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Comparison of the optimized thermal performance of square and circular ammonia-cooled microchannel heat sink with genetic algorithm

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ENERGY CONVERSION AND MANAGEMENT 2015年 102卷 59-65页

作者： Ghazali-Mohd, Normah Jong-Taek, Oh Nguyen Ba Chien Kwang-Il, Choi Ahmad, Robiah Univ Teknol Malaysia Fac Mech Engn Skudai 81300 Johor Malaysia Chonnam Natl Univ Dept Refrigerat & Aircond Engn Dunduk Dong Yeosu South Korea UTM Kuala Lumpur UTM Razak Sch Engn & Adv Technol Kuala Lumpur Malaysia

Minimization of the thermal resistance and pressure drop of a microchannel heat sink is desirable for efficient heat removal which is becoming a serious challenge due to the demand for continuous miniaturization of such cooling systems with increasing high heat generation rate. However, a reduction in the thermal resistance generally leads to the increase in the pressure drop and vice versa. This paper reports the outcome of optimization of the hydraulic diameter and wall width to channel width ratio of square and circular microchannel heat sink for the simultaneous minimization of the two objectives;thermal resistance and pressure drop. The procedure was completed with multi-objective genetic algorithm (MOGA). Environmentally friendly liquid ammonia was used as the coolant and the thermophysical properties have been obtained based on the average experimental saturation temperatures measured along an ammonia-cooled 3.0 mm internal diameter horizontal microchannel rig. The optimized results showed that with the same hydraulic diameter and pumping power, circular microchannels have lower thermal resistance. Based on the same number of microchannels per square cm, the thermal resistance for the circular channels is lower by 21% at the lowest pumping power and lower by 35% at the highest pumping power than the thermal resistance for the square microchannels. Results obtained at 10 degrees C and 5 degrees C showed no significant difference probably due to the slight difference in properties at these temperatures. (c) 2015 Elsevier Ltd. All rights reserved.

关键词： Optimization Microchannel heat sink multi-objective genetic algorithm Ammonia Thermal resistance

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multi-Stage Hybrid Rocket Conceptual Design for Micro-Satellites Launch using genetic algorithm

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TRANSACTIONS OF THE JAPAN SOCIETY FOR AERONAUTICAL AND SPACE SCIENCES 2012年第4期55卷 229-236页

作者： Kitagawa, Yosuke Kitagawa, Koki Nakamiya, Masaki Kanazaki, Masahiro Shimada, Toru Tokyo Metropolitan Univ Dept Aeronaut & Astronaut Hino Tokyo Japan Japan Aerosp Explorat Agcy ISAS JAXA Sagamihara Kanagawa Japan

The multi-objective genetic algorithm (MOGA) is applied to the multi-disciplinary conceptual design problem for a three-stage launch vehicle (LV) with a hybrid rocket engine (HRE). MOGA is an optimization tool used for multi-objective problems. The parallel coordinate plot (PCP), which is a data mining method, is employed in the post-process in MOGA for design knowledge discovery. A rocket that can deliver observing micro-satellites to the sun-synchronous orbit (SSO) is designed. It consists of an oxidizer tank containing liquid oxidizer, a combustion chamber containing solid fuel, a pressurizing tank and a nozzle. The objective functions considered in this study are to minimize the total mass of the rocket and to maximize the ratio of the payload mass to the total mass. To calculate the thrust and the engine size, the regression rate is estimated based on an empirical model for a paraffin (FT-0070) propellant. Several non-dominated solutions are obtained using MOGA, and design knowledge is discovered for the present hybrid rocket design problem using a PCP analysis. As a result, substantial knowledge on the design of an LV with an HRE is obtained for use in space transportation.

关键词： Hybrid Rocket multidisciplinary Design Optimization multi-objective genetic algorithm

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Shape Sensing in Plate Structures through Inverse Finite Element Method Enhanced by multi-objective genetic Optimization of Sensor Placement and Strain Pre-Extrapolation

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SENSORS 2024年第2期24卷 608页

作者： Del Priore, Emiliano Lampani, Luca Sapienza Univ Roma Dipartimento Ingn Meccan & Aerosp I-00184 Rome Italy

The real-time reconstruction of the displacement field of a structure from a network of in situ strain sensors is commonly referred to as "shape sensing". The inverse finite element method (iFEM) stands out as a highly effective and promising approach to perform this task. In the current investigation, this technique is employed to monitor different plate structures experiencing flexural and torsional deformation fields. In order to reduce the number of installed sensors and obtain more accurate results, the iFEM is applied in synergy with smoothing element analysis (SEA), which allows the pre-extrapolation of the strain field over the entire structure from a limited number of measurement points. For the SEA extrapolation to be effective for a multitude of load cases, it is necessary to position the strain sensors appropriately. In this study, an innovative sensor placement strategy that relies on a multi-objective genetic algorithm (NSGA-II) is proposed. This approach aims to minimize the root mean square error of the pre-extrapolated strain field across a set of mode shapes for the examined plate structures. The optimized strain reconstruction is subsequently utilized as input for the iFEM technique. Comparisons are drawn between the displacement field reconstructions obtained using the proposed methodology and the conventional iFEM. In order to validate such methodology, two different numerical case studies, one involving a rectangular cantilevered plate and the other encompassing a square plate clamped at the edges, are investigated. For the considered case studies, the results obtained by the proposed approach reveal a significant improvement in the monitoring capabilities over the basic iFEM algorithm with the same number of sensors.

关键词： structural health monitoring shape sensing sensor placement inverse finite element method smoothing element analysis multi-objective genetic algorithm

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A novel application of genetic algorithm for synthesizing optimal water reuse network with multiple objectives

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CHEMICAL ENGINEERING RESEARCH & DESIGN 2015年第Aug.期100卷 39-56页

作者： Halim, Iskandar Adhitya, Arief Srinivasan, Rajagopalan ASTAR Inst Chem & Engn Sci Singapore 627833 Singapore Indian Inst Technol Gandhinagar Ahmadabad 382424 Gujarat India

The current drive towards water conservation has compelled the process industries to seek various measures to minimize water. One such strategy is to design and synthesize an optimum water network that enables maximum reuse in between operations. Such optimization has been known to be challenging due to the presence of nonlinearities in the formulation that makes it difficult to solve using the standard deterministic techniques. Moreover, most of these techniques have focused on solving single objective problem involving freshwater usage while other decision factors such as treatment costs and network topologies are generally not accounted for as they would introduce extra complexities to the problem. In this paper, we propose to address optimal water reuse network as a multi-objective optimization problem our strategy is to apply a well-known genetic algorithm (NSGA-II) for single- or multi-objective optimization of water reuse network synthesis in a total (integrated) setting. First, a superstructure entailing all reuse opportunities between the water-using processes and wastewater treatment units is formulated. Next, decision variables in the form of various stream flows are encoded as the initial population chromosomes and then subjected to a set of genetic operators (selection, mutation, and crossover) to generate the offspring chromosomes. Coupled with these operators is an elitism strategy to improve the convergence and spread of the solutions. A key benefit of this approach is that it can be equally applied for both single objective and multi-objective cases in a robust manner. To demonstrate the efficacy of the approach, four single-objective case studies are solved with the results compared with the reported global optima. Thereafter, two multi-objective case studies are solved to generate the complete Pareto set. The key advantage of the proposed approach lies in its ability to solve complex optimization problem without resorting to simplifying heuristics

关键词： Water reuse Water network design Wastewater minimization Integrated water network multi-objective genetic algorithm Pareto optima

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genetic algorithm based search on the role of variables in the work hardening process of multiphase steels

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COMPUTATIONAL MATERIALS SCIENCE 2009年第1期45卷 158-166页

作者： Ganguly, S. Datta, S. Chakraborti, N. Bengal Engn & Sci Univ Sch Mat Sci & Engn Sibpur 711103 Howrah India Indian Inst Technol Dept Met & Mat Engn Kharagpur 721302 W Bengal India

multi-objective optimizations of strength and ductility of multiphase steels are conducted using genetic algorithms (GAs), to investigate the role of the composition and process variables in their complicated work hardening process. Neural network based computational models, describing the complex correlations between the decision parameters for processing and materials chemistry of such steels, are developed using existing data and are used for the fitness functions. The cases of both high strength low alloyed steels (HSLA) and the transformation induced plasticity (TRIP) aided steel are separately studied and the findings are compared and contrasted. The Pareto solutions are used Successfully to study the role of the parameters at different combination of strength and ductility. The findings are also utilized for qualitative assessment of the dominant mechanisms behind the work hardening of the steels. (C) 2008 Elsevier B.V. All rights reserved.

关键词： multiphase steel TRIP Strength Ductility multi-objective genetic algorithm Work hardening

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Study of multiobjective genetic algorithm on Taylor External Fixation

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IEEE ACCESS 2022年 10卷 46986-46999页

作者： Guo, Jianmin Su, Jialong Tiangong Univ Sch Comp Sci & Technol Tianjin 300387 Peoples R China

The rapid development of today's society and the rapidly increasing incidence of skeletal injuries caused by traffic and industrial accidents have led to a significant increase in the number of patients with fractures accompanied by severe soft tissue injuries, as well as an increased incidence of osteomyelitis and bone defects. Many of these patients cannot be treated with internal fixators and can only be treated with external fixators. The Taylor Bone External Fixator is the most advanced bone external fixation brace in the field of orthopedics, in which the algorithm study of the forward and backward solution of the computer software that accompanies the Taylor Bone External Fixator is the key. In this paper, we analyze the positional kinematic model based on the Taylor Spatial Frame and derive the equations for solving the six positional parameters of the fracture segment in the dynamic platform with this model. A multi-objective genetic algorithm and Pareto optimization theory are combined to propose a solution to the kinematic positive solution problem. The algorithm proposed in this paper has a minimum accuracy improvement of about 0.8 mm compared to the conventional Newton-Raphson in terms of the accuracy of the Taylor frame mounting parameters solution. Finally, based on the human tibial fracture as the test object, the healing process of the tibial fracture end was simulated using the prescription data generated by the orthopedic system, and the experimental results proved the accuracy and feasibility of the software system, and achieved the expected orthopedic effect.

关键词： Kinematics Heuristic algorithms Bones genetic algorithms Mathematical models Optimization Strain Taylor bone external fixator multi-objective genetic algorithm Pearson correlation coefficient kinematic positive solution correction prescription

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multiobjective optimization of MPLS-IP networks with a variable neighborhood genetic algorithm

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APPLIED SOFT COMPUTING 2013年第11期13卷 4403-4412页

作者： Onety, Renata E. Tadei, Roberto Neto, Oriane M. Takahashi, Ricardo H. C. Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil Politecn Torino Dept Control & Comp Engn Turin Italy FUCAPI Anal Res & Technol Innovat Ctr Manaus Amazonas Brazil Univ Fed Minas Gerais Dept Math Belo Horizonte MG Brazil

This paper presents a genetic algorithm for the optimization of multiple indices of Quality of Service of multi Protocol Label Switching (MPLS) IP networks. The proposed algorithm, the Variable Neighborhood multiobjective genetic algorithm (VN-MGA), is a genetic algorithm based on the NSGA-II, with the particular feature that solutions are encoded defining two different kinds of neighborhoods. The first neighborhood is defined by considering as decision variables the edges that form the routes to be followed by each request, whilst the second part of solution is kept constant. The second neighborhood is defined by considering the request sequence as decision variable, with the first part kept constant. Comparisons are performed with: (i) a VNS algorithm that performs a switch between the same two neighborhoods that are used in VN-MGA;and (ii) the results obtained with an integer linear programming solver, running a scalarized version of the multiobjective problem. The results indicate that the proposed VN-MGA outperforms the pure VNS algorithm, and provides a good approximation of the exact Pareto fronts obtained with Integer Linear Programming (ILP) approach, at a much smaller computational cost. Besides potential benefits of the application of the proposed approach to the optimization of packet routing in MPLS networks, this work raises the theoretical issue of the systematic application of variable encodings, which allow variable neighborhood searches, as generic operators inside general evolutionary computation algorithms. (C) 2013 Elsevier B.V. All rights reserved.

关键词： Routing on IP networks Variable Neighborhood Search multi-objective genetic algorithm

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Ensembled local mean decomposition and genetic algorithm approach to investigate tool chatter features at higher metal removal rate

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JOURNAL OF VIBRATION AND CONTROL 2022年第1-2期28卷 30-44页

作者： Gupta, Pankaj Singh, Bhagat Jaypee Univ Engn & Technol Guna 473226 Madhya Pradesh India

Improper selection of cutting parameters leads to regenerative chatter and loss in productivity. In the present work, a methodology has been proposed to select a proper combination of input cutting parameters for stable turning with improved metal removal rate. Chatter signals generated during the turning of Al6061-T6 have been acquired using a microphone. Stability lobes diagram has been plotted to access the stability regime. Further, to study the effect of feed rate on stability, the recorded signals have been processed using local mean decomposition signal processing technique, followed by the selection of dominating product functions using Fourier transform. The decomposed signals have been used to evaluate the new output parameter, that is, chatter index. Prediction models of chatter index and metal removal rate have been developed. Moreover, these prediction models have been optimized using multi-objective genetic algorithm for ascertaining the optimal range of cutting parameters for stable turning with higher metal removal rate. Finally, obtained stable range has been validated by performing more experiments.

关键词： Tool chatter local mean decomposition nonstationary signal processing response surface methodology multi-objective genetic algorithm Nakagami distribution

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Solving and optimizing a bi-objective open shop scheduling problem by a modified genetic algorithm

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2016年第5-8期85卷 1603-1613页

作者： Azadeh, Ali Goldansaz, SeyedMorteza Zahedi-Anaraki, AmirHossein Univ Tehran Sch Ind & Syst Engn Ctr Excellence Intelligent Based Expt Mech Coll EngnDept Engn Optimizat Res Tehran Iran

This paper presents a new bi-objective mixed-integer linear programming model for open shop scheduling problem. The main constraints of this model are considering independent setup times and sequence-dependent transportation times. Two objectives are presented, minimizing sum of the makespan for all jobs and minimizing sum of the machines' workload. Because of the complexity of this problem, it is known as a NP-hard problem;so, it cannot obtain an optimal solution in a reasonable time by using traditional methods. According to classic approach, some small- and medium- to large-sized instances are generated. For the small-sized ones, the Pareto-optimal solutions are produced exactly, and for the medium- to large-sized instances, we proposed a non-dominated sorting genetic algorithm (NSGA-II). To evaluate the performance of the proposed NSGA-II, the augmented epsilon-constraint method is used for the small-sized instances, and three performance metrics are proposed for the medium- to large-sized instances.

关键词： Bi-objective open shop scheduling Makespan Machine workload multi-objective genetic algorithm Optimization

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Improving fuzzy clustering model for probability density functions using the two-objective genetic algorithm

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multiMEDIA TOOLS AND APPLICATIONS 2023年第15期83卷 45291页

作者： Phamtoan, Dinh Vovan, Tai Van Lang Univ Fac Mech Elect & Comp Engn Sch Technol Ho Chi Minh City Vietnam Can Tho Univ Coll Nat Sci Can Tho Vietnam

This paper proposes a fuzzy clustering model for probability density functions (PDFs) using the two-objective genetic algorithm. In this model, the L-1-distance is used to evaluate the similarity of PDFs, and new two indexes that relate to the similarity of PDFs and clusters are proposed as the objective functions of genetic algorithm. Moreover, the operators for crossover, mutation, and selection are also updated to improve the quality of fuzzy clustering according to the corrected rand, the partition entropy, and the partition coefficients. By combining these improvements, we have an effective automatic fuzzy clustering algorithm for PDFs that can determine the appropriate number of clusters, the elements in each cluster, and the probability belonging to clusters of each element. The proposed model is tested through experiments using the established Matlab procedure, and it is also applied effectively to image data. These experiments demonstrate the superiority of the proposed model compared to other models.

关键词： Fuzzy clustering genetic algorithm Image cluster multi-objective genetic algorithm

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