Abstract

Structural optimization problems aim at increasing the performance of the structure while decreasing its costs guaranteeing, however, the applicable safety requirements. As these aspects are conflicting, the formulation of the structural optimization problem as multiobjective is natural but uncommon, and has the advantage of presenting a diverse set of solutions to the decision maker(s). The literature shows that Evolutionary Algorithms (EAs) are effective to obtain solutions in multiobjective optimization problems, and that the Differential Evolution (DE) based ones are efficient when solving structural mono-objective structural optimization problems, specially those with a real encoding of the design variables. On the other hand, one can note that DE has not yet been applied to the multiobjective version of these problems. This article presents a performance analysis of a DE-based algorithm in five multiobjective structural optimization problems. The obtained results are compared to those found in the literature, and the comparisons indicate the potential of the proposed algorithm.

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

Abstract

Floods produce enormous human and material losses every year. Evaluating their extent and severity, and especially simulating possible future scenarios can improve the response, mitigation and prevention of the effects of this phenomenon. This paper presents a methodology to reproduce the extent of floods produced by channel overflows and recorded by satellite images, identifying the maximum discharge that produced it by means of the numerical solution of the 2D shallow water equations and Differential Evolution. The objective is to minimize the difference between the extent of the flooded area recorded in the satellite images, and that obtained in the simulation by adjusting the maximum value of the flow curve used at the entrance of the channel in the solution domain. The proposal is applied to data and images corresponding to an area located south of the city of Villahermosa, in the Mexican state of Tabasco, which is an area susceptible to flooding by the overflow of the Río de la Sierra. Our proposal shows that it is possible to have more accurate information on the extent and height of water in the flooded area than that shown by the satellite, which can be used as information for prevention and mitigation plans for the adverse effects of flooding. Palabras clave: Modelado de inundaciones, gasto máximo en ríos, Iber, evolución diferencial

Abstract

Abstract

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

Abstract

Abstract

This work presents a simpler numerical design method for efficiency maximization of an Hydraulic Jet Pump (HJP) for oil-well extraction process, considering its hydraulic and geometric parameters. The design process consists in setting and solving a constrained non-linear optimization problem by taking into account the hydraulic model of the HJP in terms four design variables: throat area, nozzle area, injection flow, and injection pressure to the oil-well. The objective function of this case aims to maximize the HJP's efficiency avoiding to approach cavitation condition as well fulfilling technical constraints. A numerical technique, Differential Evolution Algorithm (DEA), has been implemented to solve the optimization problem. The proposed methodology leads to a solution set by considering only commercial geometries and feasible operating conditions for the HJP, which facilitates its practical implementation. A set of ten oil-wells with land production data, operating in the southeaster of Mexico, is used to compare and validate several Jet pump designs, i. e., through comparison with actual oil-well's operation condition.