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Decisions form the core of T&E: decisions about which tests to conduct and, especially, decisions on whether to accept or reject a system at its milestones. The traditional approach to acceptance is based on conducting tests under various conditions to ensure that key performance parameters meet certain thresholds with the required degree of confidence. In this approach, data is collected during testing, then analyzed with techniques from classical statistics in a post-action report. This work explores a new Bayesian paradigm for T&E based on one simple principle: maintaining a model of the probability distribution over system parameters at every point during testing. In particular, the Bayesian approach posits a distribution over parameters prior to any testing. This prior distribution provides (a) the opportunity to incorporate expert scientific knowledge into the inference procedure, and (b) transparency regarding all assumptions being made. Once a prior distribution is specified, it can be updated as tests are conducted to maintain a probability distribution over the system parameters at all times. One can leverage this probability distribution in a variety of ways to produce analytics with no analog in the traditional T&E framework. In particular, having a probability distribution over system parameters at any time during testing enables one to implement an optimal decision-making procedure using Bayesian Decision Theory (BDT). BDT accounts for the cost of various testing options relative to the potential value of the system being tested. When testing is expensive, it provides guidance on whether to conserve resources by ending testing early. It evaluates the potential benefits of testing for both its ability to inform acceptance decisions and for its intrinsic value to the commander of an accepted system. This talk describes the BDT paradigm for T&E and provides examples of how it performs in simple scenarios. In future work we plan to extend the paradigm to include the features, the phenomena, and the SME elicitation protocols necessary to address realistic T&E cases.

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Physical testing in the national security enterprise is often costly. Sometimes this is driven by hardware and labor costs, other times it can be driven by finite resources of time or hardware builds. Test engineers must make the most of their available resources to answer high consequence problems. Bayesian adaptive design of experiments (BADE) is one tool that should be in an engineer’s toolbox for designing and running experiments. BADE is sequential design of experiment approach which allows early stopping decisions to be made in real time using predictive probabilities (PP), allowing for more efficient data collection. BADE has seen successes in clinical trials, another high consequence arena, and it has resulted in quicker and more effective assessments of drug trials. BADE has been proposed for testing in the national security space for similar reasons of quicker and cheaper test series. Given the high-consequence nature of the tests performed in the national security space, a strong understanding of new methods is required before being deployed. The main contribution of this research is to assess the robustness of PP in a BADE under different modeling assumptions, and to compare PP results to its frequentist alternative, conditional power (CP). Comparisons are made based on Type I error rates, statistical power, and time savings through average stopping time. Simulation results show PP has some robustness to distributional assumptions. PP also tends to control Type I error rates better than CP, while maintaining relatively strong power. While CP usually recommends stopping a test earlier than PP, CP also tends to have more inconsistent results, again showing the benefits of PP in a high consequence application. An application to a real problem from Sandia National Laboratories shows the large potential cost savings for using PP. The results of this study suggest BADE can be one piece of an evidence package during testing to stop testing early and pivot, in order to decrease costs and increase flexibility. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

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Planetary Protection is the practice of protecting solar system bodies from harmful contamination by Earth life and protecting Earth from possible life forms or bioactive molecules that may be returned from other solar system bodies. Microbiologists and engineers at NASA’s Jet Propulsion Laboratory (JPL) design microbial reduction and sterilization protocols that reduce the number of microorganisms on spacecraft or eliminate them entirely. These protocols are developed using controlled experiments to understand the microbial reduction process. Many times, a phenomenological model (such as a series of differential equations) is posited that captures key behaviors and assumptions of the process being studied. A Sterility Assurance Level (SAL) – the probability that a product, after being exposed to a given sterilization process, contains one or more viable organisms – is a standard metric used to assess risk and define cleanliness requirements in industry and for regulatory agencies. Experiments performed to estimate the SAL of a given microbial reduction or sterilization protocol many times have large uncertainties and variability in their results even under rigorously implemented controls that, if not properly quantified, can make it difficult for experimenters to interpret their results and can hamper a credible evaluation of risk by decision makers. In this talk, we demonstrate how Bayesian statistics and experimentation can be used to quantify uncertainty in phenomenological models in the case of microorganism survival under short-term high heat exposure. We show how this can help stakeholders make better risk-informed decisions and avoid the unwarranted conservatism that is often prescribed when processes are not well understood. The experiment performed for this study employs a 6 kW infrared heater to test survivability of heat resistant Bacillus canaveralius 29669 to temperatures as high as 350 °C for time durations less than 30 sec. The objective of this study was to determine SALs for various time-temperature combinations, with a focus on those time-temperature pairs that give a SAL of 10^-6. Survival ratio experiments were performed that allow estimation of the number of surviving spores and mortality rates characterizing the effect of the heat treatment on the spores. Simpler but less informative fraction-negative experiments that only provide a binary sterile/not-sterile outcome were also performed once a sterilization temperature regime was established from survival ratio experiments. The phenomenological model considered here is a memoryless mortality model that underlies many heat sterilization protocols in use today. This discussion and poster will outline how the experiment and model were brought together to determine SALs for the heat treatment under consideration. Ramifications to current NASA planetary protection sterilization specifications and current missions under development such as Mars Sample Return will be discussed. This presentation/poster is also relevant to experimenters and microbiologists working on military and private medical device applications where risk to human life is determined by sterility assurance of equipment.

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The DoD maintains a broad array of systems, each one sustained by an often complex supply chain of components and suppliers. The ways that these supply chains are interlinked can have major implications for the resilience of the defense industrial base as a whole, and the readiness of multiple weapon systems. Finding opportunities to improve overall resilience requires gaining visibility of potential weak links in the chain, which requires integrating data across multiple disparate sources. By using open-source data pipeline software to enhance reproducibility, and flexible network analysis methods, multiple stovepiped data sources can be brought together to develop a more complete picture of the supply chain across systems.

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While the impact of local weather conditions on aircraft performance is well-documented, climate change has the potential to create long-term shifts in aircraft performance. Using just one metric, internal load capacity, we document operationally relevant performance changes for a UH-60L within the Indo-Pacific region. This presentation uses publicly available climate and aircraft performance data to create a representative analysis. The underlying methodology can be applied at varying geographic resolutions, timescales, airframes, and aircraft performance characteristics across the entire globe.