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Technological analysis and fuel consumption saving potential of different gas turbine thermodynamic configurations for series hybrid electric vehicles

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART D-JOURNAL OF AUTOMOBILE ENGINEERING 2020年第6期234卷 1544-1562页

作者： Nader, Wissam Bou Cheng, Yuan Nault, Emmanuel Reine, Alexandre Wakim, Samer Kabalan, Bilal Nemer, Maroun Ctr Tech Velizy PSA Grp Route Gizy F-78943 Velizy Villacoublay Velizy France ENSMP Ctr Efficacite Energet Syst CES Palaiseau France Univ Paris Saclay ENS Paris Saclay Paris France Univ Paris Saclay IFP Sch Paris France ENSPM Rueil Malmaison France IFSTTAR Bron France

Gas turbine systems are among potential energy converters to substitute the internal combustion engine as auxiliary power unit in future series hybrid electric vehicle powertrains. Fuel consumption of these auxiliary power units in the series hybrid electric vehicle strongly relies on the energy converter efficiency and power-to-weight ratio as well as on the energy management strategy deployed on-board. This paper presents a technological analysis and investigates the potential of fuel consumption savings of a series hybrid electric vehicle using different gas turbine-system thermodynamic configurations. These include a simple gas turbine, a regenerative gas turbine, an intercooler regenerative gas turbine, and an intercooler regenerative reheat gas turbine. An energetic and technological analysis is conducted to identify the systems' efficiency and power-to-weight ratio for different operating temperatures. A series hybrid electric vehicle model is developed and the different gas turbine-system configurations are integrated as auxiliary power units. A bi-level optimization method is proposed to optimize the powertrain. It consists of coupling the non-dominated sorting genetic algorithm to the dynamic programming to minimize the fuel consumption and the number of switching ON/OFF of the auxiliary power unit, which impacts its durability. Fuel consumption simulations are performed on the worldwide-harmonized light vehicles test cycle while considering the electric and thermal comfort vehicle energetic needs. Results show that the intercooler regenerative reheat gas turbine-auxiliary power unit presents an improved fuel consumption compared with the other investigated gas turbine systems and a good potential for implementation in series hybrid electric vehicles.

关键词： Gas turbine systems series hybrid electric vehicle technological analysis energy management strategy non-dominated sorting genetic algorithm dynamic programming

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Integrative numerical modeling and thermodynamic optimal design of counter-flow plate-fin heat exchanger applying neural networks

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INTERNATIONAL JOURNAL OF HEAT AND MASS TRANSFER 2020年 159卷 120097-120097页

作者： Richter do Nascimento, Carlos Augusto Mariani, Viviana Cocco Coelho, Leandro dos Santos Pontificia Univ Catolica Parana Dept Mech Engn Curitiba Parana Brazil Univ Fed Parana Dept Elect Engn Curitiba Parana Brazil Pontificia Univ Catolica Parana Ind & Syst Engn Grad Program Curitiba Parana Brazil

In this study, an optimization technique combined with a Random Vector Functional Link (RVFL) network in the form of a surrogate-assisted approach was carried out for the optimal design of counter-flow plate-fin compact heat exchanger (PFHE) with offset-strip fins considering different mass flow rates. Computational Fluid Dynamics (CFD) simulations generated dataset for training and testing by RVFL network, reducing processing time and assuring PFHE accuracy when applied in many-objective optimization based on non-dominated sorting genetic algorithm III (NSGA-III). Next, the NSGA-III was designed to achieve the maximum effectiveness, minimum volume, and pressure drop at the hot and cold sides in PFHE design. For experimental validation the Shear Stress Transport (SST) k-omega turbulence model was used to simulate the flow and heat transfer in PFHE showing 4.36% and 3.27% of error, respectively, to pressure drop and convective heat transfer coefficient measurements. The optimized results by NSGA-III indicated that the volume and effectiveness values are in agreement with the literature, decreasing approximately 55.4% and 72.3% of pressure drop at the hot and cold sides, respectively, which showed that the convective heat transfer was enhanced and the flow resistance was also significantly reduced. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Plate-fin heat exchanger Offset-strip fin Many-objective optimization non-dominated sorting genetic algorithm Random vector functional link network Effectiveness

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A multi-objective medical process mining model using event log and causal matrix

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Healthcare Analytics 2023年 3卷 100188-100188页

作者： Hejazi, Seyed Mojtaba Zandieh, Mostafa Mirmozaffari, Mirpouya Faculty of Industrial and Mechanical Engineering Qazvin Azad University Qazvin Iran Department of Industrial Management and Information Technology Management and Accounting Faculty Shahid Beheshti University Tehran Iran Department of Industrial Engineering Dalhousie University 5269 Morris Street Halifax B3H 4R2 NS Canada

Medical analytics in genetic process mining have developed models with multiple and often conflicting criteria. Some studies have reduced the complexities inherent in multiple and conflicting criteria by eliminating some criteria and solving the problem with a single or linear objective function. This study proposes a multi-objective model for obtaining Pareto without removing any conflicting criteria. Two multi-objective Pareto-based models of non-dominated Ranked genetic algorithm (NRGA) and non-dominated sorting genetic algorithm (NSGAII) are developed. A comprehensive examination and statistical analysis are performed to analyze the performance of the two algorithms with five metrics of Mean Ideal Distance (MID), Cover Surface (CS), diversity, spacing, and time. In addition, sensitivity analysis examines the problem size and the coefficients of the different criteria on the objective functions. Finally, for the performance evaluation, a Data Envelopment Analysis (DEA) is developed based on some instances and benchmarks to assess the efficiency of the algorithms. We show that NSGAII outperforms the NRGA according to the performance metrics and statistical hypothesis testing. © 2023 The Author(s)

关键词： Causal matrix Data envelopment analysis Event log non-dominated ranked genetic algorithm non-dominated sorting genetic algorithm Process mining

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Development of operation multi-objective model of dam reservoir under conditions of temperature variation and loading using NSGA-II and DANN models: a case study of Karaj/Amir Kabir dam

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SOFT COMPUTING 2020年第16期24卷 12469-12499页

作者： Tabari, Mahmoud Mohammad Rezapour Azadani, Mitra Nasr Kamgar, Reza Univ Mazandaran Dept Engn Babol Sar Mazandaran Iran Shahrekord Univ Civil Engn Hydraul Struct Shahrekord Iran Shahrekord Univ Dept Civil Engn Shahrekord Iran

For determining optimal operation policies, it is of vital importance to the structural stability of dams, besides meeting the downstream water demands during the operation period. Since water level variations in upstream dam cause changes in loadings of the dam body, ignoring factors, which are effective in the structural stability of dams, might result in cracks and instability of dams in the long term. Therefore, in the present study, a multi-objective model was developed by integrating the reservoir structure simulation model and optimization approach in order to meet water supply demands and maintain the dam structural stability. Linear static analysis of Amir Kabir dam was performed using ABAQUS 6.14.3 software to extract the structural parameters, indicating the dam structural stability. For this purpose, a dynamic artificial neural network was also used as an interfaced model to relate the results of the simulator model to those of the proposed approach. The best solution was extracted from the optimal trade-off curve, which was developed using non-dominated sorting genetic algorithm (NSGA-II) and multi-criteria decision-making methods. Results showed that the allocation to the downstream water demands was applied in the best possible way based on the stability of the dam. A comparison between the existing and optimal conditions indicated that the value of reliability and vulnerability coefficients was improved in optimal conditions in comparison with the existing conditions. According to the results, there was a 7% rise in the fuzzy stability index in optimal conditions compared to the existing conditions that indicate better performance in the optimal model. Therefore, as to consider the downstream water demands, summer was the most deficient and spring was the least deficient with the water supply of 30% and 72%, respectively. During summer, the average optimal allocation and the average demand were 20.51 MCM and 67.50 MCM, respectively. However, this sea

关键词： Optimal operation policy ABAQUS simulator model Dynamic artificial neural network non-dominated sorting genetic algorithm Multi-criteria decision-making methods Karaj dam

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Pareto Optimal Characterization of a Microwave Transistor

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IEEE ACCESS 2020年 8卷 47900-47913页

作者： Gunes, Filiz Uluslu, Ahmet Mahouti, Peyman Yildiz Tech Univ Istanbul Dept Elect & Commun Engn TR-34467 Istanbul Turkey Istanbul Cerrahpasa Univ Vocat Sch Tech Sci Dept Elect & Automat TR-34452 Istanbul Turkey

Herein, noise, gain and port mismatchings of a microwave small-signal transistor are expressed as all the set of acceptable Pareto optimal solutions and trade-off relations within the device operation (VDS, IDS, f) domain without any need of expert knowledge of microwave device. In this multi-objective optimization problem, non-dominated sorting genetic algorithm (NSGA) -III is applied to an ultra-low noise amplier (LNA) transistor NE3511S02 (HJ-FET) where the noise Freq >= Fmin and output mismatching Voutreq >= 1 are preferred as the reference points, while the input mismatching Vinopt >= 1 and gain GTmax are optimized with respect to source ZS and load ZL within the unconditionally stable working area. Thus, diverse set of the Pareto optimal (the required noise Freq, the optimum input Vinopt, the required output Voutreq, the maximum transducer gain GTmax) quadruples are resulted from a fast search of the solution space. Furthermore, the optimum bias condition (VDS, IDS) and sensitivities of the terminations to fabrication tolerances are also determined using the cost analysis in the operation domain for the required Pmax, IDSmax and performance quadruple. Finally, this work is expected to enable a designer to provide the feasible design target space (FDTS) consisting of all trade-off relations among all the transistor's performance ingredients to be used in the challenging LNA designs.

关键词： Pareto optimization Transistors Microwave transistors genetic algorithms Performance evaluation Microwave amplifiers non-dominated sorting genetic algorithm Pareto optimal solutions optimization impedance mismatching transducer gain noise figure

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Modeling and solving a bi-objective joint replenishment-location problem under incremental discount: MOHSA and NSGA-II

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OPERATIONAL RESEARCH 2020年第4期20卷 2365-2396页

作者： Pasandideh, Seyed Hamid Reza Niaki, Seyed Taghi Akhavan Abdollahi, Reza Kharazmi Univ Dept Ind Engn Fac Engn Tehran Iran Sharif Univ Technol Dept Ind Engn Tehran Iran Islamic Azad Univ Qazvin Branch Fac Mech & Ind Engn Qazvin Iran

In this paper, the joint replenishment-location problem of some distribution centers (DCs) with a centralized decision maker who is responsible for ordering and dispatching shipments of a single product is modeled. The warehouse spaces of the DCs are limited and the product is sold under an incremental discount policy. The model seeks to minimize the total cost of the supply chain under the joint replenishment policy along with minimizing the cost of locating the DCs in potential sites as the first objective. The second objective is to minimize the warehouse space of all DCs using the revisable approach. As the proposed model is a bi-objective integer non-linear optimization problem (NP-Hard) and cannot be solved by an exact method in a reasonable computational time, a multi-objective harmony search algorithm is developed to solve it. Since there is no benchmark available in the literature, the non-dominated sorting genetic algorithm II is utilized as well to validate the results obtained. As the performance of a meta-heuristic algorithm is largely influenced by the choice of its parameters, the response surface methodology is employed to tune the parameters. Several numerical illustrations are provided at the end to not only demonstrate the application of the proposed methodology, but also to analyze and compare the performances of the two solution algorithms in terms of several multi-objective measures.

关键词： Joint replenishment problem Location Revisable approach Incremental discount Multi-objective harmony search non-dominated sorting genetic algorithm

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Modelling Cross-Docking in a Three-Level Supply Chain with Stochastic Service and Queuing System: MOWFA algorithm

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algorithmS 2022年第8期15卷 265-265页

作者： Rostami, Parinaz Darestani, Soroush Avakh Movassaghi, Mitra Islamic Azad Univ Fac Ind & Mech Engn Dept Ind Engn Qazvin *** Iran London Metropolitan Univ Guidhall Sch Business & Law London N7 8DB England

In today's competitive world, it is essential to provide a new method through which maximum efficiency can be created in the production and supply cycle. In many production environments, sending goods directly from the producer to the consumer brings many problems. Therefore, an efficient transport system should be established between producers and consumers. Such a system is designed in the field of supply chain management knowledge. Supply chain management is the evolutionary result of warehousing management and is one of the important infrastructural foundations of business implementation, in many of which the main effort is to shorten the time between the customer's order and the actual delivery of the goods. In this research, the supply chain consists of three levels. Suppliers are placed on the first level, cross-docks on the second level, and factories on the third level. In this system, a number of suppliers send different raw materials to several different cross-docks. Each channel is assigned to a cross-dock for a specific product. The main goal of this article is to focus on optimizing the planning of incoming and outgoing trucks with the aim of minimizing the total operation time within the supply chain. The arrival rate of goods from suppliers to the cross-dock is stochastic with a general probability distribution. On the other hand, the time required to prepare and send the goods is random with a general probability distribution. The service time in each cross-dock depends on the number of its doors. Therefore, each cross-dock can be modeled as a G/G/m queueing system where m represents the number of doors. The mathematical model of the research has been developed based on these assumptions. Since the problem is NP-hard, the time to solve it increases drastically with the increase in the dimensions of the problem. Therefore, three metaheuristics, including multi-objective water flow, non-dominated sorting genetic, and a multi-objective simulated anneal

关键词： cross-docks multi-objective water flow algorithm non-dominated sorting genetic algorithm multi-objective simulated annealing algorithm

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Scheduling synchronization in urban rail transit networks: Trade-offs between transfer passenger and last train operation

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TRANSPORTATION RESEARCH PART A-POLICY AND PRACTICE 2020年 138卷 463-490页

作者： Guo, Xin Wu, Jianjun Sun, Huijun Yang, Xin Jin, Jian Gang Wang, David Z. W. Beijing Jiaotong Univ Key Lab Transport Ind Big Data Applicat Technol C Minist Transport Beijing 100044 Peoples R China Beijing Jiaotong Univ State Key Lab Rail Traff Control & Safety Beijing 100044 Peoples R China Nanyang Technol Univ Sch Civil & Environm Engn 50 Nanyang Ave Singapore 639798 Singapore Shanghai Jiao Tong Univ Sch Naval Architecture Ocean & Civil Engn Shanghai 200240 Peoples R China

The scheduling synchronization problem in this paper is to obtain an optimal schedule by optimizing running time, departure time, dwell time and arrival time of the last train in urban rail transit networks. Operators are often faced with multiple confiding requirements simultaneously such as high passenger's service quality and less cost, smooth transfer events and less passenger's travel time, and high accessibility and less operation time. Thus, researchers in this industry concentrate on utilizing operations and management decisions to solve the scheduling problem while balancing the service trade-offs. In this paper, we formulate a mixed integer programming approach for the last train schedule planning, and passengers benefit from smoother transfer in the form of maximizing the transfer synchronization events, while operators simultaneously can benefit with lower operation costs by minimizing the worst big difference between last trains. We propose an improved non-dominated sorting approach embedded in a genetic algorithm to obtain close-to-optimal solutions in a much shorter time for a sophisticated, real-world and large-scale Beijing subway network. Results demonstrate that significant service performance gains (76.33% for Just-missed, 32.01% for successful transfer, 15.39% for non-equity for last trains and 45.25% for variance indicators, etc.), which indicate the effectiveness of the proposed modeling framework and solution algorithm. The operator formulates an efficient schedule for the actual operation of the urban rail transit network by the proposed application method, and it also would be applicable to solving schedule problems among a large-scale network in other industries.

关键词： Last trains Scheduling Transfer synchronization non-dominated sorting genetic algorithm

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Modal parameter identification and finite element model updating of a long-span aqueduct structure based on ambient excitation

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JOURNAL OF VIBROENGINEERING 2020年第4期22卷 896-908页

作者： Sun, Liguo Xu, Yezhuang Hohai Univ Coll Mech & Mat Nanjing 211100 Peoples R China Highway Dev Ctr Dept Transport Jiangsu Prov Nanjing 210001 Peoples R China

In this paper, PSV-500 laser vibration detector and 941B vibration pick-up are used to measure the ambient vibration of an actual aqueduct in China, and the peak picking method is used to identify the modal parameters of the aqueduct. The finite element model of the aqueduct is established, and a model updating method based on multi-objective optimization algorithm is proposed. Based on the sensitivity analysis, the parameters to be updated are selected. The model is updated by the fast non dominated sorting genetic algorithm, and the Pareto optimal solution set of the multi-objective optimization problem is obtained. The comparison between the measured and calculated results shows that the results of static displacement and modal parameters are in good agreement with the measured values. The result of the research shows that the static and dynamic finite element model updating method based on multi-objective optimization can achieve satisfactory results for long-span aqueduct structure, and the updated finite element model can accurately and comprehensively simulate the actual structure.

关键词： modal parameter identification peak picking method model updating non-dominated sorting genetic algorithm

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Pareto-Based Bi-Objective Optimization Method of Sensor Placement in Structural Health Monitoring

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BUILDINGS 2021年第11期11卷 549页

作者： nong, Shao-Xiao Yang, Dong-Hui Yi, Ting-Hua Shijiazhuang Tiedao Univ State Key Lab Mech Behav & Syst Safety Traff Engn Shijiazhuang 050043 Hebei Peoples R China Dalian Univ Technol Sch Civil Engn Dalian 116023 Peoples R China

For a practical structural health monitoring (SHM) system, the traditional single objective methods for optimal sensor placement (OSP) cannot always obtain the optimal result of sensor deployment without sacrificing other targets, which creates obstacles to the efficient use of the sensors. This study mainly focuses on establishing a bi-objective optimization method to select the sensor placement positions. The practical significance of several single-objective criteria for OSP is firstly discussed, based on which a novel bi-objective optimization method is proposed based on the Pareto optimization process, and the corresponding objective functions are established. Furthermore, the non-dominated sorting genetic algorithm is introduced to obtain a series of the Pareto optimal solutions, from which the final solution can be determined based on a new defined membership degree index. Finally, a numerical example of a plane truss is applied to illustrate the proposed method. The Pareto optimization-based bi-objective OSP framework presented in this study could be well suited for solving the problem of multi-objective OSP, which can effectively improve the efficiency of the limited sensors in SHM system.

关键词： structural health monitoring optimal sensor placement bi-objective optimization Pareto optimization non-dominated sorting genetic algorithm

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