Verification of a Specialized Hydrodynamic Simulation Code for Modeling Deflagration and Detonation of High Explosives

A specialized hydrodynamic simulation code has been developed to simulate one-dimensional unsteady problems involving the detonation and deflagration of high explosives. To model all the relevant physical processes in these problems, a code is required to simulate compressible hydrodynamics, unsteady thermal conduction and chemical reactions with complex rate laws. Several verification exercises are presented which test the implementation of these capabilities. The code also requires models for physics processes such as equations of state and conductivity for pure materials and mixtures as well as rate laws for chemical reactions. Additional verification tests are required to ensure these models are implemented correctly. Though this code is limited in the types of problems it can simulate, its computationally efficient formulation allow it to be used in calibration studies for reactive burn models for high explosives.

Uncertainty Quantification of Time-Dependent Quantities in a System with Adjustable Level of Smoothness

We summarise the results of a computational study involved with Uncertainty Quantification (UQ) in a benchmark turbulent burner flame simulation. UQ analysis of this simulation enables one to analyse the convergence performance of one of the most widely-used uncertainty propagation techniques, Polynomial Chaos Expansion (PCE) at varying levels of system smoothness. This is possible because in the burner flame simulations, the smoothness of the time-dependent temperature, which is the study's QoI is found to evolve with the flame development state. This analysis is deemed important as it is known that PCE cannot accurately surrogate non-smooth QoIs and thus perform convergent UQ. While this restriction is known and gets accounted for, there is no understanding whether there is a quantifiable scaling relationship between the PCE's convergence metrics and the level of QoI's smoothness. It is found that the level of QoI-smoothness can be quantified by its standard deviation allowing to observe the effect of QoI's level of smoothness on the PCE's convergence performance. It is found that for our flow scenario, there exists a power-law relationship between a comparative parameter, defined to measure the PCE's convergence performance relative to Monte Carlo sampling, and the QoI's standard deviation, which allows us to make a more weighted decision on the choice of the uncertainty propagation technique.

On the Generation of Tuned Test Problems for Stress Concentrations

When stress concentration factors are not available in handbooks, finite element analysis has become the predominant method for determining their values. For such determinations, there is a need to know if they have sufficient accuracy. Tuned Test Problems can provide a way of assessing the accuracy of stress concentration factors found with finite elements. Here we offer a means of constructing such test problems for stress concentrations within boundaries that have local constant radii of curvature. These problems are tuned to their originating applications by sharing the same global geometries and having slightly higher peak stresses. They also have exact solutions, thereby enabling a precise determination of the errors incurred in their finite element analysis.

Experimental and Modeling Uncertainty Considerations for Determining the First Item Ignited in a Compartment Using a Bayesian Method

Fire scene reconstruction and determining the fire evolution (i.e. item-to-item ignition events) using the post-fire compartment is an extremely difficult task because of the time-integrated nature of the observed damages. Bayesian methods are ideal for making inferences amongst hypotheses given observations and are able to naturally incorporate uncertainties. A Bayesian methodology for determining probabilities to items that may have initiated the fire in a compartment from damage signatures is developed. Exercise of this methodology requires uncertainty quantification of these damage signatures. A simple compartment configuration was used to quantify the uncertainty in damage predictions by Fire Dynamics Simulator (FDS), and a compartment evolution program, JT-risk as compared to experimentally derived damage signatures. Surrogate sensors spaced within the compartment use heat flux data collected over the course of the simulations to inform damage models. Experimental repeatability showed up to 4% uncertainty in damage signatures between replicates . Uncertainties for FDS and JT-risk ranged from 12% up to 32% when compared to experimental damages. Separately, the evolution physics of a simple three fuel package problem with surrogate damage sensors were characterized in a compartment using experimental data, FDS, and JT-risk predictions. An simple ignition model was used for each of the fuel packages. The Bayesian methodology was exercised using the damage signatures collected, cycling through each of the three fuel packages, and combined with the previously quantified uncertainties. Only reconstruction using experimental data was able to confidently predict the true hypothesis from the three scenarios.

Lid-Driven Square Cavity Flow: A Benchmark Solution with an 8192x8192 Grid

The incompressible steady-state fluid flow inside a lid-driven square cavity was simulated using the mass conservation and Navier-Stokes equations. This system of equations is solved for Reynolds numbers of up to 10,000 to the accuracy of the computational machine round-off error. The computational model used was the second-order accurate finite volume method. A stable solution is obtained using the iterative multigrid methodology with 8192 × 8192 volumes, while degree-10 interpolation and Richardson extrapolation were used to reduce the discretization error. The solution vector comprised five entries of velocities, pressure, and location. For comparison purposes, 65 different variables of interest were chosen, such as velocity profile, its extremum values and location, extremum values and location of the stream function. The discretization error for each variable of interest was estimated using two types of estimators and their apparent order of accuracy. The variations of the 11 selected variables are shown across 38 Reynolds number values between 0.0001 and 10,000. In this study, we provide a more accurate determination of the Reynolds number value at which the upper secondary vortex appears. The results of this study were compared with those of several other studies in the literature. The current solution methodology was observed to produce the most accurate solution till date for a wide range of Reynolds numbers.

Verification and Validation of High-Fidelity Open-Source Simulation Tools for Supersonic Aircraft Aerodynamic Analysis

Small supersonic vehicle concepts used as research platforms to test new aerospace technologies, such as advanced propulsion systems or large sensor payloads, require major modifications to conventional, large-scale, manned, supersonic airframe design. High-fidelity numerical simulation of these concepts in academic settings often requires the use of in-house or available open-source tools instead of expensive commercial software or those with export-control restrictions. A verification and validation analysis of two widely-used open-source compressible-flow solvers, rhoCentralFoam (rCF) and SU2, is performed for several flow problems relevant to the supersonic aerodynamics of small-scale, autonomous aircraft concepts. The one-dimensional shock tube problem, two-dimensional supersonic turbulent boundary layer, and three-dimensional delta wing are simulated with both solvers. The effects of flux scheme, flux limiters, and Courant-Friedrichs-Lewy (CFL) number on solution accuracy, stability, and solver speed are assessed. The solvers' limitations and their usefulness as supersonic aircraft design tools in a holistic sense are discussed.

Developing Uncertainty Quantification Strategies in Electromagnetic Problems Involving Highly Resonant Cavities

High-quality factor resonant cavities are challenging structures to model in electromagnetics owing to their large sensitivity to minute parameter changes. Therefore, uncertainty quantification strategies are pivotal to understanding key parameters affecting the cavity response. We discuss here some of these strategies focusing on shielding effectiveness properties of a canonical slotted cylindrical cavity that will be used to develop credibility evidence in support of predictions made using computational simulations for this application.

Credibility Assessment of Machine Learning in a Manufacturing Process Application

We present a framework for establishing credibility of a machine learning model used to predict a key process control variable setting to maximize product quality in a nuclear component manufacturing application. Our model coupled a purely data-based machine learning model with a physics-based adjustment that encoded subject matter expertise of the physical process. Establishing credibility of the resulting model provided the basis for eliminating a costly intermediate testing process that was previously used to determine the control variable setting.

Embedded Error Bayesian Calibration of Thermal Decomposition of Organic Materials

Organic materials are an attractive choice for structural components due to their light weight and versatility. However, because they decompose at low temperatures relative to tradiational materials they pose a safety risk due to fire and loss of structural integrity. To quantify this risk, analysts use chem- ical kinetics models to describe the material pyrolysis and oxidation using thermogravimetric analysis. This process requires the calibration of many model parameters to closely match experimental data. Previous e?orts in this field have largely been limited to finding a single best-fit set of parame- ters even though the experimental data may be very noisy. Furthermore the chemical kinetics models are often simplified representations of the true de- composition process. The simplification induces model-form errors that the fitting process cannot capture. In this work we propose a methodology for calibrating decomposition models to thermogravimetric analysis data that accounts for uncertainty in the model-form and experimental data simul- taneously. The methodology is applied to the decomposition of a carbon fiber epoxy composite with a three-stage reaction network and Arrhenius kinetics. The results show a good overlap between the model predictions and thermogravimetric analysis data. Uncertainty bounds capture devia- tions of the model from the data. The calibrated parameter distributions are also presented. The distributions may be used in forward propagation of uncertainty in models that leverage this material.

Effect of the Heat Transfer Coefficient Reference Temperatures On Validating Numerical Models of Supercritical CO2

Validating turbulence models for cooling supercritical carbon dioxide (sCO2) in a horizontal pipe is challenging due to the lack of experimental data with spatially resolved local temperature measurements. Although many variables may be present to cause discrepancies between numerical and experimental data, this study focuses on how the choice of reference temperatures (both wall reference temperature and fluid bulk reference temperature) when calculating the heat transfer coefficient influences turbulence-model validation results. While it may seem straightforward to simply use the same parameters as the experimental setup, this has not been observed in practice. In the present work, numerical simulations are performed for cooling sCO2 in a horizontal pipe for p = 8 MPa, d = 6 mm, G = 200 and 400 kg/(m2.s), and qw = 12, 24, and 33 kW/m2. Local and average heat transfer coefficients with different reference temperatures, found to be frequently used in the literature, are presented and compared with commonly used experimental data. It was found that the choice of reference temperatures has a significant influence on the results of the numerical validation. Historically, the higher heat flux cases have been more difficult to validate, theorized due to using reference temperatures differing from the experiment; however, good agreement was found here using the reference temperatures that most closely matched the experiment. This not only highlights the need for careful selection of reference temperatures in simulations, but also the importance of clearly defining the reference temperature employed when reporting experimental results.