Launch Vehicle Cavity Venting: Modeling Concepts & Validatio

Abstract

Accurate and bounding predictions of pressure decay rates for gas containing ventilated cavities that are exposed to time-varying source/drain pressures are a subject of interest for many engineering problems and are often a significant source of pressure induced loading on local structures. This type of problem is routinely encountered on launch vehicles and centers around predictions for payload cavity pressure decay during ascent; where the relative rate of pressure decay between the local free-stream and internal cavity pressure will drive local differential pressure levels on cavity walls and internal payloads. Due to the relatively large volumes to vent areas involved the current state of the art used to predict these gas flows for multiple simulation trajectory sets, which can contain thousands of individual trajectory ascent paths, is to couple flow discharge coefficient models to ideal solutions of the 1-D steady energy equation at the vent and 1-D unsteady energy equation for the volume as opposed to running transient CFD simulations, due to efficiency and timing demands. The overarching goal of this paper is to bring together various validated compressible flow discharge models in one text as a resource for the fluid flow modeling community. We will discuss and present various discharge coefficient models that have successfully predicted the change in venting rate as the flow through the vent transitions to choked flow, as well as the sharp reduction in venting efficiency as the vent flows Reynolds number drops below 10,000. Both flight data and detailed CFD simulations are utilized to confirm the appropriateness of these predictions methods. In addition since the fidelity of the external surface pressures near the vent are of equal importance to the overall gas flow prediction, a discussion on the observed rapid change in surface pressure transonically as the Mach wave passes over the vent will also be included with supporting information.

An Optimized Neural Network Approach for Rapid Unpressurized Compartment Venting Predictions

“…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.…”