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SCIENCE AND TECHNOLOGY FOR ENERGY TRANSITION 2024年 79卷

作者： Manzoor, Awais Akram, Waseem Judge, Malik Ali Khan, Naveed Khattak, Hasan Ali COMSATS Univ Islamabad Dept Comp Sci Islamabad 44500 Pakistan Univ Palermo Dept Engn I-90133 Palermo Italy Ulster Univ Fac Comp Engn & Built Environm Newtownabbey BT37 0QB North Ireland King Fahd Univ Petr & Minerals KFUPM Informat & Comp Sci Dept Dhahran 31261 Saudi Arabia Sohar Univ Fac Comp & Informat Technol Sohar 311 Oman Natl Univ Sci & Technol NUST Islamabad Pakistan

Machine learning provides a powerful mechanism to enhance the capabilities of the next generation of smart cities. Whether healthcare monitoring, building automation, energy management, or traffic management, use cases of capability enhancement using machine learning have been significant in recent years. This paper proposes a modeling approach for scheduling energy consumption within smart homes based on a non-dominated sorting genetic algorithm (NSGA). Distributed energy management plays a significant role in reducing energy consumption and carbon emissions as compared to centralized energy generation. Multiple energy consumers can schedule energy-consuming household tasks using home energy management systems in coordination to reduce economic costs and greenhouse gas emissions. In this work, such a home energy management system is used to collect energy price data from the electricity company via an embedded device-enabled smart meter and schedule energy consumption tasks based on this data. We schedule daily power consumption tasks using a multiobjective optimization method that considers environmental and economic sustainability. Two conflicting objectives are minimizing daily energy costs and reducing carbon dioxide emissions. Based on electricity tariffs, CO2 intensity, and the window of time during which electricity is consumed, energy consumption tasks involving distributed energy resources (DERs) and electricity consumption are scheduled. The proposed model is implemented in a model smart building consisting of 30 homes under 3 pricing schemes. The energy demand is spread out across a 24-hour period for points A2-A4 under CPP-PDC, which produces a more flattened curve than point A1. There are competing goals between electricity costs and carbon footprints at points B2-B4 under the CPP-PDC, where electricity demand is set between 20:00 and 0:00. Power grids' peak energy demand is comparatively low when scheduling under CPP-PDC for points A5 and B5. Reducing

关键词： Micro grid Demand response Multiobjective optimization Home energy management Distributed energy resources non-dominated sorting genetic algorithm

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Parametric optimization of micro-tool fabrication through sheet-EDG using nature-inspired algorithms

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JOURNAL OF THE BRAZILIAN SOCIETY OF MECHANICAL SCIENCES AND ENGINEERING 2024年第2期46卷 1-17页

作者： Acharya, Biswesh Ranjan Sethi, Abhijeet Das, Amit Kumar Saha, Partha Pratihar, Dilip Kumar Indian Inst Technol Kharagpur Kharagpur India Indian Inst Management Mumbai India Indian Inst Technol Kharagpur Dept Mech Engn Kharagpur India

Sheet electrical discharge grinding (sheet-EDG) is a recently developed novel method for producing high aspect ratio micro-tools. However, due to the presence of varying nature of initial tool eccentricity for fabricating each micro-tool, determining the optimal process parameters for the mentioned process is a difficult task. To overcome the above issue, an alternative experimental investigation called the slit-cut process was conducted, in which tool rotation was not used. Face-centered central composite design-based response surface methodology (RSM) was conducted to produce 20 slit-cuts on sheet electrodes with varying input parameters like voltage, sheet-EDG parameter, and radial infeed. Later, these slit-cuts were assessed and responses like volume removal rate (VRR) and electrode wear rate (EWR) were evaluated. An analysis of variance (ANOVA) is conducted for both VRR and EWR and statistically validated regression equations were developed. Also, the effects of various input parameters on responses are discussed based on the main effect plots and surface plots. Both VRR and EWR were simultaneously optimized using desirability function approach and a variety of nature-inspired optimization algorithms, including the multi-objective bonobo optimizer (MOBO), multi-objective particle swarm optimization (MOPSO), multi-objective grey wolf optimizer (MOGWO), and non-dominated sorting genetic algorithm (NSGA II). Based on the quality of the Pareto front, MOBO performed better than the other techniques. MOBO suggested changing the design ranges of voltage and radial feed from (140-180 V) to (140-156.8 V) and from (2-6 mu m/pass) to (5.2-6 mu m/pass), respectively.

关键词： Sheet electrical discharge grinding Micro-tool non-dominated sorting genetic algorithm Multi-objective particle swarm optimization Multi-objective bonobo optimizer Multi-objective grey wolf optimizer

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Multi-objective optimization for scheduling multi-load automated guided vehicles with consideration of energy consumption

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TRANSPORTATION RESEARCH PART C-EMERGING TECHNOLOGIES 2024年 161卷

作者： Huo, Xiang He, Xiaozheng Xiong, Zhongxia Wu, Xinkai Beihang Univ Sch Transportat Sci & Engn Beijing 100191 Peoples R China Beijing Robint Technol Co Ltd Xueyuan Rd Beijing 100191 Peoples R China Rensselaer Polytech Inst Dept Civil & Environm Engn Troy NY 12180 USA

The widespread implementation of modern logistics has led to the extensive use of multi-load automatic guided vehicles (m-AGVs) in automated sorting centers. This paper tackles one of the most challenging problems in this context, i.e., the scheduling of m-AGVs for express package handling. We comprehensively consider the loading and unloading delay of packages and energy consumption of m-AGVs, formulating the problem as a multi-objective mixed integer program (MO-MIP). To solve the proposed optimal scheduling problem, we apply the non-dominated sorting genetic algorithm (NSGA-II). A series of simulation experiments on three sorting centers have been further conducted to demonstrate the effectiveness and efficiency of the proposed model and solution algorithm.

关键词： Multi-load automatic guided vehicles (m-AGVs) Multi-objective mixed integer program (MO-MIP) Scheduling non-dominated sorting genetic algorithm (NSGA-II)

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Combined genetic algorithm and response surface methodology-based bi-optimization of a vertical-axis wind turbine numerically simulated using CFD

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ENERGY SCIENCE & ENGINEERING 2024年第10期12卷 4532-4548页

作者： Roshani, Mahdi Pourfayaz, Fathollah Gholami, Ali Univ Tehran Coll Interdisciplinary Sci & Technol Sch Energy & Sustainable Resources Engn Tehran Iran

In this study, computational fluid dynamic (CFD) simulation of a vertical axis wind turbine (VAWT) geometry based on the Unsteady Reynolds-Averaged Navier-Stokes equations was investigated. In addition, the relationship between the geometric parameters of the VAWT and the two response variables, that is, moment and lift force, was determined using response surface methodology (RSM). Then, the non-dominated sorting genetic algorithm (NSGA-II) was used to solve the multi-objective optimization problem. The results obtained from the RSM showed that the lift force of the turbine is more sensitive to the change in the blade chord length, and the output moment of the turbine is more sensitive to the change in the rotor radius. Using the NSGA-II multi-objective optimization algorithm and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) method, it was determined that among the optimal values of the independent variable, the most optimal response occurs in blade chord length = 0.18 m, rotor radius = 0.4 m, blade pitch angle = -3.27 degrees and number of blades = 4. In these optimal values of the independent variables, the values of the dependent variables, which included the turbine's moment and the blades' lift force, were obtained as 9.58 N m and 57.89 N, respectively. The optimum values of moment and lift force for a vertical axis wind turbine were determined by a multi-objective optimization methodology including response surface methodology (RSM), computational fluid dynamic (CFD), non-dominated sorting genetic algorithm (NSGA-II), and the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Under the optimal conditions of blade chord length = 0.18 m, rotor radius = 0.4 m, blade pitch angle = -3.27 degrees, and number of blades = 4, the values of the turbine's moment and the blades' lift force were obtained as 9.58 N m and 57.89 N, respectively. image

关键词： CFD Darrieus wind turbine non-dominated sorting genetic algorithm response surface methodology TOPSIS method wind energy

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Simultaneously maximizing microalgal biomass and lipid productivities by machine learning driven modeling, global sensitivity analysis and multi-objective optimization for sustainable biodiesel production

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APPLIED ENERGY 2024年 358卷

作者： Kumar, Ravi Ranjan Sarkar, Debasis Sen, Ramkrishna Indian Inst Technol Kharagpur Dept Biotechnol Kharagpur India Indian Inst Technol Kharagpur Dept Chem Engn Kharagpur India

Simultaneous enhancement in the productivities of microalgal biomass and lipids that are inversely correlated to each other has been a long-standing challenge before the scientists engaged in algal biodiesel research and innovation. This study, develops an Artificial Neural Network (ANN) model and uses multi-objective optimization approach to determine the process parameters, namely, light intensity, CO2%, air flow rate, and C/N ratio to maximize biomass and lipid productivities of microalga, Chlorella sorokiniana, simultaneously. Experimental data based on central composite design (CCD) were used to train a feed-forward multilayer ANN with four critical parameters. The global sensitivity analysis was performed on the trained ANN model using Sobol's method to assess the relative significance of the four process variables. Since the objectives of maximizing biomass and lipid productivities conflict with each other, multi-objective optimization approach was used for deriving the optimal process parameters, experimental validation of which resulted in approximately 3 times increase in biomass productivity and 7 times increase in lipid productivity. On transesterification of the lipid, the biodiesel product with saturated to unsaturated fatty acids ratio of 48:52 conformed very well to the international standards, ASTM D6751 and EN14214, thereby making it an environment friendly green biofuel. Thus, the present study showcases the successful application of machine learning tool in analysing the global sensitivity analysis of process variables, and employ it for multi -objective optimization towards achieving maximum microalgal biomass and lipid productivities concomitantly for potentially sustainable production of biodiesel.

关键词： Algal biomass and lipid productivities Simultaneous enhancement Machine learning approaches Artificial neural network modeling Sobol's sensitivity analysis non-dominated sorting genetic algorithm Multi-objective optimization Sustainable biodiesel production

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Mechanisms of characteristic parameters of plasma actuator on film cooling and turbulent transport based on multi-objective optimization

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APPLIED THERMAL ENGINEERING 2024年 240卷

作者： Sun, Jie Xie, Gongnan Northwestern Polytech Univ Sch Marine Sci & Technol Xian 710072 Peoples R China Northwestern Polytech Univ Sch Marine Sci & Technol POB 24 Xian 710072 Peoples R China

In this paper, film cooling effectiveness and pressure loss coefficient are taken as the objective functions to optimize the plasma characteristic parameters such as electrode position, spacing, length and actuation intensity for enhancing film cooling performance. Regression equations of two objective functions are accessed by the Response Surface Method, and non-dominated sorting genetic algorithm is applied for bi-objective optimization. Two sets of Pareto optimal solutions are available via the Technique for Order Preference by Similarity to an Ideal Solution decision method. Pareto optimal solutions are calculated by Detached Eddy Simulation to clarify the turbulent transport and heat transfer characteristics induced by the plasma. Wall-averaged cooling effectiveness and pressure loss coefficient without plasma power supply are 64.3 % and 3.9 % superior to that without electrode, respectively. The plasma fragments the hairpin vortex into a striped structure and induces a large scale streamwise vortex behind the bare electrode. The plasma weakens the velocity diffusion and turbulent heat transport in the ionization zone. Overlapping electrodes render the root-mean-square pulsation velocity and Reynolds shear stress of the optimal model 1 both less than those of the optimal model 2 without overlapping electrodes, and flow field disorder is decreased. Electrode position interferes with the action location of plasma on cooling jet. Actuation intensity affects the ionization intensity and has a maximum value of 8.88 to optimize the actuator performance. Compared with no plasma actuation, wall-averaged cooling effectiveness of optimal model 1 and optimal model 2 are enhanced by 30.7 % and 21.8 %, and pressure loss coefficient are declined by 7.7 % and 7.1 %, respectively.

关键词： Film cooling Plasma aerodynamic actuation non-dominated sorting genetic algorithm Response surface method Detached eddy simulation

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Investigating the potential for realizing life cycle net-zero energy buildings in Europe using multi-objective optimization

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ENERGY REPORTS 2024年 12卷 5648-5670页

作者： Shadram, Farshid Mukkavaara, Jani Uppsala Univ Dept Civil & Ind Engn Div Civil Engn & Built Environm S-75105 Uppsala Sweden

In pursuit of the long-term target of achieving climate neutrality by 2050, it is crucial to strive for buildings with net-zero energy balance from a life cycle perspective. This study employs multi-objective optimization, specifically the non-dominated sorting genetic algorithm II, to explore the feasibility of achieving a life cycle net-zero energy balance for a building case in diverse European climate conditions: including Gothenburg's humid continental climate (characterized by warm to hot summers and cold, snowy winters), Frankfurt's oceanic climate (featuring short, cool summers and long, relatively mild winters), and Madrid's Mediterranean climate (marked by wet, mild winters and dry, hot summers). The study examines this feasibility across three building lifespan durations: the standard 50 years, commonly applied in life cycle analyses, as well as extended lifespans of 100 and 150 years. This examination is vital due to the impact of longer building lifespans, resulting in increased operational energy demand and higher embodied energy due to the more frequent replacements of building components and systems. The findings demonstrate the feasibility of realizing a life cycle net-zero energy building across diverse European climatic conditions and lifespan durations through the identification of optimal passive, active, and renewable measures. The results emphasize the essential role of renewable measures, such as photovoltaic (PV) panels, in effectively offsetting any surplus energy use throughout the building's life cycle, ultimately enabling the achievement of a life cycle net-zero energy building. It was observed that a minimum of 276 m2 (approximate to 63.5 kWp), 246 m2 (approximate to 56.5 kWp), and 131 m2 (approximate to 30.1 kWp) of PV panels would be required to realize a life cycle net-zero energy building in Gothenburg, Frankfurt, and Madrid, respectively. Across all locations and lifespan durations, these PV panels were integrated with optimal acti

关键词： Embodied energy Energy efficiency measures Life cycle net-zero energy building Multi-objective optimization non-dominated sorting genetic algorithm Operational energy

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Reliability analysis and optimization design of magnetic fluid dynamic seal shell structure under thermal/mechanical load

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JOURNAL OF MAGNETISM AND MAGNETIC MATERIALS 2024年 597卷

作者： Mi, Chengji Liu, Junhao Zhang, Chen Deng, Yingjian Zhang, Ling Yuan, Shiyong Tang, Jiachang Hunan Univ Technol Dept Mech Engn Zhuzhou 412007 Peoples R China

In order to study the reliability of Magnetic fluid dynamic seal shell structure under thermal/mechanical load, the improving cooling structure, calculating fuzzy fatigue reliability, and collaborative optimization design of fatigue reliability and lightweight are studied in this paper. The temperature model of the shell structure under four temperature conditions is established to analyze the influence of the thermal deformation caused by excessive local temperature. The cooling structure is improved, and the maximum temperature under the maximum temperature condition is reduced by 40.154 degrees C. For the sake of calculating the reliability of the new shell structure, a strength analysis model under thermal/mechanical load is built, and the fuzzy fatigue reliability function is obtained based on Latin hypercube sampling and the second-order response surface method. Four kinds of reliability are acquired by coding the functional functions and four kinds of membership functions with MATLAB, but all of them are lower than 90 % of the industrial requirements. To improve the reliability of the new shell structure and carry out the anti-fatigue lightweight design at the same time, an approximate model was constructed based on the interval analysis method and second-order response surface method for analysis. The accuracy of the approximate model was verified by re-sampling and finite element analysis. Based on the nondominated sorting genetic algorithm, the optimal solution is determined. The optimization results show that the stress target value is reduced by 19.3 %, the weight target value is reduced by 3 %, and the fuzzy fatigue reliability reaches 94.65 %, 94.58 %, 93.89 %, and 96.31 % respectively. A new cooling structure of the Magnetic fluid dynamic seal is obtained and the maximum temperature is reduced. Moreover, the reliability of the new shell structure under thermal/mechanical load coupling is improved.

关键词： Thermal deformation Structure optimization Strength analysis Reliability function Interval analysis method non-dominated sorting genetic algorithm

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System reliability-based robust design of deep foundation pit considering multiple failure modes

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Geoscience Frontiers 2024年第2期15卷 169-182页

作者： Li Hong Xiangyu Wang Wengang Zhang Yongqin Li Runhong Zhang Chunxia Chen School of Civil Engineering Chongqing UniversityChongqing 400045China School of Design and the Built Environment Curtin UniversityBentley 6102Western AustraliaAustralia Key Laboratory of New Technology for Construction of Cities in Mountain Area Chongqing UniversityChongqing 400045China Institute of Smart City and Intelligent Transportation Southwest Jiaotong UniversityChengdu 610097China China Southwest Geotechnical Investigation&Design Institute Co. LtdChengdu 610052SichuanChina

Recently,reliability-based design is a universal method to quantify negative influence of uncertainty in geotechnical ***,for deep foundation pit,evaluating the system safety of retaining structures and finding cost-effective design points are main *** address this,this study proposes a novel system reliability-based robust design method for retaining system of deep foundation pit and illustrated this method via a simplified case history in Suzhou,*** proposed method included two parts:system reliability model and robust design *** Propagation Neural Network(BPNN)is used to fit limit state functions and conduct efficient reliability *** common source random variable(CSRV)model are used to evaluate correlation between failure modes and determine the system ***,based on the system reliability model,a robust design method is *** method aims to find cost-effective design *** solve this problem,the third generation non-dominated genetic algorithm(NSGA-III)is *** efficiency and accuracy of whole computations are improved by involving BPNN models and NSGA-III *** proposed method has a good performance in locating the balanced design point between safety and construction ***,the proposed method can provide design points with reasonable stiffness distribution.

关键词： System reliability Machine learning method non-dominated sorting genetic algorithm Robust design Multiple objective optimization models

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Multi-objective optimization of bias current coefficient based on NSGA- III for active magnetic bearing with redundant electromagnetic actuators

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ENGINEERING COMPUTATIONS 2024年第6期41卷 1549-1571页

作者： Deng, Shuai Cheng, Xin Wu, Huachun Hu, Yefa Wuhan Univ Technol Wuhan Peoples R China Res Ctr Magnet Suspens Hubei Prov Engn Technol Wuhan Peoples R China

PurposeThe multi-objective optimization configuration strategy is proposed due to the configuration of EMAs in fault-tolerant control of active magnetic bearing with redundant electromagnetic actuators involving high-dimensional, nonlinear, conflicting ***/methodology/approachA multi-objective optimization model for bias current coefficients is established based on the nonlinear model of active magnetic bearings with redundant electromagnetic actuators. Based on the non-dominated sorting genetic algorithm III, a numerical method is used to obtain feasible and non-inferior sets for the bias current ***(1) The conflicting relationship among the three optimization objectives was analyzed for various failure modes of EAMs. (2) For different EMAs' failure modes, the multi-objective optimization configuration strategy can simultaneously achieve the optimal or sub-optimal effective EMF, flux margins, and stability of EMF. Moreover, the characteristics of the optimal Pareto front are consistent with the physical properties of the AMB. (3) Compared with the feasible configuration of C0, the non-inferior configurations can significantly improve the performance of AMB, and the advantages of the multi-objective optimization configuration strategy become more prominent as the asymmetry of the residual supporting structure ***/valuei) Considering the variation of the rotor displacement during the support reconstruction, a decision-making model that can accurately characterize the dynamic performance of AMB is presented. (ii) The interaction law between AMB and rotor under different failure modes of EMAs is analyzed, and the configuration principles for redundant EMAs are proposed. (iii) Based on the dynamic characteristics of AMB during the support reconstruction, effective EMF, energy consumption, and the Pearson correlation coefficient between the desired EMFs and the decoupled control currents are used as objective functions. iv. T

关键词： Active magnetic bearing (AMB) Electromagnetic actuators (EMAs) Multi-objective optimization non-dominated sorting genetic algorithm

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