Thermal protection system designers rely heavily on computational simulation tools for design optimization and uncertainty quantification. Because high-fidelity analysis tools are computationally expensive, analysts primarily use low-fidelity or surrogate models instead. In this work, we explore an alternative approach wherein projection-based reduced-order models (ROMs) are used to approximate the computationally infeasible high-fidelity model. ROMs are preferable to alternative approximation approaches for high-consequence applications due to the presence of rigorous error bounds. This work presents the first application of ROMs to ablation systems. In particular, we present results for Galerkin and least-squares Petrov-Galerkin ROMs of 1D and 2D ablation system models.
High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models, which can be very expensive for hypersonic flows. Additionally, UQ approaches are many-query problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model (FOM). In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems, including subsonic CFD applications. This allows LSPG ROM simulations to replace the FOM simulations in UQ studies, making UQ tractable even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application, the Hypersonic International Flight Research Experimentation 1 (HIFiRE-1) in a three-dimensional, turbulent Mach 7.1 flow. This paper shows the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.
High-speed aerospace engineering applications rely heavily on computational fluid dynamics (CFD) models for design and analysis due to the expense and difficulty of flight tests and experiments. This reliance on CFD models necessitates performing accurate and reliable uncertainty quantification (UQ) of the CFD models. However, it is very computationally expensive to run CFD for hypersonic flows due to the fine grid resolution required to capture the strong shocks and large gradients that are typically present. Additionally, UQ approaches are “many-query” problems requiring many runs with a wide range of input parameters. One way to enable computationally expensive models to be used in such many-query problems is to employ projection-based reduced-order models (ROMs) in lieu of the (high-fidelity) full-order model. In particular, the least-squares Petrov–Galerkin (LSPG) ROM (equipped with hyper-reduction) has demonstrated the ability to significantly reduce simulation costs while retaining high levels of accuracy on a range of problems including subsonic CFD applications [1, 2]. This allows computationally inexpensive LSPG ROM simulations to replace the full-order model simulations in UQ studies, which makes this many-query task tractable, even for large-scale CFD models. This work presents the first application of LSPG to a hypersonic CFD application. In particular, we present results for LSPG ROMs of the HIFiRE-1 in a three-dimensional, turbulent Mach 7.1 flow, showcasing the ability of the ROM to significantly reduce computational costs while maintaining high levels of accuracy in computed quantities of interest.
This report documents the initial testing of the Sandia Parallel Aerodynamics and Reentry Code (SPARC) to directly simulate hypersonic, turbulent boundary layer flow over a sharp 7- degree half-angle cone. This type of computation involves a tremendously large range of scales both in time and space, requiring a large number of grid cells and the efficient utilization of a large pool of resources. The goal of the simulation is to mimic and verify a wind tunnel experiment that seeks to measure the turbulent surface pressure fluctuations. These data are necessary for building a model to predict random vibration loading in the reentry flight environment. A low-dissipation flux scheme in SPARC is used on a 2.7 billion cell mesh to capture the turbulent fluctuations in the boundary layer flow. The grid is divided into 115200 partitions and simulated using the Knight's Landings (KNL) partition of the Trinity system. The parallel performance of SPARC is explored on the Trinity system, as well as some of the other new architectures. Extracting data from the simulation shows good agreement with the experiment as well as a colleague's simulation. The data provide a guide for which a new model can be built for better prediction of the reentry random vibration loads.
We propose a probabilistic framework for assessing the consistency of an experimental dataset, i.e., whether the stated experimental conditions are consistent with the measurements provided. In case the dataset is inconsistent, our framework allows one to hypothesize and test sources of inconsistencies. This is crucial in model validation efforts. The framework relies on statistical inference to estimate experimental settings deemed untrustworthy, from measurements deemed accurate. The quality of the inferred variables is gauged by its ability to reproduce held-out experimental measurements; if the new predictions are closer to measurements than before, the cause of the discrepancy is deemed to have been found. The framework brings together recent advances in the use of Bayesian inference and statistical emulators in fluid dynamics with similarity measures for random variables to construct the hypothesis testing approach. We test the framework on two double-cone experiments executed in the LENS-XX wind tunnel and one in the LENS-I tunnel; all three have encountered difficulties when used in model validation exercises. However, the cause behind the difficulties with the LENS-I experiment is known, and our inferential framework recovers it. We also detect an inconsistency with one of the LENS-XX experiments, and hypothesize three causes for it. We check two of the hypotheses using our framework, and we find evidence that rejects them. We end by proposing that uncertainty quantification methods be used more widely to understand experiments and characterize facilities, and we cite three different methods to do so, the third of which we present in this paper.
This is the second of three related conference papers focused on verifying and validating a CFD model for laminar hypersonic flows. The first paper deals with the code verification and solution verification activities. In this paper, we investigate whether the model can accurately simulate laminar, hypersonic experiments of flows over double-cones, conducted in CUBRC’s LENS-I and LENS-XX wind-tunnels. The approach is to use uncertainty quantification and sensitivity analysis, along with a careful examination of experimental uncertainties, to perform validation assessments. The validation assessments use metrics that probabilistically incorporate both parametric (i.e. freestream input) uncertainty and experimental uncertainty. Further validation assessments compare these uncertainties to iterative and convergence uncertainties described in the first paper in our series of related papers. As other researchers have found, the LENS-XX simulations under-predict experimental heat flux measurements in the laminar, attached region of the fore-cone. This is observed for a deterministic simulation, as well as a probabilistic approach to creating an ensemble of simulations derived from CUBRC-provided estimates of uncertainty for freestream conditions. This paper will conclude with possible reasons that simulations cannot bracket experimental observations, and motivate the third paper in our series, which will further examine these possible explanations. The results in this study emphasize the importance of careful measurement of experimental conditions and uncertainty quantification of validation experiments. This study, along with its sister papers, also demonstrates a process of verification, uncertainty quantification, and quantitative validation activities for building and assessing credibility of computational simulations.
This project will enable high-fidelity aerothermal simulations of hypersonic vehicles to be employed (1) to generate large databases with quantified uncertainties and (2) for rapid interactive simulation. The databases will increase the volume/quality of A4H data; rapid interactive simulation can enable arbitrary conditions/designs to be simulated on demand. We will achieve this by applying reduced-order-modeling techniques to aerothermal simulations.