DATAWorks Sessions Archive

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

An important property of any experimental design is its ability to detect active factors. For supersaturated designs, in which model parameters outnumber experimental runs, power is even more critical. In this talk, we review several popular supersaturated design construction criteria and analysis methods. We then demonstrate how simulation studies can be useful for practitioners in selecting a supersaturated design with regards to power to detect active factors. One of our findings based on an extensive simulation study is that although differences clearly exist among analysis methods, most supersaturated design construction methods are indistinguishable in terms of power. This conclusion can be reassuring for practitioners as supersaturated designs can then be sensibly chosen based upon convenience. For instance, the Bayesian D-optimal supersaturated designs can be easily constructed in JMP and SAS for any run size and number of factors. On the other hand, software for constructing E(s2)-optimal supersaturated designs is not as accessible.

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.

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

The Icing Research Tunnel (IRT) at NASA Glenn Research Center follows the recommended practice for icing tunnel calibration outlined in SAE’s ARP5905 document. The calibration team has followed the schedule of a full calibration every five years with a check calibration done every six months following. The liquid water content of the IRT has maintained stability within in the specifications presented to customers that the variation is within +/- 10% of the calibrated, target measurement. With recent measurements and instrumentation errors, a more thorough assessment of error source was desired. By constructing statistical process control charts, the ability to determine how the instrument varies in the short term, mid term, and long term was gained. The control charts offer a view of instrument error, facility error, or installation changes. It was discovered that there was a shift from target to mean baseline thus leading to the study of the overall capability indices of the liquid water content measuring instrument to perform within specifications defined in the IRT. This presentation describes data processing procedures for the Multi-Element Sensor in the IRT, including collision efficiency corrections, canonical correlation analysis, Chauvenet’s Criterion for rejection of data, distribution check of data, and mean, median and mode for construction of control charts. Further data is presented to describe the repeatability of the IRT with the Multi-Element Sensor and the ability to maintain a stable process for the defined calibration schedule.

Problems faced in defense and aerospace often go well beyond textbook problems presented in academic settings. Textbook problems are typically very well defined, and can be solved through application of one “correct” tool. Conversely, many defense and aerospace problems are ill-defined, at least initially, and require an overall strategy to attack, one involving multiple tools and often multiple disciplines. Statistical engineering is an approach recently developed for addressing large, complex, unstructured problems, particularly those for which data can be effectively utilized. This session will present a brief overview of statistical engineering, and how it can be applied to engineer solutions to complex problems. Following this introduction, two case studies of statistical engineering will be presented, to illustrate the concepts.

Problems faced in defense and aerospace often go well beyond textbook problems presented in academic settings. Textbook problems are typically very well defined, and can be solved through application of one “correct” tool. Conversely, many defense and aerospace problems are ill-defined, at least initially, and require an overall strategy to attack, one involving multiple tools and often multiple disciplines. Statistical engineering is an approach recently developed for addressing large, complex, unstructured problems, particularly those for which data can be effectively utilized. This session will present a brief overview of statistical engineering, and how it can be applied to engineer solutions to complex problems. Following this introduction, two case studies of statistical engineering will be presented, to illustrate the concepts.

This presentation will use a notional armament system case-study to illustrate the use of M&S DOE, surrogate modeling, sensitivity analysis, multi-objective optimization and model calibration during early lifecycle development and design activities in the context of a new armament system. In addition to focusing on the statistician’s, data scientist’s, or analyst’s role and the key statistical techniques in engineering DoD systems, this presentation will also emphasize the non-statistical / engineering domain-specific aspects in a multidisciplinary design and development process which make uses of these statistical approaches at the subcomponent and subsystem-level as well as the end-to-end system modeling. A statistical engineering methodology which emphasizes the use of ‘virtual’ DOE-based model emulators developed at the subsystem-level and integrated using a systems-engineering architecture framework can yield a more tractable engineering problem compared to traditional ‘design-build-test-fix’ cycles or direct simulation of computationally expensive models. This supports a more informed prototype design for physical experimentation while providing a greater variety of materiel solutions thereby reducing development and testing cycles and time to field complex systems.

The spread of COVID-19 across the United States provides an interesting case study in the modeling of spatio-temporal data. In this breakout session we will provide an overview of commonly used spatio-temporal models and demonstrate how Bayesian Inference can be performed using both exact and approximate inferential techniques. Using COVID data, we will demonstrate visualization techniques in R and introduce “off-the-shelf” spatio-temporal models. We will introduce participants to the Integrated Nested LaPlace approximation (INLA) methodology and show how results from this technique compare to using Markov Chain Monte Carlo (MCMC) techniques. Finally, we will demonstrate the short-falls in using “off-the-shelf” models and show how epidemiological motivated partial differential equations can be used to generate spatio-temporal models and discuss inferential issues when we move away from common models.

To identify the robust settings of the control factors, it is very important to understand how they interact with the noise factors. In this article, we propose space-filling designs for computer experiments that are more capable of accurately estimating the control-by-noise interactions. Moreover, the existing space-filling designs focus on uniformly distributing the points in the design space, which are not suitable for noise factors because they usually follow non-uniform distributions such as normal distribution. This would suggest placing more points in the regions with high probability mass. However, noise factors also tend to have a smooth relationship with the response and therefore, placing more points towards the tails of the distribution is also useful for accurately estimating the relationship. These two opposing effects make the experimental design methodology a challenging problem. We propose optimal and computationally efficient solutions to this problem and demonstrate their advantages using simulated examples and a real industry example involving a manufacturing packing line.