Discrete sizing, cross-sectional shape, topology optimization, and material selection of a framed automotive body

This study proposed a discrete structural optimization method for a framed automotive body. Up to four types of discrete design variables are considered simultaneously, that is, the sizing, cross-sectional shape, topology, and material variables. Firstly, to solve the nonconvex and nonlinear optimization problem, the original non-dominated sorting genetic algorithm, the third version (NSGA-III), is adapted. An improved extreme points identification scheme and a new mutation operator are proposed to stabilize the normalization of the population and accommodate the manufacturing constraints, respectively. Two test problems demonstrate that the modified NSGA-III can handle continuous and discontinuous multiple objective optimization. Subsequently, the classical 10-bar truss is used to illustrate the proposed method. A weight reduction of 4.5 kg is achieved as compared to previous optimal designs in the literature. Finally, a framed automotive body is optimized for maximizing the first order natural frequency and minimizing the total mass, the maximum stresses and the maximum displacements in different load cases and the manufacturing cost. The results obtained by different optimization procedures are presented and discussed. The results demonstrate the feasibility and effectiveness of the proposed method. A weight reduction of 17.59% is achieved while other structural performances satisfy the design requirements.

A methodological approach for the optimal design of the toroidal field coils of a Tokamak device using artificial intelligence

As prototypes of future commercial Tokamaks, DEMOs nuclear fusion power plants are expected to be able to produce cost-effective electrical power. In this view, an optimized design becomes crucial in the whole engineering workflow. Up to now, the design of one of the most critical components, the cross-section of each of the toroidal field coils inner leg winding pack, was performed using a sequential trial-and-error procedure. In this work, a novel comprehensive approach is proposed to include all the main design aspects into a unified tool taking advantage of Artificial Neural Networks (ANNs) for faster computation in finding optimal design configurations. This procedure overcomes several difficulties including dealing with both real-valued and discrete design variables, the significant CPU-time of magneto-structural analysis and also guarantees the optimality for the winding pack configuration. The proposed methodology was demonstrated for the 2019 ENEA DEMO configuration which includes 16 toroidal field coils, made-up of 6 3 double layers and a Wind & React manufacturing technique.

Objective and algorithm considerations when optimizing the number and placement of turbines in a wind power plant

Optimizing turbine layout is a challenging problem that has been extensively researched in the literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant in a land-constrained site. We optimized several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72 % of the profit compared to the wind power plant optimized for maximum profit, which corresponded to a loss of about USD 2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

Meta-Models and Genetic Algorithm Application to Approximate Optimization with Discrete Variables for Fire Resistance Design of A60 Class Bulkhead Penetration Piece

A60 class bulkhead penetration piece is a fire-resistance apparatus installed on bulkhead compartments to protect lives and to prevent flame diffusion in case of fire accident in ships and offshore plants. In this study, approximate optimization with discrete variables was carried out for the fire-resistance design of an A60 class bulkhead penetration piece (A60 BPP) using various meta-models and multi-island genetic algorithms. Transient heat transfer analysis was carried out to evaluate the fire-resistance design of the A60 class bulkhead penetration piece, and we verified the results of the analysis via a fire test. The design of the experiment’s method was applied to generate the meta-models to be used for the approximate optimization, and the verified results of the transient heat transfer analysis were integrated with the design of the experiment’s method. The meta-models used in the approximate optimization were response surface model, Kriging, and radial basis function-based neural network. In the approximate optimization, the bulkhead penetration piece length, diameter, material type, and insulation density were applied to discrete design variables, and constraints that were considered include temperature, productivity, and cost. The approximate optimum design problem based on the meta-model was formulated such that the discrete design variables were determined by minimizing the weight of the A60 class bulkhead penetration piece subject to the limit values of constraints. In the context of approximate accuracy, the solution results from the approximate optimization were compared to actual analysis results. It was concluded that the radial basis function-based neural network, among the meta-models used in the approximate optimization, showed the most accurate optimum design results for the fire-resistance design of the A60 class bulkhead penetration piece.

Objective and Algorithm Considerations When Optimizing the Number and Placement of Turbines in a Wind Power Plant

Optimizing turbine layout is a challenging problem that has been extensively researched in literature. However, optimizing the number of turbines within a given boundary has not been studied as extensively and is a difficult problem because it introduces discrete design variables and a discontinuous design space. An essential step in performing wind power plant layout optimization is to define the objective function, or value, that is used to express what is valuable to a wind power plant developer, such as annual energy production, cost of energy, or profit. In this paper, we demonstrate the importance of selecting the appropriate objective function when optimizing a wind power plant. We optimize several different wind power plants with different wind resources and boundary sizes. Results show that the optimal number of turbines varies drastically depending on the objective function. For a simple, one-dimensional, land-based scenario, we found that a wind power plant optimized for minimal cost of energy produced just 72 % of the profit as the wind power plant optimized for maximum profit, which corresponded to a loss of about $2 million each year. This paper also compares the performance of several different optimization algorithms, including a novel repeated-sweep algorithm that we developed. We found that the performance of each algorithm depended on the number of design variables in the problem as well as the objective function.

Optimization of an On-Grid Inverter for PV Applications Using Genetic Algorithms

A new approach to the optimal design of power inverters for on-grid photovoltaic systems that uses genetic algorithms (GA) is provided in this article. The nonlinear average model is adopted to model the conversion stage in order to accurately evaluate and quickly estimate the power losses of the power devices. The hysteresis current control that guarantees a quasi-sinusoidal output current is applied to generate the inverter control signals. The design of the solar inverter must meet three contradictory objectives that need to be optimized at the same time. In fact, the aim is to maximize the efficiency of the converter while minimizing its size and price under electrical constraints. The problem variables are the output current ripple and the passive and active components available on the market (IGBTs/MOSFETs, Diodes, Inductors). NSGA-II (Elitist Nondominated Sorting Genetic Algorithm) is appropriate in the case where discrete design variables are used to search for optimal Pareto solutions. It carries out a systematic and efficient search among the developed databases for a set of components which define the optimal structures of the inverter. The introduced method makes the design task easier since the best solutions depend on the components available on the market and significantly reduces the time to market for manufacturers.

Comparative Structural Optimization Study of Composite and Aluminum Horizontal Tail Plane of a Helicopter

This work presents structural optimization studies of aluminum and composite material horizontal tail plane of a helicopter by using MSC. NASTRAN SOL200 optimization capabilities. Structural design process starts from conceptual design phase, and structural layout design is performed by using CATIA. In the preliminary design phase, study focuses on the minimum weight optimization with multiple design variables and similar constraints for both materials. Aerodynamic load calculation is performed using ANSYS and the finite element model of the horizontal tail plane is created by using MSC.PATRAN. According to the characteristics of materials, design variables are chosen. For the aluminum horizontal tail, thickness and flange areas are used as the design variables; and for composite horizontal tail, attention is mainly focused on the ply numbers and ply orientation angles of the laminated composite panels. By considering the manufacturability issues, discrete design variables are used. For three different mesh densities, different initial values of the design variables, and similar design constraints, optimizations are repeated and the results of optimizations are examined and compared with each other. In the optimizations performed, constraints are taken as strength and local buckling constraints. It is shown that the optimization methodology used in this study gives confident results for optimizing structures in the preliminary design phase.

A Brief Review of Firefly Algorithm: Application in Structural Optimization Problem

Firefly Algorithm is a recently developed meta-heuristic algorithm, which is inspired by the flashing behaviour of Firefly. Initially, Firefly algorithm was used to solve the optimization problems of continuous search domain. Further, many researchers have successfully implemented this algorithm in several discrete optimization problems. Although the firefly algorithm behaves like another meta-heuristic method (i.e. Particle Swarm Optimization particle), however, the firefly is robust than that. Due to the presence of an exponential term in its movement equation, firefly algorithm is capable to search optimum value more efficiently than others. This study is, mainly, focused to show the strength of the firefly algorithm to solve the complex problems and to explore the possible research area on the structural engineering field. This study shows about the robustness of the firefly algorithm on the basis of recently published papers that was used to solve the size, shape and topology optimization of the spatial truss structure with discrete design variables. The review result shows that the performance of the Firefly Algorithm is remarkable compared to other nature-inspired-algorithms, such as particle swarm optimization. This study concludes with some remarkable points that will be more beneficial to the future researchers of this area.