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Optimization of novel heat exchanger design for the application to low temperature lift heat pump

ENERGY 2012年第1期42卷 204-212页

作者： Lee, Hoseong Saleh, Khaled Hwang, Yunho Radermacher, Reinhard Univ Maryland Dept Mech Engn Ctr Environm Energy Engn College Pk MD 20742 USA

The low temperature lift heat pump (LTLHP) utilizes a small difference between the condensing and evaporating temperatures of a working fluid. It requires a larger heat transfer area, a higher volume flow rate, and a higher temperature of heat source fluid, as compared to the typical heat pump system. In order to improve the performance of conventional plate heat exchangers, a novel heat exchanger with new geometries has been developed for application in the LTLHP. The main design development strategies were regulating the flow area ratio and offsetting plates in order to balance the heat transfer and pressure drop of the heat exchanger. The design parameters of the novel heat exchanger design were optimized with multi-scale approaches. Once the refrigerant-side geometry is properly designed according to the water-side performance, the overall heat transfer capacity of the novel heat exchanger is predicted to be higher than that of PHX by 53-204%. This can decrease the cost of the heat exchanger and increase the performance of the LTLHP system. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Low temperature lift heat pump multi-objective optimization Kriging meta-model Approximation-assisted optimization multi-objective genetic algorithm

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Integration of life cycle assessment, artificial neural networks, and metaheuristic optimization algorithms for optimization of tomato-based cropping systems in Iran

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INTERNATIONAL JOURNAL OF LIFE CYCLE ASSESSMENT 2020年第3期25卷 620-632页

作者： Pishgar-Komleh, Seyyed Hassan Akram, Asadollah Keyhani, Alireza Sefeedpari, Paria Shine, Philip Brandao, Miguel Univ Tehran Fac Agr Engn & Technol Dept Agr Machinery POB 4111 Karaj Iran State Res Inst Inst Soil Sci & Plant Cultivat Dept Bioecon & Syst Anal Pulawy Poland Czech Univ Life Sci Fac Econ & Management Dept Econ Prague Czech Republic Cork Inst Technol Dept Proc Energy & Transport Engn Cork Ireland KTH Royal Inst Technol Dept Sustainable Dev Environm Sci & Engn Seed Div Sustainabil Assessment & Management S-10044 Stokholm Sweden

Purpose The main purpose of this study was to evaluate the use of an integrated life cycle assessment (LCA), artificial neural network, and metaheuristic optimization model to improve the sustainability of tomato-based cropping systems in Iran. The model outputs the combination of input usage in a tomato cropping system, which leads to the highest economic output and the least environmental impact. Methods The LCA inventory was created using data from 114 open-field tomato farms in the Alborz Province of Iran during one growing period in 2015. Among all management components, the main focus was on irrigation management systems. The optimization problem was designed by integrating three indicators: carbon footprint (CF), benefit-cost ratio (BCR), and energy use efficiency (EUE) as the objective of field tomato production. The functional unit was 1 kg of tomato aligned with the system boundary of the cradle to market life cycle. Three artificial neural networks (ANNs) were applied to model relationships between the inputs and three indices (CF, BCR, and EUE) as the objective functions. multi-objective genetic algorithm (MOGA) and multi-objective particle swarm optimization (MOPSO) were used to minimize the CF and maximize the BCR and EUE indicators. The abovementioned aims have been pursued by developing codes in MATLAB software. Results and discussion CF, BCR, and EUE were calculated to be 0.26 kg CO2-eq (kg tomato)(-1), 1.8, and 0.5, respectively. MOGA results envisage the possibility of an increase of 86% and 50% in the EUE and BCR and a 43% reduction in the CF of tomato production systems. Moreover, EUE and BCR increased by 83% and 49%, and CF was reduced by 39% from the optimum results obtained from the MOPSO algorithm. It was revealed that in order to optimize field tomato production with the target objectives of this study, a large additional use for irrigation pipes, plastic, and machinery in comparison to current situation is required, while a large reduction

关键词： Artificial neural networks Benefit-cost ratio Carbon footprint Energy use efficiency Field tomato Iran Life cycle assessment multi-objective genetic algorithm multi-objective particle swarm optimization

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Capacity planning in thin film transistor - Liquid crystal display cell process

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JOURNAL OF MANUFACTURING SYSTEMS 2016年 39卷 63-78页

作者： Chen, James C. Chen, Tzu-Li Pratama, Bayu Rezki Tu, Qian-Fang Natl Tsing Hua Univ Dept Ind Engn & Engn Management Hsinchu 30013 Taiwan Fu Jen Catholic Univ Dept Informat Management New Taipei Taiwan Natl Taiwan Univ Sci & Technol Dept Ind Management New Taipei Taiwan

The cell process in a thin film transistor-liquid crystal display (TFT-LCD) manufacturing is characterized as a complicated multi-stage and parallel-machine production system. This system has multiple products, sequence-dependent setup times, multi-step production, multi-objective, and matching constraints between color filter lines and array lines. This complex and capital-intensive production environment needs an efficient capacity planning decision that determines the order release schedule, estimated machine start and finished time of each order, and machine allocations to improve the performance of the entire production process. This research first proposes a finite capacity planning system (FCPS) to cope with the capacity planning problem faced by the TFT-LCD cell process based on the pull philosophy and assumption of finite capacity. A modified multi-objective genetic algorithm (MOGA) embedded in FCPS is also developed to efficiently generate the order release decision, which simultaneously minimizes the machine workload balance, makespan, and lateness. The preliminary experiment identifies the fine MOGA tuning parameters using a two-level factorial experimental design. Finally, the full factorial experiment evaluates the significance and robustness of the proposed modified MOGA algorithm compared with other competitive algorithms under various circumstances. (C) 2016 The Society of Manufacturing Engineers. Published by Elsevier Ltd. All rights reserved.

关键词： Capacity planning TFT-LCD Cell process Finite capacity multi-objective genetic algorithm

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Seismic control of modularized suspended structures with optimal vertical distributions of the secondary structure parameters

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ENGINEERING STRUCTURES 2019年 183卷 160-179页

作者： Ye, Zhihang Feng, De-Cheng Wu, Gang Southeast Univ Key Lab Concrete & Prestressed Concrete Struct Minist Educ Nanjing 210096 Jiangsu Peoples R China

Suspended building systems with vibration control features dissipate seismic energy by the interaction between their main parts and the suspended parts;they are also architecturally appealing. A modularized suspended structure has been previously proposed to overcome the fragility of its secondary structure and to enhance overall attenuation. However, the full potential of modularization is yet to be achieved via the previous configuration, especially in terms of multi-mode control. In this study, the protection effect of prefabricated modules is further harnessed in such a way that drastic vertical-irregularities of inter-story stiffness and dampers within the suspended segment are allowed. Vertical distribution vectors of structural parameters were set as the variables in genetic-algorithm optimizations, with the maximum mean square moment of the primary structure being the main objective. The results show considerably improved attenuation of responses in multiple modes instead of only the fundamental mode. In the optimized distributions, peaks of damping coefficient occur at the troughs of inter-story stiffness, but without a highly concentrated pattern. Models with different irregularity levels have well-separated Pareto fronts;this indicates that comprehensive improvement can be obtained at compromised choices. The main mechanism is that, with the well-designed irregularities, the secondary structure provides satisfactory dissipation and tuning to the primary structure in the major modes. The analysis with non-stationary excitations reveals that optimized vertical distributions further quicken the vibration decay. The time-history performance verifications and the structural uncertainty analysis are also carried out.

关键词： Suspended structure Mega-substructure system Modular construction Vertical irregularity Passive vibration control multi-objective genetic algorithm

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A feature-driven crossover operator for multi-objective and evolutionary optimization of product line architectures

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JOURNAL OF SYSTEMS AND SOFTWARE 2016年 121卷 126-143页

作者： Colanzi, Thelma Elita Vergilio, Silvia Regina State Univ Maringa UEM Informat Dept DIN BR-87020900 Maringa Parana Brazil Fed Univ Parana UFPR Comp Sci Dept DInf BR-81531970 Curitiba Parana Brazil

The optimization of a Product Line Architecture (PLA) design can be modeled as a multi-objective problem, influenced by many factors, such as feature modularization, extensibility and other design principles. Due to this it has been properly solved in the Search Based Software Engineering (SBSE) field. However, previous empirical studies optimized PLA design using the multi-objective and evolutionary algorithm NSGA-II, without applying one of the most important genetic operators: the crossover. To overcome this limitation, this paper presents a feature-driven crossover operator that aims at improving feature modularization in PLA design. The proposed operator was applied in two empirical studies using NSGA-II in comparison with another version of NSGA-II that uses only mutation operators. The results show the usefulness and applicability of the proposed operator. The NSGA-II version that applies the feature-driven crossover found a greater diversity of solutions (potential PLA designs), with higher feature-based cohesion, and less feature scattering and tangling. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Product line architecture design multi-objective genetic algorithm Crossover operator Empirical study

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Optimization of Triangular Airfoils for Martian Helicopters Using Direct Numerical Simulations

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AIAA JOURNAL 2023年第11期61卷 4935-4945页

作者： Caros, Lidia Buxton, Oliver Vincent, Peter Imperial Coll London Dept Aeronaut London SW7 2AZ England

Mars has a lower atmospheric density than Earth, and the speed of sound is lower due to its atmospheric composition and lower surface temperature. Consequently, Martian rotor blades operate in a low-Reynolds-number compressible regime that is atypical for terrestrial helicopters. Nonconventional airfoils with sharp edges and flat surfaces have shown improved performance under such conditions, and second-order-accurate Reynolds-averaged Navier-Stokes (RANS) and unsteady RANS (URANS) solvers have been combined with genetic algorithms to optimize them. However, flow over such airfoils is characterized by unsteady roll-up of coherent vortices that subsequently break down/transition. Accordingly, RANS/URANS solvers have limited predictive capability, especially at higher angles of attack where the aforementioned physics are more acute. To overcome this limitation, we undertake optimization using high-order direct numerical simulations (DNSs). Specifically, a triangular airfoil is optimized using DNSs. multi-objective optimization is performed to maximize lift and minimize drag, yielding a Pareto front. Various quantities, including lift spectra and pressure distributions, are analyzed for airfoils on the Pareto front to elucidate flow physics that yield optimal performance. The optimized airfoils that form the Pareto front achieve up to a 48% increase in lift or a 28% reduction in drag compared to a reference triangular airfoil studied in the Mars Wind Tunnel at Tohoku University. The work constitutes the first use of DNSs for aerodynamic shape optimization.

关键词： Computational Fluid Dynamics Low Reynolds Number Martian Aerodynamics Direct Numerical Simulation multi-objective genetic algorithm Aerodynamic Shape Optimization

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multi-objective optimization design for the blade angles of hydraulic torque converter with adjustable pump

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART C-JOURNAL OF MECHANICAL ENGINEERING SCIENCE 2020年第13期234卷 2523-2536页

作者： Zheng, Zhi-Ying Liu, Quan-Zhong Deng, Yong-Kang Li, Biao Harbin Inst Technol Sch Energy Sci & Engn Harbin 150001 Peoples R China

To improve the efficiency of a hydraulic torque converter with adjustable pump at low load and thus increase the operation scope of high efficiency, multi-objective optimization design is carried out for the blade angles by incorporating three-dimensional steady computational fluid dynamics numerical simulation, design of experiments, Kriging surrogate model and multi-objective genetic algorithm. The results show that the angle of blade trailing edge in first-stage stator is the main influencing factor of the efficiency of hydraulic torque converter with adjustable pump. All the peak efficiencies of hydraulic torque converter with adjustable pump at three openings of the pump are improved after optimization, and the increased extent increases with decreasing opening of the pump. The operation scope of high efficiency consequently increases from 2.46 to 2.67. Besides, the improvement for the efficiency of hydraulic torque converter with adjustable pump is achieved by increasing the efficiency of the pump. The increase of angle of blade trailing edge in first-stage stator and the decrease of angle of blade leading edge in second-stage turbine after optimization induce the positive angle of attack at the inlet of second-stage turbine, thus realizing the performance optimization of hydraulic torque converter with adjustable pump. This also explains the increased proportion of the torque of second-stage turbine at larger speed ratios after optimization and the fact that the angle of blade trailing edge in first-stage stator is the main influencing factor of the efficiency of hydraulic torque converter with adjustable pump. The established multi-objective optimization method provides a reference solution for the optimization design of blade angles and for the improvement of integrated efficiency of hydraulic torque converter.

关键词： Hydraulic torque converter adjustable pump design of experiments Kriging surrogate model multi-objective genetic algorithm CFD numerical simulation

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multi-objective optimization of an auto panel drawing die face design by mesh morphing

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COMPUTER-AIDED DESIGN 2007年第10期39卷 863-869页

作者： Liu, Wei Yang, Yuying Harbin Inst Technol Sch Mat Sci & Engn Harbin 150006 Peoples R China

In order to facilitate the tryout or simulation process at the end of a manual auto panel drawing die face design process, we use finite element analysis (FEA) and a multi-objective genetic algorithm (MOGA) to find all the Pareto optimal solutions in one go and to achieve the optimal design of an auto panel drawing die face instead of transforming multi-objective functions into a single objective function, and employ a novel mesh morphing technique to achieve fast modification of parametric or non-parametric addendum surfaces and binder surfaces on drawing die faces without going back to CAD for reconstruction of geometric models or to FEA for remodeling. We use an auto panel drawing die face design process as an example to illustrate the application and effectiveness of this proposed approach, and come to the conclusion that the proposed approach is more effective than the traditional manual FEA method and the 'trial-and-error' approach in optimizing an auto panel drawing die face design. (c) 2007 Elsevier Ltd. All rights reserved.

关键词： multi-objective genetic algorithm mesh morphing die face auto panel finite element analysis (FEA)

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Optimal design of micro-dimples on crankpin bearing surface for improving engine's lubrication and friction

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INDUSTRIAL LUBRICATION AND TRIBOLOGY 2021年第1期73卷 52-59页

作者： Jiao, Renqiang Nguyen, Vanliem Le, Vanquynh Bui, Vancuong Hubei Polytech Univ Sch Mech & Elect Engn Huangshi Hubei Peoples R China Thai Nguyen Univ Technol Fac Automot & Power Machinery Engn Thai Nguyen Vietnam

Purpose The purpose of this paper is to investigate the optimal design of micro-dimples on the bearing surface of the crankpin bearing (CB) to ameliorate the engine's lubrication and friction (ELF). Design/methodology/approach A hydrodynamic model of the CB considering the influence of the asperity contact is built under the impact of the dynamic loading of the slider-crank-mechanism. The micro-dimples on non-slip surface of the bearing are designed and optimized based on the lubrication model and multi-objective genetic algorithm. The performance of optimal micro-dimples on ameliorating the ELF is analyzed and compared with that of optimal CB dimensions via the reduction of the solid contact force, friction force and friction coefficient between the crankpin and bearing surfaces;and the increase of the oil film pressure. Findings The optimal design of micro-dimples on the bearing surface may not only greatly ameliorate the ELF but also make the rotation of the crankpin inside the bearing more stable in comparison with the optimization of CB dimensions. Originality/value This study results not only clearly ameliorates the ELF but also can be applied to the slip/non-slip surface pairs of other journal bearings to enhance their lubrication performance.

关键词： Crankpin bearing Micro-dimples Slip non-slip surfaces Lubrication and friction multi-objective genetic algorithm

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multi-objective decision-making methodology to create an optimal design chain partner combination

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COMPUTERS & INDUSTRIAL ENGINEERING 2012年第4期63卷 875-889页

作者： Chiang, Tzu-An Natl Taipei Coll Business Dept Business Adm Taipei 100 Taiwan

In today's highly competitive business environment, many companies adopt the time-to-market strategy to obtain a competitive advantage. To reduce the time and cost of product development and to employ global product development resources, design chain partner evaluation and selection has become a crucial issue. Thus, establishing an optimal design chain partner combination has received significant attention because it has a far-reaching effect on the results of product development. With this perspective, this paper develops an integrated decision-making methodology to assist enterprises as they create an optimal design chain partner combination. First, this study establishes the framework and evaluation models of the criteria for the different roles of design chain partners, including system integration, functional module development and software and component development. Then, this paper applies a weight-restricted DEA (data envelopment analysis) approach to create the models for performance analysis of design chain partners to acquire the performance value of each candidate and select the efficient design chain partners. Moreover, this paper employs the multi-objective performance evaluation model proposed in this paper to analyze the synthesized performance of design chain combinations. Moreover, this research uses a multi-objective genetic algorithm (GA) to search efficiently for the optimal design chain partner combination to minimize product development cost and time and maximize product reliability. Finally, this study employs a derivative new product development project for a digital TV box as a case study to illustrate the efficacy of the proposed methodology. (C) 2012 Elsevier Ltd. All rights reserved.

关键词： Design chain partner combination DEA multi-objective genetic algorithm Product development

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