DATAWorks Sessions Archive

Development of new technology always incorporates model testing. This is certainly true for hypersonics, where flight tests are expensive and testing of component- and system-level models has significantly advanced the field. Unfortunately, model tests are often limited in scope, being only approximations of reality and typically only partially covering the range of potential realistic conditions. In this talk, we focus on the problem of real-time detection of the shock train leading edge in high-speed air-breathing engines, such as dual-mode scramjets. Detecting and controlling the shock train leading edge is important to the performance and stability of such engines, and a problem that has seen significant model testing on the ground and some flight testing. Often, methods developed for shock train detection are specific to the model used. Thus, they may not generalize well when tested in another facility or in flight as they typically require a significant amount of prior characterization of the model and flow regime. A successful method for shock train detection needs to be robust to changes in features like isolator geometry, inlet and combustor states, flow regimes, and available sensors. Such data can be difficult or impossible to obtain if the isolator operating regime is large. To this end, we propose the an approach for real-time detection of the isolator shock train. Our approach uses real-time pressure measurements to adaptively estimate the shock train position in a data-driven manner. We show that the method works well across different isolator models, placement of pressure transducers, and flow regimes. We believe that a data-driven approach is the way forward for bridging the gap between testing and reality, saving development time and money.

The recent emergence of affordable, high-quality augmented-, mixed-, and virtual-reality (AR, MR, VR), technologies presents an opportunity to dramatically change the way users consume and interact with information. It has been shown that these immersive systems can be leveraged to enhance comprehension and accelerate decision-making in situations where data can be linked to spatial information, such as maps or terrain models. Furthermore, when immersive technologies are networked together, they allow for decentralized collaboration and provide perspective-taking not possible with traditional displays. However, enabling this shared space requires novel techniques in intelligent information management and data exchange. In this experiment, we explored a framework for leveraging distributed AI/ML processing to enable clusters of low-power, limited-functionality devices to deliver complex capabilities in aggregate to users distributed across the country collaborating simultaneously in a shared virtual environment. We deployed a motion detecting camera and triggered detection events to send information using a distributed request/reply worker framework to a remotely located YOLO image classification cluster. This work demonstrates the capability for various IoT and IoBT systems to invoke functionality without a priori knowledge of the specific endpoint to use to execute that functionality but by submitting a request based on a desired capability concept (e.g. image classification) with requiring only: 1) the knowledge of the broker location, 2) valid public/private key pair required to authenticate with the broker, and 3) the capability concept UUID and knowledge of request/reply formats used by that concept.

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.

In design of experiments, setting exact replicates of factor settings enables estimation of pure-error; a model-independent estimate of experimental error useful in communicating inherent system noise and testing of model lack-of-fit. Often in practice, the factor levels for replicates are precisely measured rather than precisely set, resulting in near-replicates. This can result in inflated estimates of pure-error due to uncompensated set-point variation. In this article, we review previous strategies for estimating pure-error from near-replicates and propose a simple alternative. We derive key analytical properties and investigate them via simulation. Finally, we illustrate the new approach with an application.

Nearly all land, air, and sea maneuver systems (e.g. vehicles, ships, aircraft, and missiles) are becoming more software-reliant and blending internal communication across both Internet Protocol (IP) and non-IP buses. IP communication is widely understood among the cybersecurity community, whereas expertise and available test tools for non-IP protocols such as Controller Area Network (CAN) and MIL-STD-1553 are not as commonplace. However, a core tenet of operational cybersecurity testing is to asses all potential pathways of information exchange present on the system, to include IP and non-IP. In this presentation, we will introduce a few non-IP protocols (e.g. CAN, MIL-STD-1553) and provide a live demonstration of how to attack a CAN network using malicious message injection. We will also discuss how potential cyber effects on non-IP busses can lead to catastrophic mission effects to the target system.

The Scientific Test and Analysis Techniques (STAT) process is designed to provide structure for a test team to progress from a requirement to decision quality information. The four phases of the STAT process are Plan, Design, Execute, and Analyze. Within the Test and Evaluation (T&E) community we tend to focus on the quantifiable metrics and the hard science of testing, which are the Design and the Analyze phases. At the STAT Center of Excellence (COE) we have emphasized an increased focus on the planning phase and in this presentation we focus on the elements necessary for a comprehensive planning session. In order to efficiently and effectively test a system it is vital that the test team understand the requirements, the System Under Test (SUT) to include any subsystems that will be tested, and the test facility. To accomplish this the right team members with the necessary knowledge must be in the room and prepared to present their information and have an educated discussion to arrive at a comprehensive agreement about the desired end stated of the test. Our recommendations for the initial planning meeting are based on a thorough study of the STAT process and on lessons learned from actual planning meetings.

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.

This session will focus on the novel application of a design of experiments approach to optimize a propulsion charge configuration for a U.S. Army artillery round. The interdisciplinary design effort included contributions from subject matter experts in statistics, propulsion charge design, computational physics and experimentation. The process, which we will present in this session, consisted of an initial, low fidelity modeling and simulation study to reduce the parametric space by eliminating inactive variables and reducing the ranges of active variables for the final design. The final design used a multi-tiered approach that consolidated data from multiple sources including low fidelity modeling and simulation, high fidelity modeling and simulation and live test data from firings in a ballistic simulator. Specific challenges of the effort that will be addressed include: integrating data from multiple sources, a highly constrained design space, functional response data, multiple competing design objectives and real-world test constraints. The result of the effort is a final, optimized propulsion charge design that will be fabricated for live gun firing.

Receiver Operating Characteristic curves (ROC curves) are often used to assess the performance of detection and classification systems. ROC curves can have unexpected subtleties that make them difficult to interpret. For example, the Strategic Environmental Research and Development Program and the Environmental Security Technology Certification Program (SERDP/ESTCP) is sponsoring the development of novel systems for the detection and classification of Unexploded Ordnance (UXO) in underwater environments. SERDP is also sponsoring underwater testbeds to demonstrate the performance of these novel systems. The Institute for Defense Analyses (IDA) is currently designing and implementing the scoring process for these underwater demonstrations that addresses the subtleties of ROC curve interpretation. This presentation will provide an overview of the main considerations for ROC curve parameter selection when scoring underwater demonstrations for UXO detection and classification.

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