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Multi-objective optimization of turbulent heat transfer flow in novel outward helically corrugated tubes

APPLIED THERMAL ENGINEERING 2018年 138卷 795-806页

作者： Wang, Wei Zhang, Yaning Li, Yongji Han, Huaizhi Li, Bingxi Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Heilongjiang Peoples R China Harbin Engn Univ Coll Power & Energy Engn Harbin 150001 Heilongjiang Peoples R China

This paper reports a triple-objective optimization of a novel outward helically corrugated tube to obtain the equilibrium performance for heat transfer, pressure drop, and energy benefit. The response surface method is employed to design the numerical work with three objective functions and three factors. Variance analysis and sensitivity analysis are performed to ensure the regression model and clarify the effect of each factor on the responses. The results show that the regression model obtained after removing the insignificant terms shows good agreement with the numerical data and the error is within +/- 10%. The Reynolds number (Re) has the most effect on Nusselt number (Nu), and its sensitivity coefficient is five times higher than that for corrugation pitch-to-diameter ratio (pl/D) and corrugation height-to-diameter ratio (HUD). The Pareto optimal solution is obtained by the multi-objective genetic algorithm (MOGA), which can be selected by the designer according to the operation conditions and the emphases target values. From a balanced consideration, the variable range of Re = 18,375-23,858, pl/D = 0.92-0.75 and Hl/D = 0.11-0.13 is better for decision-making. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Outward helically corrugated tube Triple-objective optimization non-dominated sorting genetic algorithm ii Heat transfer enhancement Response surface method

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A systematic approach for watershed ecological restoration strategy making: An application in the Taizi River Basin in northern China

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SCIENCE OF THE TOTAL ENVIRONMENT 2018年 637卷 1321-1332页

作者： Li, Mengdi Fan, Juntao Zhang, Yuan Guo, Fen Liu, Lusan Xia, Rui Xu, Zongxue Wu, Fengchang Chinese Res Inst Environm Sci State Key Lab Environm Criteria & Risk Assessment Beijing 100012 Peoples R China Beijing Normal Univ Coll Water Sci Beijing 100875 Peoples R China

Aiming to protect freshwater ecosystems, river ecological restoration has been brought into the research spotlight. However, it is challenging for decision makers to set appropriate objectives and select a combination of rehabilitation acts from numerous possible solutions to meet ecological, economic, and social demands. In this study, we developed a systematic approach to help make an optimal strategy for watershed restoration, which incorporated ecological security assessment andmulti-objectives optimization (MOO) into the planning process to enhance restoration efficiency and effectiveness. The river ecological security status was evaluated by using a pressure-state-function-response (PSFR) assessment framework, and MOO was achieved by searching for the Pareto optimal solutions via non-dominated sorting genetic algorithm ii (NSGA-ii) to balance tradeoffs between different objectives. Further, we clustered the searched solutions into three types in terms of different optimized objective function values in order to provide insightful information for decision makers. The proposed method was applied in an example rehabilitation project in the Taizi River Basin in northern China. The MOO result in the Taizi River presented a set of Pareto optimal solutions that were classified into three types: I-high ecological improvement, high cost and high benefits solution;ii-medial ecological improvement, medial cost and medial economic benefits solution;iiI-low ecological improvement, low cost and low economic benefits solution. The proposed systematic approach in our study can enhance the effectiveness of riverine ecological restoration project and could provide valuable reference for other ecological restoration planning. (C) 2018 Elsevier B.V. All rights reserved.

关键词： River restoration Rehabilitation Multi-objective optimization Ecological security non-dominated sorting genetic algorithm ii

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Optimal planning of electric-heating integrated energy system in low-carbon park with energy storage system

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JOURNAL OF ENERGY STORAGE 2024年 99卷

作者： Hu, Yuanweiji Yang, Bo Wu, Pengyu Wang, Xuetong Li, Jiale Huang, Yuanping Su, Rui He, Guobin Yang, Jin Su, Shi Wang, Jingbo Jiang, Lin Sang, Yiyan Kunming Univ Sci & Technol Fac Elect Power Engn Kunming 650500 Peoples R China Kunming Univ Sci & Technol Fac Sci Kunming 650500 Peoples R China Yunnan Power Grid Co Ltd Dali Power Supply Bur Dali 671000 Peoples R China Yunnan Power Grid Co Ltd Elect Power Res Inst Kunming 650032 Peoples R China Univ Liverpool Dept Elect Engn & Elect Liverpool L69 3GJ England Shanghai Univ Elect Power Coll Elect Engn Shanghai 200090 Peoples R China

Electric-heating integrated energy system (EH-IES) is pivotal for advancing energy structure reforms, and proper planning of EH-IES components can markedly enhance the operation economy, environmental sustainability, and system stability. nonetheless, the inherent randomness and intermittency of renewable energy sources, along with the peak and valley characteristics of the load, cause output fluctuations in EH-IES energy supply equipment, posing significant threats to system stability. To address these challenges, a multi-objective bi-layer EH-IES planning model considering energy storage system is established, aiming at optimizing both economic performance and stability. This model employs non-dominated sorting genetic algorithm ii (NSGA ii) to optimally plans the capacity and location of EH-IES's equipment under 13-node district heating network (DHN), IEEE-33, and IEEE-69 node test systems. Simulation results show that DHN's thermal customer satisfaction is improved by 77.19 % (7.076 degrees C), with the total cost is $234,310.09/day. For the distributed network (DN), the net load fluctuation is reduced by 1.8491 MW (23.38 %) and 2.6083 MW (26.16 %), and the voltage deviation is reduced by 0.3479 p.u. (44.23 %) and 1.7349 p.u. (61.89 %), with respective daily costs of $403.17 and $898.36. Consequently, the proposed method can improve the cost efficiency and sustainability of the system operation.

关键词： Electric-heating integrated energy system Hybrid energy storage system non-dominated sorting genetic algorithm ii Net load fluctuation Voltage deviation

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Optimizing the Building Refurbishment Process Using Improved Evolutionary algorithms

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ENERGIES 2024年第9期17卷 2022-2022页

作者： Nicolae, Adriana Elena Necula, Horia Carutasiu, Bogdan Mihail Natl Univ Sci & Technol POLITEHN Bucharest Fac Energy Engn Bucharest 060042 Romania

An optimization model that may be applied to analyze building retrofit strategies is presented in this research. The aim of this research paper is to identify the optimal thermal envelope configuration that will assure the minimum energy requirement for heating in the case of a residential building, while also considering price restrictions obtained through a specific market survey. To achieve this, several values for the following parameters are considered: thermal insulation materials' conductivities and thicknesses, windows' overall heat transfer coefficients and total solar energy transmittance and doors' thermal proprieties. Additionally, this paper presents a method used to find the best option from among the available heat pumps that could cover most of the energy requirements for heating and domestic hot water systems, also considering the products' prices. The proposed method is based on a non-dominated sorting genetic algorithm ii (NSGA-ii) model developed in the Pymoo (Multi-Objective Optimization in Python) library. The result shows that the energy requirement for heating can be reduced by up to approximately 75% compared to that obtained in the case of a non-insulated building by using suitable insulation materials and doors and windows with superior thermal proprieties chosen by the NSGA-ii.

关键词： non-dominated sorting genetic algorithm ii optimization techniques building refurbishment process cost-efficient strategies

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Spatial Multi-Objective Optimization Approach for Land Use Allocation Using NSGA-ii

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IEEE JOURNAL OF SELECTED TOPICS IN APPLIED EARTH OBSERVATIONS AND REMOTE SENSING 2014年第3期7卷 906-916页

作者： Shaygan, Mehran Alimohammadi, Abbas Mansourian, Ali Govara, Zohreh Shams Kalami, S. Mostapha KN Toosi Univ Technol Tehran *** Iran Univ Tehran Ctr Remote Sensing Tehran *** Iran

Analysis and evaluation of land use patterns are of prime importance for natural resources management. Recent studies on land use allocation have been mainly based on linear programming optimization. Although these methods have the ability to solve multi-objective problems, spatial aspects of optimization are not considered when they are used for land use management. This study applied the non-dominated sorting genetic algorithm ii (NSGA-ii) to optimize land-use allocation in the Taleghan watershed, northwest of Karaj, Iran. The four land use classes of irrigated farming, dry farming, rangeland, and other uses were extracted from the ETM+ image. The objective functions of the proposed model were erosion, economic return, suitability, and compactness-compatibility. A novel crossover operator called exchange randomly block (ERB) was used to exchange information between individuals. Results showed that the optimization model can find a set of optimal land use combinations in accordance with the proposed conditions. For comparison purposes, land use allocation was also done using the combined goal attainment-multi-objective land allocation (GoA-MOLA) approach. The results showed that NSGA-ii performance acceptably when compared to GoA-MOLA.

关键词： Multi-objective optimization land use non-dominated sorting genetic algorithm ii taleghan watershed

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Radial basis function-based shape optimization of centrifugal impeller using sequential sampling

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART G-JOURNAL OF AEROSPACE ENGINEERING 2015年第4期229卷 648-665页

作者： Khalfallah, Smail Ghenaiet, Adel Ecole Mil Plytech Lab Thermal Power Syst Appl Mech Algiers Algeria Univ Sci & Technol USTHB Fac Mech Engn Proc Engn Algiers Algeria

Multi-objective optimization algorithms become more appropriate in general aerodynamic design problems, despite the number of objective functions evaluated by computational fluid dynamics (CFD) calculations. This paper presents a multi-objective aerodynamic shape optimization (ASO) procedure implemented to minimize the optimization cycle time. Its main ideas consist in replacing some of CFD evaluations by those provided by an approximate meta-model known as the radial basis functions (RBFs). In order to minimize the size of training samples when constructing the RBF meta-model, a sequential sampling strategy based on an iterative refinement of training samples database is considered. When compared with one-stage sampling, the sequential sampling has allowed more reduction in the computational time and a better accuracy in the approximation of the objectives by RBF meta-model. The MASSOUD multidisciplinary aero/struc shape optimization using deformation and Bspline techniques are combined to reduce the number of control points for the shape deformation or optimization variables. Once the database composed of a set of geometries is constructed and CFD is used to evaluate the objectives for each element of the database, RBF is used to approximate the objectives with respect to the optimization variables, and finally the NSGA-ii non-dominated sorting genetic algorithm is applied to produce the pareto optimal fronts. An application aimed to optimize the NASA low speed centrifugal compressor (LSCC) resulted in an optimal design within a reasonable computational time and produced the Pareto fronts of trade-offs enhancing the baseline performance.

关键词： Centrifugal compressor computational fluid dynamics aerodynamic performance blade shape optimization non-dominated sorting genetic algorithm ii radial basis function multidisciplinary aero struc shape optimization using deformation sequential sampling

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Crushing stress and vibration fatigue-life optimization of a battery-pack system

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STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION 2023年第3期66卷 1-16页

作者： Zhang, Xiaoxi Xiong, Yue Pan, Yongjun Du, Haifeng Liu, Binghe Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China China Automot Engn Res Inst Chongqing 401122 Peoples R China

The mechanical failure of battery-pack systems (BPSs) under crush and vibration conditions is a crucial research topic in automotive engineering. Most studies evaluate the mechanical properties of BPSs under a single operating condition. In this study, a dual-objective optimization method based on non-dominated sorting genetic algorithm ii (NSGA-ii) is proposed to evaluate the crushing stress of BPS modules and the vibration fatigue life of the BPS. This method can obtain better combinations of the thicknesses of the BPS components, which helps engineers achieve robust and efficient designs. First, a nonlinear finite element (FE) model of a BPS is developed and experimentally verified. The crush and vibration simulations are performed, and the FE analysis data are obtained. Second, two third-order response surface models are created to characterize the relationship between the input (thicknesses of the BPS components) and the output (crushing stress of the BPS modules and vibration fatigue life of the BPS). Finally, a linear weighting model and an NSGA-ii model are used to conduct dual-objective optimization. The solution of the linear weighting method and the non-dominated Pareto solution set of the thicknesses of the BPS components are obtained and compared. Furthermore, a reasonable interval in the Pareto frontier is defined and considered the best solution to the dual-objective optimization problem. Therefore, the reliability of the BPS is improved to ensure the safety of electric vehicles in crushing and vibration environments. This method offers an effective solution to the problem of evaluating the mechanical responses of BPSs under various operating conditions. It can be used to generate a robust design for safe and durable BPSs.

关键词： Battery-pack system Crushing stress Vibration fatigue life Third-order response surface model Multi-objective optimization non-dominated sorting genetic algorithm ii

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Modular design method for filament winding process equipment based on GGA and NSGA-ii

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INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY 2018年第5-8期94卷 2057-2076页

作者： Xu, Xiaoming Zhang, Wuxiang Ding, Xilun Beihang Univ Sch Mech Engn & Automat Beijing 100191 Peoples R China

The traditional filament winding machine design method leads to high costs and a long delivery cycle if the winding method or process parameters are changed. To solve this problem, the modular design and configuration design methods are proposed to cluster components into standard modules that can be changed or replaced and to combine the types of modules to create a complete system according to the customer's requirements. The system structure is first analyzed, and the relationship matrices between the components and the influence of the modular driving forces (MDFs) on each component are subsequently established with consideration for function and stability in the life cycle. The grouping genetic algorithm (GGA) method is later employed to conduct modular optimization to maximize the interaction and stability between the components within modules. Several different types are designed based on the result of the modular design method. The relationship matrices between the module types, the parameter performance, and the customer's requirement are established, and a multi-objective optimization method based on the non-dominated sorting genetic algorithm ii (NSGA-ii) is proposed to create a complete system that combines the different types with simultaneous consideration of performance and processing cost. Sensitivity analysis of GGA and NSGA-ii demonstrates that the proposed method is robust. An example for the requirements of the filament winding is used to demonstrate the validity of the proposed method. The general procedure of the modular design method using GGA and NSGA-ii is proposed, and the key parameters that influence the results are discussed.

关键词： Filament winding process Modular design Configuration optimization Grouping genetic algorithm non-dominated sorting genetic algorithm ii

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Multi-objective energy-saving scheduling for a permutation flow line

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2018年第5期232卷 879-888页

作者： Li, Shunjiang Liu, Fei Zhou, Xiaona Chongqing Univ State Key Lab Mech Transmiss Chongqing 400030 Peoples R China

Nowadays, manufacturing enterprises, as larger energy consumers, face the severe environmental challenge and the mission of reducing energy consumption. Therefore, how to reduce energy consumption becomes a burning issue for manufacturing. Production scheduling provides a feasible scheme for energy saving on the system level. However, the existing researches of energy-saving scheduling rarely focus on the permutation flow line scheduling problem. This article proposes an energy-saving method for permutation flow line scheduling problem. First, a mathematical model for the permutation flow line scheduling problem is developed based on the principle of multiple energy source system of the computer numerical control machine tool. The optimization objective of this model is to simultaneously minimize the total flowtime and the fixed energy consumption. Since permutation flow line scheduling problem is a well-known NP-hard problem, the non-dominated sorting genetic algorithm ii is adopted to solve the multi-objective permutation flow line scheduling problem. Finally, the effectiveness of this method is verified by numerical illustration. The computation results show that a significant trade-off between total flowtime and fixed energy consumption for the permutation flow line scheduling problem, and there would be potential for saving energy consumption by using the proposed method.

关键词： Permutation flow line scheduling energy saving multi-objective optimization fixed energy consumption non-dominated sorting genetic algorithm ii

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A hybrid approach to solve a bi-objective optimization problem of a capacitated-flow network with a time factor

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RELIABILITY ENGINEERING & SYSTEM SAFETY 2020年 204卷 107191-107191页

作者： Yeh, Cheng-Ta Fu Jen Catholic Univ Dept Informat Management New Taipei Taiwan

The problem of system reliability evaluation for a capacitated-flow network has been extended by involving the characteristics of the quickest path problem. System reliability is thus defined as the probability that d units of demand can be successfully delivered from a source node to a sink node within time t. System reliability optimization considering the time factor is important for demand transmission, especially for communication systems. However, this factor has not been taken into account in previous research. To this end, in this study, the time factor is employed to adopt a component assignment strategy to maximize the system reliability and minimize the assignment cost. Because the considered problem is bi-objective, a hybrid approach integrating the non-dominated sorting genetic algorithm ii, a concept of minimal path, information entropy, and simple additive weighting is developed to determine a set of non-dominated solutions and to subsequently determine the compromise solution from an objective viewpoint. Numerical examples are utilized to demonstrate the computational efficiency of the proposed method.

关键词： Capacitated-flow network Quickest path problem Time factor System reliability Component assignment non-dominated sorting genetic algorithm ii Minimal path Information entropy Simple additive weighting

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