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Thermally regenerative electrochemical refrigerators decision-making process and multi-objective optimization

ENERGY CONVERSION AND MANAGEMENT 2022年 252卷 115060-115060页

作者： Kamali, Hamed Mehrpooya, Mehdi Mousavi, Seyed Hamed Ganjali, Mohammad Reza Univ Tehran Caspian Fac Engn Coll Engn Tehran Iran Univ Tehran Fac New Sci & Technol Dept Renewable Energies & Environm Tehran Iran Univ Tehran Coll Sci Ctr Excellence Electrochem Sch Chem Tehran Iran Univ Tehran Med Sci Biosensor Res Ctr Endocrinol & Metab Mol Cellular Sci Inst Tehran Iran

Thermally regenerative electrochemical refrigerators have drawn tremendous attention due to their reliability, quietness, eco-friendliness, non-emission of CFC gases, and potential for various applications. This work was conducted to model a thermally regenerative electrochemical refrigerator based on finite-time analysis. The proposed system is analyzed in four diverse temperature ranges, and all losses are considered for more accurate modeling by Python. Moreover, sensitivity analysis was performed for these temperature ranges. The thermodynamic analysis of these states has been demonstrated in detail for the first time. A multi-objective genetic algorithm in MATLAB software was used to achieve the maximum cooling capacity and COP and minimum input power. The optimal values, including system temperature, cell materials, and parameters related to heat exchangers and output results of the genetic algorithm, were prioritized using the weighted aggregated sum product assessment method. The results revealed that in the temperature ranges, 263K < TL < 283K, 297K < TH < 301K, which are the temperature ranges of cold and hot cells, respectively, the system indicated better performance. Meanwhile, selecting materials with higher specific charging/discharging capacity, isothermal coefficient, and smaller specific heat and internal resistance improves the system's performance. The optimum values of cooling capacity and system coefficient of performance were acquired as 367.01 W and 0.7301. This paper is expected to pave the way for the lab-scale design of thermally regenerative electrochemical refrigerators.

关键词： Coefficient of performance Cooling capacity Performance analysis multi-objective genetic algorithm multi-Criteria decision-making Thermally regenerative electrochemical refrigerator

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Optimal development for a 3D-printed gripper for biomedical and micromanipulation applications by non-parametric regression-based metaheuristic technique

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART E-JOURNAL OF PROCESS MECHANICAL ENGINEERING 2022年

作者： Ngoc Thoai Tran Minh Phung Dang Ngoc Le Chau Shankar, Subramaniam Buddhi, Dharam 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 Kongu Engn Coll Dept Mechatron Engn Erode India Uttaranchal Univ Div Res & Innovat Dehra Dun 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

With the advancement of bioengineering and robotic engineering, medical robots have been increasing concern about manipulating the microobject or cells. Although the rigid robots have a stable operation, they inherit many limitations such as the complex assembly process of joints-coupled rigid links and expensive costs. Especially, clearances between kinematic joints cause vibrations that damage microobject. To cope with such problems, a flexure-based polylactic acid (PLA) gripper is developed to realize precise motion in medical robots. The proposed gripper is 3D printed by fused deposition modeling technique with advantages of monolithic structure, jointless and cheap cost. Prior to the gripper fabrication, the optimization development of the gripper is conducted employing a combination of the non-parametric regression (NPR) and multi-objective genetic algorithm (MOGA). Numerical data samples are collected by the finite element method. The modeling results were well formulated utilizing the NPR method with the R-2 value greater than 0.9. The Pareto-optimum design results identified that the gripper can provide a high displacement of 2 mm and a small stress of 41 MPa via MOGA. Additionally, the proposed flexure-based compliant PLA gripper can work with a safety factor higher than 1.6. The experiment tests on the prototype of the gripper are close to the estimated values.

关键词： 3D printing additive manufacturing fused deposition modeling PLA gripper non-parametric regression multi-objective genetic algorithm

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PERFORMANCE OF TWO-SIDED EWMA CEV CONTROL CHARTS WITH multiPLE CENSORED DATA

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INTERNATIONAL JOURNAL OF INDUSTRIAL ENGINEERING-THEORY APPLICATIONS AND PRACTICE 2022年第4期29卷 487-498页

作者： Lee, Pei-Hsi Torng, Chau-Chen Kao, Pei-Hsuan Chou, Chao-Yu Fujian Univ Technol Sch Internet Econ & Business Fuzhou Peoples R China Natl Yunlin Univ Sci & Technol Inst Ind Engn & Management Touliu Taiwan Natl Taichung Univ Sci & Technol Dept Finance Taichung Taiwan

Control charts are widely applied in process control. Life testing is time-consuming and expensive;thus, this testing typically has a termination condition that is based on the assumption of a single failure mode. The lifetimes of unfailed units are known as right-censored data, and conditional expected value (CEV) control charts are effective for monitoring right-censored data. However, life testing may produce multiple censored data due to there being multiple failure modes. The occurrence of these failure modes is unpredictable, and thus the censored proportion cannot be known in advance. The exponentially weighted moving average (EWMA) control chart has a high sensitivity for detecting small mean shifts. In this paper, two charting methods for monitoring multiple censored data were developed: a two-sided EWMA CEV control chart and a combination of two single-sided EWMA CEV charts. A multi-objective model was established to optimize the design parameter values for the two types of control charts by using the multi-objective genetic algorithm. The charting methods were compared theoretically and with the practical example of liquid crystal display modules. The results demonstrate that the two-sided EWMA CEV control chart is generally more effective than the combination of two single-sided EWMA CEV control charts for monitoring multiple censored data. Moreover, if a large range and high censoring proportion are used to optimize the model design, increasing the sample size can always improve the performance of the EWMA CEV control chart.

关键词： EWMA Control Chart Conditional Expected Value multiple Censored Data multi-objective genetic algorithm

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Aerodynamic Optimization and Characterization of a Ducted Tail for a Box-Launched Aircraft

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APPLIED SCIENCES-BASEL 2024年第15期14卷 6496页

作者： Jia, Huayu Zheng, Huilong Zhou, Hong Zhang, Qian Chinese Acad Sci Inst Engn Thermophys Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China

The tail wing of box-launched aircraft needs to be folded in the launch box, which can easily cause malfunctions during flight deployment. This article presents a ducted tail wing aircraft that does not require folding of the tail wing. To address the nonlinear problem of lift coefficient in the ducted tail, an aerodynamic optimization method for ducted tails based on the sparrow search algorithm with back-propagation (SSA-BP) neural network approximate model and multi-objective genetic algorithm fusion is proposed, with the goal of improving the lift-to-drag ratio and linearization degree of the lift curve. The linearization degree of the optimized tail lift coefficient curve is significantly improved, and the lift-to-drag ratio is significantly improved under cruising conditions. Based on this optimization result, the shape of the tail wing and fuselage combination was optimized, and the optimal configuration of the ducted tail wing aircraft was selected, providing a reference for the design of ducted tail wing aircraft.

关键词： box-launched aircraft ducted tail aerodynamic optimization SSA-BP approximate model multi-objective genetic algorithm

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Analysis and multi-objective optimization of SOFC/GT/SCO2 hybrid power system based on thermodynamics and economics

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APPLIED THERMAL ENGINEERING 2023年第1期232卷

作者： Xia, Minjie Yao, Shouguang Ying, Chao Jiangsu Univ Sci & Technol Sch Energy & Power Zhenjiang 212000 Peoples R China

SOFC/GT hybrid power systems with the advantages of high energy efficiency and low emissions are important directions of energy research. This study proposes a new SOFC/GT/SCO2 hybrid power system, in which the SCO2 cycle is placed inside the SOFC/GT subsystem, and the waste heat of the SCO2 cycle is used for heating the air of the system to achieve good thermal matching between the system. After system construction, the system is thermodynamically and economically evaluated, and the influence of parameters on system operation performance is investigated. The system's energy efficiency and the electricity production cost are optimized by a multi-objective genetic algorithm. The optimized net output power, energy efficiency, and exergy efficiency of the system are increased to 226.374 kW, 60.42%, and 63.03%, respectively, and the electricity production cost decreases to 0.079 $/kWh. Results show that placing the SCO2 cycle inside the SOFC/GT subsystem instead of at the tail can improve the energy efficiency of the system and effectively realize gradient energy utilization in the SOFC/GT/SCO2 hybrid power system.

关键词： SOFC GT SCO2 Energy efficiency Exergy efficiency Electricity production cost multi-objective genetic algorithm

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Research on Contact Anchoring Theory and Contact Optimization of Underwater Pipeline Recovery Tools

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PROCESSES 2023年第11期11卷 3166页

作者： Gong, Haixia Ping, Zhuoran Zhao, Tong Hou, Shuping Zu, Fuqiang Qiu, Pengyue Qin, Jianguo Harbin Engn Univ Coll Mech & Elect Engn 145 Nantong St Harbin 150001 Peoples R China Inner Mongol Univ Technol Coll Mech Engn 49 Aimin St Hohhot 010051 Peoples R China

Technology for recycling abandoned undersea pipelines is crucial for lowering the cost of installing new submarine pipelines, polluting the ocean less, and improving recycling efficiency. A popular area of study is how to lessen the harm that underwater pipeline recycling instruments do to the inner wall of the pipeline during recycling. In order to recover pipelines, this study examines the anchoring theory and damage process of submerged pipeline recovery equipment. The effect of the contact body's diameter and radius of the rounded corner on the depth of the pressed-in pipeline and the slip distance is examined using the contact body structure optimization design approach of the underwater pipeline recovery tool, which is based on a multi-objective genetic algorithm. Dynamic simulations of the insertion mechanism as a whole are performed using the Adams simulation program to make sure that the optimized contact body can exert enough contact force on the pipeline's inner wall. According to the optimization results, the ideal design parameters are D = 57 mm and R = 11.5 mm. While still satisfying the criteria, the improved contact body has higher stability.

关键词： anchoring theory damage mechanism structural optimization multi-objective genetic algorithm approximate model

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Optimizing space cooling of a nearly zero energy building via model predictive control: Energy cost vs comfort

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ENERGY AND BUILDINGS 2023年 278卷

作者： Ascione, Fabrizio Masi, Rosa Francesca De Festa, Valentino Mauro, Gerardo Maria Vanoli, Giuseppe Peter Univ Napoli Federico II DII Dept Ind Engn Piazzale Tecchio 80 Napoli I-80125 Naples Italy Univ Sannio Dept Engn DING Piazza Roma 21 I-82100 Benevento Italy Univ Molise Dept Med & Hlth Sci Vincenzo Tiberio via G Paolo 2contrada Tappino I-86100 Campobasso Italy

The space conditioning of buildings is liable for more than 10 % of World final energy uses and related CO2-eq emissions. Such share must be drastically reduced to pursue sustainability by optimizing both energy design and devices control. In this frame, space cooling is assuming an increasing weight owing to climate change. Accordingly, this study applies a simulation- and optimization-based framework for the model predictive control (MPC) of space cooling systems. The case study is a nearly zero energy building located in Benevento - Southern Italy, Mediterranean climate - featuring an efficient air-source multi-split system for cooling. The framework is envisioned to provide optimal values of setpoint temperatures on a day-ahead planning horizon to minimize energy cost and thermal discomfort, based on weather forecasts. Accordingly, a Pareto multi-objective approach is applied considering different discomfort indicators to compare the Fanger theory with the adaptive one of ASHRAE 55. The optimization problem is solved by running a genetic algorithm - variant of NSGA II - under MATLAB (R) environment. The objective functions are assessed via the coupling between MATLAB (R) and EnergyPlus, using a validated building energy model. The multi-criteria decision-making is performed by setting a limit to discomfort to pick an optimal Pareto solution. The framework is tested addressing a typical day of the cooling season and using monitored weather data to simulate weather forecasts. Different optimal solutions are provided to fit different comfort categories. Compared to a reference control at fixed setpoint 26 degrees C - the proposed solutions with similar comfort performance ensure cost savings around 28 %. Besides the proposed hypothetical implementation, the framework can be integrated in automation systems for real-time MPC. The novel contributions of this study lie in the methodology to combine MPC with different thermal comfort models as well as in the results, w

关键词： Zero energy buildings Space cooling HVAC systems Model predictive control Thermal comfort multi-objective genetic algorithm

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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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Optimal policies for control of the novel coronavirus disease (COVID-19) outbreak

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CHAOS SOLITONS & FRACTALS 2020年 136卷 109883-109883页

作者： Yousefpour, Amin Jahanshahi, Hadi Bekiros, Stelios Univ Tehran Coll Engn Sch Mech Engn Tehran 111554563 Iran Univ Manitoba Dept Mech Engn Winnipeg MB R3T 5V6 Canada European Univ Inst Dept Econ Via Fontanelle 18 I-50014 Florence Italy Wilfrid Laurier Univ RCEA LH3079 75 Univ Ave W Waterloo ON N2L 3C5 Canada

Understanding the early transmission dynamics of diseases and estimating the effectiveness of control policies play inevitable roles in the prevention of epidemic diseases. To this end, this paper is concerned with the design of optimal control strategies for the novel coronavirus disease (COVID-19). A mathematical model of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission based on Wuhan's data is considered. To solve the problem effectively and efficiently, a multi-objective genetic algorithm is proposed to achieve high-quality schedules for various factors including contact rate and transition rate of symptomatic infected individuals to the quarantined infected class. By changing these factors, two optimal policies are successfully designed. This study has two main scientific contributions that are: (1) This is pioneer research that proposes policies regarding COVID-19, (2) This is also the first research that addresses COVID-19 and considers its economic consequences through a multi-objective evolutionary algorithm. Numerical simulations conspicuously demonstrate that by applying the proposed optimal policies, governments could find useful and practical ways for control of the disease.

关键词： Novel coronavirus Mathematical modeling Optimal control multi-objective genetic algorithm

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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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