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J. Verif. Valid. Uncert. 2018;3(1):011001-011001-9. doi:10.1115/1.4040476.

One major problem in the design of aerospace components is the nonlinear changes in the response due to a change in the geometry and material properties. Many of these components have small nominal values and any change can lead to a large variability. In order to characterize this large variability, traditional methods require either many simulation runs or the calculations of many higher-order derivatives. Each of these paths requires a large amount of computational power to evaluate the response curve. In order to perform uncertainty quantification (UQ) analysis, even more simulation runs are required. The hyper-dual meta-model (HDM) is introduced and used to characterize the response curve with the use of basis functions. The information of the response is generated with the utilization of the hyper-dual (HD) step to determine the sensitivities at a few number of simulation runs to greatly enrich the response space. This paper shows the accuracy of this method for two different systems with parameterizations at different stages in the design analysis.

Commentary by Dr. Valentin Fuster
J. Verif. Valid. Uncert. 2018;3(1):011002-011002-12. doi:10.1115/1.4040431.

Achieving good reproducibility in fluid flow experiments can be challenging, in particular in scenarios where the experimental boundary conditions are obscure. We use computational uncertainty quantification (UQ) to evaluate the influence of uncertain inflow conditions on the reproducibility of experiments with swirling flow. Using a nonintrusive polynomial chaos method in combination with a computational fluid dynamics (CFD) code, we obtain the expectation and variance of the velocity fields downstream from symmetric and asymmetric swirl disturbance generators. Our results suggest that the flow patterns downstream from the asymmetric swirl disturbance generator are more reproducible than the flow patterns downstream from the symmetric swirl disturbance generator. This confirms that the inherent breaking of symmetry eliminates instability mechanisms in the wake of the disturber, thereby creating more stable swirling patterns that make the experiments more reproducible.

Commentary by Dr. Valentin Fuster
J. Verif. Valid. Uncert. 2018;3(1):011003-011003-16. doi:10.1115/1.4039836.

Uncertainty quantification (UQ) and propagation are critical to the computational assessment of structural components and systems. In this work, we discuss the practical challenges of implementing uncertainty quantification for high-dimensional computational structural investigations, specifically identifying four major challenges: (1) Computational cost; (2) Integration of engineering expertise; (3) Quantification of epistemic and model-form uncertainties; and (4) Need for V&V, standards, and automation. To address these challenges, we propose an approach that is straightforward for analysts to implement, mathematically rigorous, exploits analysts' subject matter expertise, and is readily automated. The proposed approach utilizes the Latinized partially stratified sampling (LPSS) method to conduct small sample Monte Carlo simulations. A simplified model is employed and analyst expertise is leveraged to cheaply investigate the best LPSS design for the structural model. Convergence results from the simplified model are then used to design an efficient LPSS-based uncertainty study for the high-fidelity computational model investigation. The methodology is carried out to investigate the buckling strength of a typical marine stiffened plate structure with material variability and geometric imperfections.

Commentary by Dr. Valentin Fuster
J. Verif. Valid. Uncert. 2018;3(1):011004-011004-13. doi:10.1115/1.4040585.

A validation/uncertainty quantification (VUQ) study was performed on the 1.5 MWth L1500 furnace, an oxy-coal fired facility located at the Industrial Combustion and Gasification Research Facility at the University of Utah. A six-step VUQ framework is used for studying the impact of model parameter uncertainty on heat flux, the quantity of interest (QOI) for the project. This paper focuses on the first two steps of the framework. The first step is the selection of model outputs in the experimental and simulation data that are related to the heat flux: incident heat flux, heat removal by cooling tubes, and wall temperatures. We describe the experimental facility, the operating conditions, and the data collection process. To obtain the simulation data, we utilized two tools, star-ccm+ and Arches. The star-ccm+ simulations captured flow through the complex geometry of the swirl burner while the Arches simulations captured multiphase reacting flow in the L1500. We employed a filtered handoff plane to couple the two simulations. In step two, we developed an input/uncertainty (I/U) map and assigned a priority to 11 model parameters based on prior knowledge. We included parameters from both a char oxidation model and an ash deposition model in this study. We reduced the active parameter space from 11 to 5 based on priority. To further reduce the number of parameters that must be considered in the remaining steps of the framework, we performed a sensitivity analysis on the five parameters and used the results to reduce the parameter set to two.

Commentary by Dr. Valentin Fuster

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