ALBERT

Description: This paper examines the role of unexplained systematic variation on the reproducibility of wind tunnel test results. Sample means and variances estimated in the presence of systematic variations are shown to be susceptible to bias errors that are generally non-reproducible functions of those variations. Unless certain precautions are taken to defend against the effects of systematic variation, it is shown that experimental results can be difficult to duplicate and of dubious value for predicting system response with the highest precision or accuracy that could otherwise be achieved. Results are reported from an experiment designed to estimate how frequently systematic variations are in play in a representative wind tunnel experiment. These results suggest that significant systematic variation occurs frequently enough to cast doubts on the common assumption that sample observations can be reliably assumed to be independent. The consequences of ignoring correlation among observations induced by systematic variation are considered in some detail. Experimental tactics are described that defend against systematic variation. The effectiveness of these tactics is illustrated through computational experiments and real wind tunnel experimental results. Some tutorial information describes how to analyze experimental results that have been obtained using such quality assurance tactics.

Description: Two conventional One Factor At a Time (OFAT) test matrices under consideration for an Orion Landing System subscale soil impact study are reviewed. Certain weaknesses in the designs, systemic to OFAT experiment designs generally, are identified. An alternative test matrix is proposed that is based in the Modern Design of Experiments (MDOE), which achieves certain synergies by combining the original two test matrices into one. The attendant resource savings are quantified and the impact on uncertainty is discussed.

Description: Computational tools have been developed to estimate thermal and mechanical reentry loads experienced by the Space Shuttle Orbiter as the result of cavities in the Thermal Protection System (TPS). Such cavities can be caused by impact from ice or insulating foam debris shed from the External Tank (ET) on liftoff. The reentry loads depend on cavity geometry and certain Shuttle state variables, among other factors. Certain simplifying assumptions have been made in the tool development about the cavity geometry variables. For example, the cavities are all modeled as shoeboxes , with rectangular cross-sections and planar walls. So an actual cavity is typically approximated with an idealized cavity described in terms of its length, width, and depth, as well as its entry angle, exit angle, and side angles (assumed to be the same for both sides). As part of a comprehensive assessment of the uncertainty in reentry loads estimated by the debris impact assessment tools, an effort has been initiated to quantify the component of the uncertainty that is due to imperfect geometry specifications for the debris impact cavities. The approach is to compute predicted loads for a set of geometry factor combinations sufficient to develop polynomial approximations to the complex, nonparametric underlying computational models. Such polynomial models are continuous and feature estimable, continuous derivatives, conditions that facilitate the propagation of independent variable errors. As an additional benefit, once the polynomial models have been developed, they require fewer computational resources to execute than the underlying finite element and computational fluid dynamics codes, and can generate reentry loads estimates in significantly less time. This provides a practical screening capability, in which a large number of debris impact cavities can be quickly classified either as harmless, or subject to additional analysis with the more comprehensive underlying computational tools. The polynomial models also provide useful insights into the sensitivity of reentry loads to various cavity geometry variables, and reveal complex interactions among those variables that indicate how the sensitivity of one variable depends on the level of one or more other variables. For example, the effect of cavity length on certain reentry loads depends on the depth of the cavity. Such interactions are clearly displayed in the polynomial response models.