DATAWorks Sessions Archive

Over the past 10 years, a number of activities have incorporated statistical methods for the purpose of database development and associated uncertainty modeling. This presentation will highlight approaches taken in aerodynamic database development for space vehicle projects, specifically the Orion spacecraft and abort vehicle, and the Space Launch System (SLS) launch vehicle. Additionally, statistical methods have been incorporated into the Commercial Supersonic Transport Project for test technique development and optimization as well as a certification prediction methodology, which is planned to be verified with the Low-Boom Flight Demonstrator data. Discussion will conclude with the use of statistical methods for quality control and assurance in the NASA Langley Research Center ground testing facilities related to our Check Standard Project and characterization and calibration testing.

https://s3.amazonaws.com/workshop-archives-2016/IDA+Workshop+2016/testsciencemeeting.ida.org/pdfs/1b-ExperimentalDesignMethodsandApplications.pdf

Rigorous characterization of system capabilities is essential for defensible decisions in test and evaluation (T&E). Analysis of designed experiments is not usually associated “big” data analytics as there are typically a modest number of runs, factors, and responses. The Mars Rover program has recently conducted several disciplined DOEs on prototype coring drill performance with approximately 10 factors along with scores of responses and hundreds of recorded covariates. The goal is to characterize the ‘atthis-time’ capability to confirm what the scientists and engineers already know about the system, answer specific performance and quality questions across multiple environments, and inform future tests to optimize performance. A ‘rigorous’ characterization required that not just one analytical path should be taken, but a combination of interactive data visualization, classic DOE analysis screening methods, and newer methods from predictive analytics such as decision trees. With hundreds of response surface models across many test series and qualitative factors, these methods used had to efficiently find the signals hidden in the noise. Participants will be guided through an end-to-end analysis workflow with actual data from many tests (often Definitive Screening Designs) of the Rover prototype coring drill. We will show data assembly, data cleaning (e.g. missing values and outliers), data exploration with interactive graphical designs, variable screening, response partitioning, data tabulation, model building with stepwise and other methods, and model diagnostics. Software packages such as R and JMP will be used.

NASA has instituted requirements for establishing Agency-level safety thresholds and goals that define “long-term targeted and maximum tolerable levels of risk to the crew as guidance to developers in evaluating ‘how safe is safe enough’ for a given type of mission.” With the adoption of this policy for human space flight and with ongoing Agency efforts to increase formality in the development and review of the basis for risk acceptance, the decision-support demands placed on risk models are becoming more stringent at NASA. While these models play vital roles in informing risk acceptance decisions, they are vulnerable to incompleteness of risk identification, as well as to incomplete understanding of probabilities of occurrence, potentially leaving a substantial portion of the actual risk unaccounted for, especially for new systems. This presentation argues that management of uncertainty about the “actual” safety performance of a system must take into account the contribution of unknown and/or underappreciated (UU) risk. Correspondingly, responsible risk-acceptance decision-making requires the decision-maker to think beyond the model and address factors (e.g., organizational and management factors) that live outside traditional engineering risk models. This presentation advocates the use of a safety-case approach to risk acceptance decision-making.

Rigorous characterization of system capabilities is essential for defensible decisions in test and evaluation (T&E). Analysis of designed experiments is not usually associated “big” data analytics as there are typically a modest number of runs, factors, and responses. The Mars Rover program has recently conducted several disciplined DOEs on prototype coring drill performance with approximately 10 factors along with scores of responses and hundreds of recorded covariates. The goal is to characterize the ‘atthis-time’ capability to confirm what the scientists and engineers already know about the system, answer specific performance and quality questions across multiple environments, and inform future tests to optimize performance. A ‘rigorous’ characterization required that not just one analytical path should be taken, but a combination of interactive data visualization, classic DOE analysis screening methods, and newer methods from predictive analytics such as decision trees. With hundreds of response surface models across many test series and qualitative factors, these methods used had to efficiently find the signals hidden in the noise. Participants will be guided through an end-to-end analysis workflow with actual data from many tests (often Definitive Screening Designs) of the Rover prototype coring drill. We will show data assembly, data cleaning (e.g. missing values and outliers), data exploration with interactive graphical designs, variable screening, response partitioning, data tabulation, model building with stepwise and other methods, and model diagnostics. Software packages such as R and JMP will be used.

The American National Standards Institute (ANSI) maintains a set of test standards that provide methods to characterize and determine the acceptability of radiation detection systems for use in Homeland security. With a focus on the environmental, electromagnetic, and mechanical functionality tests, we describe the test formulation and discuss challenges faced in administering the standard to include the assurance of comparable evaluations across multiple test facilities and the handling of systems that provide a non-standard, unit-less response. We present proposed solutions to these difficulties that are currently being considered in updated versions of the ANSI standards. We briefly describe a decision analytic approach that could allow for the removal of minimum performance requirements from the standards and enable the end user to determine system suitability based on operation-specific requirements.

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.

This paper presents the results of two hypervelocity impact failure probabilistic risk assessments for critical wire bundles exposed aboard the Joint Polar Satellite System (JPSS-1) to an increased orbital debris environment at its 824 km, 98.8 deg inclination orbit. The first “generic” approach predicted the number of wires broken by orbital debris ejecta emerging from normal impact with multi-layer insulation (MLI) covering 36-, 18-, and 6-strand wire bundles at a 5 cm standoff using the Smooth Particle Hydrodynamic (SPH) code. This approach also included a mathematical approach for computing the probability that redundant wires were impacted then severed within the bundle. Based in part on the high computed risk of a critical wire bundle failure from the generic approach, an enhanced orbital debris protection design was examined, consisting of betacloth-reinforced MLI suspended at a 5 cm standoff over a seven layer betacloth and Kevlar blanket, draped over the exposed wire bundles. A second SPH-based risk assessment was conducted that also included the beneficial effects from the high (75 degree) obliquity of orbital debris impact and shadowing by other spacecraft components, and resulted in a considerably reduced likelihood of critical wire bundle failure compared to the original baseline design.

For the past 7 years, USAF DT&E has been exploring ways to adapt the principles of experimental design to rapidly evolving developmental test articles and test facilities – often with great success. This paper discusses three case studies that span the range of USAF DT&E activities from EW to Ground Test to Flight Test and shows the truly revolutionary impact Fisher’s DOE can have on development. The Advanced Strategic and Tactical Expendable (ASTE) testing began in 1990 to develop, enhance, and test new IR flares, flare patterns, and dispense tactics. More than 60 aircraft & flare types have been tested. The typical output is a “Pancake Plot” of 200+ “cases” of flare, aspect angle, range, elevation, and flare effectiveness using a stop-light chart (red-yellow-green) approach. In usual testing – ~3000 flare engagements costing $1M to participate. The response, troublingly enough, is binary – 15-30 binomial response trials measuring P(Decoy). Binary responses are information-poor. Legacy testing does not assess present statistical power in reporting P(decoy) results. Analysts investigated replacing P(Decoy) w/ continuous metrics – e.g. time to decoy. This research is ongoing. We found we could spread the replicates out to examine 3x to 5x more test conditions without affecting power materially. Analysis with the Generalized Linear Model (GLZ) replaced legacy “cases” analysis with 75% improvement to confidence intervals with same data. We are seeking to build a Monte-Carlo simulation to estimate how many runs are required in a logistics regression model to achieve adequate power. We hope to reduce customer expenditures for flare information by as much as 50%. Co-authors J. Higdon, B, Knight. AEDC completed a major upgrade with new nozzle hardware to vary Mach. The Arnold Transonic Wind Tunnel 4T spans the range of flows from subsonic to approximately M9. The new wind tunnel was to be computer controlled. A number of key instrumentation improvements were made at the same time. The desire was to calibrate the resulting modified tunnel. The last calibration of 4T was 25 years ago in 1990. The calibration ranged across the full range of Mach and pressure capabilities, spanning a four-D space: pressure, Mach, wall angle, and wall porosity. Both the traditional OFAT effort – vary one factor at a time – and a parallel DOE effort were run to compare design, execution, modeling, and prediction capabilities against cost and time to run. The robust embedded face-centered CCD DOE design (J. Simpson and D. Landman ’05) employed 75 vs. 176 OFAT runs. The RSM design achieved 57% run savings. Due to an admirable discipline in randomization during the DOE trials, the smaller design required longer to run. As a result of using the DOE approach, engineers found it easier predict offcondition tunnel operating characteristics using RSM models, optimize facility flow quality for any given test condition. In addition, the RSM regression models support future “spot check” calibration in future by comparing predictions to measured values. If the measurement falls within the prediction interval, the existing calibration still appropriate. AEDC is using split-plot style designs a for current wind tunnel probe calibration. Co-author: Dr Dough Garrard.

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.

Co-authors: Dr. Darryl Ahner, Director, STAT COE Dr. Lenny Truett, STAT COE Mr. Scott Wacker, 96 TG/OL-ACS. This presentation will present the benefits of sequential testing and demonstrate how sequential testing can be used to address complex test conditions by developing well controlled early experiments to explore basic questions before proceeding to full-scale testing. This approach can result in increased knowledge and decreased cost. As of FY13 the Air Force had spent an estimated $47M on dry bay fire testing making fire the largest cost contributor for Live Fire Test and Evaluation (LFT&E) programs. There is currently an estimated 60% uncertainty in total platform vulnerable area (Av) driven by probability of kill (PK) due to ballistically ignited fires. A large part of this uncertain comes from the fact that current spurt modeling does not predict fuel spurt delay with reasonable accuracy despite a large amount of test data. A low-cost sequential approach was developed to improve spurts models. Initial testing used a spherical projectile to test 10 different factors in a definitive screening design. Once the list of factors was refined, a second phase of testing determined if a suitable methodology could be developed to scaled results using water as a surrogate for JP-8 fuel. Finally testing was performed with cubical projectiles to evaluate the effect of fragment orientation. The entire cost for this effort was less than one or two typical full-scale live fire tests.