DATAWorks Sessions Archive

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.

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.

The growing complexity of interactive systems requires increasing amounts of effort to ensure reliability and usability. Testing is an effective approach for finding and correcting problems with implemented systems. However, testing is often regarded as the most intellectual-demanding, time-consuming, and expensive part of system development. Furthermore, it can be difficult (if not impossible) for testers to anticipate all of the conditions that need to be evaluated. This is especially true of human-machine systems. This is because the human operator (who is attempting to achieve his or her task goals) is an additional concurrent component of the system and one whose behavior is not strictly governed by the implementation of designed system elements. To address these issues, researchers have developed approaches for automatically generating test cases. Among these are formal methods: rigorous, mathematical languages, tools, and techniques for modeling, specifying, and verifying (proving properties about) systems. These support model-based approaches (almost exclusively used in computer engineering) for creating tests that are efficient and provide guarantees about their completeness (at least with respect to the model). In particular, model checking can be used for automated test case generation. In this, efficient and exhaustive algorithms search a system model to find traces (test cases) through that model that satisfy specified coverage criteria: descriptions of the conditions the tests should encounter during execution. This talk focuses on a formal automated test generation method developed in my lab for creating cases for human-system interaction. This approach makes use of task models. Task models are a standard human factors method for describing how humans normatively achieve goals when interacting with a system. When these models are given formal semantics, they can be paired with models of system behavior to account for human-system interaction. Formal, automated test case generation can then be performed for coverage criteria asserted over the system (for example, to cover the entire human interface) or human task (to ensure all human activities or actions are performed). Generated tasks, when manually executed with the system, can serve two purposes. First, testers can observe whether the human behavior in test always produces the system behavior from the test. This can help analysts validate the models and, if no problems are found, be sure that any desirable properties exhibited by the model hold in the actual system. Second, testers will be able to use their insights about system usability and performance to subjectively evaluate the system under all of conditions contained in the tests. Given the coverage guarantees provided by the process, this means that testers can be confident they have seen every system condition relevant to the coverage criteria. In this talk, I will describe this approach to automated test case generation and illustrate its utility with a simple example. I will then describe how this approach could be extended to account for different dimensions of human cognitive performance and emerging challenges in human-autonomy interaction.

The process for testing military systems which are largely software intensive involves techniques and procedures often different from those for hardware-based systems. Much of the testing can be performed in laboratories at many of the acquisition stages, up to operational testing. Testing software systems is not different from testing hardware-based systems in that testing earlier and more intensively benefits the acquisition program in the long run. Automated testing of software systems enables more frequent and more extensive testing, allowing for earlier discovery of errors and faults in the code. Automated testing is beneficial for unit, integrated, functional and performance testing, but there are costs associated with automation tool license fees, specialized manpower, and the time to prepare and maintain the automation scripts. This presentation discusses some of the features unique to automated software testing and offers a framework organizations can implement to make the business case for, to organize for, and to execute and benefit from automating the right aspects of their testing needs. Automation has many benefits in saving time and money, but is most valuable in freeing test resources to perform higher value tasks.

The process for testing military systems which are largely software intensive involves techniques and procedures often different from those for hardware-based systems. Much of the testing can be performed in laboratories at many of the acquisition stages, up to operational testing. Testing software systems is not different from testing hardware-based systems in that testing earlier and more intensively benefits the acquisition program in the long run. Automated testing of software systems enables more frequent and more extensive testing, allowing for earlier discovery of errors and faults in the code. Automated testing is beneficial for unit, integrated, functional and performance testing, but there are costs associated with automation tool license fees, specialized manpower, and the time to prepare and maintain the automation scripts. This presentation discusses some of the features unique to automated software testing and offers a framework organizations can implement to make the business case for, to organize for, and to execute and benefit from automating the right aspects of their testing needs. Automation has many benefits in saving time and money, but is most valuable in freeing test resources to perform higher value tasks.

Jones and Nachtsheim (2011) introduced a class of three-level screening designs called definitive screening designs (DSDs). The structure of these designs results in the statistical independence of main effects and two-factor interactions; the absence of complete confounding among two-factor interactions; and the ability to estimate all quadratic effects. Because quadratic effects can be estimated, DSDs can allow for the screening and optimization of a system to be performed in one step, but only when the number of terms found to be active during the screening phase of analysis is less than about half the number or runs required by the DSD (Errore, et al., 2016). Otherwise, estimation of second-order models requires augmentation of the DSD. In this paper we explore the construction of series of augmented designs, moving from the starting DSD to designs capable of estimating the full second-order model. We use power calculations, model-robustness criteria, and model-discrimination criteria to determine the number of runs by which to augment in order to identify the active second-order effects with high probability.

Under the Artemis program, the Exploration Extravehicular Mobility Unit (xEMU) spacesuit will ensure the safety of NASA astronauts during the targeted 2024 return to the moon. Efforts are currently underway to finalize and certify the xEMU design. There is a delicate balance between producing a spacesuit that is robust enough to safely withstand potential fall events while still satisfying stringent mass and mobility requirements. The traditional approach of considering worst case-type loading and applying conservative factors of safety (FoS) to account for uncertainties in the analysis was unlikely to meet the narrow design margins. Thus, the xEMU design requirement was modified to include a probability of no impact failure (PnIF) threshold that must be verified through probabilistic analysis. As part of a broader one year effort to help integrate modern uncertainty quantification (UQ) methodology into engineering practice at NASA, the certification of the xEMU spacesuit was selected as the primary case study. The project, led by NASA Langley Research Center (LaRC) under the Engineering Research & Analysis (R&A) Program in 2020, aimed to develop an end-to-end UQ workflow for engineering problems and to help facilitate reliability-based design at NASA. The main components of the UQ workflow included 1) sensitivity analysis to identify the most influential model parameters, 2) model calibration to quantified model parameter uncertainties using experimental data, and 3) uncertainty propagation for producing probabilistic model predictions and estimating reliability. Particular emphasis was placed on overcoming the common practical barrier of prohibitive computational expense associated with probabilistic analysis by leveraging state-of-the-art UQ methods and high performance computing (HPC). In lieu of mature computational models and test data for the xEMU at the time of the R&A Program, the UQ workflow for estimating PnIF was demonstrated using existing models and data from the previous generation of spacesuits (the Z-2). However, the lessons learned and capabilities developed in the process of the R&A are directly transferable to the ongoing xEMU certification effort and are currently being integrated in 2021. This talk provides an overview of the goals of and findings under NASA’s UQ R&A project, focusing on the spacesuit certification case study. The steps of the UQ workflow applied to the Z-2 spacesuit using the available finite element method (FEM) models and impact test data will be detailed. The ability to quantify uncertainty in the most influential subset of FEM model input parameters and then propagate that uncertainty to estimates of PnIF is demonstrated. Since the FEM model of the full Z-2 assembly took nearly 1 day to execute just once, the advanced UQ methods and HPC utilization required to make the probabilistic analysis tractable are discussed. Finally, the lessons learned from conducting the case study are provided along with planned ongoing/future work for the xEMU certification in 2021.

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.

Model validation is a process for determining how accurate a model is when compared to a true value. The methodology uses uncertainty analysis in order to assess the discrepancy between a measured and predicted value. In the literature, there have been several area metrics introduced to handle these type of discrepancies. These area metrics were applied to problems that include aleatory uncertainty, epistemic uncertainty, and mixed uncertainty. However, these methodologies lack the ability to fully characterize the true dierences between the experimental and prediction data when mixed un- certainty exists in the measurements and/or in the predictions. This work will introduce a new area metric validation approach which aims to com- pensate for the shortcomings in current techniques. The approach will be described in detail and comparisons between existing metrics will be shown. To demonstrate its applicability the new area metric will be applied to a stagnation point calibration probe’s surface predictions for a low-enthalpy conditions. For this application, testing was preformed in the Hypersonic Materials Environmental Test System (HYMETS) facility located at NASA Langley Research Center.

Sensors abound in aerospace testing and while many scientists look at the data from a physics perspective, the comparative statistics information is what drives decisions. A multi-company project was comparing launch data from the 1980’s to a current set of data that included 30 sensors. Each sensor was designed to gather 3000 data points during the 3-second launch event. The data included temperature, acceleration, and pressure information. This talk will compare the data analysis methods developed for this project as well as the use of the new Functional Data Analysis tool within JMP for its ability to discern in-family launch performances.

Statistical engineering has been defined as: “The study of how to best utilize statistical concepts, methods, and tool, and integrate them with IT and other relevant sciences to generate improved results.” A key principle is that significant untapped benefits are often achievable by integrating multiple methods in novel ways to address a problem, without having to invent new statistical techniques. In this presentation, we discuss the application of statistical engineering to the problem of design and analysis of mixture experiments when process variables are also involved. In such cases, models incorporating interaction between the mixture and process variables have been developed, but tend to require large designs and models. By considering models nonlinear in the parameters, also well known in the literature, we demonstrate how experimenters can utilize an alternative, iterative strategy in attacking such problems. We show that this strategy potentially saves considerable experimental time and effort, while producing models that are nearly as accurate as much larger linear models. These results are illustrated using two published data sets and one new data set, all involving interactive mixture and process variable problems.

Recent work at the National Transonic Facility (NTF) at the NASA Langley Research Center has shown that a substantial reduction in freestream pressure fluctuations can be achieved by positioning the moveable model support walls and plenum re-entry flaps to choke the flow just downstream of the test section. This choked condition reduces the upstream propagation of disturbances from the diffuser into the test section, resulting in improved Mach number control and reduced freestream variability. The choked conditions also affect the Mach number gradient and distribution in the test section, so a calibration experiment was undertaken to quantify the effects of the model support wall and re-entry flap movements on the facility freestream flow using a centerline static pipe. A design of experiments (DOE) approach was used to develop restricted-randomization experiments to determine the effects of total pressure, reference Mach number, model support wall angle, re-entry flap gap height, and test section longitudinal location on the centerline static pressure and local Mach number distributions for a reference Mach number range from 0.7 to 0.9. Tests were conducted using air as the test medium at a total temperature of 120 °F as well as for gaseous nitrogen at cryogenic total temperatures of -50, -150, and -250 °F. The resulting data were used to construct quadratic polynomial regression models for these factors using a Restricted Maximum Likelihood (REML) estimator approach. Independent validation data were acquired at off-design conditions to check the accuracy of the regression models. Additional experiments were designed and executed over the full Mach number range of the facility (0.2 £ Mref £ 1.1) at each of the four total temperature conditions, but with the model support walls and re-entry flaps set to their nominal positions, in order to provide calibration regression models for operational experiments where a choked condition downstream of the test section is either not feasible or not required. This presentation focuses on the design, execution, analysis, and results for the two experiments performed using air at a total temperature of 120 °F. Comparisons are made between the regression model output and validation data, as well as the legacy NTF calibration results, and future work is discussed.

Recent work at the National Transonic Facility (NTF) at the NASA Langley Research Center has shown that a substantial reduction in freestream pressure fluctuations can be achieved by positioning the moveable model support walls and plenum re-entry flaps to choke the flow just downstream of the test section. This choked condition reduces the upstream propagation of disturbances from the diffuser into the test section, resulting in improved Mach number control and reduced freestream variability. The choked conditions also affect the Mach number gradient and distribution in the test section, so a calibration experiment was undertaken to quantify the effects of the model support wall and re-entry flap movements on the facility freestream flow using a centerline static pipe. A design of experiments (DOE) approach was used to develop restricted-randomization experiments to determine the effects of total pressure, reference Mach number, model support wall angle, re-entry flap gap height, and test section longitudinal location on the centerline static pressure and local Mach number distributions for a reference Mach number range from 0.7 to 0.9. Tests were conducted using air as the test medium at a total temperature of 120 °F as well as for gaseous nitrogen at cryogenic total temperatures of -50, -150, and -250 °F. The resulting data were used to construct quadratic polynomial regression models for these factors using a Restricted Maximum Likelihood (REML) estimator approach. Independent validation data were acquired at off-design conditions to check the accuracy of the regression models. Additional experiments were designed and executed over the full Mach number range of the facility (0.2 £ Mref £ 1.1) at each of the four total temperature conditions, but with the model support walls and re-entry flaps set to their nominal positions, in order to provide calibration regression models for operational experiments where a choked condition downstream of the test section is either not feasible or not required. This presentation focuses on the design, execution, analysis, and results for the two experiments performed using air at a total temperature of 120 °F. Comparisons are made between the regression model output and validation data, as well as the legacy NTF calibration results, and future work is discussed.

Hardly a week goes by without news of a cybersecurity breach or an attack by cyber adversaries against a nation’s infrastructure. These incidents have wide-ranging effects, including reputational damage and lawsuits against corporations with poor data handling practices. Further, these attacks do not require the direction, support, or funding of technologically advanced nations; instead, significant damage can be – and has been – done with small teams, limited budgets, modest hardware, and open source software. Due to the significance of these threats, it is critical to analyze past events to predict trends and emerging threats. In this document, we present an implementation of a cybersecurity taxonomy and a methodology to characterize and analyze all stages of a cyberattack. The chosen taxonomy, MITRE ATT&CK™, allows for detailed definitions of aggressor actions which can be communicated, referenced, and shared uniformly throughout the cybersecurity community. We translate several open source cyberattack descriptions into the analysis framework, thereby constructing cyberattack data sets. These data sets (supplemented with notional defensive actions) illustrate example Red Team activities. The data collection procedure, when used during penetration testing and Red Teaming, provides valuable insights about the security posture of an organization, as well as the strengths and shortcomings of the network defenders. Further, these records can support past trends and future outlooks of the changing defensive capabilities of organizations. From these data, we are able to gather statistics on the timing of actions, detection rates, and cyberattack tool usage. Through analysis, we are able to identify trends in the results and compare the findings to prior events, different organizations, and various adversaries.

Cyber system defenders face the challenging task of continually protecting critical assets and information from a variety of malicious attackers. Defenders typically function within resource constraints, while attackers operate at relatively low costs. As a result, design and development of resilient cyber systems that support mission goals under attack, while accounting for the dynamics between attackers and defenders, is an important research problem. This talk will highlight decision support modeling challenges under uncertainty within non-cooperative cybersecurity settings. Multiple attacker-defender game formulations under uncertainty are discussed with steps for further research.

A question about whether how post-processing affects the flammability of a given metal an oxygen enriched environment could be answered with the sensitivity of a standard test that is typically used to measure differences in flammability between different metals. The principal investigator was familiar with design of experiments (DOE) and wanted to optimize the value of the information to be gained. This talk will focus on the interchange between the engineer and a statistician. Their negotiations will illustrate the process of clarifying the problem, planning the test including choosing factors and responses, running the experiment and analyzing and reporting on the data. It will focus on the choices made to help squeeze extra knowledge from each test run and leverage statistics to result in increased engineering insight.

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.

Selection decisions, such as procurement decisions, are often based on multiple performance attributes whose values are estimated using data (samples) collected through experimentation. Because the sampling (measurement) process has uncertainty, more samples provide better information. With a limited test budget to collect information to support such a selection decision, determining the number of samples to observe from each alternative and attribute is a critical information gathering decision. In this talk we present a sequential allocation scheme that uses Bayesian updating and maximizes the probability of selecting the true best alternative when the attribute value samples contain Gaussian measurement error. In this sequential approach, the test-designer uses the current knowledge of the attribute values to identify which attribute and alternative to sample next; after that sample, the test-designer chooses another attribute and alternative to sample, and this continues until no more samples can be made. We present the results of a simulation study that illustrates the performance advantage of the proposed sequential allocation scheme over simpler and more common fixed allocation approaches.