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Optimization of a boron nitride nanotubes nanofluid-cooled microchannel heat sink at different concentrations

JOURNAL OF THERMAL ANALYSIS AND CALORIMETRY 2023年第8期148卷 3117-3127页

作者： Yusof, Nur Liyana Nabihah Shamsuddin, Hielfarith Suffri Estelle, Patrice Mohd-Ghazali, Normah Univ Teknol Malaysia Fac Engn Sch Mech Engn Utm Johor Bahru 81310 Johor Malaysia Univ Rennes LGCGM F-35000 Rennes France

The microchannel heat sink (MCHS) has become the most relevant micro-heat exchanger for a small area in need of an effective high heat removal system. However, heat dissipation from the microchips where the MCHS is utilized-the microprocessor and microcontroller-is getting higher with the sizes getting smaller. A more effective coolant is needed to address the increasing heat load from the microchip and nanofluid, nanosized particles dispersed in a base fluid, is among those explored. This paper reports a new potential use of non-metallic nanofluid, boron nitride nanotube (BNN), that is capable of improving the overall performance of a rectangular MCHS. A heuristic method, multi-objective genetic algorithm (MOGA), is employed to simultaneously minimize the thermal resistance and pressured drop of a boron nitride nanotubes (BNN) nanofluid-cooled MCHS to obtain optimized dimensions at various mass concentrations. The method is capable in achieving conflicting objectives, minimization of the thermal resistance and the pressure drop;an increase in the former decreases the latter and vice versa. In addition, experimental thermophysical properties of the BNN nanofluid are used to provide reliability to the optimization outcomes in identifying the best BNN concentration for cooling of a MCHS at 50 degrees C. The optimization results showed that as the thermal resistance decreases, the pressure drop decreases. For mass concentrations of 0.001, 0.003, 0.005, 0.01 and 0.03% the thermal resistance is the lowest, 0.0711 K/W at 0.01 mass.%. at 0.0115 W. The thermal resistance is lowered by 5.34% compared to water for the same operating conditions. These results indicate the great potential of BNN nanofluid as a coolant in the electronics cooling system. Optimization with the MOGA provides a fast analysis into the potentials of any coolants for MCHS applications as shown here, reliability being provided by experimentally obtained thermophysical properties.

关键词： Microchannel heat sink multi-objective genetic algorithm Optimization Boron nitride nanotubes nanofluid

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multi-objective optimal droop control of solid oxide fuel cell based integrated energy system

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2023年第30期48卷 11382-11389页

作者： Zhou, Yujie Hua, Qingsong Liu, Ping Sun, Li Southeast Univ Key Lab Thermal Sci & Power Engn Minist Educ THU Nanjing 210096 Jiangsu Peoples R China Beijing Normal Univ Coll Nucl Sci & Technol Key Lab Beam Technol Minist Educ Beijing 100875 Peoples R China Sun Yat sen Univ Guangzhou 510275 Guangdong Peoples R China Shenzhen Faroad Intelligent Equipment Co Ltd Shenzhen 518125 Guangdong Peoples R China 135 West Xingang Rd Guangzhou Peoples R China

Solid oxide fuel cell (SOFC) based integrated energy system (IES) is promising in the future low-carbon power generation market, due to the high efficiency and flexibility. However, it is challenging for the dynamic control design in dealing with the conflicting objectives in terms of fast power tracking and overall efficiency during the transient process of load response. To this end, this paper develops a multi-objective optimal droop control strategy for the real-time power dispatch of the IES. Firstly, a nonlinear implicit dynamic model consisting of SOFC, lithium-ion battery, photovoltaic array and DC-DC converter is devel-oped. Then, a multi-objective optimization is formulated to balance the power tracking performance and transient efficiency. Non-dominated sorting genetic algorithm-II (NSGA-II) is adopted to search the optimal parameters for droop controller. Simulation results demonstrates that the electricity loss of the proposed method can be reduced by 96.26% with a slight compromise in power tracking performance.(c) 2022 Hydrogen Energy Publications LLC. Published by Elsevier Ltd. All rights reserved.

关键词： Solid oxide fuel cell Integrated energy system Dynamic model Optimal control multi-objective genetic algorithm

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multi-objective Optimization of Rolling Schedules for Tandem Hot Rolling Based on Opposition Learning multi-objective genetic algorithm

Multi-objective Optimization of Rolling Schedules for Tandem...

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第25届中国控制与决策会议

作者： Yong Li Xinhua Zhao Yu Wang Mingxu Ren School of Electrical Engineering Shenyang University of Technology Department of Automatic Control Shenyang Aerospace University

ISBN: (纸本)9781467355322

An opposition learning multi-objective genetic algorithm based multi-objective optimization approach of rolling schedules for tandem hot rolling is proposed. According to actual rolling process, the power distribution and rolling energy consumption are selected as cost functions. Then the multi-objective model of rolling schedules is established. The opposition learning multi-objective genetic algorithm is applied to the model. Result shows that the proposed method decreases the values of two objective functions simultaneously compared to actual rolling schedules. Furthermore the proposed approach archives better Pareto solution set of rolling schedules with less running time compared with NSGA II. The validity of the proposed approach is confirmed.

关键词： opposition based learning multi-objective genetic algorithm tandem hot rolling optimization of rolling schedules

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Modal Expansion Analysis, Inverse Design, and Experimental Verification of a Broadband High-Aperture Efficiency Circular Short Backfire Antenna Loaded With Anisotropic Impedance Surfaces

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IEEE TRANSACTIONS ON ANTENNAS AND PROPAGATION 2023年第6期71卷 4783-4798页

作者： Bie, Mengyuan Peng, Manxin Jiang, Zhi Hao Werner, Douglas H. Southeast Univ Sch Informat Sci & Engn State Key Lab Millimeter Waves Nanjing 210096 Peoples R China Purple Mt Labs Nanjing 211111 Peoples R China Penn State Univ Dept Elect Engn University Pk PA 16802 USA

In this article, a broadband highly efficient circular short backfire antenna (SBFA) loaded with cylindrical anisotropic impedance surfaces (AISs) is proposed. An efficient modal expansion method (MEM) is developed to facilitate the analysis of the AIS-loaded SBFA with a finite-sized flange, which considerably reduces the computational resources as compared to commercial full-wave solvers. By combining the MEM with a multiobjective genetic algorithm, the geometrical dimensions of the antenna and the dispersive properties of the AIS are optimized through an inverse design strategy. As a proof-of-concept example, an S-band AIS-loaded SBFA with a broad bandwidth and high aperture efficiency is demonstrated, showing a good agreement between measured results and simulation predictions. An aperture efficiency of greater than 83% is experimentally achieved over a broad S-11 < -10 dB bandwidth of 35.3%, with a peak aperture efficiency of about 100%. Moreover, the underlying operating principle of the antenna is revealed by the investigation of the main propagating modes in the SBFA. The extraordinary performance demonstrates that the proposed AIS-loaded SBFA represents a promising candidate for various wireless communication systems.

关键词： Anisotropic impedance surfaces (AISs) broadband high-aperture efficiency mode expansion method multi-objective genetic algorithm short backfire antenna (SBFA)

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Machine Learning-Based Optimization of a Mini-Channel Heatsink Geometry

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ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING 2023年第9期48卷 12107-12124页

作者： Saeed, Muhammed Kalule, Ramanzani S. S. Berrouk, Abdallah S. S. Alshehhi, Mohamed Almatrafi, Eydhah 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 Abu Dhabi Maritime Acad POB 54477 Abu Dhabi U Arab Emirates King Abdulaziz Univ Mech Engn Dept POB 80200 Jeddah Saudi Arabia

The research on cooling electronics devices in workstations, servers, and other devices has become one of the rapidly growing technologies today, with the generation of high heat fluxes associated with component compactness and increased power consumption. Hence, the current study aims to enhance the thermohydraulic performance of a compact mini-channel heat removal system based on a machine learning-based optimization technique. The design variables considered for the optimization study are the channel's Reynolds number (Re), fin thickness (t(f)), fin spacing (s(f)), and fin height (h(f)). Initially, the performance of the heat sink geometry with different fin configurations is computed using 3D-RANS simulations. The generated data are then used to train six regression techniques in machine learning to propose the best approach capable of accurately predicting the heat transfer coefficient and pressure drop data. The selected machine learning model is then coupled with the multi-objective genetic algorithm to find the optimal heat sink geometry. It is found that the multilayered perceptron approach re-designed to a deep neural network model efficiently predicts both the heat transfer coefficient and the pressure drop from the available data. The overall performance of the optimized channel geometry is found up to 2.1 times improved than the best available channel configuration in the data pool. Further, the optimized channel geometry's heat transfer coefficient was 14% higher, and the corresponding pressure drop was five times lower.

关键词： Heatsinks Machine learning Neural networks Mini channels multi-objective genetic algorithm

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Enhancing agricultural sustainability: Optimizing crop planting structures and spatial layouts within the water-land-energy-economy-environment-food nexus

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GEOGRAPHY AND SUSTAINABILITY 2025年第3期6卷

作者： Wu, Haowei Li, Zhihui Deng, Xiangzheng Zhao, Zhe Chinese Acad Sci Inst Geog Sci & Nat Resources Res Beijing 100101 Peoples R China Chinese Acad Sci Key Lab Land Surface Pattern & Simulat Beijing 100101 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Liaoning Univ Fac Econ Sch Econ Shenyang 110136 Peoples R China

In the new phase of sustainable development, agriculture is seeking sustainable management of the water-land-energy-economy-environment-food nexus. At present, there are few studies on optimizing crop planting structure and analyzing its spatial layout with consideration of natural and socio-economic factors. Herein, we proposed a framework for addressing this issue. In this framework, the NSGA-II algorithm was used to construct the multi-objective optimization model of crop planting structures with consideration of water and energy consumption, greenhouse gas (GHG) emissions, economic benefits, as well as food, land, and water security constraints, while the model for planting spatial layout optimization was established with consideration of crop suitability using the MaxEnt model and the improved Hungarian algorithm. This framework was further applied in the Black Soil Region of Northeast China (BSRNC) for analyzing optimized crop planting structures and spatial layouts of three main crops (rice, maize, and soybean) under various scenarios. This study showed that the sown area of rice in the BSRNC decreased by up to 40.73 % and 35.30 % in the environmental priority scenario and economic-environmental balance scenario, respectively, whereas that of soybean increased by up to 112.44 % and 63.31 %, respectively. In the economic priority scenario, the sown area of rice increased by up to 93.98 %. Expanding the sown area of soybean was effective in reducing GHG emissions. On the contrary, rice production led to greater environmental costs though it provided higher economic returns. Among the three crops, maize exhibited an advantage in balancing environmental and economic benefits. Hegang-Jixi area in the northeast of the BSRNC was identified as the key area with the most intense crop planting transfer among different scenarios. Overall, this framework provides a new methodology for optimizing crop planting structures and spatial layouts with consideration of the nexus

关键词： Planting structure optimization Crop spatial layout multi-objective genetic algorithm Food security Agricultural sustainability Water-land-energy-economy-environment-food nexus

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A circular approach to foster additive manufacturing early design stages sustainability: a methodological proposal

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年第2期18卷 815-836页

作者： Faveto, Alberto Lombardi, Franco Chiabert, Paolo Segonds, Frederic Politecn Torino Dept Management & Prod Engn Corso Duca Abruzzi 24 I-10129 Turin Italy HESAM Univ LCPI Arts & Metiers Inst Technol 151 Blvd Hop F-75013 Paris France

The design of new products is now influenced by shifting consumer demands and technological advancements. Products must satisfy high-quality standards and have a low environmental impact. New phenomena such as distributed and urban manufacturing are emerging to cope with this. A new manufacturing era is coming where methods that prevent waste, support small workshops and encourage do-it-yourself are crucial. In the early design stage, the process knowledge is minimal, and the decision taken is vital. For this reason, it is essential to support designers in anticipating the impact of decisions on the final product. This paper establishes the groundwork for decision-support methodologies for sustainable design in One-of-a-Kind additive manufacturing prototyping. Our proposed method is applied to a Fused Filament Fabrication case study, wherein we evaluate the impact of nine variables on factors such as process time, energy and material consumption, environmental footprint, and product quality. The initial step aims to generate fresh insights through Taguchi experimentation, while the subsequent step formulates and resolves a multi-objective optimization problem using the NSGA-II algorithm. The resulting Pareto-optimal solutions serve as the basis for a novel visual-based design support tool. The proposed approach can evaluate the trade-offs between product quality and environmental impact by offering users a visual heatmap based on quantitative data. This heatmap can guide the user in the material and production parameter selection. Integrating the decision support tool into the product design process can empower designers to create environmentally responsible products while fostering innovation.

关键词： Additive manufacturing One-of-a-Kind production Sustainability Sustainable manufacturing multi-objective genetic algorithm Design support tool

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Optimizing Stakeholder Behavior for Groundwater Management in Arid and Semi-arid Climates: A multi-agent System Approach in Karaj Plain, Iran

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Iranian Journal of Science and Technology - Transactions of Civil Engineering 2024年第6期48卷 4751-4767页

作者： Khajeem Moghadam, Atena Saghafian, Bahram Najarchi, Mohsen Delavar, Majid Department of Civil Engineering Islamic Azad University Arak Branch Arak Iran Civil Department Science and Research Branch Islamic Azad University Tehran Iran Department of Water Resources Engineering Tarbiat Modares University Tehran Iran

The ever-increasing water demand has not only disrupted the demand-supply balance, causing conflicts among multiple stakeholders, but has also led to environmental impacts due to aquifer depletion. Today, agent-based modeling (ABM) is recommended for studying the effects of stakeholders’ social aspects on water resources, particularly groundwater resources. Aim of this study proposes a model for optimizing stakeholder behavior, which is coupled with numerical groundwater resource simulation and water resource optimization models in Karaj Plain, Iran. The simulation results of the aquifer showed that continuing the current situation will cause the aquifer level to drop by about 5 m. However, implementing the water resource optimization model, with goals of reducing water shortages and improving underground water conditions, will increase the aquifer reserve. This model will also provide 99.7% of the region’s drinking water at a rate of 3.03 MCM per year, raising the aquifer level by 3 m. Additionally, the optimization model of beneficiaries’ behavior and the analysis of proposed scenarios demonstrated that reducing agricultural water rights, combined with policies aimed at changing agents’ behavior in the agent base model, would increase the average groundwater level across the entire aquifer by about 5.5 m. Taking into account the local knowledge of the main water consumers, it is recommended to use multi-agent system models to incorporate the ideas of other agents (decision-makers) and develop the proposed cultivation pattern for effectively managing water resources and reducing environmental risks in arid and semi-arid climates. © The Author(s), under exclusive licence to Shiraz University 2024.

关键词： Agent-based model Environment Groundwater multi-objective genetic algorithm Optimal water allocation Simulation

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multi-classification of Alzheimer's Disease by NSGA-II Slices Optimization and Fusion Deep Learning 17th

Multi-classification of Alzheimer's Disease by NSGA-II Slice...

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17th Italian Workshop on Artificial Life and Evolutionary Computation (WIVACE)

作者： Rojas-Valenzuela, Ignacio Rojas, Ignacio Delgado-Marquez, Elvira Valenzuela, Olga Univ Granada ETSIIT Granada Spain Univ Leon Dept Econ & Stat Leon Spain Univ Granada Fac Sci Granada Spain

ISBN: (纸本)9783031574290;9783031574306

This paper presents a two-phase hierarchical classifier for determining the different states in Alzheimer's disease (AD). In the first phase, an evolutionary system is developed to determine the most relevant slices (in both X-axis and Y-axis) of the magnetic resonance imaging (MRI) for the construction of a classifier. To obtain the image features, the biorthogonal wavelet transform 3.3 was used at level 2. Due to the high number of coefficients, a dimensionality reduction is performed using minimum Redundancy - Maximum Relevance algorithm (mRMR) and Principal Component Analysis (PCA). An evolutionary algorithm on a high-performance computer with GPU was used to optimize the slides. Support vector machine (SVM) was used in the fitness function to estimate the features of the classifier in a computationally simple way. In the second phase, using the different solutions of the Pareto front obtained by the evolutionary algorithm, a multiple deep learning system was developed, each of the systems having as input one of the selected slices of the analyzed solution. The solution with three slices (trade-off between complexity and accuracy) was used as the solution. The obtained hierarchical deep learning system fused the information from each system and analyzed the probabilities obtained for each class. As a final result, an accuracy of 92% was obtained for the six classes. A total of 1,200 patients from the Alzheimer's disease neuroimaging initiative (ADNI) database were used, corresponding to six different classes of patients (with varying degrees of dementia).

关键词： Alzheimer's Disease multiclass Classification multi-objective genetic algorithm Hierarchical System Deep Learning Ensemble System

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A design approach of bonding head guiding platform for die to wafer hybrid bonding application using compliant mechanism

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INTERNATIONAL JOURNAL OF INTERACTIVE DESIGN AND MANUFACTURING - IJIDEM 2023年第6期17卷 2855-2866页

作者： Duc Nam Nguyen Minh Phung Dang Dixit, Saurav Thanh-Phong Dao Ind Univ Ho Chi Minh City Fac Mech Engn Ho Chi Minh City Vietnam Ho Chi Minh City Univ Technol & Educ Fac Mech Engn Ho Chi Minh City Vietnam Uttaranchal Univ Div Res & Innovat Dehra Dun 248007 Uttarakhand India Ton Duc Thang Univ Inst Computat Sci Div Computat Mechatron Ho Chi Minh City Vietnam Ton Duc Thang Univ Fac Elect & Elect Engn Ho Chi Minh City Vietnam

In semiconductor manufacturing process, the wafer direct bonding, so-called die-to-wafer hybrid bonding, still has unexpected voids on the boning surface which decreases the bonding efficiency. Therefore, this paper proposes a design approach for the bonding head guiding platform (BHGP) based on compliant mechanism for the die-to-wafer hybrid bonding. Firstly, a bridge amplification module (BAM) is designed and simulated. Secondly, a dual parallelogram module (DPM) is designed and analyzed. Then, the BAM is combined with the DPM to create a full design of the BHGP. To investigate the parametric effect of the geometric inputs on the output behaviors of the platform, the parameter sensitivity is conducted. The multi-objective optimization study is performed using multi-objective genetic algorithm. The results determined the displacement can gain up to 191.88 mu m, the parasitic error has a small value of 1.47 mu m, and the von Mises stress is minimal value of 52 MPa. The crosstalk of the platform is about 3.82%, and this ratio ensures a linear characteristic of the platform. A small value of stress allows a high safety factor during operation. The result of this study is expected be utilized for removing voids in the die-to-wafer hybrid bonding process.

关键词： Die-to-wafer hybrid bonding Bonding head guiding platform Bridge amplification module Dual parallelogram module multi-objective genetic algorithm

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