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Browsing by Subject "Multi-objective optimization"
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Item An Integrated Nanocomposite Proximity Sensor: Machine Learning-Based Optimization, Simulation, and Experiment(MDPI, 2022-04-08) Moheimani, Reza; Gonzalez, Marcial; Dalir, Hamid; Mechanical and Energy Engineering, School of Engineering and TechnologyThis paper utilizes multi-objective optimization for efficient fabrication of a novel Carbon Nanotube (CNT) based nanocomposite proximity sensor. A previously developed model is utilized to generate a large data set required for optimization which included dimensions of the film sensor, applied excitation frequency, medium permittivity, and resistivity of sensor dielectric, to maximize sensor sensitivity and minimize the cost of the material used. To decrease the runtime of the original model, an artificial neural network (ANN) is implemented by generating a one-thousand samples data set to create and train a black-box model. This model is used as the fitness function of a genetic algorithm (GA) model for dual-objective optimization. We also represented the 2D Pareto Frontier of optimum solutions and scatters of distribution. A parametric study is also performed to discern the effects of the various device parameters. The results provide a wide range of geometrical data leading to the maximum sensitivity at the minimum cost of conductive nanoparticles. The innovative contribution of this research is the combination of GA and ANN, which results in a fast and accurate optimization scheme.Item Multiobjective Optimization of Composite Square Tube for Crashworthiness Requirements Using Artificial Neural Network and Genetic Algorithm(2021-12) Zende, Pradnya; Dalir, Hamid; Agarwal, Mangilal; Tovar, AndresDesign optimization of composite structures is of importance in the automotive, aerospace, and energy industry. The majority of optimization methods applied to laminated composites consider linear or simplified nonlinear models. Also, various techniques lack the ability to consider the composite failure criteria. Using artificial neural networks approximates the objective function to make it possible to use other techniques to solve the optimization problem. The present work describes an optimization process used to find the optimum design to meet crashworthiness requirements which includes minimizing peak crushing force and specific energy absorption for a square tube. The design variables include the number of plies, ply angle and ply thickness of the square tube. To obtain an effective approximation an artificial neural network (ANN) is used. Training data for the artificial neural network is obtained by crash analysis of a square tube for various samples using LS DYNA. The sampling plan is created using Latin Hypercube Sampling. The square tube is considered to be impacted by the rigid wall with fixed velocity and rigid body acceleration, force versus displacement curves are plotted to obtain values for crushing force, deceleration, crush length and specific energy absorbed. The optimized values for the square tube to fulfill the crashworthiness requirements are obtained using an artificial neural network combined with Multi-Objective Genetic Algorithms (MOGA). MOGA finds optimum values in the feasible design space. Optimal solutions obtained are presented by the Pareto frontier curve. The optimization is performed with accuracy considering 5% error.Item Physics-Based Modelling and Simulation Framework for Multi-Objective Optimization of Lithium-Ion Cells in Electric Vehicle Applications(2022-05) Gaonkar, Ashwin; El-Mounayri, Hazim; Tovar, Andres; Zhu, Likun; Shin, HosopIn the last years, lithium-ion batteries (LIBs) have become the most important energy storage system for consumer electronics, electric vehicles, and smart grids. The development of lithium-ion batteries (LIBs) based on current practice allows an energy density increase estimated at 10% per year. However, the required power for portable electronic devices is predicted to increase at a much faster rate, namely 20% per year. Similarly, the global electric vehicle battery capacity is expected to increase from around 170 GWh per year today to 1.5 TWh per year in 2030--this is an increase of 125% per year. Without a breakthrough in battery design technology, it will be difficult to keep up with the increasing energy demand. To that end, a design methodology to accelerate the LIB development is needed. This can be achieved through the integration of electro-chemical numerical simulations and machine learning algorithms. To help this cause, this study develops a design methodology and framework using Simcenter Battery Design Studio® (BDS) and Bayesian optimization for design and optimization of cylindrical cell type 18650. The materials of the cathode are Nickel-Cobalt-Aluminum (NCA)/Nickel-Manganese-Cobalt-Aluminum (NMCA), anode is graphite, and electrolyte is Lithium hexafluorophosphate (LiPF6). Bayesian optimization has emerged as a powerful gradient-free optimization methodology to solve optimization problems that involve the evaluation of expensive black-box functions. The black-box functions are simulations of the cyclic performance test in Simcenter Battery Design Studio. The physics model used for this study is based on full system model described by Fuller and Newman. It uses Butler-Volmer Equation for ion-transportation across an interface and solvent diffusion model (Ploehn Model) for Aging of Lithium-Ion Battery Cells. The BDS model considers effects of SEI, cell electrode and microstructure dimensions, and charge-discharge rates to simulate battery degradation. Two objectives are optimized: maximization of the specific energy and minimization of the capacity fade. We perform global sensitivity analysis and see that thickness and porosity of the coating of the LIB electrodes that affect the objective functions the most. As such the design variables selected for this study are thickness and porosity of the electrodes. The thickness is restricted to vary from 22microns to 240microns and the porosity varies from 0.22 to 0.54. Two case studies are carried out using the above-mentioned objective functions and parameters. In the first study, cycling tests of 18650 NCA cathode Li-ion cells are simulated. The cells are charged and discharged using a constant 0.2C rate for 500 cycles. In the second case study a cathode active material more relevant to the electric vehicle industry, Nickel-Manganese-Cobalt-Aluminum (NMCA), is used. Here, the cells are cycled for 5 different charge-discharge scenarios to replicate charge-discharge scenario that an EVs battery module experiences. The results show that the design and optimization methodology can identify cells to satisfy the design objective that extend and improve the pareto front outside the original sampling plan for several practical charge-discharge scenarios which maximize energy density and minimize capacity fade.