Abstract

Teaching–Learning-Based Optimization (TLBO) algorithms simulate the teaching–learning phenomenon of a classroom to solve multi-dimensional, linear and nonlinear problems with appreciable efficiency. In this paper, the basic TLBO algorithm is improved to enhance its exploration and exploitation capacities by introducing the concept of number of teachers, adaptive teaching factor, tutorial training and self motivated learning. Performance of the improved TLBO algorithm is assessed by implementing it on a range of standard unconstrained benchmark functions having different characteristics. The results of optimization obtained using the improved TLBO algorithm are validated by comparing them with those obtained using the basic TLBO and other optimization algorithms available in the literature.

Abstract

Optimization of mechanical components is an important aspect of the engineering process; a well designed system will lead to money saving during the production phase and better machine life. In oil refineries and petrochemical plants, furnaces are incorporated into the process to supply thermal energy (heat) generated by fuel combustion, which is transmitted to a fluid flowing within a tube bundle, i.e., a coil. An important internal component of the furnace oil refinery is the system that supports the tube coil. This work is part of a research whose goal is to generate a new methodology for the investigation and search of the supports of the radiation zone refining furnace. This methodology represents as a whole the possibility of application of some evolutionary algorithms in the area of mechanical design of furnaces. For the optimization of the lateral supports two evolutionary algorithms are used, genetic algorithm and particle swarm algorithm. In this work a comparison between the two optimization methods chosen is carried out. Recent techniques of dynamic penalty, specifically adaptive penalty method are incorporated into the algorithms. The serviceability and strength requirements are considered in the design problem as specified in API560. The results show that the model converges to a very efficient solution without any engineer intervention. A real example is included to demonstrate the efficiency of the algorithm.

Abstract

Abstract

Shape optimization is a largely studied problem in aeronautics. It can be applied to many disciplines in this field, namely efficiency improvement of engine blades, noise reduction of engine nozzles, or reduction of the fuel consumption of aircraft. Optimization for general purposes is also of increasing interest in many other fields. Traditionally, optimization procedures were based on deterministic methodologies as in Hamalainen et al (2000), where the optimum working point was fixed. However, not considering what happens in the vicinity of the defined working conditions can produce problems like loose of efficiency and performance. That is, in many cases, if the real working point differs from the original, even a little distance, efficiency is reduced considerably as pointed out in Huyse and
Lewis (2001). Non deterministic methodologies have been applied to many fields (Papadrakakis, Lagaros and Tsompanakis, 1998; Plevris, Lagaros and Papadrakakis, 2005). One of the most extended nondeterministic methodologies is the stochastic analysis. The time consuming calculations required on Computational Fluid Dynamics (CFD) has prevented an extensive application of the stochastic analysis to shape optimization. Stochastic analysis was firstly developed in structural mechanics, several years ago. Uncertainty quantification and variability studies can help to deal with intrinsic errors of the processes or methods. The result to consider for design optimization is no longer a point, but a range of values that defines the area where, in average, optimal output values are obtained. The optimal value could be worse than other optima, but considering its
vicinity, it is clearly the most robust regarding input variability. Uncertainty quantification is a topic of increasing interest from the last few years. It provides several techniques to evaluate uncertainty input parameters and their effects on the outcomes.

This research presents a methodology to integrate evolutionary algorithms and stochastic analysis, in order to deal with uncertainty and to obtain robust solutions.

Abstract

This work analyzes the influence of the discretization error associated with the finite element (FE) analyses of each design configuration proposed by the structural shape optimization algorithms over the behavior of the algorithm. The paper clearly shows that if FE analyses are not accurate enough, the final solution provided by the optimization algorithm will neither be optimal nor satisfy the constraints. The need for the use of adaptive FE analysis techniques in shape optimum design will be shown. The paper proposes the combination of two strategies to reduce the computational cost related to the use of mesh adaptivity in evolutionary optimization algorithms: (a) the use of an algorithm for the mesh generation by projection of the discretization error, which reduces the computational cost associated with the adaptive FE analysis of each geometrical configuration and (b) the successive increase of the required accuracy of the FE analyses in order to obtain a considerable reduction of the computational cost in the early stages of the optimization process.

Abstract

Evolutionary methods are a powerful and robust tool for the solution of structural shape optimization problems. Nevertheless, the use of these methods requires the structural analysis of an important number of different designs, this making the computational cost of the analysis of each design a critical issue. For this reason, each design must be analyzed at a minimum computational cost but ensuring a minimum quality of the results.

It is well known that the cheapest mesh for producing a solution with a fixed quality at minimum cost is an adapted one. Nevertheless, traditional adapted meshes are obtained from adaptive remeshing strategies, where each design has to be analyzed more than once, thereby also causing a high computational cost.

This work presents a new strategy that allows generating an adapted mesh for each design without the necessity of performing a full adaptive remeshing procedure for each of them. It is based on the use of sensitivity analysis of all magnitudes related with adaptive remeshing (location of nodes, error estimation, etc.) with respect to the design variables. This sensitivity analysis is performed only once using a geometry of reference and it is used to project the results of the corresponding analysis to all other designs to be analyzed. The projected information allows generating an appropriate adapted mesh for each new design in one shot, greatly reducing the computational cost compared with standard strategies.

Abstract

This paper presents a research work on stacking sequence design optimisation for multilayered composite plate using a parallel/distributed evolutionary algorithm. The stacking sequence of fibres has a dramatic influence on the strength of multilayered composite plates. Multiple layers of fibre-reinforced material systems offer versatility in engineering material design due to the fact that the stacking sequence of each orthotropic layer can offer full advantage of superior mechanical properties. Numerical results show that the optimal composite structures have lower weight, higher stiffness and also affordable cost when compared to the extreme and intermediate composite structures. In addition, the benefits of using a parallel optimisation system are also presented.

Abstract

There are many applications in aeronautical/aerospace engineering where some values of the design parameters/states cannot be provided or determined accurately. These values can be related to the geometry (wingspan, length, angles) and or to operational flight conditions that vary due to the presence of uncertainty parameters (Mach, angle of attack, air density and temperature, etc.). These uncertainty design parameters cannot be ignored in engineering design and must be taken into the optimisation task to produce more realistic and reliable solutions. In this paper, a robust/uncertainty design method with statistical constraints is introduced to produce a set of reliable solutions which have high performance and low sensitivity. Robust design concept coupled with Multi-Objective Evolutionary Algorithms (MOEAs) is defined by applying two statistical sampling formulas; mean and variance/standard deviation associated with the optimisation fitness/objective functions. The methodology is based on a canonical evolution strategy and incorporates the concepts of hierarchical topology, parallel computing and asynchronous evaluation. It is implemented for two practical Unmanned Aerial System (UAS) design problems; the first case considers robust multi-objective (single-disciplinary: aerodynamics) design optimisation and the second considers a robust multidisciplinary (aero-structures) design optimisation. Numerical results show that the solutions obtained by the robust design method with statistical constraints have a more reliable performance and sensitivity in both aerodynamics and structures when compared to the baseline design. KeywordsRobust/uncertainty design–Multi-objective–Multidisciplinary–UAS–Evolutionary algorithms

Abstract

In this paper, the aerodynamic shape optimization problems with uncertain operating conditions have been addressed. After a review of robust control theory and the possible approaches to take into account uncertainties, the use of Taguchi robust design methods in order to overcome single point design problems in aerodynamics is proposed. Under the Taguchi concept, a design with uncertainties is converted into an optimization problem with two objectives which are the mean performance and its variance, so that the solutions are as less sensitive to the uncertainty of the input parameters as possible. Furthermore, the modified non‐dominated sorting genetic algorithms are used to capture a set of compromised solutions (Pareto front) between these two objectives. The flow field is analyzed by Navier–Stokes computation using an unstructured mesh. In order to reduce the number of expensive evaluations of the fitness function a response surface modeling is employed to estimate the fitness value using the polynomial approximation model. During the solution of the optimization problem, a semi‐torsional spring analogy is used for the adaption of the computational mesh to all the obtained geometrical configurations. The proposed approach is applied to the robust optimization of the 2D high‐lift devices of a business aircraft by maximizing the mean and minimizing the variance of the lift coefficients with uncertain free‐stream angle of attack at landing flight condition.

Abstract

This work analyzes the influence of the discretization error contained in the Finite Element (FE) analyses of each design configuration proposed by structural shape optimization algorithms over the behaviour of the algorithm. If the FE analyses are not accurate enough, the final solution will neither be optimal nor satisfy the constraints. The need for the use of adaptive FE analysis techniques in shape optimum design will be shown. The paper also proposes the use of the algorithm described in [1] in order to reduce the computational cost associated to the adaptive FE analysis of each geometrical configuration when evolutive optimization algorithms are used.