DATAWorks Sessions Archive

Combining physical measurements with computational models is key to many investigations involving validation and uncertainty quantification (UQ). This talk surveys some of the many approaches taken for validation and UQ, with large-scale computational models. Experience with such applications suggests classifications of different types of problems with common features (e.g. data size, amount of empiricism in the model, computational demands, availability of data, extent of extrapolation required, etc.). More recently, social and social-technical systems are being considered for similar analyses, bringing new challenges to this area. This talk will approaches for such problems and will highlight what might be new research directions for application and methodological development in UQ.

The concept of sequential testing has many disparate meanings. Often, for statisticians it takes on a purely mathematical context while possibly meaning multiple disconnected test events for some practitioners. Here we present a pedagogical approach to creating test designs involving constrained factors using JMP software. Recent experiences testing one of the U.S. military’s fast jet life support systems (LSS) serves as a case study and back drop to support the presentation. The case study discusses several lessons learned during LSS testing, applicable to all practitioners of scientific test and analysis techniques (STAT) and design of experiments (DOE). We conduct a short analysis to specifically determine a test region with a set of factors pertinent to modeling human breathing and the use of breathing machines as part of the laboratory setup. A comparison of several government and industry laboratory test points and regions with governing documentation is made, along with the our proposal for determining a necessary and sufficient test region for tests involving human breathing as a factor.

This talk focuses on a means to characterize the variability in multivariate data. This characterization, given in terms of both probability distributions and closed sets, is instrumental in assessing and improving the robustness/reliability properties of system designs. To this end, we propose the Sliced-Normal (SN) class of distributions. The versatility of SNs enables modeling complex multivariate dependencies with minimal modeling effort. A polynomial mapping is defined which injects the physical space into a higher dimensional (so-called) feature space on which a suitable normal distribution is defined. Optimization-based strategies for the estimation of SNs from data in both physical and feature space are proposed. The formulations in physical space yield non-convex optimization programs whose solutions often outperform the solutions in feature space. However, the formulations in feature space yield either an analytical solution or a convex program thereby facilitating their application to problems in high dimensions. The superlevel sets of a SN density have a closed semi-algebraic form making them amenable to rigorous uncertainty quantification methods. Furthermore, we propose a chance-constrained optimization framework for identifying and eliminating the effects of outliers in the prescription of such regions. These strategies can be used to mitigate the conservatism intrinsic to many methods in system identification, fault detection, robustness/reliability analysis, and robust design caused by assuming parameter independence and by including outliers in the dataset.

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.

The Military Global Positioning System (GPS) User Equipment (MGUE) program is the user segment of the GPS Enterprise—a program on the Deputy Assistant Secretary of Defense for Developmental Test and Evaluation (DASD(DT&E)) Space and Missile Defense Systems portfolio. The MGUE program develops and test GPS cards capable of using Military-Code (M Code) and legacy signals. The program’s DT&E strategy is challenging. The GPS cards provide new, untested capabilities. Milestone A was approved on 2012 with sole source contracts released to three vendors for Increment 1. An Acquisition Decision Memorandum directs the program to support a Congressional Mandate to provide GPS M Code-capable equipment for use after FY17. Increment 1 provides GPS receiver form factors for the ground domain interface as well as for the aviation and maritime domain interface. When reviewing DASD(DT&E) Milestone B (MS B) Assessment Report, Mr. Kendall expressed curiosity about how the Developmental Evaluation Framework (DEF) and Design of Experiments (DOE) help. This presentation describes how the DEF and DOE methods help producing more informative and more economical developmental tests than what was originally under consideration by the test community—decision-quality information with a 60% reduction in test cycle time. It provides insight into how the integration of the DEF and DOE improved the overall effectiveness of the DT&E strategy, illustrates the role of modeling and simulation (M&S) in the test design process, provides examples of experiment designs for different functional and performance areas, and illustrates the logic involved in balancing risks and test resources. The DEF and DOE methods enables the DT&E strategy to fully exploit early discovery, to maximize verification and validation opportunities, and to characterize system behavior across the technical requirements space.

The fundamental principles of experiment design are factorization, replication, randomization, and local control of error. In many industries, however, departure from these principles is commonplace. Often in our experiments complete randomization is not feasible because the factor level settings are hard, impractical, or inconvenient to change or the resources available to execute under homogeneous conditions are limited. These restrictions in randomization lead to split-plot experiments. We are also often interested in fitting second-order models leading to second-order split-plot experiments. Although response surface methodology has grown tremendously since 1951, the lack of alternatives for second-order split-plots remains largely unexplored. The literature and textbooks offer limited examples and provide guidelines that often are too general. This deficit of information leaves practitioners ill prepared to face the many roadblocks associated with these types of designs. This presentation provides practical strategies to help practitioners in dealing with second-order split-plot and by extension, split-split-plot experiments, including an innovative approach for the construction of a response surface design referred to as second-order sub-array Cartesian product split-plot design. This new type of design, which is an alternative to other classes of split-plot designs that are currently in use in defense and industrial applications, is economical, has a low prediction variance of the regression coefficients, and low aliasing between model terms. Based on an assessment using well accepted key design evaluation criterion, second-order sub-array Cartesian product split-plot designs perform as well as historical designs that have been considered standards up to this point.

Inherent system processes, restrictions on collection, or cost may impact the practical execution of an operational test. This study presents the use of blocking and split-plot designs when complete randomization is not feasible in operational test. Specifically, the USAF B-52 Radar Modernization Program test design is used to present tradeoffs of different design choices and the impacts of those choices on cost, operational relevance, and analytical rigor.

Difficult choices are often required in a decision-making process where resources and budgets are increasingly constrained. This talk demonstrates a structured decision-making approach using layered Pareto fronts to prioritize the allocation of funds between munitions stockpiles based on their estimated reliability, the urgency of needing available units, and the consequences if adequate numbers of units are . This case study illustrates the process of first identifying appropriate metrics that summarize important dimensions of the decision, and then eliminating non-contenders from further consideration in an objective stage. The final subjective stage incorporates subject matter expert priorities to select the four stockpiles to receive additional maintenance and surveillance funds based on understanding the trade-offs and robustness to various user priorities.

Co-Authors: Sean A. Commo, Ph.D., P.E. and Peter A. Parker, Ph.D., P.E. NASA Langley Research Center, Hampton, Virginia, USA Austin D. Overmeyer, Philip E. Tanner, and Preston B. Martin, Ph.D. U.S. Army Research, Development, and Engineering Command, Hampton, Virginia, USA. The application of statistical engineering to helicopter wind-tunnel testing was explored during two powered rotor entries. The U.S. Army Aviation Development Directorate Joint Research Program Office and the NASA Revolutionary Vertical Lift Project performed these tests jointly at the NASA Langley Research Center. Both entries were conducted in the 14- by 22-Foot Subsonic Tunnel with a small segment of the overall tests devoted to developing case studies of a statistical engineering approach. Data collected during each entry were used to estimate response surface models characterizing vehicle performance, a novel contribution of statistical engineering applied to powered rotor-wing testing. Additionally, a 16- to 47-times reduction in the number of data points required was estimated when comparing a statistically-engineered approach to a conventional one-factor-at-a-time approach.

During the summer of 2001, Microsoft launched Windows XP, which was lauded by many users as the most reliable and usable operating system at the time. Miami Herald columnist, Dave Berry, responded to this praise by stating that “this is like saying asparagus is the most articulate vegetable ever.” Whether you agree or disagree with Dave Berry, these users’ reactions are relative (to other operating systems and to other past and future versions). This is due to an array of technological factors that have facilitated human-system improvements. Automation is often cited as improving human-system performance across many domains. It is true that when the human and automation are aligned, performance improves. But, what about the times that this is not the case? This presentation will describe the myths and facts about human-system performance and increasing levels of automation through examples of human-system R&D conducted on a satellite ground system. Factors that affect human-system performance and a method to characterize mission performance as it relates to increasing levels of automation will also be discussed.

Abstract: In applications where modeling and simulation runs are quick and cheap, space filling designs will give the tester all the information they need to make decisions about their system. In some applications however, this luxury does not exist, and each M&S run can be time consuming and expensive. In these scenarios, a sequential test approach provides an efficient solution where an initial screening is conducted, followed by an augmentation to fit specified models of interest. Until this point, no dedicated screening designs for UQ applications in resource constrained situations existed. Due to the Army’s frequent exposure to this type of situation, the need sparked a collaboration between Picatinny’s Statistical Methods and Analysis group and Professor V. Roshan Joseph of Georgie Tech, where a new type of UQ screening design was created. This paper provides a brief introduction to the design, its intended use, and a case study in which this new methodology was applied.

For the past several years, researchers at NASA Langley have been engaged in a series of projects to study the degree to which existing facilities and capabilities, originally created for work on full-scale aircraft, are extensible to smaller scales – those of the small unmanned aerial systems (sUAS, also UAVs and, colloquially, `drones’) that have been showing up in the nation’s airspace. This paper follows an effort that has led to an initial human-subject psychoacoustic test regarding the annoyance generated by sUAS noise. This effort spans three phases: 1. the collection of the sounds through field recordings, 2. the formulation and execution of a psychoacoustic test using those recordings, 3. the analysis of the data from that test. The data suggests a lack of parity between the noise of the recorded sUAS and that of a set of road vehicles that were also recorded and included in the test, as measured by a set of contemporary noise metrics.

Sensors that record sequences of measurements are now embedded in many products from wearable exercise watches to chemical and semiconductor manufacturing equipment. There is information in the shapes of the sensor stream curves that is highly predictive of a variety of outcomes such as the likelihood of a product failure event or batch yield. Despite this data now being common and readily available, it is often being used either inefficiently or not at all due to lack of knowledge and tools for how to properly leverage it. In this presentation, we will propose fitting splines to sensor streams and extracting features called functional principal component scores that offer a highly efficient low dimensional compression of the signal data. Then, we use these features as inputs into machine learning models like neural networks and LASSO regression models. Once one sees sensor data in this light, answering a wide variety of applied questions becomes a straightforward two stage process of data cleanup/functional feature extraction followed by modeling using those features as inputs.

Uncertainty is an inescapable reality that can be found in nearly all types of engineering analyses. It arises from sources like measurement inaccuracies, material properties, boundary and initial conditions, and modeling approximations. For example, the increasing use of numerical simulation models throughout industry promises improved design and insight at significantly lower costs and shorter timeframes than purely physical testing. However, the addition of numerical modeling has also introduced complexity and uncertainty to the process of generating actionable results. It has become not only possible, but vital to include Uncertainty Quantification (UQ) in engineering analysis. The competitive benefits of UQ include reduced development time and cost, improved designs, better understanding of risk, and quantifiable confidence in analysis results and engineering decisions. Unfortunately, there are significant cultural and technical challenges which prevent organizations from utilizing UQ methods and techniques in their engineering practice. This presentation will introduce UQ methodology and discuss the past and present strategies for addressing these challenges, making it possible to use UQ to enhance engineering processes with fewer resources and in more situations. Looking to the future, anticipated challenges will be discussed along with an outline of the path towards making UQ a common practice in engineering.