NASA NTRS · 20220004976
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Erin Hubbard, Frank Semmelmayer. Uncertainty Analysis Considerations for Wind Tunnel Managers. https://ntrs.nasa.gov/citations/20220004976
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The purpose of this handbook is to provide an in-depth reference for the practice of risk management in NASA, updating the guidance offered in its original version, NASA/SP-2011-3422 (November 2011), and closely aligning the updated guidance with the current NASA Procedural Requirements for Agency Risk Management, NPR 8000.4, and the parent NASA Policy Directive for NASA Governance and Strategic Management, NPD 1000.0. NPD 1000.0 introduces with emphasis the concept of “Risk Leadership,” making it a fundamental tenet and pillar of the risk management culture that it advocates for the Agency. NPR 8000.4 applies this concept and establishes Risk Management (RM) requirements for the Agency as an integrated enterprise, as well as the RM requirements for portfolio elements within the enterprise. Such elements include the various programs and projects that contribute to the Agency’s objectives and the various institutional activities carried out by entities that contribute to mission support. The present version of the handbook also emphasizes the integration of risk management processes across activity and project life cycles and their coordination and interaction with day-to-day programmatic and organizational functions. Areas of application of risk assessment and management that were not covered with specific guidance in the preceding version are addressed in this version with in-depth examples. The handbook is structured into two parts, whose chapters are in turn organized in a sequential order intended to facilitate a gradual and progressive introduction of the reader to risk management principles and practices. Part 1 of the handbook is dedicated to the introduction of the basic foundations of the NASA integrated risk management framework, the related fundamental risk concepts, the description of the risk management and decision processes that are to be implemented within the framework, the discussion of the risk assessment techniques that should be utilized in support of such processes, and the management and organizational interactions and interfaces that should be enabled to implement an effective integration of risk management activities within the Agency. Part 2 provides self-contained, end-to-end examples of application of the processes and techniques introduced in Part 1, in the context of both programmatic (i.e., project and/or mission related) and institutional activities.
This chapter discusses the ongoing development of combined uncertainty and error bound estimates for computational fluid dynamics (CFD) calculations subject to imposed random parameters and random fields. An objective of this work is the construction of computable error bound formulas for output uncertainty statistics that guide CFD practitioners in systematically determining how accurately CFD realizations should be approximated and how accurately uncertainty statistics should be approximated for output quantities of interest. Formal error bounds formulas for moment statistics that properly account for the presence of numerical errors in CFD calculations and numerical quadrature errors in the calculation of moment statistics have been previously presented in [8]. In this past work, hierarchical node-nested dense and sparse tensor product quadratures are used to calculate moment statistics integrals. In the present work, a framework has been developed that exploits the hierarchical structure of these quadratures in order to simplify the calculation of an estimate of the quadrature error needed in error bound formulas. When signed estimates of realization error are available, this signed error may also be used to estimate output quantity of interest probability densities as a means to assess the impact of realization error on these density estimates. Numerical results are presented for CFD problems with uncertainty to demonstrate the capabilities of this framework.
This paper presents a computational framework for uncertainty characterization and propagation, and sensitivity analysis under the presence of aleatory and epistemic un- certainty, and develops a rigorous methodology for efficient refinement of epistemic un- certainty by identifying important epistemic variables that significantly affect the overall performance of an engineering system. The proposed methodology is illustrated using the NASA Langley Uncertainty Quantification Challenge (NASA-LUQC) problem that deals with uncertainty analysis of a generic transport model (GTM). First, Bayesian inference is used to infer subsystem-level epistemic quantities using the subsystem-level model and corresponding data. Second, tools of variance-based global sensitivity analysis are used to identify four important epistemic variables (this limitation specified in the NASA-LUQC is reflective of practical engineering situations where not all epistemic variables can be refined due to time/budget constraints) that significantly affect system-level performance. The most significant contribution of this paper is the development of the sequential refine- ment methodology, where epistemic variables for refinement are not identified all-at-once. Instead, only one variable is first identified, and then, Bayesian inference and global sensi- tivity calculations are repeated to identify the next important variable. This procedure is continued until all 4 variables are identified and the refinement in the system-level perfor- mance is computed. The advantages of the proposed sequential refinement methodology over the all-at-once uncertainty refinement approach are explained, and then applied to the NASA Langley Uncertainty Quantification Challenge problem.