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Analysis of the effect of roughness and concentration of Fe₃O₄/water nanofluid on the boiling heat transfer using the artificial neural network: An experimental and numerical study

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INTERNATIONAL JOURNAL OF THERMAL SCIENCES 2021年 163卷 106863-106863页

作者： Peng, Yeping Ghahnaviyeh, Milad Boroumand Ahmad, Mohammad Nazir Abdollahi, Ali Bagherzadeh, Seyed Amin Azimy, Hamidreza Mosavi, Amirhosein Karimipour, Aliakbar Shenzhen Univ Coll Mech & Control Engn Guangdong Key Lab Electromagnet Control & Intelli Shenzhen 518060 Peoples R China Islamic Azad Univ Najafabad Branch Dept Mech Engn Najafabad Iran Univ Kebangsaan Malaysia Inst IR 4 0 Bangi 43600 Selangor Malaysia Ton Duc Thang Univ Environm Qual Atmospher Sci & Climate Change Res Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Environm & Labour Safety Ho Chi Minh City Vietnam Duy Tan Univ Inst Res & Dev Da Nang 550000 Vietnam

Since experimental studies in the field of nanofluid pool boiling requires costly and time-consuming tests, numerical methods such as artificial neural networks with higher predictability and nonlinear features are suitable for prediction and modeling of problem parameters. In this paper, 180 pool boiling laboratory data of Fe3O4/water nanofluid are employed as datasets used for network training to determine the effect of different parameters of nanofluid pool boiling on Boiling Heat Transfer Coefficient (BHTC) and wall superheat. The concerned input parameters for the neural network include concentration, roughness, and heat flux, while the network outputs are the BHTC and wall superheat. Finally, it becomes clear that the trainbr training algorithm with the optimal quantity of 41 neurons within the hidden layer shows the best performance. In addition, the present model can accurately predict the BHTC and wall superheat with correlation coefficients (R) of 0.99936 and 0.9986 and the mean square error (mse) of 0.103 and 0.013, respectively. Also, given the optimization objectives considered in this research, including maximizing the heat transfer coefficient and minimizing the wall superheat in the nanofluid pool boiling process, the multi-objective genetic algorithm has been used to optimize the two objective functions concerned.

关键词： Artificial neural network multi-objective genetic algorithm Pool boiling Fe3O4/Water nanofluid BHTC Surface roughness

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multi-objective optimization of cooling air distributions of grate cooler with different clinker particles diameters and air chambers by genetic algorithm

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APPLIED THERMAL ENGINEERING 2017年 111卷 77-86页

作者： Shao, Wei Cui, Zheng Cheng, Lin Shandong Univ Inst Thermal Sci & Technol Jinan 250061 Peoples R China

The paper proposes a multi-objective optimization model of cooling air distributions of grate cooler in cement plant based on convective heat transfer principle and entropy generation minimization analysis. The heat transfer and flow models of clinker cooling process are brought out at first. Then the modified entropy generation numbers caused by heat transfer and viscous dissipation are considered as objective functions respectively which are optimized by genetic algorithm simultaneously. The design variables are superficial velocities of air chambers and thicknesses of clinker layer on different grate plates. The model is verified by a set of Pareto optimal solutions and scattered distributions of design variables. Sensitive analysis of average diameters of clinker particles and amount of air chambers are carried out based on the optimization model. The optimal cooling air distributions are compared by heat recovered, energy consumption of cooling fans and heat efficiency of grate cooler. And all of them are selected from the Pareto optimal solutions based on energy consumption of cooling fans minimization. The results show that the most effective and economic average diameter of clinker particles is 0.02 m and the amount of air chambers is 9. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Cooling air distribution of grate cooler multi-objective genetic algorithm Clinker particles diameter Air chambers amount

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Optimized Design of Neural Networks for a River Water Level Prediction System

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SENSORS 2021年第19期21卷 6504-6504页

作者： Lineros, Miriam Lopez Luna, Antonio Madueno Ferreira, Pedro M. Ruano, Antonio E. Univ Seville Design Engn Dept Seville 41013 Spain Univ Seville Aerosp Engn & Fluid Mech Dept Seville 41013 Spain Univ Lisbon Dept Informat LASIGE Fac Ciencias P-1749016 Lisbon Portugal Univ Algarve Fac Sci & Technol P-8005139 Faro Portugal Univ Lisbon Inst Super Tecn IDMEC P-1049001 Lisbon Portugal

In this paper, a multi-objective genetic algorithm (MOGA) framework for the design of Artificial Neural Network (ANN) models is used to design 1-step-ahead prediction models of river water levels. The design procedure is a near-automatic method that, given the data at hand, can partition it into datasets and is able to determine a near-optimal model with the right topology and inputs, offering a good performance on unseen data, i.e., data not used for model design. An example using more than 11 years of water level data (593,178 samples) of the Carrion river collected at Villoldo gauge station shows that the MOGA framework can obtain low-complex models with excellent performance on unseen data, achieving an RMSE of 2.5 x 10(-3), which compares favorably with results obtained by alternative design.

关键词： multi-objective genetic algorithm artificial neural networks river stage data

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Scheduling of a flexible job-shop using a multi-objective genetic algorithm

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JOURNAL OF ADVANCES IN MANAGEMENT RESEARCH 2012年第2期9卷 178-188页

作者： Agrawal, Rajeev Pattanaik, L. N. Kumar, S. Birla Inst Technol Dept Prod Engn Mesra India GLA Univ Dept Mech Engn Mathura India

Purpose - The purpose of this paper is to solve a flexible job shop scheduling problem where alternate machines are available to process the same job. The study considers the Flexible Job Shop Problem (FJSP) having n jobs and more than three machines for scheduling. Design/methodology/approach - FJSP for n jobs and more than three machines is non polynomial (NP) hard in nature and hence a multi-objective genetic algorithm (GA) based approach is presented for solving the scheduling problem. The two objective functions formulated are minimizations of the make-span time and total machining time. The algorithm uses a unique method of generating initial populations and application of genetic operators. Findings - The application of GA to the multi- objective scheduling problem has given optimum solutions for allocation of jobs to the machines to achieve nearly equal utilisation of machine resources. Further, the make span as well as total machining time is also minimized. Research limitations/implications - The model can be extended to include more machines and constraints such as machine breakdown, inspection etc., to make it more realistic. Originality/value - The paper presents a successful implementation of a meta-heuristic approach to solve a NP-hard problem of FJSP scheduling and can be useful to researchers and practitioners in the domain of production planning.

关键词： Programming and algorithm theory Production scheduling Job sequence loading Flexible job shop problem Make-span Scheduling multi-objective genetic algorithm

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Optimization of multiple Products Transportation Under the Background of Industrial Symbiosis Network

Optimization of Multiple Products Transportation Under the B...

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IEEE International Conference on Industrial Engineering and Engineering Management (IEEM)

作者： Gao, Na Li, Yuting Mai, Yuxi Xu, Haoxuan Northwestern Polytech Univ Sch Management Xian Peoples R China

ISBN: (纸本)9781538672204

Starting from the hotspots of solid waste recycling and reuse, and considering the characteristics of waste transportation in eco-industrial symbiotic network, this paper takes the transportation and distribution process of waste and by-product recycling and reuse in upstream and downstream enterprise clusters as the research object. Different from the traditional transportation research which a single goal-oriented and multi-product independent, Considering model the economic cost performance and environmental performance, as well as the "transport price correlation" between various wastes, this paper constructs a multi-objective transport planning model of Interacted multi-product. Finally, on the basis of the multi-product and multiobjective model, the paper refers to the related method theory of the second generation fast non-dominated sorting genetic algorithm (NSGA-II) which based on the elite selection strategy. By carrying out calculation and result analysis on the basis of algorithm implementation, the paper verifies the effectiveness of the model and algorithm.

关键词： Industrial symbiosis network interacted multi-product multi-objective genetic algorithm multi-product transportation

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Optimization of rapeseed production using exergy analysis methodology

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 43卷

作者： Esmaeilpour-Troujeni, Mahdi Rohani, Abbas Khojastehpour, Mehdi Ferdowsi Univ Mashhad Dept Biosyst Engn Mashhad Razavi Khorasan Iran

The exergy analysis helps to determine the energy losses of different inputs through identifying the actual amount of energy consumed in a process. In this regard, a new approach was applied in this study to predict and optimize the rainfed production systems (RPS) and irrigated production systems (IPS) of rapeseed in the north of Iran, by using exergy analysis methodology. Two indices of cumulative degree of perfection (CDP) and renewability index (RI) were used to evaluate the sustainability of systems. The Support Vector Machine (SVM) and multi-objective genetic algorithm (MOGA) were also employed to predict and optimize the rapeseed production systems, respectively. In the study of conventional system, CDP and RI were determined to be 2.19 and 0.72, on average, respectively, in which, the values of both indices were higher in the RPS than IPS. Optimization results indicated that by reducing the consumption of irrigation water, electricity and biocides as well as increasing the use of chemical fertilizers, farmyard manure and diesel fuel, the yield increased by 24.55%. Sustainability assessment in optimal scenario revealed that CDP and RI increased to 2.75 and 0.81, respectively. As a result, under optimal conditions, the production process becomes more environmentally friendly.

关键词： Exergy multi-objective genetic algorithm Optimization Rapeseed Sustainability

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A heuristic solution and multi-objective optimization model for life-cycle cost analysis of solar PV/GSHP system: A case study of campus residential building in Korea

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SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS 2021年 47卷 101490-101490页

作者： Sim, Minjeong Suh, Dongjun Kyungpook Natl Univ Dept Convergence & Fus Syst Engn Sangju 37224 South Korea

Because of the unavailability of standard renewable power generation capacity and the ambiguous sales methods for each building type, there has been indiscriminate installation of renewable energy sources to increase the mandatory installation rate of renewable energy. Importantly, campus buildings are high energy-consuming buildings that have considerable energy-saving potential. Herein, we present a model that maximizes the total life cycle cost (LCC) of a building using energy simulation programs (EnergyPlus & DesignBuilder) by considering various costs and efficiencies depending on the capacity and efficiency of the photovoltaic (PV) and ground-source heat pumps (GSHP) systems and optimization analysis using heuristic solution and multi-objective genetic algorithm. Based on economic factors, a Korean campus residential building was used as a case study, and the expense of the optimization models of seven applicable scenarios were analyzed. Each scenario is a method for selling renewable energy power, and it is possible to determine the building type and the effect of the sales method by analyzing the results of these scenarios. This method could be used to develop installation guidelines for integration of renewable energy systems into newly built buildings and provide the basis for decision-making by studying retrofitting of existing buildings to enhance energy efficiency.

关键词： Optimization PV GSHP LCC multi-objective genetic algorithm

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A metamodeling with CFD method for hydrodynamic optimisations of deflectors on a multi-wing trawl door

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OCEAN ENGINEERING 2021年 232卷 109045-109045页

作者： Wang, Gang Huang, Liuyi Wang, Lei Zhao, Fenfang Li, Yuyan Wan, Rong Ocean Univ China Coll Fisheries Qingdao 266003 Peoples R China East China Sea Fisheries Res Inst Shanghai 200090 Peoples R China Natl Engn Res Ctr Ocean Fisheries Shanghai 201306 Peoples R China Shanghai Ocean Univ Coll Marine Sci Shanghai 201306 Peoples R China

In the present work, a metamodeling with Computational Fluid Dynamics (CFD) method for the hydrodynamic optimisations of deflectors on the multi-wing trawl door is introduced. Then a comparative analysis of Kriging and Artificial Neural Networks metamodeling methods is carried out, using the effects of relative camber, thickness and the installed angles of deflectors on the hydrodynamics as a case study for optimisations. Based on CFD simulations (verified by the wind tunnel experiment), a strict procedure including metamodeling and multi-objective genetic algorithm is established. Efficiency and accuracy between the two methods are discussed. Finally, this study studied the flow patterns of the optimal otter board in the higher working efficiency state. The results showed that Kriging performs better than Artificial Neural Networks methods in 2-factor metamodeling. However, in the 3-factor investigation, Artificial Neural Networks has more advantages in the prediction of drag, while Kriging is better in the lift component of hydrodynamics. The relative camber has the considerable impacts on the hydrodynamics of the trawl door, whereas the thickness of deflectors shows the prominent influence on the resistances, followed by lift forces. The effect of installed angles on the hydrodynamic loadings is more remarkable than that of thickness and relative camber. In flow visualisation, the disappearance of the vortex on the deflector suction side indicates the rationality of the newly-proposed procedure used for optimisations.

关键词： Metamodeling Artificial Neural Networks Kriging multi-objective genetic algorithm Trawl door Hydrodynamics

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Feasibility of micro-hole machining in fiber laser trepan drilling of 2.5D Cf/SiC composite: Experimental investigation and optimization

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OPTIK 2021年 242卷 167186-167186页

作者： Liu, Chang Zhang, Xianze Gao, Lei Jiang, Xiaogeng Li, Cheng Yang, Tao Tiangong Univ Tianjin Key Lab Adv Mechatron Equipment Technol Sch Mech Engn Tianjin 300387 Peoples R China

The special structure characteristics make 2.5D C-f/SiC composite a key material in high-temperature parts of aerospace field. However, the micro-hole machining based on fiber laser has been a challenge due to effect of taper and heat affected zone. This paper proposed the feasibility on high-quality micro-hole machining of the 2.5D C-f/SiC composite by using fiber laser trepan drilling, including experimental investigation and optimization analysis for the first time. The key laser parameters, such as scanning speed, pulse frequency, pulse width, defocus amount, and energy percentage are introduced as the optimization parameters, and taper and average thickness of heat affected zone (HAZ(a)) are chosen as objectives. A comprehensive analysis method including Taguchi L-16(4(5)) orthogonal array, analysis of variance (ANOVA), regression modeling, and multi-objective genetic algorithm (MOGA-II) is presented on fiber laser trepan drilling to minimize the taper and the HAZ(a). The importance and interaction of the laser parameters on the taper and HAZ(a) are obtained. The results show that the fiber laser trepan drilling integrated with the proposed experimental and optimization analysis can achieve the high-quality micro-hole machining of 2.5D C-f/SiC composite. The presented methodology can be also used in high-quality machining of other fields.

关键词： Fiber laser 2.5D c(f)/SiC composite Micro-hole machining Feasibility analysis multi-objective genetic algorithm

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Spatial Geometry Design of a Low Earth Orbit Constellation for Iranian Regional Navigation Satellite System

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JOURNAL OF AEROSPACE TECHNOLOGY AND MANAGEMENT 2021年 13卷

作者： Zardashti, Reza Emami, Shiva Malek Ashtar Univ Technol Fac Aerosp Tehran Iran

The regional navigation satellite system (RNSS) is recently used in some countries to cover or enhance their local navigation. The most important satellite navigation systems to date are in the Earth medium orbit (MEO) and Earth synchronous Earth orbit (GEO), which are characterized by big satellites, and high launch, construction, and operation costs. In contrast, low Earth orbit (LEO) small-satellite constellations have recently become attractive because of their advantages, such as a significant reduction in system cost, an increase in communication volume, and a reduction in latency. Therefore, in this study, the spatial geometry of a LEO constellation is designed for Iran to increase the required regional navigation performance. For this purpose, the optimal constellation configuration is obtained through a multi-objective genetic algorithm (MOGA) utilizing a cost function with a combination of the geometry dilution of precision (GDOP), the number of satellites, and orbital height in the form of a design procedure. Moreover, reducing the feasible region of longitude of the ascending nodes of orbit planes is applied in the design process to reduce the search space. The simulation results indicate the constellation designed performance.

关键词： Regional navigation satellite system Satellite constellation multi-objective genetic algorithm Geometry dilution of precision Low Earth orbit

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