Abstract

After definition of the discrete grey stochastic variable and its expected value, the expected probability degree is defined. For multi-criteria decision-making problems, in which the criteria weights are incompletely certain and the criteria values of alternatives are in the form of grey stochastic variables, a grey stochastic multi-criteria decision-making approach is proposed. In this method, the evaluation value of each alternative under each criterion can be transformed to comprise the expected probability degree judgment matrix, based on which, a non-linear programming model can be enacted. In the end, the genetic algorithm is used to solve the model to attain the criteria weights, and the ranking of alternatives can be produced consequently. The feasibility and validity of this approach are illustrated by an example.

Abstract

Abstract

Abstract

Concrete gravity dams are the most commonly used hydraulic structures in engineering, for the designer these structures, the overall stability (external forces) must be guaranteed as well as the internal stresses that element is subjected to. The scaling mechanisms these structures are presented in the manual Critérios de Projeto Civil de Usinas Hidroelétricas (2003) from Eletrobras, which is strongly influenced by the U.S. Bureau of Reclamation (1976). After an analysis of the proposed design forms, it was observed a tendency to oversize these structures, in order to reduce this oversizing and to obtain slender forms of dam, the present work brings an optimization study for the cross sections of concrete gravity dams, this optimization will be made by means of routines used in Matlab software, the genetic algorithm technique will be used to reduce the cross-section of this type of dam and consequently reduce the volume of concrete used in its construction.

Abstract

Concrete gravity dams have trapezoidal shape in their cross section and shall guarantee the global stability against acting loads like hydrostatic and uplift pressures through his gravitational actions (self-weight and others). This study focuses on the shape optimization of concrete gravity dams using genetic algorithms. In this case, the dam cross section area is considered as the objective function and the design variables are the geometric parameters of the gravity dam. The optimum cross-section of a concrete gravity dam is achieved by the Genetic Algorithm (GA) through a Matlab routine developed by the author. Sliding, overturning and floating verifications are implemented in the program. In order to assess the efficiency of the proposed methodology for gravity dams optimization, one application is presented adopting the concrete gravity dam of Belo Monte Hydropower Plant (HPP), considering normal loading condition and others assumptions presented.

Abstract

Practical size structural optimization problems can involve a large number of variables and constraints that meet regulatory requirements for safety and structural performance. Most optimization problems tend to find the minimum value of the objective function within a feasible set that satisfies the constraints. Among evolutionary computation techniques, genetic algorithms (GAs) have been successfully used for the optimization of structures, including lattice systems. This article proposes an automated interactive methodology for the optimization of structures based on the integration of two commercial programs: Ansys and Matlab. The developed script uses the Finite Element Method for the analysis of the structure, together with the Genetic Algorithms for the optimization. The objective of the article is to evaluate the applicability, precision and efficiency of the proposed methodology. Two numerical examples of trusses were solved with the proposed methodology, classic truss of the literature and truss with normative restrictions. The results show that the methodology is adequate for the solution of size structural optimization problems with a good precision of the results.

Abstract

Since 1947, when Schubauer and Skramstad established the basis of the technology with its revolutionary work about steady state tools and mechanisms for the flow management, the progress of the flow control technology and the development of devices have progressed constantly. Anyway, the applicability of such devices is limited, and only few of them have arrived to the assembly workshop. The problem is that the range of actuation is still limited. Despite their operability limitations, flow control devices are of great interest for the aeronautical industry. The number of projects investigating this technology demonstrates the relevance of in the Fluid Dynamic field. The scientific interest focus not only on the industrial applications and the improvement of the technology, but also on the deep understanding of the physical phenomena associated to the flow separation, turbulence formation associated to the final drag reduction aim. A clear example of what has been mentioned is the EC MARS research project (MARS project, FP7 project number 266326, [2]). Its objectives are aimed to a better understanding of the Reynolds Stress and turbulent flow related to both drag reduction and flow control. The research was carried out through the analysis of several flow control devices and the optimization of the parameters for some of them was an important element of the research. When solving a traditional fluid dynamics optimisation problem numerical flow analysis are used instead of experimental ones due to their lower cost and shorter needed time for evaluation of candidate solutions. Nevertheless, in the particular case of the selected flow control plasma devices the experimental measurement of the performance of each candidate configuration has been much quicker than a numerical analysis. For this reason, the corresponding optimisation problem has been solved by coupling an evolutionary optimization algorithm with an experimental device. This paper discusses the design quality and efficiency gained by this innovative coupling.

Abstract

Inter-domain traffic engineering is a key issue when QoS-aware resource optimization is concerned. Mapping inter-domain traffic flows into existing service level agreements is, in general, a complex problem, for which some algorithms have recently been proposed in the literature. In this paper a modified version of a multi-objective genetic algorithm is proposed, in order to optimize the utilization of domain resources from several perspectives: bandwidth, monetary cost, and routing trustworthiness. Results show trade-off solutions and “optimal” solutions for each perspective. The proposal is a useful tool in inter-domain management because it can assist and simplify the decision process. Document type: Part of book or chapter of book

Abstract

Abstract