Configuration Determination of a Reusable First Stage Booster Utilizing Monte Carlo Based Inverse Design

Robertson, Bradford E.; Crowley, Daniel; Douglas, Rebecca; Mavris, Dimitri N.; Hellman, Barry

doi:10.2514/6.2011-7280

Cited by 3 publications

(2 citation statements)

References 6 publications

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“…A preliminary study 24 showed that a small minority of vehicles, less than six percent of the possible configurations, were considered likely to trim for all relevant flight conditions. Furthermore, even for a single flight condition, only a small portion of the design space may be viable.…”

Section: Exploring the Design Spacementioning

confidence: 99%

Reducing the Computational Expense to Train Euler-level Aerodynamic Surrogate Models for a Family of Vehicle Configurations

Crowley¹,

Douglas²,

Mavris³

et al. 2012

AIAA SPACE 2012 Conference &Amp; Exposition

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The aerodynamic performance of vehicles which experience uncommon aerodynamic phenomena, such as high angles of attack or nonlinear effects, may not be accurately captured by typical design tools. More complex analysis tools, such as Euler or NavierStokes CFD, may be required to accurately predict vehicle behavior. Unfortunately, increased analysis complexity carries with it increased analysis cost, and this increased cost may quickly become excessive. Even surrogate modeling of these tools may be infeasible if the design space has many dimensions. Two promising techniques for reducing the cost of developing surrogate models of Euler-level CFD results have been identified: contour-based sampling and multi-fidelity modeling. In this effort, those techniques are applied to a simple two-dimensional test problem to assess the degree to which they reduce the cost of developing accurate surrogate models. Contour-based sampling was found to significantly reduce the number of samples required when only a certain portion of the response is of interest. When combined with the contour-based sampling technique, multi-fidelity techniques did not produce significant improvements for this small test problem; however, multi-fidelity techniques may still offer benefits for larger-scale applications. Nomenclature CFD= computational fluid dynamics C m = pitching moment coefficient OML = outer mold line  = angle of attack, in degrees

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Section: Exploring the Design Spacementioning

confidence: 99%

Reducing the Computational Expense to Train Euler-level Aerodynamic Surrogate Models for a Family of Vehicle Configurations

Crowley¹,

Douglas²,

Mavris³

et al. 2012

AIAA SPACE 2012 Conference &Amp; Exposition

View full text Add to dashboard Cite

show abstract

“…The option to redesign the wing exists because an experimental vehicle like this is known to have issues with pitch trim stability. [113,112] Finally, an option exists to add stiffness to account for flutter.…”

Section: Vehicle Structuresmentioning

confidence: 99%

Aeromechanics and Vehicle Configuration Demonstrations. Volume 3: A Hybrid Probabilistic Method for Estimate Design Margin

Robertson¹,

Edwards²,

Mavris³

2014

Self Cite

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Using Government drawings, specifications, or other data included in this document for any purpose other than Government procurement does not in any way obligate the U.S. Government. The fact that the Government formulated or supplied the drawings, specifications, or other data does not license the holder or any other person or corporation; or convey any rights or permission to manufacture, use, or sell any patented invention that may relate to them. This report was cleared for public release by the USAF 88th Air Base Wing (88 ABW) Public Affairs Office (PAO) and is available to the general public, including foreign nationals. Copies may be obtained from the Defense Technical Information Center (DTIC) (http://www.dtic.mil).

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Adaptive Sampling to Improve Aerodynamic Predictions for Reusable Rocket-Powered Vehicles

Crowley

Edwards

Mavris

et al. 2013

AIAA SPACE 2013 Conference and Exposition

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Configuration Determination of a Reusable First Stage Booster Utilizing Monte Carlo Based Inverse Design

Cited by 3 publications

References 6 publications

Reducing the Computational Expense to Train Euler-level Aerodynamic Surrogate Models for a Family of Vehicle Configurations

Reducing the Computational Expense to Train Euler-level Aerodynamic Surrogate Models for a Family of Vehicle Configurations

Aeromechanics and Vehicle Configuration Demonstrations. Volume 3: A Hybrid Probabilistic Method for Estimate Design Margin

Adaptive Sampling to Improve Aerodynamic Predictions for Reusable Rocket-Powered Vehicles

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