscopic, microscopic and nanoscale dosages of steel fibers, carbon fibers and nanocarbon black as conductive phases were taken as the influencing factors, and a prediction model was established to predict the compressive strength of concrete and the absolute maximum of the fractional amplitude of resistivity change under uniaxial compression cyclic loading(|Delta FCR|max) as the performance index, to analyze the influence of each factor on the performance index, and further to obtain the optimal proportioning parameters of conductive phase by multi-objective optimization with objective weight assignment-grey relational degree analysis method. The results show that response surface modeling for each performance index has high accuracy in the test range. The objective weight assignment-grey relational degree analysis method enables optimal design of three-phase intrinsic self-sensing concrete mix proportion based on multi-objective performance requirements. Under certain test conditions, the compressive strength of the three-phase intrinsic self-sensing concrete was comparable to that of the matrix concrete and had good sensing performance when the dosages of steel fibers, carbon fibers and carbon black nanoparticles were 0.80 vol%, 0.50 vol% and 0.20 wt %, respectively.

关键词： Steel fibre Carbon fibre Carbon black nanoparticle objective weight assignment-grey relational degree analysis method multi-objective optimization

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multi-objective optimization OF A356 ENGINE BLOCK CASTING PROCESS PARAMETERS BASED ON RESPONSE SURFACE METHOD AND NSGA-II GENETIC ALGORITHM

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INTERNATIONAL JOURNAL OF METALCASTING 2025年 1-19页

作者： Deng, Wei Song, Zhongmo Lei, Jilin Luo, Kun Zhang, Yong Yu, Mingjiang Kunming Univ Sci & Technol Prov Key Lab Internal Combust Engine Kunming Yunnan Peoples R China Chengdu Zhengheng Power Co Ltd Chengdu Sichuan Peoples R China Kunming Yunnei Power CO LTD Kunming 650200 Yunnan Peoples R China

In order to improve the casting quality of the engine block and reduce the casting cost, this paper takes the an A356 alloy engine block as the research object, takes the alloy pouring temperature, mold preheating temperature, filling time and holding pressure four process parameters as the influencing factors, and takes the solidification time and shrinkage volume as the evaluation objectives, and uses the response surface method(RSM) to design 29 sets of test schemes, and conducts multi-objective analysis based on NSGA-II genetic algorithm and satisfaction function. The results show that the optimized process parameters are as follows: alloy pouring temperature 680 degrees C, mold preheating temperature 20 degrees C, filling time 12s and holding pressure 50 kPa. Compared with the initial process, the solidification time was shortened by 12.55% and the shrinkage volume was improved by 2.24% under the combination of optimized process parameters. After production verification, the casting molding quality is good and the mechanical properties to meet the requirements, which well verifies the rationality of the optimized process parameters, and the research can provide effective guidance for the actual production of engine blocks.

关键词： engine block response surface method NSGA-II genetic algorithm multi-objective optimization production validation

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multi-objective optimization Design of a Segmented Asymmetric V-Type Interior Permanent Magnet Synchronous Motor

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IEEE TRANSACTIONS ON MAGNETICS 2025年第1期61卷

作者： Wang, Luyao Zhang, Xueyi Wu, Jinke Wang, Mingjun Hu, Wenjing Geng, Huihui Pang, Xingzhe Shandong Univ Technol Coll Transportat & Vehicle Engn Zibo 255000 Peoples R China Shandong Bentu New Energy Vehicle Ind Co Ltd Tai An 271200 Peoples R China Rizhao Wuzheng Elect Vehicle Co Ltd Rizhao 276800 Peoples R China

A segmented asymmetric V-type permanent magnet structure is proposed to address the issues of high output torque ripple and large cogging torque in traditional permanent magnet synchronous motors. First, the relationship between the pole-span angles of the segmented asymmetric V-type permanent magnet structure and the output torque, torque ripple, and cogging torque is investigated. Then, the rationality of the segmented asymmetric V-type permanent magnet structure and the correctness of the theoretical derivation are verified by the finite-element simulation. Subsequently, with the optimization objectives of increasing the average output torque of the motor, reducing the torque ripple, and decreasing the cogging torque, the optimal pole-span angle combination of the motor is determined by using the Pareto frontier distribution and the weight analysis method. The effectiveness of the optimized design is finally verified by the finite-element simulation and prototype tests. The results reveal that the segmented asymmetric V-type permanent magnet structure can ensure ample output torque while having low cogging torque and torque ripple in output characteristics.

关键词： Permanent magnets Torque Magnetic flux Saturation magnetization Forging Rotors Torque measurement Magnetic cores Bridges Stator cores Asymmetric structure interior permanent magnet synchronous motor multi-objective optimization pole segmentation

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multi-objective optimization framework for generative design of horseshoe-shaped pipe arrangement in pre-stressed underground bundles

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TUNNELLING AND UNDERGROUND SPACE TECHNOLOGY 2025年 158卷

作者： He, Wen Pan, Yue Hou, Yongmao Chen, Jin-Jian Shanghai Jiao Tong Univ Dept Civil Engn State Key Lab Ocean Engn Shanghai Key Lab Digital Maintenance Buildings & I 800 Dongchuan Rd Shanghai 200240 Peoples R China Shanghai Tunnel Engn Co Ltd 1099 Wanpingnan Rd Shanghai 200082 Peoples R China

The underground bundle composite pipe integrated by transverse pre-stressing (UBIT) is an emerging method for underground excavation. It offers advantages such as the enhanced structural performance, reduced deformation, simplified construction process, and improved economic efficiency. However, the complexity inherent in the UBIT structure, coupled with the subjective nature of individual experience, renders the manual design of the UBIT pipe arrangement within tunnel cross-sections a challenging task. To address this, this research proposes an intelligent design method named multi-objective optimization-based generative design (MOOGD), which combines multi-objective optimization principles with generative design techniques. The proposed MOOGD has two main innovations: (a) It enables an "end-to-end" design process that enhances efficiency while minimizing human errors;(b) It incorporates five classic multi-objective optimization algorithms, offering flexibility for engineers to select or substitute algorithms based on specific project needs. The effectiveness of MOOGD is validated through a case study of the Pingli Station construction project on Shanghai Metro Line 20. It is a twolayer underground station in Shanghai Metro Line 20, with a horseshoe-shaped circular tunnel cross-section. Compared to the original engineering design, the MOOGD-based UBIT pipe arrangement design solutions using five candidate multi-objective optimization algorithms achieve significant improvements of 11.03 %, 10.64 %, 10.95 %, 10.89 %, and 10.80 %, respectively. MOOGD provides a highly efficient and reliable design tool for engineers, facilitating a rapid exploration of design alternatives and the automated generation of optimal UBIT pipe arrangements that adhere to real-world engineering specifications.

关键词： UBIT Generative design multi-objective optimization Horseshoe-shaped cross-section

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multi-objective optimization design of foam concrete mechanical properties through the integration of FEM and DL

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ADVANCED ENGINEERING INFORMATICS 2025年 64卷

作者： Ruan, Bin Li, Juncheng Zhou, Zhenglong Huang, Hao Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Hubei Peoples R China Nanjing Tech Univ Inst Geotech Engn Nanjing 210009 Jiangsu Peoples R China

The factors influencing the microstructural characteristics and energy absorption capacity of foam concrete are numerous and complex, making it challenging to accurately characterize them using mathematical functions. This paper proposes an integral implementation framework based on convolutional neural network (CNN), gated recurrent unit (GRU) and channel attention mechanism. By utilizing X-ray Computed Tomography (X-CT) scanning, the complete pore structure of foam concrete is obtained, and establish its finite element method (FEM) model. A comprehensive analysis of the regularities related to material parameters and external loading conditions is conducted, focusing on the compressive strength 6 c and energy absorption ability WEA. Parameters that significantly influence 6 c and W EA are selected as input variables for the deep learning (DL) model. Employing the combined CNN-GRU-Attention neural network as the predictive model, and integrating mechanisms for prediction error compensation and dynamic updating of the dataset, the model achieves accurate predictions of foam concrete performance. Based on the DL model, objective functions for 6 c and W EA are developed, and the Non-dominated Sorting Genetic Algorithm II (NSGA-II) is used to solve for the optimal design characteristics that correspond to maximum 6 c and WEA, thus realizing the optimal performance design of foam concrete. The results demonstrate that: (1) the stress-strain curve outcomes from numerical simulations align well with the experimental findings, accurately reflecting the mechanical behaviour of foam concrete. (2) The CNN-GRU-Attention model facilitates precise predictions of 6 c and WEA, with R 2 values of the training set reaching 0.993 and 0.981, maintaining high predictive precision even with small data samples. (3) Following optimization using the NSGAII algorithm, 6 c increased by 24.17%, and W EA by 27.46%, signifying a significant enhancement in the performance of foam concrete.

关键词： Foam concrete Deep learning multi-objective optimization Numerical simulation Compressive strength Energy absorption capacity

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multi-objective optimization of grooved circular pin-finned heat sink with phase change material

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APPLIED THERMAL ENGINEERING 2025年 262卷

作者： Moustafa, Sherin Wei, Gaosheng Hamid, Hossam El-Hamid, M. Abd North China Elect Power Univ Sch Energy Power & Mech Engn Key Lab Power Stn Energy Transfer Convers & Syst Minist Educ Beijing 102206 Peoples R China Helwan Univ Fac Engn Mataria Mech Power Engn Dept Cairo 11718 Egypt

Phase change material heat sink can be used to effectively cool electronic device, and internal pin-fins can be utilized to improve heat transfer process. However, the latent heat storage capacity of phase change material will also be lowered due to inclusion of fins. In order to attain an ideal trade-off between two main objectives: maximizing the melting time and minimizing the base temperature of heat sink, a parametric study is conducted utilizing seven pin-fin geometries, which are analyzed numerically, followed by a multi-objective optimization of the grooved circular pin-fin heat sink aiming to maximize the phase change material melting time and minimize the base temperature of heat sink. To develop an optimal structure of pin-finned phase change material heat sink, the optimization design factors include fin diameter, orientation, thickness, and height in addition to the heater base thickness simultaneously with the thermophysical properties of the phase change material. The NSGA-II methodology, integrated with the Kriging interpolative model, is used as the optimization method in combination with ANSYS-FLUENT to decide the optimal parameters of the heat sink design. The results reveal that grooved pin fin is the best selection for phase change material heat sink in seven examined structures. The optimal grooved circular pin-finned phase change material heat sink with melting temperature of 44.28 degrees C, fin height of 16.67 mm and a fin rotation angle of 53.23 degrees, attaining 1.3 longer melting time and a lower base temperature than the referenced model. Findings highlight that the thermal conductivity of phase change material and the thickness of pin-fins serve as key determinants in improving thermal performance. By combining geometric and material properties, this study introduces a novel strategy for enhancing phase change materialbased heat sinks thermal management.

关键词： Pin-finned heat sink multi-objective optimization Phase change material Electronics cooling Numerical simulation

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multi-objective optimization for efficient CO2 storage under pressure buildup constraint in saline aquifer

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APPLIED ENERGY 2025年 382卷

作者： Liu, Jianqiao Liu, Jia Zhu, Yiheng Sun, Wenyue Zhang, Daowei Pan, Huanquan China Univ Geosci Wuhan Dept Petr Engn Wuhan Peoples R China China Univ Petr East China Dept Petr Engn Qingdao Peoples R China

CO2 storage within saline aquifers represents a pivotal strategy for mitigating climate change. Continuous injection of CO2 into saline aquifers can lead to a sharp increase information pressure, potentially reducing storage efficiency and escalating the risks of CO2 leakage and seismic activities resulting from stress- induced changes. Developing an optimal injection strategy that maximizes CO2 storage while minimizing leakage risk is critical for storage projects. Current research primarily focuses on optimization tasks that incorporate geomechanical considerations, often necessitating extensive flow-geomechanics coupled forward simulations. These simulations are computationally intensive, posing challenges in convergence and making them impractical for field-scale CO2 storage deployments. To tackle these challenges, we propose a novel optimization framework that synergistically optimizes the open time of production wells and the control strategies for both injection and production wells to enhance CO2 storage efficiency and safety. This framework innovatively addresses the co-optimization objectives of CO2 injection volume and safety simultaneously, ensuring a uniform increase information pressure during the injection process and mitigating the risks associated with localized pressure buildup. This approach maintains storage efficiency and safety while also substantially reducing computational complexity and enhancing the practicality of field-scale CO2 storage deployments. Additionally, to facilitate the implementation of complex multi-objective optimization tasks in CO2 storage, the framework integrates surrogate models with the Non- dominated Sorting Genetic Algorithm II (NSGA-II) and introduces a novel network architecture combining the Enhanced Fourier Neural Operator (U-FNO) with transformer encoders. This integration enables rapid and accurate predictions of cumulative CO2 injection volume, pressure and saturation dynamic evolution, effectively satisfying

关键词： Carbon dioxide capture and storage Saline aquifer multi-objective optimization Deep learning Leakage risk

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multi-objective optimization and experimental investigation of friction stir welding under Minimum quantity lubrication

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2025年第2期239卷 993-1004页

作者： Al-Wajidi, Wisam Eltaggaz, Abdelkrem Defersha, Fantahun M. Deiab, Ibrahim Univ Guelph Sch Engn Adv Mfg Lab Guelph ON Canada Mustansiriyah Univ Fac Engn Mat Engn Dept Baghdad Iraq

Aluminium is widely used in the aerospace, marine, and transportation industries. However, achieving defect-free, high-quality welds using conventional welding processes is challenging. Friction stir welding (FSW) is a promising solid-state welding process that is environmentally friendly, produces high-quality welds, and improves the mechanical and other properties of aluminium and other lightweight materials. This study examines the welding force, power consumption, and surface roughness of friction stir-welded 6061-T651 and 5052-H32 aluminium alloys under similar and dissimilar circumstances using the minimum quantity lubrication (MQL) process. The main variables of the MQL system, including flow rate, nozzle orientation, and nozzle diameter, were analysed using analysis of variance. A multi-objective model was used to predict the optimal levels, and grey relational analysis techniques were applied for optimization. Furthermore, this study provides a clear mechanism for using MQL during FSW. The results indicate that a flow rate of 7.5 ml/h reduces welding forces by 25% and 4% compared to 5 ml/h and 10 ml/h, respectively. Additionally, the 7.5 ml/h flow rate reduces power consumption by 20% and 10% compared to 5 ml/h and 10 ml/h, respectively. In addition, it improves the surface quality. The orientation nozzle angle of 60 degrees yielded slightly better results than those at the other levels. Similarly, for the nozzle diameter, both 2.5 mm and 3.75 mm showed slightly better results in terms of welding forces, power consumption, and surface roughness.

关键词： Friction stir welding minimum quantity lubrication aluminium alloys analysis of variance multi-objective optimization

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multi-objective optimization of geometrical parameters of a pipe with internal baffle using artificial neural networks and genetic algorithm

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APPLIED THERMAL ENGINEERING 2025年 262卷

作者： Shoaei, Mersad Kowsary, Farshad Univ Tehran Sch Mech Engn Tehran Iran Univ Tehran Dept Renewable Energies & Environm Engn Tehran Iran

The significant importance of energy and its extensive utilization in recent years has compelled researchers to conduct investigations in order to discover methods for conserving and utilizing energy resources in the most efficient manner. This study examines the influence of the geometric characteristics of a baffled pipe on its thermal efficiency, including heat transfer rate and power consumption, by employing Computational Fluid Dynamics (CFD) methods. Subsequently, the neural network was employed on the numerical simulation data, utilizing evolutionary algorithms and machine learning techniques. The NSGA-II method will then be utilized to optimize the two inverse objectives of heat transfer and power consumption through reduction. The investigated Reynolds values vary from 10,000 to 70,000, and the fluids used are water and air. The findings indicate that when the Reynolds number increases, there is a drop in the maximum rate of heat transfer and an increase in the amount of power consumption needed. In addition, the best values of geometric variables remain unaffected by the kind of fluid at particular velocity intervals. Furthermore, the proportion between the length of the baffle and the diameter of the pipe will significantly enhance heat transfer. Consequently, pipes with baffles exhibit superior thermal efficiency compared to pipes without baffles.

关键词： Baffled heat exchanger Heat transfer multi-objective optimization Neural networks NSGA-II

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multi-objective optimization of the appendages of a sailing yacht using the Normal Boundary Intersection method

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MATHEMATICS AND COMPUTERS IN SIMULATION 2025年 229卷 885-895页

作者： Peri, Daniele CNR Ist Applicazioni Calcolo M Picone Via Taurini 19 I-00185 Rome Italy

In this paper, a multidisciplinary design optimization algorithm, the Normal Boundary Intersection (NBI) method, is applied to the design of some devices of a sailing yacht. The full Pareto front is identified for two different design problems, and the optimal configurations are compared with standard devices. The great efficiency of the optimization algorithm is demonstrated by the wideness and density of the identified Pareto front.

关键词： multi-objective optimization Normal boundary intersection Yacht design Sailing yacht

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