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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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Energy Consumption Optimization Strategy of Hybrid Vehicle Based on nsga-ii genetic algorithm

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PROCESSES 2023年第6期11卷 1735页

作者： Wang, Xuanxuan Ji, Wujun Gao, Yun Henan Polytech Zhengzhou 450046 Peoples R China

Hybrid electric vehicles (HEVs) have certain advantages over internal combustion engines in terms of energy consumption and emission performance. However, the transmission system parameters are uncertain. The low matching between the engine and the power transmission system makes it a big problem to improve the efficiency of hybrid vehicles. Therefore, the multi-objective optimization design of hybrid vehicles is studied. The transmission system parameters of hybrid vehicles are analyzed from the objective function, decision variables, and constraints. The nsga-ii algorithm with elite strategy is introduced to realize the optimal selection of parameters and formulation of energy consumption optimization strategy. The results showed that the multi-objective optimization algorithm could adjust the position of the working point of the engine and improve the efficiency by more than 10%. There was an average difference of 2.15% after the improvement in the fuel consumption of four-gear vehicles. The fuel consumption per 100 km decreases by more than 3%. The maximum climbing gradient of the whole vehicle was 33.9%. The power factor of the direct gear of the maximum power factor increases by 15% after the improvement. The multi-objective energy consumption optimization design of hybrid vehicles proposed in the study can effectively improve the economic and dynamic performance of the whole vehicle and reduce fuel consumption. It provides a reference for the optimization of the hybrid vehicle transmission system.

关键词： nsga-ii genetic algorithm hybrid vehicle power economy drive system

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Multi-objective optimization of active cooling and compressive load-bearing performance of truss-cored sandwich panel using genetic algorithm

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2025年 236卷

作者： Peng, Shibin Sheng, Yinglong Ren, Jiaxin Jin, Feng Feng, Shangsheng Xi An Jiao Tong Univ State Key Lab Strength & Vibrat Mech Struct Xian 710049 Peoples R China Southwest China Res Inst Elect Equipment Chengdu 610036 Peoples R China Xi An Jiao Tong Univ Minist Educ Sch Life Sci & Technol Key Lab Biomed Informat Engn Xian 710049 Peoples R China Xi An Jiao Tong Univ Bioinspired Engn & Biomech Ctr BEBC Xian 710049 Peoples R China

Sandwich panels with truss cores offer versatile capabilities, such as heat dissipation and load-bearing, making them promising options for multifunctional applications. However, optimizing thermal and mechanical performance often requires different structural parameters for the sandwich cores. Therefore, it is necessary to balance thermal and mechanical performance during structural design. In this study, a pyramid lattice-cored sandwich panel was evaluated under simultaneous thermal and pressure loads. The multifunctional design of the sandwich panel was optimized using the nsga-ii algorithm to enhance active cooling efficiency, load-bearing capacity, and lightweight index. The design variables included core parameters such as strut diameter, inclination angle, and core height. The active cooling performance of the sandwich panel was assessed using CFD simulation with the k-omega SST turbulence model. Meanwhile, the compressive load-bearing capacity and lightweight index were evaluated using theoretical relations. To validate the optimization results, forced convection experiments and quasi-static out-of-plane compression tests were conducted. From the Pareto solutions predicted by the nsga-ii algorithm, the optimal design point was identified. The predicted heat transfer coefficient and collapse strength of the optimal design were within 15 % and 6.3 %., respectively, of the experimental data. Compared to the initial design, the optimal design increased the heat transfer coefficient by 79.1 % and the collapse strength by 40.6 %, while maintaining nearly the same relative density.

关键词： Forced convection Lightweight sandwich panels Multifunctional design Multi-objective optimization nsga-ii genetic algorithm

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PCA analysis and data-driven multi-objective optimization of geometric parameters of an intermediate-temperature methane pre-reformer for automotive SOFC systems

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FUEL 2025年 385卷

作者： Dai, Shengli Ren, Jing Wang, Enhua Zhang, Hongguang Beijing Univ Technol Coll Mech & Energy Engn Key Lab Enhanced Heat Transfer & Energy Conservat Beijing Key Lab Heat Transfer & Energy Convers Beijing 100124 Peoples R China Beijing Inst Technol Sch Mech Engn Beijing 100081 Peoples R China

Metal-supported solid oxide fuel cell (MS-SOFC) is an emerging technology suitable for vehicular applications. Utilizing the off-gas from MS-SOFC to realize methane steam reforming is an attractive approach to avoid the challenges of hydrogen storage and transportation. However, the operation temperature of such a pre-reformer will be lower than conventional high-temperature reformer. How to optimize the geometric parameters of such a pre-reformer needs to be clarified. In this study, a data-driven multi-objective optimization method is proposed. The critical parameters of a methane steam pre-reformer are determined using the principal component analysis based on Taguchi method and a data-driven multi-objective optimization based on artificial neural network (ANN) and nsga-ii genetic algorithm. A heterogeneous mathematical model is established based on the Langmuir-Hinshelwood method. The effects of critical geometric parameters of the pre-reformer on the performance under intermediate-temperature conditions are studied using Taguchi orthogonal experimental design and analysis of variance (ANOVA). A surrogate ANN model for predicting the performance of the methane prereformer is setup. Then a multi-objective genetic algorithm optimization is performed based on the surrogate model to maximize the methane conversion rate and minimize the total cost. The results indicate that the significances of the four parameters are different. The number of tubes and tube length demonstrate the most significant effects on the methane conversion rate,hydrogen production yield and total heat transfer cost, whereas the bed porosity and tube inner diameter exhibit the lowest degree of influences. Using a surrogate ANN model during the multi-objective optimization process can alleviate the computation load significantly while maintains a high prediction precision. The methane conversion rate increases by 9.5% and the total cost declines by 18.4% compared to the original design.

关键词： Steam methane reforming Heterogeneous model Taguchi method Data-driven method nsga-ii genetic algorithm

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Machine learning-based optimization for D-shaped PCF SPR refractive index sensor

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OPTICS COMMUNICATIONS 2025年 575卷

作者： Dogan, Yusuf Katirci, Ramazan Erdogan, Ilhan Sivas Univ Sci & Technol Dept Elect & Elect Engn TR-58000 Sivas Turkiye Sivas Univ Sci & Technol Dept Comp Engn TR-58000 Sivas Turkiye

In this research, we investigated common machine learning algorithms to estimate the highest sensitivity of a Dshaped PCF SPR sensor by optimizing the performance parameters. Extreme gradient boosting (XGBoost), random forest model, and PyTorch neural network machine learning algorithms were compared to build a model and accurately predict the results, and sensor parameters were optimized through Non-dominated Sorting genetic algorithm (nsga-ii). The XGBoost technique demonstrated exceptional prediction capability, achieving an impressive R2 value of 99.64% and the trained model served as the objective function. The maximum sensitivity of 4529.75 nm/RIU was achieved in the standard optimization approach, However, with the guidance of nsgaii, this sensitivity increased to 4814.14 nm/RIU, representing an improvement of 6.28%. The developed model enables rapid, reliable, and computationally cost-effective parameter predictions. Additionally, it provides a comprehensive understanding of the intricate relationships between input parameters and sensitivity, thus contributing significantly to the existing literature in the quest for optimal parameter identification through the application of machine learning algorithms.

关键词： Surface plasmon resonance (SPR) Photonic crystal fiber (PCF) Optical fiber sensors nsga-ii genetic algorithm Artificial intelligence Machine learning

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Thermodynamic Cycle Analysis of Combined Cycle Propulsion Technology Based on Parallel Combustion

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2025年 1-19页

作者： Li, Baozhong Zhang, Jingyu He, Xiaomin Yao, Kanghong Nanjing Univ Aeronaut & Astronaut Coll Energy & Power Engn Nanjing 210016 Peoples R China

This paper presents a novel combined cycle propulsion scheme based on parallel combustion. The parallel combustion cycle engine (PCCE) comprises turbine engine components that form an internal bypass flow path, an outer combustor of parallel combustion set up in the external bypass, and an intake adjustment device set up in the intake to regulate the air input of the internal and external bypasses. Furthermore, a shunt adjustment body is installed at the outlet of the internal bypass compressor, which allows a portion of the high-pressure airflow compressed by the compressor to be diverted into the outer combustor. This can be utilized in the propulsion system of Ma0-7 wide-speed range flight vehicle. The paper constructs the principle structure of PCCE, describes the four operating modes and its working principle, and establishes its ideal thermodynamic cycle process. The results of the thermodynamic cycle analysis demonstrate that the PCCE in dual-combustion mode (Ma0.7-2) exhibits a broader speed range and higher thermal efficiency. In the transition mode (Ma2-4), the shunt adjustment body facilitates the utilization of compressed air to enhance specific thrust at Ma2.5 by 2.1% and at Ma3.2 by 5.8% in comparison to the over-under TBCC engine.

关键词： Combined cycle Thermodynamic cycle optimization Parallel combustion nsga-ii genetic algorithm

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The Mechanical Performance of Pre-folded Re-entrant Honeycombs Under In-plane Quasi-static Loading

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IRANIAN JOURNAL OF SCIENCE AND TECHNOLOGY-TRANSACTIONS OF MECHANICAL ENGINEERING 2025年 1-20页

作者： Wang, Dingchuan Chen, Chaoda Jiang, Hui Hu, Jinpeng Jiang, Zhansi Guilin Univ Elect Technol Dept Mech Engn Guilin 541004 Peoples R China Zhaoqing Univ Coll Mech & Automot Engn Zhaoqing 526061 Peoples R China Guangzhou Maritime Univ Sch Intelligent Transportat & Engn Guangzhou 510725 Peoples R China Guangzhou Maritime Univ Sch Naval Architecture & Ocean Engn Guangzhou 510725 Peoples R China

This paper proposes a pre-folded re-entrant honeycomb (PRH) structure, which is constructed based on the traditional re-entrant hexagonal honeycomb and cell stacking method, and has both negative Poisson's ratio and Miura origami part properties. The prominent in-plane energy absorption capability of the PRH structure will be widely applied in the field of energy absorbers. First, the internal and external deformation patterns and energy absorption properties of the structure under quasi-static loading were investigated using a validated finite element model, and the effects of the stacking parameters on the mechanical properties of the structure under internal and external compression were discussed. Secondly, a multi-objective optimization model with plateau stress and specific energy absorption (SEA) under in-plane loading of the PRH structure was developed and solved using the response surface methodology, nsga-ii genetic algorithm and TOPSIS decision making method. The best solution of the proxy model for plateau stress and SEA under in-plane loading of the PRH structure was found, and it was compared with similar models such as pre-folded hexagonal honeycomb (PHH) and origami-based re-entrant honeycomb (OBH). It was found that the energy absorption properties of this structure are better under in-plane loading, and the SEAm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${SEA}_{m}$$\end{document} and SEAv\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${SEA}_{v}$$\end{document} of PRH were 24% and 53.23% higher than those of PHH. The SEAm\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasys

关键词： Pre-folded re-entrant honeycomb Specific energy absorption Plateau stress Response surface methodology nsga-ii genetic algorithm

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Multi-objective genetic optimization of the thermoelectric system for thermal management of proton exchange membrane fuel cells

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APPLIED ENERGY 2018年 217卷 314-327页

作者： Kwan, Trevor Hocksun Wu, Xiaofeng Yao, Qinghe Sun Yat Sen Univ Sch Engn Guangzhou Guangdong Peoples R China Univ Sydney Sch AMME Sydney NSW Australia

As a clean power system with a narrow temperature range of typically 60-95 degrees C, the low temperature (LT) proton exchange membrane fuel cell (PEMFC) requires an effective thermal management system to enhance its efficiency and durability. This paper focuses on a genetic algorithm based optimization of the thermoelectric generator (TEG) as applied to the PEMFC system. The genetic algorithm approach is advantageous over similar previous research in that it enables multi-objective optimization where the various TEG module parameters can be configured towards critical objectives such as maximum output power, minimal mass and maintaining the PEMFC within its operating temperature range. A second case study is also studied where the combined efficiency of the PEMFC and TEG is selected as an objective in replacement of the maximum TEG output power. Optimization results suggest that, in both cases, there is a trade-off situation between maximum output TEG power or maximum system efficiency with respect to system mass. It is also shown that the most important benefit of increasing the cooling convection coefficient is that it increases the system's specific power where the heat sink areas can be smaller to achieve the same cooling rate.

关键词： Thermoelectric generator PEMFC nsga-ii genetic algorithm Optimization 1-D thermodynamics

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Multi-objective shape optimization of autonomous underwater glider based on fast elitist non-dominated sorting genetic algorithm

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OCEAN ENGINEERING 2018年 157卷 339-349页

作者： Fu, Xiaoyun Lei, Lei Yang, Gang Li, Baoren Huazhong Univ Sci & Technol Sch Mech Sci & Engn Wuhan 430074 Hubei Peoples R China

Autonomous underwater glider (AUG) equips with limited battery capacity, and needs to optimize the shape of AUG to reduce power consumption and improve voyage. This paper presents a new method of the multi-objective optimization of AUG shape based on the fast elitist non-dominated sorting genetic algorithm (nsga - ii). The slender ellipsoid line is chosen as the reference model and the volume of the model is constrained to keep 100 L. The hull drag and the hull surface pressure are two key technical performance indicators. Variables are used for sensitivity analysis based on One-At-a-time (OAT) method. Comparisons between towing tank experiments and numerical simulation method is conducted to prove that this method is used for hydrodynamic analysis. The original shape, the nsga-ii optimization shape, the Spray shape and the multi-island genetic algorithm (MIGA) optimization shape are analyzed to verify the validity of the optimization method in this paper by comparing hydrodynamic performance and power conversion efficiency. The simulation results indicate that the nsga-ii shape obtains a better hydrodynamic performance than the others. At the same wing configuration and gliding depth, the voyage of the nsga-ii shape is more than the original shape 12%, which has great significance for reducing power consumption.

关键词： Autonomous underwater glider Shape optimization Multi -objectives nsga-ii genetic algorithm Hydrodynamic Power conversion efficiency

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Multi-objective optimization of a high speed on/off valve for dynamic performance improvement and volume minimization

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Chinese Journal of Aeronautics 2024年第10期37卷 435-444页

作者： Qi ZHONG Junxian WANG Enguang XU Cheng YU Yanbiao LI College of Mechanical Engineering Zhejiang University of TechnologyHangzhou 310023China Key Laboratory of Special Purpose Equipment and Advanced Processing Technology Ministry of Education and Zhejiang ProvinceZhejiang University of TechnologyHangzhou 310023China

Hydraulic circuits with high speed on/off valve(HSV)for servo control have become commonplace in ***,the individual valve that is not volume-optimized results in a large total size of hydraulic control system,diminishing the *** address this issue,the high-precision equivalent reluctance model of the HSV is established by employing an equivalent magnetic circuit,on which the dynamic characteristic of the HSV,as well as the effects of structural parameters on switching behaviour,are *** on this model,multi-objective optimization is adopted to design an HSV with faster dynamic performance and smaller volume,nsga-ii genetic algorithm is applied to obtain the Pareto front of the desired *** assess the impact before and after optimization,an HSV based on the optimized structure is manufactured and *** experimental results show that the optimized HSV reduces 47.1%of its solenoid volume while improving opening and closing dynamic performance by 14.8%and 43.0%respectively,increasing maximum switching frequency by 6.2%,and expanding flow linear control area by 6.7%.These results validate the optimized structure and indicate that the optimization method provided in the paper is beneficial for developing superior HSV.

关键词： High speed on/off valve Dynamic response Volume Multiobjective optimization nsga-ii genetic algorithm

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