Thomas K. West

Sonic Boom Pressure Signature Uncertainty Calculation and Propagation to Ground Noise

The objective of this study was to outline an approach for the quantification of uncertainty in sonic boom measurements and to investigate the effect of various near-field uncertainty representation approaches on ground noise predictions. These approaches included a symmetric versus asymmetric uncertainty band representation and a dispersion technique based on a partial sum Fourier series that allows for the inclusion of random error sources in the uncertainty. The near-field uncertainty was propagated to the ground level, along with additional uncertainty in the propagation modeling. Estimates of perceived loudness were obtained for the various types of uncertainty representation in the near-field. Analyses were performed on three configurations of interest to the sonic boom community: the SEEB-ALR, the 69 Delta Wing, and the LM 1021-01. Results showed that representation of the near-field uncertainty plays a key role in ground noise predictions. Using a Fourier series based dispersion approach can double the amount of uncertainty in the ground noise compared to a pure bias representation. Compared to previous computational fluid dynamics results, uncertainty in ground noise predictions were greater when considering the near-field experimental uncertainty.

Uncertainty Analysis and Robust Design of Low-Boom Concepts using Atmospheric Adjoints

This paper seeks to quantify the uncertainty associated with atmospheric conditions when propagating shaped pressure disturbances from a vehicle flying at supersonic speeds. A discrete adjoint formulation is used to obtain sensitivities of boom metrics to atmospheric inputs such as temperature, wind, and relative humidity profiles in addition to deterministic inputs such as the near-field pressure distribution. This study uses a polynomial chaos theory approach to couple these adjoint-derived gradients with uncertainty quantification to enable robust design by using gradient-based optimization techniques. The effectiveness of this approach is demonstrated over an axisymmetric body of revolution and a low-boom concept. Results show that the mean and standard deviation of sonic-boom loudness are simultaneously reduced using robust optimization. Unlike the conventional optimization approaches, the robust optimization approach has the added benefit of generating probability distributions of the sonic-boom met...

Reliability-Based Design of Thermal Protection Systems with Support Vector Machines

The primary objective of this work was to develop a computationally efficient and accurate approach to reliability analysis of thermal protection systems using support vector machines. An adaptive sampling approach was introduced informs a iterative support vector machine approximation of the limit state function used for measuring reliability. The proposed sampling approach efficient adds samples along the limit state function until the reliability approximation is converged. This methodology is applied to two sample, mathematical functions to test and demonstrate the applicability. Then, the adaptive samplingbased support vector machine approach is applied to the reliability analysis of a thermal protection system. The results of all three problems highlight the potential capability of the new approach in terms of accuracy and computational saving in determining thermal protection system reliability.

Multifidelity Uncertainty Quantification of a Commercial Supersonic Transport

The objective of this work was to develop a multifidelity uncertainty quantification approach for efficient analysis of a commercial supersonic transport concept. An approach based on point-collocation, non-intrusive polynomial chaos was formulated in which a low-fidelity model could be corrected using multiple higher-fidelity models. The formulation and methodology also allows for the addition of uncertainty sources not present in the lower fidelity models. To demonstrate the applicability and potential computational savings of the multifidelity polynomial chaos approach, two model problems were explored. The first was a supersonic airfoil with three levels of modeling fidelity, each capturing a gradual increase in modeling of the underlying flow physics. As much as 50% computational cost reduction was observed using the mutlifidelity approach, while predicting nearly the same amount of uncertainty in drag. The second problem was a commercial supersonic transport. This model had three levels of fidelity that included two different modeling approaches and the addition of physics between the fidelity levels. Results of this analysis yielded nearly a 70% computational savings to predict a comparable amount of uncertainty in ground noise. Both problems illustrate the applicability and significant computational savings of the multifidelity method for efficient and accurate uncertainty quantification.

Integrated Uncertainty Quantification for Risk and Resource Management: A NASA Langley Perspective (Invited)

Uncertainty management in the design process can improve both risk and resource management while building confidence in the design. This paper postulates how uncertainty could be managed in a program or project to the benefit of scarce resources. An assessment space is introduced to aid in the management of the design cycle progression through its stages of maturity. The importance of classification and integration of uncertainties is discussed with some examples provided.