Evaluation of Multidisciplinary Optimazation (MDO) Techniques Applied to a Reusable Launch Vehicle

Brown, Nichols F.; Olds, John R.

doi:10.2514/6.2005-707

Cited by 18 publications

(8 citation statements)

References 8 publications

(8 reference statements)

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“…Significant research has been performed in rocket-based vehicle design optimization using various evolutionary techniques [8][9][10][11][12][13][14][15][16][17][18][19] . Cost has also been considered in some studies [20][21][22] .…”

Section: B Recent Launch Vehicle Optimization Workmentioning

confidence: 99%

Design Optimization of a Space Launch Vehicle Using a Genetic Algorithm

Bayley

Hartfield

Burkhalter

et al. 2007

48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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This paper describes an effort to optimize the design of an entire space launch vehicle to low-Earth (circular) orbit, consisting of multiple stages using a genetic algorithm (GA) with the goal of minimizing vehicle weight and ultimately vehicle cost. The entire launch vehicle system is analyzed using various multistage configurations to reach low-Earth orbit. Specifically, three and four-stage solid propellant vehicles have been analyzed. The vehicle performance modeling requires that analysis from four separate disciplines be integrated into the design optimization process. The disciplines of propulsion characteristics, aerodynamics, mass properties and flight dynamics have been integrated to produce a high fidelity system model of the entire vehicle. In addition, the system model has been validated using existing launch vehicle data. The cost model is mass-based and uses extensive historical data to produce a cost estimating relationship for a solid propellant vehicle. For the design optimization, the goals of the problem are for the GA to minimize the differences between the desired and actual orbital parameters. This ensures the payload achieves the desired orbit. One final goal is to minimize the overall vehicle mass thus minimizing the system cost per launch. The paper will represent the first effort of its kind to minimize solid propellant launch vehicle cost at the preliminary design level using a GA.

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Section: B Recent Launch Vehicle Optimization Workmentioning

confidence: 99%

Design Optimization of a Space Launch Vehicle Using a Genetic Algorithm

Bayley

Hartfield

Burkhalter

et al. 2007

48th AIAA/ASME/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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show abstract

“…In previous BLISS studies 3,8,9 , polynomial RS models have been used. The response surface methodology 10 was originally developed in experimental sciences, but it has become popular to fit computer simulation results.…”

Section: Choice Of Surrogate Model For Blissmentioning

confidence: 99%

Flexible Approximation Model Approach for Bi-Level Integrated System Synthesis

Kim¹,

Ragon²,

Soremekun³

et al. 2004

10th AIAA/ISSMO Multidisciplinary Analysis and Optimization Conference

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Bi-Level Integrated System Synthesis (BLISS) is an approach that allows design problems to be naturally decomposed into a set of subsystem optimizations and a single system optimization. In the BLISS approach, approximate mathematical models are used to transfer information from the subsystem optimizations to the system optimization. Accurate approximation models are therefore critical to the success of the BLISS procedure. In this paper, new capabilities that are being developed to generate accurate approximation models for BLISS procedure will be described. The benefits of using flexible approximation models such as Kriging will be demonstrated in terms of convergence characteristics and computational cost. An approach of dealing with cases where subsystem optimization cannot find a feasible design will be investigated by using the new flexible approximation models for the violated local constraints.

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“…In order to fully capture the performance impact of the fairing design, a multidisciplinary optimization (MDO) is required which captures the interactions of structural loading, aerodynamic stability, and trajectory performance must be included in an integrated fashion. MDO efforts of rocket systems to date have focused primarily on system-level parameters 3,4,5 (e.g. staging ratio) rather than directly optimizing the physical variables of the vehicle (e.g.…”

Section: Introductionmentioning

confidence: 99%