DATAWorks Sessions Archive

Biomechanical experiments investigating the failure modes of biological tissue require a significant investment of time and money due to the complexity of procuring, preparing, and testing tissue. Furthermore, the potentially destructive nature of these tests makes repeated testing infeasible. This leads to experiments with notably small sample sizes in light of the high variance common to biological material. When the goal is to estimate parameters for an analytic artifact such as an injury risk curve (IRC), which relates an input quantity to a probability of injury, small sample sizes result in undesirable uncertainty. One way to ameliorate this effect is through a Bayesian approach, incorporating expert opinion and previous experimental data into a prior distribution. This has the advantage of leveraging the information contained in expert opinion and related experimental data to obtain faster convergence to an appropriate parameter estimation with a desired certainty threshold. We explore several ways of implementing Bayesian methods in a biomechanical setting, including permutations on the use of expert knowledge and prior experimental data. Specifically, we begin with a set of experimental data from which we generate a reference IRC. We then elicit expert predictions of the 10th and 90th quantiles of injury, and use them to formulate both uniform and normal prior distributions. We also generate priors from qualitatively similar experimental data, both directly on the IRC parameters and on the injury quantiles, and explore the use of weighting schemes to assign more influence to better datasets. By adjusting the standard deviation and shifting the mean, we can create priors of variable quality. Using a subset of the experimental data in conjunction with our derived priors, we then re-fit the IRC and compare it to the reference curve. For all methods we will measure the certainty, speed of convergence, and accuracy relative to the reference IRC, with the aim of recommending a best practices approach for the application of Bayesian methods in this setting. Ultimately an optimized approach for handling small samples sizes with Bayesian methods has the potential to increase the information content of individual biomechanical experiments by integrating them into the context of expert knowledge and prior experimentation.

The spread of COVID-19 across the United States provides an interesting case study in the modeling of spatio-temporal data. In this breakout session we will provide an overview of commonly used spatio-temporal models and demonstrate how Bayesian Inference can be performed using both exact and approximate inferential techniques. Using COVID data, we will demonstrate visualization techniques in R and introduce “off-the-shelf” spatio-temporal models. We will introduce participants to the Integrated Nested LaPlace approximation (INLA) methodology and show how results from this technique compare to using Markov Chain Monte Carlo (MCMC) techniques. Finally, we will demonstrate the short-falls in using “off-the-shelf” models and show how epidemiological motivated partial differential equations can be used to generate spatio-temporal models and discuss inferential issues when we move away from common models.

Problems faced in defense and aerospace often go well beyond textbook problems presented in academic settings. Textbook problems are typically very well defined, and can be solved through application of one “correct” tool. Conversely, many defense and aerospace problems are ill-defined, at least initially, and require an overall strategy to attack, one involving multiple tools and often multiple disciplines. Statistical engineering is an approach recently developed for addressing large, complex, unstructured problems, particularly those for which data can be effectively utilized. This session will present a brief overview of statistical engineering, and how it can be applied to engineer solutions to complex problems. Following this introduction, two case studies of statistical engineering will be presented, to illustrate the concepts.

Problems faced in defense and aerospace often go well beyond textbook problems presented in academic settings. Textbook problems are typically very well defined, and can be solved through application of one “correct” tool. Conversely, many defense and aerospace problems are ill-defined, at least initially, and require an overall strategy to attack, one involving multiple tools and often multiple disciplines. Statistical engineering is an approach recently developed for addressing large, complex, unstructured problems, particularly those for which data can be effectively utilized. This session will present a brief overview of statistical engineering, and how it can be applied to engineer solutions to complex problems. Following this introduction, two case studies of statistical engineering will be presented, to illustrate the concepts.

Generating aerodynamic coefficients can be computationally expensive, especially for the viscous CFD solvers in which multiple complex models are iteratively solved. When filling large design spaces, utilizing only a high accuracy viscous CFD solver can be infeasible. We apply state-of-the-art methods for design and analysis of computer experiments to efficiently develop an emulator for high-fidelity simulations. First, we apply a cokriging model to leverage information from fast low-fidelity simulations to improve predictions with more expensive high-fidelity simulations. Combining space-filling designs with a Gaussian process model-based sequential sampling criterion allows us to efficiently generate sample points and limit the number of costly simulations needed to achieve the desired model accuracy. We demonstrate the effectiveness of these methods with an aerodynamic simulation study using a conic shape geometry. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. Release Number: LLNL-ABS-818163

The Advanced Joint Effectiveness Model (AJEM) is a joint forces model developed by the U.S. Army that is used in vulnerability and lethality (V/L) predictions for threat/target interactions. This complex model primarily generates a probability response for various components, scenarios, loss of capabilities, or summary conditions. Sensitivity analysis (SA) and uncertainty quantification (UQ), referred to jointly as SA/UQ, are disciplines that provide the working space for how model estimates changes with respect to changes in input variables. A comparative measure that will be used to characterize the effect of an input change on the predicted outcome was developed and is reviewed and illustrated in this presentation. This measure provides a practical context that stakeholders can better understand and utilize. We show graphical and tabular results using this measure.

Physics-based simulation of nondestructive evaluation (NDE) inspection can help to advance the inspectability and reliability of mechanical systems. However, NDE simulations applicable to non-idealized mechanical components often require large compute domains and long run times. This has prompted development of custom NDE simulation software tailored to high performance computing (HPC) hardware. Verification and validation (V&V) is an integral part of developing this software to ensure implementations are robust and applicable to inspection problems, producing tools and simulations suitable for computational NDE research. This presentation addresses factors common to V&V of several elastodynamic simulation codes applicable to ultrasonic NDE. Examples are drawn from in-house simulation software at NASA Langley Research Center, ranging from ensuring reliability in a 1D heterogeneous media wave equation solver to the V&V needs of 3D cluster-parallel elastodynamic software. Factors specific to a research environment are addressed, where individual simulation results can be as relevant as the software product itself. Distinct facets of V&V are discussed including testing to establish software reliability, employing systematic approaches for consistency with fundamental conservation laws, establishing the numerical stability of algorithms, and demonstrating concurrence with empirical data. This talk also addresses V&V practices for small groups of researchers. This includes establishing resources (e.g. time and personnel) for V&V during project planning to mitigate and control the risk of setbacks. Similarly, we identify ways for individual researchers to use V&V during simulation software development itself to both speed up the development process and reduce incurred technical debt.