Compartment Venting on the Orion Crew Module During Atmospheric Re-entry

Smith, Ries N.; Exploration, Lockheed Martin

doi:10.2514/6.2011-427

Cited by 5 publications

(3 citation statements)

References 4 publications

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“…Spacecraft, launch vehicle, and air vehicle venting analysis is the prediction of the pressure time histories for unpressurized internal compartments of the craft [1][2][3][4][5][6][7][8] . The vehicle may contain one or more interconnected compartments that are usually connected to the ambient air by a surface-mounted vent.…”

Section: Introductionmentioning

confidence: 99%

An Optimized Neural Network Approach for Rapid Unpressurized Compartment Venting Predictions

Rodi

2014

6th AIAA Atmospheric and Space Environments Conference

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An examination has been performed of using artificial Neural Networks to predict the unpressurized compartment pressure time history for spacecraft, launch vehicle, and air vehicle applications. In this paper, the development of and results from using artificial Neural Networks to predict the unpressurized compartment pressure time history for a space capsule reentering the Earth's atmosphere is presented. A Neural Network-based approach was selected for their ability to mimic the compartment pressure time histories using only a small number of high fidelity venting solutions. This approach employs simple multi-layer feed-forward Neural Network architectures with inputs based on the freestream static pressure and Mach time histories, pressure time derivatives and pressure moving averages. The time periods over which the pressure averages are calculated were optimized using a Genetic Algorithm. For repressurization of the Apollo aft bay during reentry, the Neural Networks correctly identified the worst three trajectories, and 18 of the worst 20 cases, out of a 100,000 set of dispersed trajectory data. For the worst case venting trajectory the Neural Network predictions were within 1.3% and 0.25 seconds the peak of the deltapressure value and time-at-peak-pressure, respectively. This new tool runs in the Matlab environment, and after training the Neural Network can predict the compartment pressure time history for a new trajectory within a few seconds. Nomenclature h = altitude t = time P i = static pressure in compartment i P ∞ = freestream static pressure ∆P = i th compartment delta-pressure (P i -P ∞ )

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Section: Introductionmentioning

confidence: 99%

An Optimized Neural Network Approach for Rapid Unpressurized Compartment Venting Predictions

Rodi

2014

6th AIAA Atmospheric and Space Environments Conference

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“…The problems of drainage of volumes in a vehicle descending into the dense atmospheric layers are solved in [19]. The solutions developed for the Titan IV CR (USA) on its atmospheric flight phase are used in this work.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

“…The solutions developed for the Titan IV CR (USA) on its atmospheric flight phase are used in this work. In fact, work [19] considers the inverse problem. The solved issues remained the same: ensuring the predicted process of draining dry compartments and searching for peak pressure values.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%