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Thermo-mechanical Evaluation of Slurry-Sprayed multi-layered Coatings

ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2020年第11期45卷 9449-9470页

作者： Naseem, Muhammed Verma, Rajeev Kango, Saurabh Dr BR Ambedkar Natl Inst Technol Dept Ind & Prod Engn Jalandhar 144011 Punjab India Dr BR Ambedkar Natl Inst Technol Dept Mech Engn Jalandhar 144011 Punjab India

Thermal barrier coatings (TBCs) represent a relatively thin layer of ceramic with the favourable insulating properties, which are generally used to improve the temperature stability of the engineering component such as turbine blades. One of the prime prerequisites of TBCs is to determine the optimised coating configuration with the desired thermo-mechanical properties and enhanced service life. In a typical functionally graded coating structure, this could be achieved by having a trade-off between the thermal insulation and fatigue toughness offered by ceramic in top coat and metal towards the substrate, respectively. In this work, a computational method was used to analyse and optimise the parameters pertaining to thermo-mechanical evaluation of the slurry spray-coated (SST) mullite-nickel ASTM 1018 steel. The composite material properties have been predicted using the classical mean-field micromechanics model and rule of mixtures and the finite element simulation package ANSYS. Experimental validation has been performed to analyse the relative thermal and structural properties of the composite layers of the coatings using transient plane source (TSP)-based thermal constants analyser. The predicted and experimental results were further analysed and optimised for various process parameters using response surface optimisation and multi-objective genetic algorithm, which evaluated the optimum results considering the boundary conditions with a temperature reduction of nearly 306 degrees C. Further, the research results indicate that suitable thickness of the coating configuration of the slurry-sprayed mullite-nickel TBC system included coating thickness of 57.2 mu m, 147.1 mu m, and 143.9 mu m for bond, intermediate, and top coats, respectively. The material properties were found dependent on the coating composition across the FG structure, and the predicted, computed, and experimental results were in reasonable agreement.

关键词： Thermo-mechanical evaluation Slurry spray technique Mean-field micromechanics Rule of mixtures Finite element simulation Transient plane source Optimisation multi-objective genetic algorithm

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The Portfolio Model Based on Temporal Convolution Networks and the Empirical Research on Chinese Stock Market 2021

The Portfolio Model Based on Temporal Convolution Networks a...

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2021 5th International Conference on Computer Science and Artificial Intelligence

作者： Rui Zhang Zuoquan Zhang Marui Du Xiaomin Wang Beijing Jiaotong University China

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Robustness of Time-multiplexed Hyperthermia to Temperature Dependent Thermal Tissue Properties

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IEEE JOURNAL OF ELECTROMAGNETICS RF AND MICROWAVES IN MEDICINE AND BIOLOGY 2020年第2期4卷 126-132页

作者： Cappiello, Grazia Paulides, Margarethus M. Drizdal, Tomas O'Loughlin, Declan O'Halloran, Martin Glavin, Martin van Rhoon, Gerard Jones, Edward Natl Univ Ireland Coll Engn & Informat Discipline Elect & Elect Engn Galway H91 TK33 Ireland Erasmus MC Canc Inst Dept Radiat Oncol Sect Hyperthermia NL-3015 GD Rotterdam Netherlands Eindhoven Univ Technol Fac Elect Engn NL-5612 AZ Eindhoven Netherlands Czech Tech Univ Dept Biomed Technol Fac Biomed Engn Kladno 27201 Czech Republic Natl Univ Ireland Lambe Inst Translat Res Translat Med Device Lab Galway H91 TK33 Ireland

Microwave hyperthermia is a promising cancer treatment used in combination with radio- and chemotherapy. Typically, hyperthermia systems involve several antennas that transfer electromagnetic energy into the tissue. The principal need in hyperthermia treatment is to optimally focus the heating into the target while minimising heating in the surrounding healthy tissue. Patient-specific treatment planning is done to optimize the specific absorption rate and the resulting temperature distribution. Uncertainties associated with the thermal model used for temperature simulations represent an important challenge. Our previous work has demonstrated that the occurrence of hotspots can be reduced and target heating enhanced using time-multiplexed steering procedures. In this paper, the robustness of time-multiplexed hyperthermia against temperature dependent thermal tissue properties is investigated. Temperature simulations are used to predict the time-dependent heating achieved by multiple antenna phase and amplitude configurations that are generated by a multi-objective genetic algorithm and applied sequentially. The proposed strategy is compared with the heating obtained using one single heating setting obtained by particle swarm optimization as typically used in clinical hyperthermia. Thermal performance of the static and time-multiplexed methods are assessed by applying two thermal models, one that uses constant properties of blood perfusion and thermal conductivity of tumor, muscle and fat, and a second one that uses temperature dependent perfusion values. This study shows that time-multiplexed hyperthermia enhances target heating and limits the hotspot appearance regardless of the thermal model used in thermal simulations.

关键词： Hyperthermia Hyperthermia Treatment Planning multi-objective genetic algorithm Thermal Tissue Properties

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A Hybrid multi-objective genetic algorithm for Bandwidth multi-Coloring Problem

A Hybrid Multi-Objective Genetic Algorithm for Bandwidth Mul...

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12th International Conference on Hybrid Intelligent Systems (HIS)

作者： Bayindir, Ismail Ugur Mercan, Engin Korkmaz, Emin Erkan Yeditepe Univ Dept Comp Engn Istanbul Turkey

ISBN: (纸本)9781467351157;9781467351164

Standard genetic algorithm (GA) yields poor performance on the Graph Coloring Problem (GCP) and its variants. This paper presents a multi-objective genetic algorithm (MOGA) for Bandwidth multi-Coloring Problem (BMCP). The problem is a generalization of GCP. In the proposed method, genetic operations are replaced with new ones which suit better to the structure of the problem. Performance of this MOGA framework is further boosted by hybridizing it with a Local Search (LS) algorithm. The aim of this hybrid approach is to increase the variety within the population through the genetic operations and to improve those individuals further by using LS. Several tests were conducted on a collection of benchmarks from GEOM series and promising results are obtained.

关键词： Graph Coloring Problem Bandwidth multi-Coloring Problem Hybrid genetic algorithm multi-objective genetic algorithm

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Geometric optimization of two-stage thermoelectric generator using genetic algorithms and thermodynamic analysis

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ENERGY 2019年 171卷 37-48页

作者： Sun, Henan Ge, Ya Liu, Wei Liu, Zhichun Huazhong Univ Sci & Technol Sch Energy & Power Engn Wuhan 430074 Peoples R China Huazhong Univ Sci & Technol China EU Inst Clean & Renewable Energy Wuhan 430074 Peoples R China

multi-objective genetic algorithms are used to optimize the structure, assignment of configuration and load resistance of a two-stage thermoelectric generator, where Skutterudite and Bi2Te3 are chosen as upper stage and lower stage TE leg materials, respectively. Heat convection and radiation are considered on the top of the upper substrate. In the optimization process, the specific power and entropy generation rate are considered synchronously as objective functions to maximize the power output per unit area and to minimize the irreversibilities. The FEM is adopted in the simulation model, and the Seebeck effect, together with the Peltier effect, Joule heating, Thomson effect, and Fourier heat conduction phenomena are all considered in the simulation process. Shannon's entropy method is applied to select the best solution from the Pareto Frontier. Besides, the exergy destruction rate is analyzed, the results show that the exergy destruction rate increases as the load resistance increases. In addition, the different relationships between the load resistance and the voltage, power output, efficiency and entropy generation rate are presented. The principle of performance enhancement is also explained by comparing the ZT value along the TE legs. The optimization is important to the development of more compact and high-efficiency thermoelectric generators. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Thermoelectric generator multi-objective genetic algorithm Specific power Entropy generation rate Exergy destruction rate

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An Interactive Evaluation Method of Decentralized Procurement Plan by multi-objective genetic algorithm

An Interactive Evaluation Method of Decentralized Procuremen...

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International Conference on Data Communication Networking

作者： Ryoji Kobayashi Masakatsu Mori Masaki Samejima Norihisa Komoda Graduate School of Information Science and Technology Osaka University Yokohama Research Laboratory Hitachi Ltd.

ISBN: (纸本)9789898565723

This paper addresses evaluation of a decentralized procurement plan for the support of the discussion among decision-makers with considering a catastrophic disaster. For the evaluation of the decentralized procurement plan, we have formulated the decentralized procurement planning problem as 3-objective optimization problem. However, multiple-objective genetic algorithms (MOGA) to solve the problem take several minutes and display many Pareto solutions. We propose the interactive evaluation method of the decentralized procurement plan that is an expanded interactive MOGA (iMOGA) with loss evaluation simulator and solution selection by characteristics of the decentralized procurement plan. Experimental results show that the proposed method can allow the decision-makers to find their preference solutions with 38% fewer interactions than the basic iMOGA can.

关键词： Supply Chain Decentralized Procurement Plan multi-objective genetic algorithm Interactive Evaluation

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Performance of Supercritical CO2 Power Cycle and Its Turbomachinery with the Printed Circuit Heat Exchanger with Straight and Zigzag Channels

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ENERGIES 2021年第1期14卷 62页

作者： Saeed, Muhammed Alawadi, Khaled Kim, Sung Chul Khalifa Univ Sci & Technol Dept Mech Engn POB 127788 Abu Dhabi U Arab Emirates Publ Author Appl Educ & Trainings Dept Automot & Marine Technol Kuwait 70654 Kuwait Yeungnam Univ Sch Mech Engn 280 Daehak Ro Gyongsan 38541 South Korea

Since printed circuit heat exchangers (PCHE) are the largest modules of a supercritical carbon dioxide Brayton cycle, they can considerably affect the whole system's performance and layout. Straight-channel and zigzag-channel printed circuit heat exchangers have frequently been analyzed in the standalone mode and repeatedly proposed for sCO2-BC. However, the impact of heat exchanger designs with straight and zigzag-channel configurations on the performance of the cycle and its components, i.e., the turbine and compressor, has not been studied. In this context, this study evaluates the effect of different heat exchanger designs with various values of effectiveness (epsilon), inlet Reynolds number (Re), and channel configuration (zigzag and straight channel) on the overall performance of the sCO2-BC and its components. For the design and analysis of PCHEs, an in-house PCHE design and analysis code (PCHE-DAC) was developed in the MATLAB environment. The sCO2-BC performance was evaluated utilizing an in-house cycle simulation and analysis code (CSAC) that employs the heat exchanger design code as a subroutine. The results suggest that pressure drop in PCHEs with straight-channel configuration is up to 3.0 times larger than in PCHEs with zigzag-channel configuration. It was found that a higher pressure drop in the PCHEs with straight channels can be attributed to substantially longer channel lengths required for these designs (up to 4.1 times than zigzag-channels) based on the poor heat transfer characteristics associated with these channel geometries. Thus, cycle layouts using PCHEs with a straight-channel configuration impart a much higher load (up to 1.13 times) on the recompression compressor, this in turn, results in a lower pressure ratio across the turbine. Therefore, the overall performance of the sCO2-BC using PCHEs with straight-channel configurations is found to be substantially inferior to that of layouts using PCHEs with zigzag-channel configurations. Finall

关键词： supercritical carbon dioxide cycle simulation multi-objective genetic algorithm heat exchanger optimization sCO(2)-Brayton cycle

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Turbine Design and Optimization for a Supercritical CO2 Cycle Using a multifaceted Approach Based on Deep Neural Network

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ENERGIES 2021年第22期14卷 7807页

作者： Saeed, Muhammad Berrouk, Abdallah S. Burhani, Burhani M. Alatyar, Ahmed M. Wahedi, Yasser F. Al Khalifa Univ Sci & Technol Mech Engn Dept POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Ctr Catalysis & Separat CeCas POB 127788 Abu Dhabi U Arab Emirates Khalifa Univ Sci & Technol Aerosp Engn Dept POB 127788 Abu Dhabi U Arab Emirates Abu Dhabi Maritime Acad POB 54477 Abu Dhabi U Arab Emirates

Turbine as a key power unit is vital to the novel supercritical carbon dioxide cycle (sCO(2)-BC). At the same time, the turbine design and optimization process for the sCO(2)-BC is complicated, and its relevant investigations are still absent in the literature due to the behavior of supercritical fluid in the vicinity of the critical point. In this regard, the current study entails a multifaceted approach for designing and optimizing a radial turbine system for an 8 MW sCO(2) power cycle. Initially, a base design of the turbine is calculated utilizing an in-house radial turbine design and analysis code (RTDC), where sharp variations in the properties of CO2 are implemented by coupling the code with NIST's Refprop. Later, 600 variants of the base geometry of the turbine are constructed by changing the selected turbine design geometric parameters, i.e., shroud ratio (r(s4)/r(3)), hub ratio (r(s4)/r(3)), speed ratio (?s) and inlet flow angle (a(3)) and are investigated numerically through 3D-RANS simulations. The generated CFD data is then used to train a deep neural network (DNN). Finally, the trained DNN model is employed as a fitting function in the multi-objective genetic algorithm (MOGA) to explore the optimized design parameters for the turbine's rotor geometry. Moreover, the off-design performance of the optimized turbine geometry is computed and reported in the current study. Results suggest that the employed multifaceted approach reduces computational time and resources significantly and is required to completely understand the effects of various turbine design parameters on its performance and sizing. It is found that sCO(2)-turbine performance parameters are most sensitive to the design parameter speed ratio (?s), followed by inlet flow angle (a(3)), and are least receptive to shroud ratio (r(s4)/r(3)). The proposed turbine design methodology based on the machine learning algorithm is effective and substantially reduces the computational cost of the design a

关键词： turbine design supercritical CO2 artificial neural network optimization multi-objective genetic algorithm machine learning

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A multi-objective genetic algorithm Based on Simulated Annealing

A Multi-objective Genetic Algorithm Based on Simulated Annea...

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4th International Conference on multimedia Information Networking and Security (MINES)

作者： Tang Xin-hua Chang Xu Fang Zhi-feng Shandong Univ Polit Sci & Law Dept Informat Sci & Technol Jinan Peoples R China

ISBN: (纸本)9780769548524;9781467330930

Combined the characteristic of simulated annealing, we propose a multi-objective genetic algorithm based on simulated annealing. We take the advantage of simulated annealing, improve the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm. Experimental results show that the improved algorithm improve the solution efficiency of the traditional multi-objective genetic algorithm, and avoid the premature convergence of the algorithm effectively.

关键词： Simulated annealing algorithm multi-objective genetic algorithm genetic algorithm non-inferior solution

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Hybrid Evolutionary Framework for Selection of Genes Predicting Breast Cancer Relapse

Hybrid Evolutionary Framework for Selection of Genes Predict...

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： Perino, Lorenzo Cascianelli, Silvia Masseroli, Marco Politecn Milan Dipartimento Elettron Informaz & Bioingn Milan Italy

ISBN: (纸本)9781728169262

Predicting relapse events is still one of the major challenges for breast cancer research. Despite gene expression-based classifiers may tackle this task, working on thousands of genes and only few samples jeopardizes the performances of a classifier trained without a proper gene selection. We propose a novel hybrid evolutionary gene selection framework, which uses a multi-objective genetic algorithm (MOGA) to search a wider range of gene selections and handles MOGA results in a whole new way, so as to overcome the limit of the non-easy interpretability of the MOGA broad set of solutions. To a classifier our framework provides a gene signature not only bringing the best cross-validation result, but also having noteworthy and robust performances when tested on unseen samples of an hold-out set. The robustness in hold-out showed the strength of our innovative key element: the final module of the framework, which fully exploits the high variability of MOGA outputs, rather than choosing just one of the solutions, as commonly done in the literature. It combines all MOGA results in more robust and compact gene occurrence-based signatures, under the reasonable assumption that highly recurrent genes have a more crucial biological role, more suitable clinical application and good discriminative power between relapsed and relapse-free patients, as confirmed by the obtained classification results.

关键词： Breast cancer relapse Gene selection Hybrid feature selection multi-objective genetic algorithm

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