Simple Sensitivity Analysis for Orion GNC

Abstract

The Orion GNC entry team is analyzing the performance of Orion flight software in part by running Monte Carlo simulations of Orion spacecraft flights. The simulated performance is checked for conformance with flight requirements, expressed as performance constraints. Flight requirements include guidance (e.g., touchdown distance from target) and control (e.g., control saturation) as well as performance (e.g., heat load constraints). The Monte Carlo simulations disperse hundreds of simulation input variables, for everything from mass properties to date of launch. We describe in this paper a sensitivity analysis tool ("Critical Factors Tool" or CFT) developed to find the input variables or pairs of variables which by themselves significantly influence satisfaction of requirements or significantly affect key performance metrics (e.g., touchdown distance from target). Knowing these factors can inform robustness analysis and where engineering resources are most needed, and could even affect operations. The contributions of this paper include the introduction of novel sensitivity measures, such as estimating success probability, and a technique for determining whether pairs of factors are interacting dependently or independently. The tool found that input variables such as moments, mass, thrust dispersions, and date of launch are significant factors for success of various requirements. Examples are shown in this paper as well as a summary and physics discussion of EFT-1 driving factors that the tool found. Nomenclature Cm = Aerodynamic coefficient of pitching moment Cn = Aerodynamic coefficient of yawing moment CD = Aerodynamic coefficient of drag CL = Aerodynamic coefficient of lift dCm/dq = Derivative of aerodynamic coefficient of pitching moment with respect to pitch rate

Optimization of Perturbation Parameters for Simulated Free Shear Layer Flow

“…1. The motivation is to use the resulting model as a data-driven proxy; i.e., an equivalent simulation to the physics-based Navier-Stokes equations at a greatly reduced computational burden, similar to what was performed by Pressburger et al [6]. To facilitate selection of the best performing regression method, the NMSE (Normalized Mean Square Error) metric was used.…”

“…low sensitivity to natural variation in the data). The linear regression techniques to be evaluated are linear in the parameters to be estimated only, however basis functions for the regressors themselves to be studied will involve both affine and quadratic regressors in the same vein as was presented in both [6] and [8]. As such, both linear and quadratic regressors were used, where the use of quadratic regressors include the x 2 i regressors as well as all N 2 quadratic pairs of parameters, x i and x j .…”