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Optimization investigation on configuration parameters of sine wavy fin in plate-fin heat exchanger based on fluid structure interaction analysis

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2019年 131卷 385-402页

作者： Wen, Jian Li, Ke Wang, Chunlong Zhang, Xing Wang, Simin Xi An Jiao Tong Univ Dept Refrigerat & Cryogen Engn Sch Energy & Power Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ Dept Proc Equipment & Control Engn Sch Chem Engn & Technol Xian 710049 Shaanxi Peoples R China

The comprehensive performance of sine wavy fin in plate-fin heat exchangers (PFHEs) is numerically studied based on fluid structure interaction (FSI) analysis in this paper. The analysis results of stress distribution reveal that the highest stress is located in the inlet and outlet region of fin structure, and the fluctuant stress reaches to the peak in the wave crest. By way of analyzing Full 2nd-Order Polynomial response surface (RS), the effects of inlet velocity and five configuration parameters (fin height, fin space, fin thickness, fin wavelength and double amplitude) on heat transfer, flow resistance and stress of sine wavy fin structure are quantitatively assessed. The results reveal that the j factor increases with the increase of fin space and fin height, and decreases with the increase of fin thickness, wavelength and inlet velocity. The j factor firstly increases with the increase of double amplitude and then decreases. The f factor increases with double amplitude, fin space and fin height, and decreases with fin thickness, wavelength and inlet velocity. The maximum stress increases with the increase of wavelength and fin space, and decreases with the increase of fin thickness and double amplitude. The interaction effects of input parameters on the j factor and f factor are not obvious. While the interaction effects of fin thickness and wavelength, double amplitude and wavelength on the maximum stress are obvious. Based on RS, multi-objective genetic algorithm (MOGA) is performed to optimize the fin structure comprehensively, with multiple objectives of increasing the JF factor (JF = j/f(1/3)) and decreasing the maximum stress to the best. The optimization results shows that, compared with the original design, the JF factor of optimal design 1, 2, and 3 increases by 11.0%, 8.4% and 15.9% respectively, and the maximum stress decreases by 32.3%, 42.4% and 20.7% respectively. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Plate-fin heat exchanger Wavy fin Stress analysis Response surface multi-objective genetic algorithm

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A new comprehensive framework for the multi-objective optimization of building energy design: Harlequin

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APPLIED ENERGY 2019年 241卷 331-361页

作者： Ascione, Fabrizio Bianco, Nicola Mauro, Gerardo Maria Vanoli, Giuseppe Peter Univ Naples Federico II Dept Ind Engn Piazzale Tecchio 80 I-80125 Naples Italy Univ Sannio Dept Engn Piazza Roma 21 I-82100 Benevento Italy Univ Molise Dept Med & Hlth Sci Vincenzo Tiberia Via Gazzani 47 I-86100 Campobasso Italy

The comprehensive optimization of building energy design is fundamental to promote sustainability but it is an arduous issue that involves a huge domain of variables and objectives. The proposed investigation addresses this issue through a novel comprehensive framework - Harlequin - that performs a multi-phase and multi-objective design optimization. Three phases are carried out to optimize design variables related to the whole building-plants system, considering different energy, comfort, economic and environmental performance indicators. Phase 1 implements a genetic algorithm to achieve the Pareto optimization of envelope, geometry and space conditioning set points. Phase 2 performs a smart exhaustive sampling of design scenarios to find optimal energy systems. Phase 3 provides the most sustainable, the cost-optimal and the lowest investment (but energy-efficient) design solutions. Among these, the stakeholders can choose the best solution according to their wills and needs. Harlequin uses EnergyPlus (only in phase 1) and MATLAB (R) and it is so-called because building geometry and envelope are optimized for each exposure, thereby providing "Harlequin buildings". The novelty and scientific significance consist in ensuring a reliable design optimization by investigating a domain of variables and objectives, as comprehensive as never before. As a case study, Harlequin is applied to design a typical Italian office in Milan. Compared to a reference design, significant reductions of primary energy consumption (PEC), global cost (GC) and CO2-eq emissions can be achieved, depending on the chosen solution. The maximum reductions are 43.9 kWh(p)/m(2) a for PEC, 63.9 (sic)/m(2) for GC (discount rate of 3%) and 12.3 kg/m(2) a for CO2-eq.

关键词： Building design Energy efficiency Building energy simulation Building energy optimization multi-objective genetic algorithm Cost-optimal analysis

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Modular Granular Neural Networks Optimization with multi-objective Hierarchical genetic algorithm for human recognition based on iris biometric

Modular Granular Neural Networks Optimization with Multi-Obj...

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IEEE Congress on Evolutionary Computation

作者： Daniela Sanchez Patricia Melin Oscar Castillo Fevrier Valdez Tijuana Institute of Technology

ISBN: (纸本)9781479904532

In this paper a new model of a multi-objective Hierarchical genetic algorithm (MOHGA) based on the Micro genetic algorithm (μGA) approach for Modular Neural Networks (MNNs) optimization is proposed. The proposed method can divide the data automatically into granules or sub modules, and chooses which data are for the training and which are for the testing phase. The proposed multi-objective genetic algorithm is responsible for determining the number of granules or sub modules and the percentage of data for training that can allow to have better results. The proposed method was applied to human recognition and its applicability with good results is shown, although the proposed method can be used in other applications such as time series prediction and classification.

关键词： Modular Neural Networks Hierarchical genetic algorithms multi-objective genetic algorithm Micro genetic algorithm Granular computing

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Design, optimization and control of extractive distillation for the separation of isopropanol-water using ionic liquids

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SEPARATION AND PURIFICATION TECHNOLOGY 2019年 209卷 833-850页

作者： Ma, Shoutao Shang, Xianyong Zhu, Minyan Li, Jinfang Sun, Lanyi China Univ Petr East China Coll Chem Engn State Key Lab Heavy Oil Proc Qingdao 266580 Shandong Peoples R China

As an excellent solvent, ionic liquids (ILs) have received much attention in the last two decades. One of the most important applications is to be used as an extractant for separation of azeotropic mixtures via extractive distillation (ED). In this work, 1-ethyl-3-methylimidazolium dicyanamide ([emim][N(CN)2]) is selected as the extractant to separate isopropanol (IPA)-water azeotrope based on two ED processes. multi-objective genetic algorithm (MOGA) is used to determine the operating parameters for these ED processes. Total annual cost (TAO, efficiency indicator of extractive section (E-ext), efficiency indicator of per tray in extractive section (e(ext)) and energy consumption per product flow rate (GEO are selected as objective functions in the ED process optimization, and the Pareto front provides a series of solutions satisfying the constraints with different operating parameters. The results show that TAC of the process 2 with two evaporators used in the phase of solvent recovery is 4.26% lower than that of process 1 with one evaporator. In addition, the thermodynamic efficiency (eta) and CO2 emissions are calculated for different cases, and they provide the reference for the selection of operating parameters. On this basis, the control structures of ED processes are further studied, and the traditional two-point temperature control structure works pretty well for disturbances of both feed rate and feed composition.

关键词： Ionic liquids (ILs) Extractive distillation multi-objective genetic algorithm Process optimization Dynamic control

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Strategy development for retrofitting ships for implementing shore side electricity

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TRANSPORTATION RESEARCH PART D-TRANSPORT AND ENVIRONMENT 2019年第Sep.期74卷 201-213页

作者： Yu, Jingjing Voss, Stefan Tang, Guolei Dalian Univ Technol Fac Infrastruct Engn State Key Lab Coastal & Offshore Engn Dalian 116023 Liaoning Peoples R China Univ Hamburg Inst Informat Syst Von Melle Pk 5 D-20146 Hamburg Germany

Shore side electricity (SSE) is the use of electricity from the shore to power a ship's system when it is berthing at ports. It is a solution to reduce the emissions of auxiliary engines at berth and to improve the local and regional air quality. This paper develops a multi-objective model that integrates spatial and temporal dimensions for strategic planning regarding whether and when to retrofit ships to use SSE. The strategic planning problem is to maximize the environmental benefit and to minimize the payback period of the investment over the entire planning horizon. By adopting an improved multi-objective genetic algorithm (NSGA-II), a series of retrofit strategies for container ships calling at Dalian Port in China are obtained under a projected annual visiting frequency of ships with different ship sizes and shipping lines. Depending on a selected retrofit strategy, the sum of the payback periods of ships that are determined to be retrofitted is 46 years (on average less than four years) and the environmental benefit of using SSE can be up to 128 million USD for Dalian Port in the planning horizon from 2020 to 2035. The results show that using SSE is an efficient method to decline emissions within the maritime sector and validate that the proposed methodology can be useful to ship owners to decide upon investing in SSE applications and to promote the development of green shipping and green ports.

关键词： Shore side electricity Ship retrofitting multi-objective optimization multi-objective genetic algorithm Green port

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A multi-objective approach for multi-material topology and shape optimization

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ENGINEERING OPTIMIZATION 2019年第6期51卷 915-940页

作者： Queiroz Zuliani, Joao Batista Cohen, Miri Weiss Guimaraes, Frederico Gadelha Severiano Junior, Carlos Alberto Ctr Fed Educ Tecnol Minas Gerais Timoteo Brazil Braude Coll Engn Dept Software Engn Karmiel Israel Univ Fed Minas Gerais Dept Elect Engn Belo Horizonte MG Brazil Inst Fed Minas Gerais Campus Sabara Sabara Brazil

An automated multi-material approach that integrates multi-objective Topology Optimization (TO) and multi-objective shape optimization is presented. A new ant colony optimization algorithm is presented and applied to solving the TO problem, estimating a trade-off set of initial topologies or distributions of material. The solutions found usually present irregular boundaries, which are not desirable in applications. Thus, shape parameterization of the internal boundaries of the design region, and subsequent shape optimization, is performed to improve the quality of the estimated Pareto-optimal solutions. The selection of solutions for shape optimization is done by using the PROMETHEE II decision-making method. The parameterization process involves identifying the boundaries of different materials and describing these boundaries by non-uniform rational B-spline curves. The proposed approach is applied to the optimization of a C-core magnetic actuator, with two objectives: the maximization of the attractive force on the armature and the minimization of the volume of permanent magnet material.

关键词： Topology optimization shape optimization multi-objective ant colony optimization multi-objective genetic algorithm PROMETHEE II

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Numerical investigation on the application of elliptical tubes in a spiral-wound heat exchanger used in LNG plant

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2019年 130卷 333-341页

作者： Jian, Guanping Wang, Simin Sun, Lijuan Wen, Jian Xi An Jiao Tong Univ Sch Chem Engn & Technol Xian 710049 Shanxi Peoples R China Xi An Jiao Tong Univ Sch Energy & Power Engn Xian 710049 Peoples R China

The elliptical tubes were proposed to use in a spiral-wound heat exchanger to boost the shell-side transfer rate with the decrease of shell-side pressure drop. The effects of major axis and minor axis of elliptical tubes on thermal hydraulic performance are numerically investigated. The results show that, compared with the circular tube, the SWHE with elliptical tubes have a better heat transfer ability when the shell-side pressure drop is equal to that with circular tubes. With the increase of the major axis of the elliptical tube, the friction factor f decrease firstly and then increase. While with the increase of the minor axis, the variations of the f have an opposite trend with the increase of the major axis. For the heat transfer, the Nu decreases firstly with the increase of major axis and then increases. However, Nu number increases monotonously with the minor axis because elliptical tube bundle promotes the shell-side turbulent mixing. Based on the numerical data, the multi-objective optimization is performed by genetic algorithm. The optimal results show that, by contrast with the original structure, the comprehensive performance (K/Delta P) of the optimized structures increases by an average of 315.4% and the pumping consumption decreases by an average of 65.5%. (C)2018 Elsevier Ltd. All rights reserved.

关键词： Spiral-wound heat exchanger Elliptical tube multi-objective genetic algorithm Optimization

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Interdisciplinary application of numerical and machine-learning-based models to predict half-hourly suspended sediment concentrations during typhoons

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JOURNAL OF HYDROLOGY 2019年 573卷 661-675页

作者： Huang, Cheng-Chia Fang, Hsi-Ting Ho, Hao-Che Jhong, Bing-Chen Natl Taiwan Univ Dept Civil Engn Taipei 10617 Taiwan Taiwan Integrated Disaster Prevent Technol Engn C New Taipei 23678 Taiwan Kyoto Univ Grad Sch Engn Dept Civil & Earth Resources Engn Kyoto 6158540 Japan

Accurate forecasting of hourly suspended sediment concentration is a critical issue for reservoir management, especially during typhoon periods. This research proposes a two-stage forecasting approach integrating numerical and machine-learning-based models to provide accurate real-time forecasts of half-hourly suspended sediment concentration. In the first stage, the density current model, SRH2D, was verified and applied by the historical typhoon events due to the lack of measured suspended sediment concentration. Next, in the second stage, the calculated results from SRH2D based on the spatial-temporal relation were employed as the input of the MGSVM-based model. Finally, an application in the Shi-Men reservoir was conducted to demonstrate the forecasting performance of the proposed approach. The results indicated that the calculated data from the SRH2D model corresponded to the trend of measured data in spatial-temporal variation. Moreover, the proposed forecasting approach enables reasonably acceptable 0.5- to 3-h ahead forecasts of suspended sediment concentration by using the simulated data of half-hourly suspended sediment concentration. The accurate forecast conducted by the proposed approach is expected to be properly applied to reservoir management.

关键词： Suspended sediment concentration forecasting Density current Reservoir multi-objective genetic algorithm Support vector machine Typhoon

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Stochastic partially optimized cyclic shift crossover for multi-objective genetic algorithms for the vehicle routing problem with time-windows

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APPLIED SOFT COMPUTING 2017年 52卷 863-876页

作者： Pierre, Djamalladine Mahamat Zakaria, Nordin Univ Teknol PETRONAS High Performance Comp Ctr Tronoh 32610 Perak Malaysia

This paper presents a stochastic partially optimized cyclic shift crossover operator for the optimization of the multi-objective vehicle routing problem with time windows using genetic algorithms. The aim of the paper is to show how the combination of simple stochastic rules and sequential appendage policies addresses a common limitation of the traditional genetic algorithm when optimizing complex combinatorial problems. The limitation, in question, is the inability of the traditional genetic algorithm to perform local optimization. A series of tests based on the Solomon benchmark instances show the level of competitiveness of the newly introduced crossover operator. (C) 2016 Elsevier B.V. All rights reserved

关键词： multi-objective vehicle routing problems multi-objective genetic algorithm Crossover

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Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak

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CHAOS SOLITONS & FRACTALS 2020年 136卷 109883-109883页

作者： Yousefpour, Amin Jahanshahi, Hadi Bekiros, Stelios Univ Tehran Coll Engn Sch Mech Engn Tehran 111554563 Iran Univ Manitoba Dept Mech Engn Winnipeg MB R3T 5V6 Canada European Univ Inst Dept Econ Via Fontanelle 18 I-50014 Florence Italy Wilfrid Laurier Univ RCEA LH3079 75 Univ Ave W Waterloo ON N2L 3C5 Canada

Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying the proposed optimal policies, governments could find useful and practical ways for control of the disease.

关键词： Novel coronavirus Mathematical modeling Optimal control multi-objective genetic algorithm

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