Heat Pipe Design Through Generalized Extremal Optimization

Abstract

In this paper, an application of the Generalized Extremal Optimization (GEO) algorithm to the optimization of a heat pipe (HP) for a space application is presented. The GEO algorithm is a generalization of the Extremal Optimization (EO) algorithm, devised to be applied readily to a broad class of design optimization problems regardless of the design space complexity it would face. It is easy to implement, does not make use of derivatives, and can be applied to either unconstrained or constrained problems with continuous, discrete, or integer variables. The GEO algorithm has been tested in a series of test functions and shows to be competitive to other stochastic algorithms, such as the Genetic Algorithm. In this work, it is applied to the problem of minimizing the mass of an HP as a function of a desirable heat transport capability and a given temperature on the condenser. The optimal solutions were obtained for different heat loads, heat sink temperatures, and three working fluids: ammonia, methanol, and ethanol. The present design application highlights the GEO features of being easily implemented and efficient on tackling optimization problems when the objective function presents design variables with strong nonlinear interactions and is subject to multiple constraints.

F. M. Ramos acknowledges the support of CNPq-Brazil through the research grant 300171/97-8. V. V. Vlassov acknowledges the support of CNPq-Brazil through the research grant 300034/96-2/2002.