Thermal Analysis and Shape Optimization of an In-Space Radiator Using Genetic Algorithms

Abstract The hybridization of different optimization methods have been used to find the optimum solution of design problems. While random search techniques, such as genetic algorithms and simulated annealing, have a high probability of achieving global optimality, they usually arrive at a near optimal solution due to their random nature. On the other hand direct search methods are efficient optimization techniques but linger in local minima if the objective function is multi-modal. This paper presents the optimization of C-frame cross-section using a hybrid optimization algorithm. Real coded genetic algorithms are used as a random search method, while Nelder-Mead is used as a direct search method, where the result of the genetic algorithm search is used as the starting point of direct search. Traditionally, the cross-section of C-frame belonged to a set of primitive shapes, which included I, T, trapezoidal, circular and rectangular sections. The cross-sectional shape is represented by a non-uniform rational B-Splines (NURBS) in order to give it a kind of shape flexibility. The results showed that the use of Nelder-Mead with Real coded Genetic Algorithms has been very significant in improving the optimum shape of a solid C-frame cross-section subjected to a combined tension and bending stresses. The hybrid optimization method could be extended to more complex shape optimization problems.

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Shape Optimization of a Camoid Follower Surface

Volume 1: 34th Design Automation Conference, Parts A and B ◽

10.1115/detc2008-49659 ◽

2008 ◽

Author(s):

Karim A. Aguib ◽

Keith A. Hekman ◽

Ashraf O. Nassef

Keyword(s):

Genetic Algorithms ◽

Shape Optimization ◽

Three Dimensional ◽

Characteristic Surface ◽

Design Constraints ◽

B Splines ◽

Surface Interpolation ◽

Specific Objective ◽

Interpolation Technique ◽

Real Coded Genetic Algorithms

Camoids are three dimensional cams that can produce more complex follower output than plain disc cams. A camoid follower motion is described by a surface rather than a curve. The camoid profile can be directly synthesized once the follower surface is fully described. To define a camoid follower motion surface it is required that the surface pass by all predefined constraints. Constraints can be follower position, velocity and acceleration. These design constraints are scattered all along the camoid follower surface. Hence a fitting technique is needed to satisfy these constraints which include position and its derivatives (velocity and acceleration). Furthermore if the fitting function can be of a parametric nature, then it would be possible to optimize the follower surface to obtain better performance according to a specific objective. Previous research has established a method to fit camoid follower surface positions, but did not tackle the satisfaction of derivative constraints. This paper presents a method for defining a camoid follower characteristic surface B-Splines on two steps first synthesizing the sectional cam curves then using a surface interpolation technique to generate the follower characteristic surface. The fitting technique is parametric in nature which allows for its optimization. Real coded Genetic algorithms are used to optimize the parameters of the surface to meet a specified objective function. A demonstration problem to illustrate the suggested methodology is presented.

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Multiobjective optimization of journal bearing using mass conserving and genetic algorithms

Proceedings of the Institution of Mechanical Engineers Part J Journal of Engineering Tribology ◽

10.1243/135065005x9844 ◽

2005 ◽

Vol 219 (3) ◽

pp. 235-248 ◽

Cited By ~ 8

Author(s):

H Hirani

Keyword(s):

Thermal Analysis ◽

Genetic Algorithms ◽

Film Thickness ◽

Journal Bearing ◽

Operating Conditions ◽

Radial Clearance ◽

Pareto Optimal ◽

Optimization Strategy ◽

Oil Viscosity ◽

Film Pressure

An optimal design of hydrodynamic journal bearing using mass conserving thermal analysis and genetic algorithms is presented. Simultaneous minimization of power loss and oil flow, subjected to constraints on film thickness, film pressure, and temperature rise between the bearing surfaces, is the objective of this study. The radial clearance, L/D ratio, oil groove location, feed pressure, and the oil viscosity are the design variables. The rank-based genetic algorithm is used to deal with the discrete variables and multimodal objective functions and to capture Pareto optimal points. In view of computation economics and robustness, initial guesses of oil film pressure distribution, eccentricity ratio, and attitude angle obtained by two-dimensional analytical approach are provided for mass conserving thermal analysis. The complete optimization strategy is illustrated by a step-by-step (in four steps) approach. A comparative study of thermal and isothermal analyses is illustrated. Effects of constraints on temperature, pressure, and film thickness on the design vector are enlightened. The mass conserving thermal analysis is validated against experimental results. Pareto optimal fronts for various operating conditions are presented.

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New Approach to Constrained Shape Optimization Using Genetic Algorithms

AIAA Journal ◽

10.2514/2.351 ◽

1998 ◽

Vol 36 (1) ◽

pp. 51-61 ◽

Cited By ~ 16

Author(s):

M. C. Sharatchandra ◽

Mihir Sen ◽

Mohamed Gad-El-Hak

Keyword(s):

Genetic Algorithms ◽

Shape Optimization ◽

New Approach

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