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Proceedings of the 2023 3rd Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum

作者： Xin Ning Tianji Shi Shaoru Shang Zhihua Zhu Mengyao Qin School of Mechanical and Electrical Engineering Henan Institute of Science and Technology China

ISBN: (纸本)9798400716362

To further improve the structural performance of the 32/5t-10.5m double girder bridge crane, the optimal size design of the main beam is sought. The main beam of the bridge crane is taken as the optimization object, and stress analysis is conducted on multiple load cases to identify critical points. The goal is to achieve the best structural quality of the bridge crane while ensuring its stiffness performance. Topology optimization design is performed using a multi-objective genetic algorithm and response surface analysis is employed to optimize the thickness of the left and right web plates of the main beam. By comparing the stress analysis results before and after the reconstruction of the main beam, it is found that the reconstructed main beam has a 19.8% reduction in weight while meeting the strength and stiffness requirements. This achieves the objective of simplifying the structure, reducing weight, and lowering manufacturing costs.

关键词： Bridge Crane Main Beam Finite Element Analysis multi-objective genetic algorithm Optimization Design

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multi-objective OPTIMIZATION OF HOT BLAST STOVE HEAT EXCHANGER

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INMATEH-AGRICULTURAL ENGINEERING 2023年第1期69卷 245-259页

作者： Zhang, Ye Jiang, Qing Xu, Liangyuan Huang, Chao Gan, Huasheng Anhui Agr Univ Coll Engn Hefei Anhui Peoples R China Anhui Prov Engn Lab Intelligent Agr Machinery & Eq Hefei Anhui Peoples R China

Shell-and-tube heat exchangers are widely used in many research fields and industrial production processes, but little research has been conducted on the use of heat exchangers for drying crops. This study conducted a numerical simulation of the temperature, velocity, and pressure fields based on the shell-and-tube fluids of a heat exchanger in a biomass particle hot-blast stove. The correctness of the simulation results was verified by test data before simulation, and the mesh was verified to be irrelevant. The application of a multi-objective genetic algorithm in heat exchanger design and optimization was explored, considering five design variables, such as hot tube diameter, transverse pitch, longitudinal pitch, cold flow velocity, and hot flow velocity for optimization. The Nusselt number, friction factor, and comprehensive performance coefficient were used as objective functions for 2D and 3D response surface analysis. The final design variables P1=74.91 mm, P2=104.23 mm, P3=121.37 mm, P4=4.83 m/s, and P5=8.48 m/s were obtained to improve the comprehensive performance coefficient by 16.11%. The heat transfer performance was improved by 9.55% and the resistance performance was reduced by 15%.

关键词： Heat exchanger Hot-blast stove multi-objective genetic algorithm Numerical simulation Response surface method

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multi-Physics multi-objective Optimal Design of 5-DOF Magnetic Bearing System with High-speed Permanent Magnet Synchronous Motor 24

Multi-Physics Multi-Objective Optimal Design of 5-DOF Magnet...

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24th International Conference on Electrical Machines and Systems (ICEMS)

作者： Wang, Xiaoyuan Xu, Yuhao Tianjin Univ Tianjin Peoples R China

ISBN: (纸本)9788986510218

In view of the high-precision design requirements of suspension system under high-speed conditions, this paper used multi-objective genetic algorithm (MOGA) to carry out pareto optimization design of five-degree-of-freedom magnetic bearing system, and used finite element method to analyze the structure before and after optimization based on electromagnetic field, stress field and rotor dynamics theory respectively.

关键词： five-degree-of-freedom magnetic bearing system multi-objective genetic algorithm non-dominated sorting genetic algorithm-II

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Thermally regenerative electrochemical refrigerators decision-making process and multi-objective optimization

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ENERGY CONVERSION AND MANAGEMENT 2022年 252卷 115060-115060页

作者： Kamali, Hamed Mehrpooya, Mehdi Mousavi, Seyed Hamed Ganjali, Mohammad Reza Univ Tehran Caspian Fac Engn Coll Engn Tehran Iran Univ Tehran Fac New Sci & Technol Dept Renewable Energies & Environm Tehran Iran Univ Tehran Coll Sci Ctr Excellence Electrochem Sch Chem Tehran Iran Univ Tehran Med Sci Biosensor Res Ctr Endocrinol & Metab Mol Cellular Sci Inst Tehran Iran

Thermally regenerative electrochemical refrigerators have drawn tremendous attention due to their reliability, quietness, eco-friendliness, non-emission of CFC gases, and potential for various applications. This work was conducted to model a thermally regenerative electrochemical refrigerator based on finite-time analysis. The proposed system is analyzed in four diverse temperature ranges, and all losses are considered for more accurate modeling by Python. Moreover, sensitivity analysis was performed for these temperature ranges. The thermodynamic analysis of these states has been demonstrated in detail for the first time. A multi-objective genetic algorithm in MATLAB software was used to achieve the maximum cooling capacity and COP and minimum input power. The optimal values, including system temperature, cell materials, and parameters related to heat exchangers and output results of the genetic algorithm, were prioritized using the weighted aggregated sum product assessment method. The results revealed that in the temperature ranges, 263K < TL < 283K, 297K < TH < 301K, which are the temperature ranges of cold and hot cells, respectively, the system indicated better performance. Meanwhile, selecting materials with higher specific charging/discharging capacity, isothermal coefficient, and smaller specific heat and internal resistance improves the system's performance. The optimum values of cooling capacity and system coefficient of performance were acquired as 367.01 W and 0.7301. This paper is expected to pave the way for the lab-scale design of thermally regenerative electrochemical refrigerators.

关键词： Coefficient of performance Cooling capacity Performance analysis multi-objective genetic algorithm multi-Criteria decision-making Thermally regenerative electrochemical refrigerator

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Optimal development for a 3D-printed gripper for biomedical and micromanipulation applications by non-parametric regression-based metaheuristic technique

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2022年

作者： Ngoc Thoai Tran Minh Phung Dang Ngoc Le Chau Shankar, Subramaniam Buddhi, Dharam Thanh-Phong Dao Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol & Educ Fac Mech Engn Ho Chi Minh City Vietnam Kongu Engn Coll Dept Mechatron Engn Erode India Uttaranchal Univ Div Res & Innovat Dehra Dun Uttarakhand India Ton Duc Thang Univ Inst Computat Sci Div Computat Mechatron Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

With the advancement of bioengineering and robotic engineering, medical robots have been increasing concern about manipulating the microobject or cells. Although the rigid robots have a stable operation, they inherit many limitations such as the complex assembly process of joints-coupled rigid links and expensive costs. Especially, clearances between kinematic joints cause vibrations that damage microobject. To cope with such problems, a flexure-based polylactic acid (PLA) gripper is developed to realize precise motion in medical robots. The proposed gripper is 3D printed by fused deposition modeling technique with advantages of monolithic structure, jointless and cheap cost. Prior to the gripper fabrication, the optimization development of the gripper is conducted employing a combination of the non-parametric regression (NPR) and multi-objective genetic algorithm (MOGA). Numerical data samples are collected by the finite element method. The modeling results were well formulated utilizing the NPR method with the R-2 value greater than 0.9. The Pareto-optimum design results identified that the gripper can provide a high displacement of 2 mm and a small stress of 41 MPa via MOGA. Additionally, the proposed flexure-based compliant PLA gripper can work with a safety factor higher than 1.6. The experiment tests on the prototype of the gripper are close to the estimated values.

关键词： 3D printing additive manufacturing fused deposition modeling PLA gripper non-parametric regression multi-objective genetic algorithm

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PERFORMANCE OF TWO-SIDED EWMA CEV CONTROL CHARTS WITH multiPLE CENSORED DATA

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2022年第4期29卷 487-498页

作者： Lee, Pei-Hsi Torng, Chau-Chen Kao, Pei-Hsuan Chou, Chao-Yu Fujian Univ Technol Sch Internet Econ & Business Fuzhou Peoples R China Natl Yunlin Univ Sci & Technol Inst Ind Engn & Management Touliu Taiwan Natl Taichung Univ Sci & Technol Dept Finance Taichung Taiwan

Control charts are widely applied in process control. Life testing is time-consuming and expensive;thus, this testing typically has a termination condition that is based on the assumption of a single failure mode. The lifetimes of unfailed units are known as right-censored data, and conditional expected value (CEV) control charts are effective for monitoring right-censored data. However, life testing may produce multiple censored data due to there being multiple failure modes. The occurrence of these failure modes is unpredictable, and thus the censored proportion cannot be known in advance. The exponentially weighted moving average (EWMA) control chart has a high sensitivity for detecting small mean shifts. In this paper, two charting methods for monitoring multiple censored data were developed: a two-sided EWMA CEV control chart and a combination of two single-sided EWMA CEV charts. A multi-objective model was established to optimize the design parameter values for the two types of control charts by using the multi-objective genetic algorithm. The charting methods were compared theoretically and with the practical example of liquid crystal display modules. The results demonstrate that the two-sided EWMA CEV control chart is generally more effective than the combination of two single-sided EWMA CEV control charts for monitoring multiple censored data. Moreover, if a large range and high censoring proportion are used to optimize the model design, increasing the sample size can always improve the performance of the EWMA CEV control chart.

关键词： EWMA Control Chart Conditional Expected Value multiple Censored Data multi-objective genetic algorithm

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A New multi-objective Hybrid Gene Selection algorithm for Tumor Classification Based on Microarray Gene Expression Data

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INTERNATIONAL JOURNAL OF COMPUTATIONAL INTELLIGENCE AND APPLICATIONS 2023年第4期22卷 2350019-2350019页

作者： Li, Min Wu, Bangyu Deng, Shaobo Lou, Mingzhu Nanchang Inst Technol Sch Informat Engn 289 Tianxxaing Rd Nanchang Jiangxi Peoples R China

Tumor classification based on microarray gene expression data is easy to fall into overfitting because such data are composed of many irrelevant, redundant, and noisy genes. Traditional gene selection methods cannot achieve satisfactory classification results. In this study, we propose a novel multi-target hybrid gene selection method named RMOGA (ReliefF multi-objective genetic algorithm), which aims to select a few genes and obtain good tumor recognition accuracy. RMOGA consists of two phases. Firstly, ReliefF is used to select the top 5% subset of genes from the original datasets. Secondly, a multi-objective genetic algorithm searches for the optimal gene subset from the gene subset obtained by the ReliefF method. To verify the validity of RMOGA, we conducted extensive experiments on 11 available microarray datasets and compared the proposed method with other previous methods. Two classical classifiers including Naive Bayes and Support Vector Machine were used to measure the classification performance of all comparison methods. Experimental results show that the RMOGA algorithm can yield significantly better results than previous state-of-the-art methods in terms of classification accuracy and the number of selected genes.

关键词： Gene selection reliefF multi-objective genetic algorithm microarray gene expression data

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A Calculation Method for the Journal Bearing with a Determined Load Based on Response Surface Optimization

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TRIBOLOGY TRANSACTIONS 2020年第4期63卷 647-657页

作者： Zhang, Yu Chen, Guoding Wang, Lin Northwestern Polytech Univ Sch Mech Engn Xian Peoples R China

Calculation with the determined load is more useful for engineering applications because the external load is easier to obtain than the bearing eccentricity. However, the calculation method based on the determined load for a journal bearing with a complex structure is still difficult to meet requirement. This article presents a calculation method to seek proper bearing eccentricity with a determined external load according to response surface methodology. The relationship between the relative eccentricity and attitude angle of the journal bearing and the external load is determined according to response surface optimization. The Navier-Stokes and elastic deformation equations are adopted during calculation of lubricating properties. The validity of the calculation method is verified with the experimental results. Moreover, the method is proved to be applicable to calculation of the fixed load based on the Reynolds equation.

关键词： Elastohydrodynamic lubrication response surface methodology multi-objective genetic algorithm journal bearing

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Demand Side Management Strategy for multi-objective Day-Ahead Scheduling Considering Wind Energy in Smart Grid

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ENERGIES 2022年第19期15卷 6900-6900页

作者： Ullah, Kalim Khan, Taimoor Ahmad Hafeez, Ghulam Khan, Imran Murawwat, Sadia Alamri, Basem Ali, Faheem Ali, Sajjad Khan, Sheraz Univ Engn & Technol Dept Elect Engn Mardan 23200 Pakistan Lahore Coll Women Univ Dept Elect Engn Lahore 54000 Pakistan Taif Univ Coll Engn Dept Elect Engn POB 11099 Taif 21944 Saudi Arabia Univ Engn & Technol Dept Elect Engn Peshawar 25000 Pakistan Univ Engn & Technol Dept Telecommun Engn Mardan 23200 Pakistan

Distributed energy resources (DERs) and demand side management (DSM) strategy implementation in smart grids (SGs) lead to environmental and economic benefits. In this paper, a new DSM strategy is proposed for the day-ahead scheduling problem in SGs with a high penetration of wind energy to optimize the tri-objective problem in SGs: operating cost and pollution emission minimization, the minimization of the cost associated with load curtailment, and the minimization of the deviation between wind turbine (WT) output power and demand. Due to climatic conditions, the nature of the wind energy source is uncertain, and its prediction for day-ahead scheduling is challenging. Monte Carlo simulation (MCS) was used to predict wind energy before integrating with the SG. The DSM strategy used in this study consists of real-time pricing and incentives, which is a hybrid demand response program (H-DRP). To solve the proposed tri-objective SG scheduling problem, an optimization technique, the multi-objective genetic algorithm (MOGA), is proposed, which results in non-dominated solutions in the feasible search area. Besides, the decision-making mechanism (DMM) was applied to find the optimal solution amongst the non-dominated solutions in the feasible search area. The proposed scheduling model successfully optimizes the objective functions. For the simulation, MATLAB 2021a was used. For the validation of this model, it was tested on the SG using multiple balancing constraints for power balance at the consumer end.

关键词： hybrid demand response programs smart grid renewable energy sources multi-objective genetic algorithm

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multi-objective optimization of fuel-air tube-in-tube helical coil heat exchangers for cooled cooling air system applied in aeroengines

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AEROSPACE SCIENCE AND TECHNOLOGY 2022年 130卷

作者： Liu, Penghua Han, Hongbo Bao, Zewei Sichuan Univ Sch Chem Engn Chengdu 610065 Peoples R China Sichuan Univ Minist Educ Engn Res Ctr Combust & Cooling Aerosp Power Chengdu 610065 Sichuan Peoples R China

In fuel-air heat exchangers used in cooled cooling air (CCA) systems, the power-to-weight ratio (G) has to be as high as possible and the air-side pressure drop (Delta pair) as low as possible. In this study, a numerical model of a fuel-air tube-in-tube helical coil heat exchanger used in CCA systems was developed. multi -objective optimization of the heat exchanger was performed to resolve the conflict between G and Delta pair by combining response surface methodology and multi-objective genetic algorithm. Finally, an optimal combination of structural parameters was selected using the Pareto-optimal points captured by Non -dominated Sorting genetic algorithm-II. The results indicate that the realizable k - e turbulence model makes the best prediction of thermal and hydraulic characteristics of the heat exchanger. The analysis of variance showed that the most significant parameter affecting G as well as Delta pair was the hydraulic diameter of the heat exchanger annulus side (dh), followed by the inner diameter of the heat exchanger inner tube (di,i). The optimized structural parameters were di,i = 1 mm, dh = 2.3 mm, and number of turns = 4.5, and the corresponding values of G and Delta pair were 27.6 kW/kg and 255.7 kPa, respectively. In the optimized heat exchanger, G had increased by 5.34% and Delta pair reduced by 50.8%.(c) 2022 Elsevier Masson SAS. All rights reserved.

关键词： Fuel-air heat exchanger Tube-in-tube helical coil heat exchanger Response surface method multi-objective genetic algorithm

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