Publications

Abstract not provided.

There is a dearth in the literature on how to capture the uncertainty generated by material surface evolution in thermal modeling. This leads to inadequate or highly variable uncertainty representations for material properties, specifically emissivity when minimal information is available. Inaccurate understandings of prediction uncertainties may lead decision makers to incorrect conclusions, so best engineering practices should be developed for this domain. In order to mitigate the aforementioned issues, this study explores different strategies to better capture the thermal uncertainty response of engineered systems exposed to fire environments via defensible emissivity uncertainty characterizations that can be easily adapted to a variety of use cases. Two unique formulations (one physics-informed and one mathematically based) are presented. The formulations and methodologies presented herein are not exhaustive but more so are a starting point and give the reader a basis for how to customize their uncertainty definitions for differing fire scenarios and materials. Finally, the impact of using this approach versus other commonly used strategies and the usefulness of adding rigor to material surface evolution uncertainty is demonstrated.

Surrogate models maximize information utility by building predictive models in place of computational or experimentally expensive model runs. Marine hydrokinetic current energy converters require large-domain simulations to estimate array efficiencies and environmental impacts. Meso-scale models typically represent turbines as actuator discs that act as momentum sinks and sources of turbulence and its dissipation. An OpenFOAM model was developed where actuator disc k-ε turbulence was characterized using an approach developed for flows through vegetative canopies. Turbine-wake data from laboratory flume experiments collected at two influent turbulence intensities were used to calibrate parameters in the turbulence-source terms in the k-ε equations. Parameter influences on longitudinal wake profiles were estimated using Gaussian process regression with subsequent optimization minimizing the objective function within 3.1% of those obtained using the full model representation, but for 74% of the computational cost (far fewer model runs). This framework facilitates more efficient parameterization of the turbulence-source equations using turbine-wake data.

A computational simulation of various mixing laws for gaseous equations of state using planar traveling shocks for multiple mixtures in three dimensions is analyzed against nominal experimental data. Numerical simulations use the Sandia National Laboratories shock hydrodynamic code CTH and other codes including the thermochemical equilibrium code TIGER and the uncertainty qualification and sensitivity analysis code DAKOTA. The mixtures are 1:1 and a 1:3 molar mixtures of helium and sulfur hexafluoride. The mixing laws to be analyzed are the ideal-gas law, Amagat’s law, Dalton’s law, the Becker–Kistiakowsky–Wilson equation of state (EOS), the exponential 6 EOS, and the Jacobs-Cowperthwaite-Zwisler EOS. Examination of the experimental data with TIGER revealed that the shock strength should not be strong enough to turn the mixture nonideal because the compressibility factor z was essentially unity (z ≈ 1.02). Experimental results show that none of the equations of state are able to accurately predict the properties of the shocked mixture; similar discrepancies have been observed in previous works. Kinetic molecular theory appears to introduce a parameter that offers an explanation regarding the discrepancies. Implementation of the kinetic molecular theory parameter into the EOS is left for future work.

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Abstract not provided.

When making computational simulation predictions of multiphysics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis, a verification, validation, and uncertainty quantification (VVUQ) workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification (UQ) simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias is used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes use of computing resources.

When making computational simulation predictions of multi-physics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis a verification, validation and uncertainty quantification workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias are used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes computing resources.

When making computational simulation predictions of multi-physics engineering systems, sources of uncertainty in the prediction need to be acknowledged and included in the analysis within the current paradigm of striving for simulation credibility. A thermal analysis of an aerospace geometry was performed at Sandia National Laboratories. For this analysis a verification, validation and uncertainty quantification workflow provided structure for the analysis, resulting in the quantification of significant uncertainty sources including spatial numerical error and material property parametric uncertainty. It was hypothesized that the parametric uncertainty and numerical errors were independent and separable for this application. This hypothesis was supported by performing uncertainty quantification simulations at multiple mesh resolutions, while being limited by resources to minimize the number of medium and high resolution simulations. Based on this supported hypothesis, a prediction including parametric uncertainty and a systematic mesh bias are used to make a margin assessment that avoids unnecessary uncertainty obscuring the results and optimizes computing resources.

Amagat and Dalton mixing-models were analyzed to compare their thermodynamic prediction of shock states. Numerical simulations utilized the Sandra National Laboratories (SNL) shock hydrodynamic code CTH [1]. Simulations modeled the University of New Mexico (UNM) shock tube laboratory experimental series shocking a 1:1 molar mixture of helium (He) and sulfur hexafluoride (SF6). Five input parameters were varied for sensitivity analysis: driver section pressure; driver section density; test section pressure; test section density; and mixture ratio (mole fraction). We show via incremental Latin hypercube sampling (LHS) analysis that significant differences exist between Amagat and Dalton mixing-model predictions. The differences observed in predicted shock speeds, temperatures, and pressures grow more pronounced with higher shock speeds.

The thermal degradation of two polyurethane elastomers was investigated via thermal gravimetric analysis coupled with gas chromatography/ma ss spectrometry. Decomposition occur s in a multi - step fashion with similar onset temperatures for both materials. Apparent activation energy plots were calculated inside Model - Free Kinetics software and utilized to construct conversion and isothermal conversion tables . These tables predicted material degradation as a function of temperature and time. Isothermal experiments were performed and found to be in good agreement with the predictions made from the Model - Free Kinetic s software package. Volatile products evolved during the multistep decomposition were captured at various times and analyzed using the coupled gas chromatography/mass spectrometry system . This analysis demonstrated strong correlation between the degradation products and known decomposition mechanisms for polyurethanes.

Verification of tightly coupled multiphysics computational codes is generally significantly more difficult than verification of single-physics codes. The case of coupled heat conduction and thermal radiation in an enclosure is considered, and it is extended to a manufactured solution verification test for enclosure radiation to a fully two-dimensional coupled problem with conduction and thermal radiation. Convergence results are shown using a production thermal analysis code. Convergence rates are optimal with a pairwise view-factor calculation algorithm.