Uncertainty Analysis of Radiative Heating Predictions for Titan Entry

West, Thomas K.; Brune, Andrew J.; Hosder, Serhat; Johnston, Christopher O.

doi:10.2514/1.t4620

Cited by 23 publications

(2 citation statements)

References 48 publications

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“…Previous works by West et al 6,26 considered all of the uncertain parameters in the surrogate model used in the final uncertainty analysis. However, in the present study, a much larger set of uncertain parameters will be considered.…”

Section: Dimension Reductionmentioning

confidence: 99%

Uncertainty and Sensitivity Analysis of Afterbody Radiative Heating Predictions for Earth Entry

West

Johnston

Hosder

2017

Journal of Thermophysics and Heat Transfer

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The objective of this work was to perform sensitivity analysis and uncertainty quantification for afterbody radiative heating predictions of Stardust capsule during Earth entry at peak afterbody radiation conditions. The radiation environment in the afterbody region poses significant challenges for accurate uncertainty quantification and sensitivity analysis due to the complexity of the flow physics, computational cost, and large number of uncertain variables. In this study, first a sparse collocation non-intrusive polynomial chaos approach along with global non-linear sensitivity analysis was used to identify the most significant uncertain variables and reduce the dimensions of the stochastic problem. Then, a total order stochastic expansion was constructed over only the important parameters for an efficient and accurate estimate of the uncertainty in radiation. Based on previous work, 388 uncertain parameters were considered in the radiation model, which came from the thermodynamics, flow field chemistry, and radiation modeling. The sensitivity analysis showed that only four of these variables contributed significantly to afterbody radiation uncertainty, accounting for almost 95% of the uncertainty. These included the electronicimpact excitation rate for N between level 2 and level 5 and rates of three chemical reactions influencing N, N + , O, and O + number densities in the flow field.

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Section: Dimension Reductionmentioning

confidence: 99%

Uncertainty and Sensitivity Analysis of Afterbody Radiative Heating Predictions for Earth Entry

West

Johnston

Hosder

2017

Journal of Thermophysics and Heat Transfer

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“…Uncertainty quantification (UQ) [1,2] is the science of quantitative characterization and reduction of uncertainties in applications. It tries to determine how likely certain outcomes are if some aspects of the system are not exactly known, bridging a more reliable and robust application from theoretical and experimental models to the practical world.…”

Section: Introductionmentioning

confidence: 99%

Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions

Leifsson

Grandin²,

Meeker³

et al. 2018

AIP Conference Proceedings

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Probability of detection (POD) is widely used for measuring reliability of nondestructive testing (NDT) systems. Typically, POD is determined experimentally, while it can be enhanced by utilizing physics-based computational models in combination withmodel-assisted POD (MAPOD) methods. With the development of advanced physics-basedmethods, such as ultrasonic NDTtesting, the empirical information,needed for POD methods, can bereduced. However, performing accurate numerical simulationscan be prohibitivelytimeconsuming, especially as part of stochastic analysis. In this work, stochastic surrogate models for computational physics-based measurement simulations are developed for cost savings of MAPOD methods while simultaneously ensuring sufficient accuracy. The stochastic surrogate is used to propagate the random input variables through thephysics-basedsimulation model to obtain the joint probability distribution of the output. The POD curves are then generated based on those results. Here, the stochastic surrogates are constructed using nonintrusive polynomial chaos (NIPC) expansions. In particular, the NIPC methods used are the quadrature, ordinary leastsquares (OLS), and least-angle regression sparse (LARS) techniques. The proposed approach is demonstrated on the ultrasonic testing simulation of a flat bottom hole flaw inanaluminum block. The results show that the stochastic surrogates have at least two orders of magnitude faster convergence on the statistics than direct Monte Carlo sampling (MCS). Moreover, the evaluation of the stochastic surrogate models is over three orders of magnitude faster than the underlying simulation modelfor this case,which is the UTSim2 model.

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Generalized Polynomial Chaos for Non-intrusive Uncertainty Quantification in Computational Fluid Dynamics

Couaillier¹,

Savin²

2018

Uncertainty Management for Robust Industrial Design in Aeronautics

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Uncertainty Analysis of Radiative Heating Predictions for Titan Entry

Cited by 23 publications

References 48 publications

Uncertainty and Sensitivity Analysis of Afterbody Radiative Heating Predictions for Earth Entry

Uncertainty and Sensitivity Analysis of Afterbody Radiative Heating Predictions for Earth Entry

Model-assisted probability of detection of flaws in aluminum blocks using polynomial chaos expansions

Generalized Polynomial Chaos for Non-intrusive Uncertainty Quantification in Computational Fluid Dynamics

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