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Data-driven design of ternary alloy catalysts for enhanced oxide-based gas sensors' performance

COMPUTATIONAL MATERIALS SCIENCE 2019年 161卷 255-260页

作者： Ghosal, Suman Dey, Swati Bhattacharyya, Partha Chattopadhyay, Partha Pratim Datta, Shubhabrata Indian Inst Engn Sci & Technol Dept Elect & Telecommun Engn Howrah 711103 W Bengal India Pailan Coll Management & Technol Dept Elect & Elect Engn Amgachia Rd Kolkata 700104 India Indian Inst Engn Sci & Technol Dept Aerosp Engn & Appl Mech Howrah 711103 W Bengal India Natl Inst Foundry & Forge Technol Ranchi 834003 Bihar India SRM Inst Sci & Technol Dept Mech Engn Chennai 603203 Tamil Nadu India

Informatics based approach was employed to design ternary catalytic alloy electrode from a data base of binary alloys for oxide-based gas sensor to detect methane gas efficiently. Semiconducting Zinc Oxide was used for sensing methane, where alloys of noble metals, mostly pure or binary alloys, were used to increase the sensor parameters of the device. Experimental results of such noble metals or their binary alloys were used to develop the corresponding artificial neural network model describing the three pivotal attributes of the sensor device, viz. response magnitude, response time and recovery time, in respect to ZnO size and the catalyst composition. The models were used in a novel approach to design ternary alloys with superior performance using multi-objective optimization technique, where the Pareto front thus developed was used for designing ternary catalysts. The present scheme of prescriptive data analytics seems to provide some definite clue for experimental study aiming the pre-determined set of sensor parameters.

关键词： Oxide based gas sensor Zinc oxide Ternary alloy catalyst design Response characteristics Artificial neural network multi-objective genetic algorithm

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Flight Performance Optimization Considering Environmental Impact Under multi-RTA Constraints

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INTERNATIONAL JOURNAL OF AERONAUTICAL AND SPACE SCIENCES 2019年第4期20卷 964-977页

作者： Gu, Runping Yuan, Jie Han, Xiaolan Wei, Zhiqiang Li, Na Civil Aviat Univ China Coll Air Traff Management Tianjin Peoples R China China Acad Civil Aviat Sci & Technol Beijing Peoples R China

To optimize the flight performance under multi-waypoint required time of arrival constraints, characteristics of the vertical flight trajectory during the en route descent process are studied. A multi-constraint segment sequence model including flight distance, flight altitude and arrival time is constructed. To ensure the rationality of constraints, the rationality detection model for multi-constraint is established. The control variables and their variation ranges are determined by analyzing the optimization process. Mathematical models of optimization objectives are studied from three aspects: fuel economy, greenhouse effect and variation of flight speed. Based on the multi-objective genetic algorithm, the optimization solution model is established. Effects of speed on the objectives are analyzed. The results indicate that the optimization model can effectively optimize the flight parameters with multiple RTA constraints. The impact of aviation on the environment can be effectively reduced. And the optimization method has a good trade-off between fuel consumption and temperature rise by changing weighted factors. Optimization results of the flight parameters are mainly affected by the maximum RTA time constraint. The proposed optimization method provides a reference for the optimization of flight parameters and trajectory under multi-RTA constraints in four dimensions.

关键词： Flight performance Required time of arrival multi-objective genetic algorithm Vertical profile

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CBGA-ES: A Cluster-Based genetic algorithm with Elitist Selection for Supporting multi-objective Test Optimization 10

CBGA-ES: A Cluster-Based Genetic Algorithm with Elitist Sele...

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10th IEEE International Conference on Software Testing, Verification and Validation (ICSTW)

作者： Pradhan, Dipesh Wang, Shuai Ali, Shaukat Yue, Tao Liaaen, Marius Certus V&V Ctr Simula Res Lab Oslo Norway Univ Oslo Oslo Norway Cisco Syst Oslo Norway

ISBN: (纸本)9781509060313

multi-objective search algorithms (e.g., non dominated sorting genetic algorithm II (NSGA-II)) have been frequently applied to address various testing problems requiring multi-objective optimization such as test case selection. However, existing multi-objective search algorithms have certain randomness when selecting parent solutions for producing offspring solutions. In the worse case, suboptimal parent solutions may result in offspring solutions with bad quality, and thus affect the overall quality of the next generation. To address such a challenge, we propose a cluster-based genetic algorithm with elitist selection (CBGA-ES) with the aim to reduce such randomness for supporting multi-objective test optimization. We empirically compared CBGA-ES with random search, greedy (as baselines) and four commonly used multi-objective search algorithms (e.g., NSGA-II) using two industrial and one real world test optimization problem, i.e., test suite minimization, test case prioritization, and test case selection. The results showed that CBGA-ES significantly outperformed the baseline algorithms (e.g., greedy), and the four selected search algorithms for all the three test optimization problems. CBGA-ES managed to outperform more than 75% of the objectives for all the four algorithms in each test optimization problem. Moreover, CBGA-ES was able to improve the quality of the solutions for an average of 32.5% for each objective as compared to the four algorithms for the three test optimization problems.

关键词： multi-objective test optimization Search multi-objective genetic algorithm Cluster

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Optimization of Thermoelectric Module's Number and Distribution Pattern in an Automotive Exhaust Thermoelectric Generator

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IEEE ACCESS 2019年 7卷 72143-72157页

作者： Li, Xiaolong Xie, Changjun Quan, Shuhai Shi, Ying Tang, Zebo Wuhan Univ Technol Sch Automat Wuhan 430070 Hubei Peoples R China Hubei Key Lab Adv Technol Automot Components Wuhan 430070 Hubei Peoples R China Dongfeng Motor Corp Tech Ctr Wuhan 430058 Hubei Peoples R China

Thermoelectric generators are efficient devices to recover energy from the automotive exhaust gas. In this paper, conversion efficiency of automotive thermoelectric generator (ATEG) and the maximum electrical power generated by the ATEG, defining as the power output of the ATEG excluding the energy loss caused to the engine improved by optimizing the number of thermoelectric modules (TEMs) and its distribution pattern in an ATEG. An advanced numerical model of ATEG considering the effect of the heat transfer among the adjacent TEM's rows is developed with Simulation-X software. In order to acquire the ATEG's optimal electrical performance, a 3-step optimization is applied. First, 17 independent factors (the number of TEMs in each row from 1 to 18) are assessed and the significant parameters are screened using Plackett-Burman design. Second, an experiment designed with a central composite design is performed to analyze the sensitivity of six selected factors and a surrogate model is built through response surface method. Then, conflicts in two objectives are settled with a multi-objective genetic algorithm. According to the optimization results of a given ATEG, the maximum electrical power generated by the ATEG is 139.47 W and the conversion efficiency is 2.51% under steady engine condition. Finally, the performances of the optimized design under different engine conditions are discussed. The results show that the maximum power generated by the ATEG and efficiency respectively increase by 49.8% and 106.5% after optimization when the exhaust inlet temperature is 805 K and the mass flow rate is 0.5 kg/s.

关键词： Automotive thermoelectric generator multi-objective genetic algorithm response surface method thermoelectric modules 3-step optimization

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Building envelope design: multi-objective optimization to minimize energy consumption, global cost and thermal discomfort. Application to different Italian climatic zones

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ENERGY 2019年 174卷 359-374页

作者： Ascione, Fabrizio Bianco, Nicola Mauro, Gerardo Maria Napolitano, Davide Ferdinando Univ Napoli Federico II DII Dept Ind Engn Piazzale Tecchio 80 I-80125 Naples Italy Univ Sannio DING Dept Engn Piazza Roma 21 I-82100 Benevento Italy Univ Bergamo Via Salvecchio 19 I-24129 Bergamo Italy

The paper proposes a multi-objective optimization approach to address the energy design of the building envelope. A genetic algorithm (GA) is implemented by means of the coupling between MATLAB (R) and EnergyPlus to minimize primary energy consumption (PEC), energy-related global cost (GC) and discomfort hours (DH). The design variables concern the set point temperatures, the radiative properties of plasters, the thermo-physical properties of envelope components, the window type, the building orientation. The GA performs a Pareto optimization and finally two optimal solutions are recommended: the nZEB (nearly zero energy building) optimal solution, which minimizes PEC, and the cost-optimal solution, which minimizes GC. These solutions provide the optimal design strategies for the public and private stakeholders, respectively, which represent the main actors involved in building design. The approach is applied for the design of a new typical Italian residential building. Four locations are considered to investigate the typical Italian climates. The outcomes can give precious indications to rebuild the Italian residential stock with a view to energy-efficiency and cost-optimality, given that the optimal solutions provide low values of PEC - between 62.0 and 91.9 kWh(p)/m(2)a - and of GC - between 456 and 665 (sic)/m(2) - depending on the location. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Building envelope Building energy optimization multi-objective genetic algorithm Cost-optimal analysis Nearly zero energy buildings Thermal comfort

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Thermal boundary condition optimization of ball screw feed drive system based on response surface analysis

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MECHANICAL SYSTEMS AND SIGNAL PROCESSING 2019年 121卷 471-495页

作者： Liu, Jialan Ma, Chi Wang, Shilong Wang, Sibao Yang, Bo Shi, Hu Chongqing Univ State Key Lab Mech Transmiss Chongqing 400044 Peoples R China Chongqing Univ Coll Mech Engn Chongqing 400044 Peoples R China Xi An Jiao Tong Univ Sch Mech Engn Xian 710049 Shaanxi Peoples R China Xi An Jiao Tong Univ State Key Lab Mech Syst Engn Xian 710049 Shaanxi Peoples R China

To improve the simulation accuracy of the traditional transient thermal characteristics analysis model (TCAM) of the ball screw feed drive system (BSFDS), the optimization method of the thermal boundary conditions (TBCs), including the thermal loads, the convective heat transfer coefficient and the thermal contact resistance (TCR), was proposed. A multi-objective and multi-parameter optimization model was established based on the hybrid response surface (HRS) and a multi-objective genetic algorithm (MOGA) was developed to optimize the above TBCs. To simulate the thermal characteristics of the BSFDS under different working conditions, the generality of the method was extended to predict the TBCs under different feed rates. To validate the effectiveness of the method, the thermal characteristic experiments of BSFDS were conducted. The results showed that the simulation error for the temperature field was reduced from 25% to 10% and that the simulation error for the thermal elongation was reduced from 30% to 11%. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Ball screw feed drive system Response surface analysis multi-objective genetic algorithm Finite element analysis Temperature filed Thermal deformation

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Numerical Investigation and Optimization on Shell Side Performance of A Shell and Tube Heat Exchanger with Inclined Trefoil-Hole Baffles

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ENERGIES 2019年第21期12卷 4138-4138页

作者： Sun, Yue Wang, Xinting Long, Rui Yuan, Fang Yang, Kun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China

In this work, a shell and tube heat exchanger with inclined trefoil-hole baffles (STHX-IT) is proposed, and the numerical simulation is conducted to investigate the flow and heat transfer characteristics. A shell and tube heat exchanger with segmental baffles (STHX-SG) is also studied for the performance comparison. The results show that the heat transfer coefficient and pressure drop of the STHX-IT is averagely lower by 23.89% and 44.19% than those of the STHX-SG, but the heat transfer coefficient per pressure drop is higher by 36.38% on average. Further, the parametric studies of the inclination angle theta, trefoil-hole number n, and baffle cut delta are carried out for the STHX-IT. According to the numerical results, n and delta have more notable influence on shell side performance than theta. In detail, the heat transfer coefficient and pressure drop decrease slightly with theta increasing, and the overall performance is approximately equal;both the heat transfer coefficient and pressure drop decrease with the respective rising of n and delta, but the comprehensive performance shows a growing trend. Considering the synthetic effects of structural parameters, the multi-objective structure optimization using the genetic algorithm combined with the artificial neural networks is fulfilled. As a result, the Pareto front is obtained to characterize the behaviors of the maximum heat transfer rate and minimum pressure drop. The STHX-IT with the theta = 5.38 degrees, n = 6, and delta = 43% is decided as the optimal solution by the TOPSIS method, whose Q/Delta p is 2.34 times as much as that of the original STHX-SG.

关键词： Shell and tube heat exchanger numerical simulation heat transfer enhancement artificial neural network multi-objective genetic algorithm

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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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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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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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