Investigation on Effective Sampling Strategy for Multi-objective Design Optimization of RBCC Propulsion Systems via Surrogate-assisted Evolutionary Algorithms

Tuan Quang Ho, Hideaki Ogawa, Cees Bil

Research output: Contribution to journalConference article

1 Citation (Scopus)

Abstract

Rocket-based combined cycle (RBCC) engines are an airbreathing propulsion technology that offers considerable potential for efficient access-to-space. Successful design of RBCC-powered space transport systems requires reliable databases for both vehicle and engine performance, calling for an effective sampling method to accurately resolve non-linear characteristics in vast design space. This paper presents an optimal sampling strategy based on the function gradients to realize efficient database construction based on evolutionary algorithms and assesses its effectiveness by applying the methodology to various test functions with multiple objectives as well as surrogate models representing scramjet intake characteristics for validation.

Original languageEnglish
Pages (from-to)1252-1262
Number of pages11
JournalProcedia Engineering
Volume99
DOIs
Publication statusPublished - Jan 1 2015
EventAsia-Pacific International Symposium on Aerospace Technology, APISAT 2014 - Shanghai, China
Duration: Sep 24 2014Sep 26 2014

Fingerprint

Rockets
Evolutionary algorithms
Propulsion
Sampling
Engines
Design optimization

All Science Journal Classification (ASJC) codes

  • Engineering(all)

Cite this

Investigation on Effective Sampling Strategy for Multi-objective Design Optimization of RBCC Propulsion Systems via Surrogate-assisted Evolutionary Algorithms. / Ho, Tuan Quang; Ogawa, Hideaki; Bil, Cees.

In: Procedia Engineering, Vol. 99, 01.01.2015, p. 1252-1262.

Research output: Contribution to journalConference article

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