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Analytical Optimal Design of a Two-Phase Axial-Gap Transverse Flux Motor

ENERGIES 2021年第12期14卷 3666-3666页

作者： Ballestin-Bernad, Victor Sergio Artal-Sevil, Jesus Antonio Dominguez-Navarro, Jose Univ Zaragoza Sch Engn & Architecture Dept Elect Engn Zaragoza 50018 Spain

Transverse flux motors (TFMs) are being investigated to be used in vehicle traction applications due to their high torque density. In this paper, a two-phase axial-gap transverse flux motor is designed for an electric scooter, proposing a novel analytical design method. First, the dimensioning equations of the motor are obtained based on the vehicle requirements, and the stationary dq model is calculated. Then, the motor is optimized using a multiobjective genetic algorithm, and finally a 3D-FEM verification is made. Both the motor structure and the design method aim to have a low complexity, in order to favor the sizing and manufacturing processes through a low computation time and simple core shapes. This approach has not yet been explored in axial-gap TFMs.

关键词： transverse flux motor vehicle traction analytical design multiobjective genetic algorithm finite element method

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Design of the external forced air cooling control strategy for the bulb tubular turbine generator based on multi-objective optimization

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FRONTIERS IN ENERGY RESEARCH 2024年 11卷

作者： Wei, Han Zhang, Yukun Song, Houbin Qiu, Hongbo Guan, Zhijun Wang, Shuai Xie, Wenqi Lanzhou Univ Technol Lanzhou Gansu Peoples R China Zhengzhou Univ Light Ind Zhenzhou Peoples R China Gansu Chaijiaxia Hydropower Co Ltd Lanzhou Peoples R China

The compact internal structure of the bulb tubular turbine generator (BTTG) leads to poor ventilation and heat dissipation capability (VHDC). It will result in high-temperature faults and lead to losses for the hydro-power station, if the staff at the hydropower station fail to promptly regulate the external forced air cooling for effective heat dissipation. Thus, a specific and effective ventilation control strategy (VCS) is of utmost importance for the operation of the BTTG. In this study, the finite element method (FEM) was employed to solve the magnetic-flow-thermal coupled field of a 24-MW BTTG. The distribution characteristics of electromagnetic losses and their impact on the temperature field were revealed. Experimental validation was conducted to verify the effectiveness of the numerical results. The response surface methodology (RSM) was employed to design an experimental plan by building upon this foundation. Integrated with a multiobjective genetic algorithm (MOGA), an optimal relationship between ambient temperature, fan flow rate, and heating components' temperature was established. This relationship serves as the optimized ventilation control strategy for the BTTG. This research provides a theoretical framework for the formulation of operational guidelines to ensure the safe operation of generators.

关键词： bulb tubular turbine generator ventilation control strategy finite element method response surface method multiobjective genetic algorithm

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Analysis of Supercritical CO₂ Cycle Using Zigzag Channel Pre-Cooler: A Design Optimization Study Based on Deep Neural Network

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ENERGIES 2021年第19期14卷 6227-6227页

作者： Saeed, Muhammed Berrouk, Abdallah S. Singh, Munendra Pal Alawadhi, Khaled Siddiqui, Muhammad Salman Khalifa Univ Sci & Technol Dept Mech Engn P Box 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Ctr Catalysis Separat CeCas POB 127788 Abu Dhabi U Arab Emirates Coll Technol Stud Dept Automot & Marine Engn Technol Publ Author Appl Educ & Training Kuwait 70654 Kuwait Norwegian Univ Life Sci Fac Sci & Technol N-1430 As Norway

The role of a pre-cooler is critical to the sCO(2)-BC as it not only acts as a sink but also controls the conditions at the main compressor's inlet that are vital to the cycle's overall performance. Despite their prime importance, studies on the pre-cooler's design are hard to find in the literature. This is partly due to the unavailability of data around the complex thermohydraulic characteristics linked with their operation close to the critical point. Henceforth, the current work deals with designing and optimizing pre-cooler by utilizing machine learning (ML), an in-house recuperator and pre-cooler design, an analysis code (RPDAC), and a cycle design point code (CDPC). Initially, data computed using 3D Reynolds averaged Navier-Stokes (RANS) equation is used to train the machine learning (ML) model based on the deep neural network (DNN) to predict Nusselt number (Nu) and friction factor (f). The trained ML model is then used in the pre-cooler design and optimization code (RPDAC) to generate various designs of the pre-cooler. Later, RPDAC was linked with the cycle design point code (CDPC) to understand the impact of various designs of the pre-cooler on the cycle's performance. Finally, a multi-objective genetic algorithm was used to optimize the pre-cooler geometry in the environment of the power cycle. Results suggest that the trained ML model can approximate 99% of the data with 90% certainty in the pre-cooler's operating regime. Cycle simulation results suggest that the cycle's performance calculation can be misleading without considering the pre-cooler's pumping power. Moreover, the optimization study indicates that the compressor's inlet temperature ranging from 307.5 to 308.5 and pre-cooler channel's Reynolds number ranging from 28,000 to 30,000 would be a good compromise between the cycle's efficiency and the pre-cooler's size.

关键词： pre-cooler design 1 PCHEs 2 sCO(2)-BC deep learning neural network machine learning optimization multiobjective genetic algorithm

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Rule extraction based on PROMETHEE-assisted multi-objective genetic algorithm for generating interpretable neural networks

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APPLIED SOFT COMPUTING 2024年 151卷

作者： Yedjour, Dounia Yedjour, Hayat Amri, Mohammed Bilel Senouci, Adlania Univ Sci & Technol Oran Mohamed Boudiaf USTO MB Fac Math & Comp Sci Dept Comp Sci BP 1505Mnaouer Oran 31000 Algeria

Rule extraction from artificial neural networks (ANNs) refers to the process of identifying the underlying decision-making logic used by the network in its predictions. This involves converting the complex, non-linear relationships between inputs and outputs in the network into a set of interpretable rules. The main objective of rule extraction is to enhance the interpretability of decision-making in artificial neural networks (ANNs) and make it more understandable for human experts, especially in critical domains like healthcare, where clear decision-making is essential. Despite the ability of machine learning methods to interpret ANNs, interpreting complex neural networks remains limited, as most studies tend to sacrifice one criterion, related to rule quality, in favor of another. Rule extraction methods applied directly to complex neural networks may produce overly specific rules that are tailored to the training data. Such rules can suffer from overfitting and may not generalize well to unseen data. Also, The use of multiobjective genetic algorithms for rule extraction can result in a large number of rules, making it challenging for decision-makers to interpret them. This study proposes an innovative approach to address challenges in rule extraction from complex artificial neural networks (ANNs) by integrating a multiobjective genetic algorithm with the PROMETHEE method. By integrating these techniques, the study aims to identify meaningful relationships between inputs and outputs of ANNs, generating concise and reliable rules that are ranked based on support, confidence, and lift values. The use of PROMETHEE's rule ranking mechanism enabled the consideration of rule priority during candidate selection, guiding the genetic algorithm towards discovering high-quality rule sets and efficiently exploring the search space. This approach not only enhances the quality of extracted rules but also facilitates efficient decision-making by striking a balance between rule

关键词： Artificial neural network Classification rules multiobjective genetic algorithm PROMETHEE method

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Method for Generating Multiple MRR Solutions for Application in Cost-Leveling Models

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JOURNAL OF INFRASTRUCTURE SYSTEMS 2017年第3期23卷

作者： Yoon, Yoojung Hastak, Makarand Cho, Kyuman West Virginia Univ Dept Civil & Environm Engn Morgantown WV 26506 USA Purdue Univ Div Construct Engn & Management W Lafayette IN 47907 USA Chosun Univ Dept Architectural Engn Gwangju South Korea

Cost-leveling models have been introduced recently to minimize the fluctuations in annual maintenance, repair, and rehabilitation (MRR) costs within a multiyear infrastructure asset-management plan. However, these models were found to produce limited results because they ignored the impacts on all the preceding/succeeding MRR activities in a multiyear plan when the initially identified implementation time of one MRR activity was adjusted for cost-leveling purposes. This paper proposes a method to generate multiple MRR solutions at the facility level for existing cost-leveling models at the network level. The method utilizes two concepts as follows: (1)the fast elitist nondominated sorting genetic algorithm (NSGA-II) to generate a Pareto optimal set of feasible MRR alternatives, and (2)the pseudoweight vector approach with the Euclidean distance and a confidence interval to select multiple MRR solutions from the Pareto optimal set identified by NSGA-II. The proposed method is applied to a sample concrete bridge deck as a case study in this paper;the results show that the proposed method was able to provide multiple MRR solutions having various profiles in terms of the intervention times of the MRR activities and annual MRR costs. Consequently, the proposed method advances the theoretical framework of existing cost-leveling models by combining four approaches to provide multiple MRR solutions at the facility level: (1)NSGA-II algorithm, (2)pseudoweight vector approach, (3)Euclidean distance, and (4)a confidence interval. In addition, it is expected that the proposed method can be well applied to existing multiobjective optimization models to generate a set of nondominated good policies for infrastructure asset management.

关键词： Cost-leveling Decision support system Infrastructure asset management Time float multiobjective genetic algorithm

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Multipath Error Modeling Methodology for GNSS Integrity Monitoring Using a Global Optimization Strategy

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REMOTE SENSING 2022年第9期14卷 2130-2130页

作者： Fang, Xinqi Song, Dan Shi, Chuang Fan, Lei Hu, Ziye Beihang Univ Sch Elect Informat Engn Xueyuan Rd 37 Beijing 100191 Peoples R China Minist Ind & Informat Technol Key Lab Nav & Commun Fus Technol Xueyuan Rd 37 Beijing 100191 Peoples R China Beihang Univ Res Inst Frontier Sci Xueyuan Rd 37 Beijing 100191 Peoples R China China Elect Technol Grp Corp Inst 54 Zhongshanxi Rd 589 Shijiazhuang 050011 Hebei Peoples R China

Valid multipath error model is the prerequisite for high-performance GNSS integrity monitoring. It is indispensable to civil aviation and other Safety-of-Life (SoL) users. The model must perfectly bound multipath error while preventing the constructed model from being too conservative. Nevertheless, no sound methodologies to meet both the requirements have been introduced in previous literatures, and subsequently, practices always require iterative manual trade-offs. To improve the efficiency of multipath modeling, we propose a new automatic multipath error modeling methodology. It quantifies the above requirements in the objective function of multiobjective genetic algorithm (GA) so that multipath modeling can be managed automatically. Moreover, through introducing a new model that is based on two inflation factors, conservatism of modeling results can be significantly reduced. Experiments based on a 4-month dataset of BDS-3 Medium Earth Orbit (MEO) satellites show that constructed multipath models effectively bound actual error in each elevation bin. In addition, the new model form with two inflation factors brings average CDF difference reduction of 67.4% at B1I and 50.6% at B3I, which means significantly mitigation in terms of conservatism.

关键词： multipath error modeling Cumulative Distribution Function (CDF) overbounding BDS-3 multiobjective genetic algorithm

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Optimization of stepwise varying width microchannel heat sink for high heat flux applications

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CASE STUDIES IN THERMAL ENGINEERING 2020年 18卷

作者： Abo-Zahhad, Essam M. Ookawara, Shinichi Radwan, Ali Elkady, M. F. El-Shazly, A. H. Egypt Japan Univ Sci & Technol Chem & Petrochem Engn Dept Alexandria Egypt Aswan Univ Fac Energy Engn Mech Power Engn Dept Aswan Egypt Tokyo Inst Technol Dept Chem Sci & Engn Tokyo 1528552 Japan Mansoura Univ Mech Power Engn Dept Mansoura 35516 Egypt City Sci Res & Technol Applicat Fabricat Technol Dept ATNMRI Alexandria Egypt Alexandria Univ Fac Engn Chem Engn Dept Alexandria 11432 Egypt

The semiconductor devices are widely employed for many applications. A significant amount of the heat must be dissipated properly to keep the devices' performance stable. Additionally, under high operation temperatures, a physical damage is possible due to thermal stresses that threaten the safety components and increase the failure rate. The development in the direction of more tightly packed electronic devices increases the challenges of offering an efficient cooling for these devices under high heat fluxes in a very restricted space. Hence, an advanced cooling technique is compulsory to attain the appropriate performance along with extending the lifespan of microelectronic devices. Stepwise varying width microchannel heat sink is considered one of the innovative cooling system from these microelectronic devices. In the current study, different designs of stepwise varying width microchannel heat sink were optimized, and their thermal performance was studied for more uniform cooling of microelectronics devices.

关键词： Stepwise varying width microchannel Heat sink multiobjective genetic algorithm Fin CPU

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Towards a Design Support System for Urban Walkability

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Procedia Computer Science 2015年 51卷 2157-2167页

作者： Ivan Blecic Arnaldo Cecchini Giuseppe A. Trunfio Department of Architecture Planning and Design University of Sassari Alghero Italy

In the paper we present an urban design support tool centered on pedestrian accessibility and walkability of places. Differently from standard decision support systems developed for the purpose of evaluating given pre-defined urban projects and designs, we address the inverse problem of having the software system itself generate hypotheses of projects and designs, given some (user-provided) objectives and constraints. Taking as a starting point a model for evaluating walkability, we adapt the NSGA-II multi-objective genetic algorithm to produce the front of non-dominated design alternatives to satisfy certain predefined constraints. By way of example, we briefly present an application of the system to a real urban area.

关键词： Design support system Walkability multiobjective genetic algorithm Urban design

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A multiobjective Go based Approach to Protein Complex Detection

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Procedia Technology 2012年 4卷 555-560页

作者： Sumanta Ray Moumita De Anirban Mukhopadhyay Department of Computer Science and Engineering University of Kalyani Kalyani-741235 West Bengal India

Considering the large number of proteins and the high complexity of the protein interaction network, decomposing it into smaller and manageable modules is a valuable step toward the analysis of biological processes and pathways. There are di_erent types of graph clustering approaches available for modularization of PPI networks. But most of them seek to identify groups or clusters of proteins on the basis of the local density of the interaction graph in the network. So they find the denser region in the interaction graph, as the denser regions are expected to have the high chance to form a protein complex. In this article we present a novel multiobjective Gene Ontology based Clustering (MGOC) in PPI network- to find protein complexes. We consider both graphical properties of PPI network as well as biological properties based on GO semantic similarity as objective functions. The proposed technique is demonstrated on yeast PPI data and the results are compared with that of some existing algorithms.

关键词： Protein-protein interaction protein complex multiobjective genetic algorithm gene ontology

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An optimization model for the impervious surface spatial layout considering differences in hydrological unit conditions for urban waterlogging prevention in urban renewal

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ECOLOGICAL INDICATORS 2024年 158卷

作者： Zhao, Juchao Ke, Entong Wang, Bin Zhao, Yaolong South China Normal Univ Sch Geog Guangzhou 510631 Peoples R China Minist Nat Resources Key Lab Nat Resources Monitoring Trop & Subtrop Ar Guangzhou 510631 Peoples R China South China Normal Univ Beidou Res Inst Foshan 528225 Peoples R China

The expansion of impervious surfaces during urbanization, as well as the irrationality of their spatial layout, is one of the main causes of urban waterlogging. Optimizing the spatial layout of impervious surfaces through urban renewal is important for urban waterlogging prevention. The differences in hydrological conditions among different hydrological units affect the optimization efficiency of the spatial layout of impervious surfaces in different hydrological units. Therefore, this study proposes an effective optimization model for impervious surface spatial layout, employing a hybrid approach that couples the Soil Conservation Service Curve Number (SCS-CN) model, nondominated sorting genetic algorithm 2 (NSGA2), and multiple linear programming (MLP) algorithm. This approach is applied in a case study of Guangzhou, China. The results show that (1) compared to the MLP-SCS model, the enhanced model not only provides reduced runoff coefficients but also achieves a better spatial balance of the retention water volume. (2) Compared to the conditions before optimization, medium- and high -density impervious surfaces significantly change after optimization, and the connectivity between patches is reduced. The optimized layout alters the original gradient connection method, preventing the aggregation of runoff from high -density impervious surfaces and thereby reducing the risk of urban waterlogging during heavy rain. This study can provide a reference for optimizing the spatial layout of impervious surfaces aimed at urban waterlogging prevention in urban renewal planning.

关键词： Urban renewal Urban waterlogging prevention Impervious surface optimization multiobjective genetic algorithm Hydrological model Multiple linear programming

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