Probabilistic Analysis of a Composite Crew Module

An approach for conducting reliability-based analys is (RBA) of a Composite Crew Module (CCM) is presented. The goal is to identify and quantify the benefits of probabilistic design methods for the CCM and future space vehicles. The coarse finite element model from a previous NASA Engineering and Safety Center (NESC) project is used as the baseline deterministic analysis model to evaluate t he performance of the CCM using a strength-based failure index. The first step in th e probabilistic analysis process is the determination of the uncertainty distributions for key parameters in the model. Analytical data from water landing simulations are used to dev elop an uncertainty distribution, but such data were unavailable for other load cases. T he uncertainty distributions for the other load scale factors and the strength allowables are generated based on assumed coefficients of variation. Probability of first-ply failure is est imated using three methods: the first order reliability method (FORM), Monte Carlo simulation, and conditional sampling. Results for the three methods were consistent. The reliability is shown to be driven by first ply failure in one region of the CCM at the high altitude abort load set. The final predicted probability of failure is on the order of 10 -11 due to the conservative nature of the factors of s afety on the deterministic loads. I. Introduction A. Motivation and Background A probabilistic approach is an attractive alternati ve to traditional deterministic design optimization by quantifying the level of safety (i.e. reliability) of a structure instead of a simple safe/unsafe eval uation. Probabilistic analysis and optimization can result in improved de signs considering the variability of structural mat erials and the uncertainty in loads. Traditional deterministic de sign relies on historically or arbitrarily assigned factors of safety to account for uncertainties in the design. These fac tors of safety are believed to reduce the probabili ty of mission failure to very low levels (e.g. 10 -7 or lower probability of failure) in commercial avi ation. However, NASA’s space flight program has a higher tolerance for risk (and a much higher sensitivity to mass savings) than co mmercial aviation. Probabilistic methods potentially enable the designer to trade off risk for increased mass savings, which is of great benefit to the space flight program. In early 2010, the NASA Engineering and Safety Center (NESC) initiated a Probabilistic Design Opportun ity Identification (PDOI) task to illustrate the advant ages of probabilistic design. The PDOI team select ed the Composite Crew Module (CCM) as a design problem due to its large amount of data available for structur al geometry, loads, and material models from the NESC’s recently completed design, development, test and evaluation (DDT&E) project. The CCM (Ref. 1) is a concept six crew space vehicle similar to the Orion project’s Crew Exploration Vehicle (CEV), except the CCM is manufactured using composite materials and design techniq ues. Results from this CCM study will be used to help es tablish probabilistic analysis as a design tool for future projects within NASA and to help establish probabilistic des ign requirements as an alternative to traditional f actor of safety requirements. In this paper, results are presented from phase 1 of the project, which includes reliab ility calculations for the baseline CCM design. B. Purpose and Contents The purpose of this CCM design study is to answer i mportant questions about applying reliability-based design and optimization (RBDO) to spacecraft design in general and, more specifically, to the design of the C CM. First, does the probabilistic approach require exorbitant computer resources or measured data that are unavai lable? This question is answered by estimating the computational costs of probabilistic analysis and establishing the needed and available data for the CCM. Next, what is the base line reliability of the CCM and what parameters hav e the greatest effect on reliability? Monte Carlo simulation and first order reliability methods (FORM) are used to answer this