ACADEMIA
New free version of Sandia DAKOTA software now available
A software toolkit developed Sandia created to help designers answer design questions and assist engineers in designing anything from components to sophisticated systems. The toolkit, DAKOTA version 4.0, can be downloaded for free. More information: its Web site. 
Computational modeling plays a critical role in the assessment of performance, safety, and reliability for complex, high-consequence engineering systems. The Validation and Uncertainty Quantification department at Sandia National Laboratories enables engineering designers and decision makers to understand and improve the credibility of computer simulations for a wide variety of complex systems. Sandia’s Validation and Uncertainty Quantification (V&UQ) Department was formed as part of the National Nuclear Security Administration’s (NNSA) Advanced Simulation and Computing (ASC) Program. ASC simulation capabilities are used to analyze and predict the performance, safety, and reliability of nuclear weapons and weapon components over an extraordinary range of normal, abnormal, and hostile environments. They address these high-consequence national security applications using computational tools ranging from desktop PCs up to massively parallel supercomputers. The unique mission of the V&UQ department is the research, development, and application of mathematical and experimental techniques to assess the credibility of physics-based computational simulations. The multi-disciplinary V&UQ team, experienced in simulation and experimentation, draws from a wide range of disciplines: fluid dynamics, solid and structural mechanics, heat transfer, numerical methods, optimization, statistics, and risk assessment. Overview
Computational methods developed in structural mechanics, heat transfer, fluid mechanics, shock physics, and many other fields of engineering can be an enormous aid to understanding the complex physical systems they simulate. Often, it is desired to use these simulations as virtual prototypes to obtain an acceptable or optimized design for a particular system. This effort seeks to enhance the utility of these computational methods by enabling their use as design tools, so that simulations may be used not just for single-point predictions, but also for automated determination of system performance improvements throughout the product life cycle. This allows analysts to address the fundamental engineering questions of foremost importance to our programs, such as "what is the best design?", "how safe is it?", and "how much confidence do I have in my answer?". System performance objectives can be formulated to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or to maximize performance, reliability, throughput, reconfigurability, agility, or design robustness. A systematic, rapid method of determining these optimal solutions will lead to better designs and improved system performance and will reduce dependence on prototypes and testing, which will shorten the design cycle and reduce development costs. Toward these ends, a general purpose software toolkit is under continuing development for the integration of commercial and in-house simulation capabilities with broad classes of systems analysis tools. Written in C++, the DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit is intended as a flexible, extensible interface between simulation codes and iterative systems analysis methods. In addition to optimization methods, DAKOTA implements uncertainty quantification with sampling, reliability, and stochastic finite element methods, parameter estimation with nonlinear least squares methods, and sensitivity/variance analysis with design of experiments and parameter study capabilities. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment as well as a platform for rapid prototyping of advanced methodologies which focus on increasing robustness and efficiency for computationally complex engineering problems. Several of these research programs are discussed.
Computational modeling plays a critical role in the assessment of performance, safety, and reliability for complex, high-consequence engineering systems. The Validation and Uncertainty Quantification department at Sandia National Laboratories enables engineering designers and decision makers to understand and improve the credibility of computer simulations for a wide variety of complex systems. Sandia’s Validation and Uncertainty Quantification (V&UQ) Department was formed as part of the National Nuclear Security Administration’s (NNSA) Advanced Simulation and Computing (ASC) Program. ASC simulation capabilities are used to analyze and predict the performance, safety, and reliability of nuclear weapons and weapon components over an extraordinary range of normal, abnormal, and hostile environments. They address these high-consequence national security applications using computational tools ranging from desktop PCs up to massively parallel supercomputers. The unique mission of the V&UQ department is the research, development, and application of mathematical and experimental techniques to assess the credibility of physics-based computational simulations. The multi-disciplinary V&UQ team, experienced in simulation and experimentation, draws from a wide range of disciplines: fluid dynamics, solid and structural mechanics, heat transfer, numerical methods, optimization, statistics, and risk assessment. Overview
Computational methods developed in structural mechanics, heat transfer, fluid mechanics, shock physics, and many other fields of engineering can be an enormous aid to understanding the complex physical systems they simulate. Often, it is desired to use these simulations as virtual prototypes to obtain an acceptable or optimized design for a particular system. This effort seeks to enhance the utility of these computational methods by enabling their use as design tools, so that simulations may be used not just for single-point predictions, but also for automated determination of system performance improvements throughout the product life cycle. This allows analysts to address the fundamental engineering questions of foremost importance to our programs, such as "what is the best design?", "how safe is it?", and "how much confidence do I have in my answer?". System performance objectives can be formulated to minimize weight, cost, or defects; to limit a critical temperature, stress, or vibration response; or to maximize performance, reliability, throughput, reconfigurability, agility, or design robustness. A systematic, rapid method of determining these optimal solutions will lead to better designs and improved system performance and will reduce dependence on prototypes and testing, which will shorten the design cycle and reduce development costs. Toward these ends, a general purpose software toolkit is under continuing development for the integration of commercial and in-house simulation capabilities with broad classes of systems analysis tools. Written in C++, the DAKOTA (Design Analysis Kit for Optimization and Terascale Applications) toolkit is intended as a flexible, extensible interface between simulation codes and iterative systems analysis methods. In addition to optimization methods, DAKOTA implements uncertainty quantification with sampling, reliability, and stochastic finite element methods, parameter estimation with nonlinear least squares methods, and sensitivity/variance analysis with design of experiments and parameter study capabilities. These capabilities may be used on their own or as components within advanced strategies such as surrogate-based optimization, mixed integer nonlinear programming, or optimization under uncertainty. By employing object-oriented design to implement abstractions of the key components required for iterative systems analyses, the DAKOTA toolkit provides a flexible and extensible problem-solving environment as well as a platform for rapid prototyping of advanced methodologies which focus on increasing robustness and efficiency for computationally complex engineering problems. Several of these research programs are discussed.