GEO + ES Hybrid Optimization Algorithm Applied to the Parametric Thermal Model Estimation of a 200N Hydrazine Thruster

Abstract

In the present paper, a hybrid version of the Generalized Extremal Optimization (GEO) and Evolution Strategies (ES) algorithms [1], developed in order to conjugate the convergence properties of GEO with the self-tuning characteristics present in the ES, is applied to the estimation of the temperature distribution of the film cooling near the internal wall of a thruster. The temperature profile is determined through an inverse problem approach using the hybrid. The profile was obtained for steady-state conditions, were the external wall temperature along the thruster is considered as a known input. The Boltzmann’s equation parameters [2], which define the cooling film temperature profile, are the design variables. Results using simulated data showed that this approach was efficient in recuperating those parameters. The approach showed here can be used on the design of thrusters with lower wall temperatures, which is a desirable feature of such devices.

Implementação Adaptativa de Variante do Algoritmo de Otimização Extrema Generalizada (GEO)

“…Pesquisas foram realizadas com o GEO a fim de melhorar sua eficiência e desenvolver diversas versões do mesmo, como híbrida [Galski et al 2011], paralela [Galski 2006] e multi-objetivo com codificação real [Mainenti-Lopes, Souza e De Sousa 2012]. O GEO e suas versões vem sendo aplicado com sucesso em muitos problemas de otimização em engenharia, como transferência radiativa [De Sousa et al 2007], projeto de sistemas ópticos [De Albuquerque, De Sousa e Montes 2016], veículos autônomos [Freitas, De Sousa e Macau 2018] e sistemas térmicos [Muraoka et al 2006].…”