Publications

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Training simulators have become increasingly popular tools for instructing humans on performance in complex environments. However, the question of how to provide individualized and scenario-specific assessment and feedback to students remains largely an open question. In this work, we follow-up on previous evaluations of the Automated Expert Modeling and Automated Student Evaluation (AEMASE) system, which automatically assesses student performance based on observed examples of good and bad performance in a given domain. The current study provides an empirical evaluation of the enhanced training effectiveness achievable with this technology. In particular, we found that students given feedback via the AEMASE-based debrief tool performed significantly better than students given only instructor feedback.

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As high energy laser systems evolve towards higher energies, fundamental material properties such as the laser-induced damage threshold (LIDT) of the optics limit the overall system performance. The Z-Backlighter Laser Facility at Sandia National Laboratories uses a pair of such kiljoule-class Nd:Phosphate Glass lasers for x-ray radiography of high energy density physics events on the Z-Accelerator. These two systems, the Z-Beamlet system operating at 527nm/ 1ns and the Z-Petawatt system operating at 1054nm/ 0.5ps, can be combined for some experimental applications. In these scenarios, dichroic beam combining optics and subsequent dual wavelength high reflectors will see a high fluence from combined simultaneous laser exposure and may even see lingering effects when used for pump-probe configurations. Only recently have researchers begun to explore such concerns, looking at individual and simultaneous exposures of optics to 1064 and third harmonic 355nm light from Nd:YAG [1]. However, to our knowledge, measurements of simultaneous and delayed dual wavelength damage thresholds on such optics have not been performed for exposure to 1054nm and its second harmonic light, especially when the pulses are of disparate pulse duration. The Z-Backlighter Facility has an instrumented damage tester setup to examine the issues of laser-induced damage thresholds in a variety of such situations [2] . Using this damage tester, we have measured the LIDT of dual wavelength high reflectors at 1054nm/0.5ps and 532nm/7ns, separately and spatially combined, both co-temporal and delayed, with single and multiple exposures. We found that the LIDT of the sample at 1054nm/0.5ps can be significantly lowered, from 1.32J/cm{sup 2} damage fluence with 1054/0.5ps only to 1.05 J/cm{sup 2} with the simultaneous presence of 532nm/7ns laser light at a fluence of 8.1 J/cm{sup 2}. This reduction of LIDT of the sample at 1054nm/0.5ps continues as the fluence of 532nm/7ns laser light simultaneously present increases. The reduction of LIDT does not occur when the 2 pulses are temporally separated. This paper will also present dual wavelength LIDT results of commercial dichroic beam-combining optics simultaneously exposed with laser light at 1054nm/2.5ns and 532nm/7ns.

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Adagio is a three-dimensional, implicit solid mechanics code with a versatile element library, nonlinear material models, and capabilities for modeling large deformation and contact. Adagio is a parallel code, and its nonlinear solver and contact capabilities enable scalable solutions of large problems. It is built on the SIERRA Framework [1, 2]. SIERRA provides a data management framework in a parallel computing environment that allows the addition of capabilities in a modular fashion. The Adagio 4.16 User's Guide provides information about the functionality in Adagio and the command structure required to access this functionality in a user input file. This document is divided into chapters based primarily on functionality. For example, the command structure related to the use of various element types is grouped in one chapter; descriptions of material models are grouped in another chapter. The input and usage of Adagio is similar to that of the code Presto [3]. Presto, like Adagio, is a solid mechanics code built on the SIERRA Framework. The primary difference between the two codes is that Presto uses explicit time integration for transient dynamics analysis, whereas Adagio is an implicit code. Because of the similarities in input and usage between Adagio and Presto, the user's guides for the two codes are structured in the same manner and share common material. (Once you have mastered the input structure for one code, it will be easy to master the syntax structure for the other code.) To maintain the commonality between the two user's guides, we have used a variety of techniques. For example, references to Presto may be found in the Adagio user's guide and vice versa, and the chapter order across the two guides is the same. On the other hand, each of the two user's guides is expressly tailored to the features of the specific code and documents the particular functionality for that code. For example, though both Presto and Adagio have contact functionality, the content of the chapter on contact in the two guides differs. Important references for both Adagio and Presto are given in the references section at the end of this chapter. Adagio was preceded by the codes JAC and JAS3D; JAC is described in Reference 4; JAS3D is described in Reference 5. Presto was preceded by the code Pronto3D. Pronto3D is described in References 6 and 7. Some of the fundamental nonlinear technology used by both Presto and Adagio are described in References 8, 9, and 10. Currently, both Presto and Adagio use the Exodus II database and the XDMF database; Exodus II is more commonly used than XDMF. (Other options may be added in the future.) The Exodus II database format is described in Reference 11, and the XDMF database format is described in Reference 12. Important information about contact is provided in the reference document for ACME [13]. ACME is a third-party library for contact. One of the key concepts for the command structure in the input file is a concept referred to as scope. A detailed explanation of scope is provided in Section 1.2. Most of the command lines in Chapter 2 are related to a certain scope rather than to some particular functionality.

Presto is a three-dimensional transient dynamics code with a versatile element library, nonlinear material models, large deformation capabilities, and contact. It is built on the SIERRA Framework [1, 2]. SIERRA provides a data management framework in a parallel computing environment that allows the addition of capabilities in a modular fashion. Contact capabilities are parallel and scalable. The Presto 4.16 User's Guide provides information about the functionality in Presto and the command structure required to access this functionality in a user input file. This document is divided into chapters based primarily on functionality. For example, the command structure related to the use of various element types is grouped in one chapter; descriptions of material models are grouped in another chapter. The input and usage of Presto is similar to that of the code Adagio [3]. Adagio is a three-dimensional quasi-static code with a versatile element library, nonlinear material models, large deformation capabilities, and contact. Adagio, like Presto, is built on the SIERRA Framework [1]. Contact capabilities for Adagio are also parallel and scalable. A significant feature of Adagio is that it offers a multilevel, nonlinear iterative solver. Because of the similarities in input and usage between Presto and Adagio, the user's guides for the two codes are structured in the same manner and share common material. (Once you have mastered the input structure for one code, it will be easy to master the syntax structure for the other code.) To maintain the commonality between the two user's guides, we have used a variety of techniques. For example, references to Adagio may be found in the Presto user's guide and vice versa, and the chapter order across the two guides is the same. On the other hand, each of the two user's guides is expressly tailored to the features of the specific code and documents the particular functionality for that code. For example, though both Presto and Adagio have contact functionality, the content of the chapter on contact in the two guides differs. Important references for both Adagio and Presto are given in the references section at the end of this chapter. Adagio was preceded by the codes JAC and JAS3D; JAC is described in Reference 4; JAS3D is described in Reference 5. Presto was preceded by the code Pronto3D. Pronto3D is described in References 6 and 7. Some of the fundamental nonlinear technology used by both Presto and Adagio are described in References 8, 9, and 10. Currently, both Presto and Adagio use the Exodus II database and the XDMF database; Exodus II is more commonly used than XDMF. (Other options may be added in the future.) The Exodus II database format is described in Reference 11, and the XDMF database format is described in Reference 12. Important information about contact is provided in the reference document for ACME [13]. ACME is a third-party library for contact. One of the key concepts for the command structure in the input file is a concept referred to as scope. A detailed explanation of scope is provided in Section 1.2. Most of the command lines in Chapter 2 are related to a certain scope rather than to some particular functionality.

Current computational models for proton exchange membrane fuel cells (PEMFCs) include a large number of parameters such as boundary conditions, material properties, and numerous parameters used in sub-models for membrane transport, two-phase flow and electrochemistry. In order to successfully use a computational PEMFC model in design and optimization, it is important to identify critical parameters under a wide variety of operating conditions, such as relative humidity, current load, temperature, etc. Moreover, when experimental data is available in the form of polarization curves or local distribution of current and reactant/product species (e.g., O2, H2O concentrations), critical parameters can be estimated in order to enable the model to better fit the data. Sensitivity analysis and parameter estimation are typically performed using manual adjustment of parameters, which is also common in parameter studies. We present work to demonstrate a systematic approach based on using a widely available toolkit developed at Sandia called DAKOTA that supports many kinds of design studies, such as sensitivity analysis as well as optimization and uncertainty quantification. In the present work, we couple a multidimensional PEMFC model (which is being developed, tested and later validated in a joint effort by a team from Penn State Univ. and Sandia National Laboratories) with DAKOTA through the mapping of model parameters to system responses. Using this interface, we demonstrate the efficiency of performing simple parameter studies as well as identifying critical parameters using sensitivity analysis. Finally, we show examples of optimization and parameter estimation using the automated capability in DAKOTA.

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Finding the optimal (lightest, least expensive, etc.) design for an engineered component that meets or exceeds a specified level of reliability is a problem of obvious interest across a wide spectrum of engineering fields. Various methods for this reliability-based design optimization problem have been proposed. Unfortunately, this problem is rarely solved in practice because, regardless of the method used, solving the problem is too expensive or the final solution is too inaccurate to ensure that the reliability constraint is actually satisfied. This is especially true for engineering applications involving expensive, implicit, and possibly nonlinear performance functions (such as large finite element models). The Efficient Global Reliability Analysis method was recently introduced to improve both the accuracy and efficiency of reliability analysis for this type of performance function. This paper explores how this new reliability analysis method can be used in a design optimization context to create a method of sufficient accuracy and efficiency to enable the use of reliability-based design optimization as a practical design tool.

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