Sensitivity technologies for large scale simulation.

Collis, S. Scott; Bartlett, Roscoe; Smith, Thomas M.; Heinkenschloss, Matthias; Wilcox, Lucas C.; Hill, Judith; Ghattas, Omar; Berggren, Martin; Akçelik, Volkan; Ober, Curtis C.; Waanders, Bart Gustaaf van Bloemen; Keiter, Eric R.

doi:10.2172/921606

Cited by 5 publications

(2 citation statements)

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“…The optimization parameter vector d can be either µ or f . To solve this optimization problem, a trust-region method is used, with the use of a truncated conjugate-gradient method to solve the trust-region subproblem; the gradient required by this method is calculated using an adjoint approach, described in [124].…”

Section: Optimization Problem Formulationmentioning

confidence: 99%

Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

Liu

Safta

Sargsyan

et al. 2014

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In this project we have developed atmospheric measurement capabilities and a suite of atmospheric modeling and analysis tools that are well suited for verifying emissions of greenhouse gases (GHGs) on an urban-through-regional scale. We have for the first time applied the Community Multiscale Air Quality (CMAQ) model to simulate atmospheric CO 2. This will allow for the examination of regional-scale transport and distribution of CO 2 along with air pollutants traditionally studied using CMAQ at relatively high spatial and temporal resolution with the goal of leveraging emissions verification efforts for both air quality and climate. We have developed a bias-enhanced Bayesian inference approach that can remedy the well-known problem of transport model errors in atmospheric CO 2 inversions. We have tested the approach using data and model outputs from the TransCom3 global CO 2 inversion comparison project. We have also performed two prototyping studies on inversion approaches in the generalized convection-diffusion context. One of these studies employed Polynomial Chaos Expansion to accelerate the evaluation of a regional transport model and enable efficient Markov Chain Monte Carlo sampling of the posterior for Bayesian inference. The other approach uses deterministic inversion of a convection-diffusion-reaction system in the presence of uncertainty. These approaches should, in principle, be applicable to realistic atmospheric problems with moderate adaptation. We outline a regional greenhouse gas source inference system that integrates (1) two approaches of atmospheric dispersion simulation and (2) a class of Bayesian inference and uncertainty quantification algorithms. We use two different and complementary approaches to simulate atmospheric dispersion. Specifically, we use a Eulerian chemical transport model CMAQ and a Lagrangian Particle Dispersion Model-FLEXPART-WRF. These two models share the same WRF assimilated meteorology fields, making it possible to perform a hybrid simulation, in which the Eulerian model (CMAQ) can be used to compute the initial condition needed by the Lagrangian model, while the source-receptor relationships for a large state We are very grateful to our colleagues and collaborators, Drs.

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Section: Optimization Problem Formulationmentioning

confidence: 99%

Greenhouse Gas Source Attribution: Measurements Modeling and Uncertainty Quantification

Liu

Safta

Sargsyan

et al. 2014

Self Cite

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“…In subsequent sections of the paper, our derivation of the optimality conditions for a constrained optimization problem is primarily for presentational reasons; an alternative approach could be to differentiate the unconstrained objective function and apply the chain rule. With additional algebraic manipulations, the adjoint equation could be derived [van Bloemen Waanders et al 2005].…”

Section: Introductionmentioning

confidence: 99%

Untitled

2015

J. Mech. Mater. Struct.

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This special issue contains contributions that were invited on the occasion of the Peridynamic Theory symposium held at the 17th U.S. National Congress on Theoretical and Applied Mechanics (USNCTAM) at Michigan State University in June, 2014.One theme of these papers is exploiting the potential of peridynamics in contemporary technology, particularly by including multiple physical effects and applying it to nanoscale mechanics. The paper by Turner, Val Bloemen Waanders, and Parks develops an inverse method to determine heterogeneous nonlocal material properties from experimental data, including digital image correlation (DIC). Wildman and Gazonas couple peridynamic mechanics with a model for electrical conduction and Joule heating to simulate fracture caused by high voltage dielectric breakdown. Application of peridynamics to friction and wear at the nanoscale is demonstrated in the paper by Ebrahim, Steigmann, and Komvopoulos.Another general theme of the special issue is making peridynamic mechanics more practical as a general analysis tool for applications involving fracture. The paper by Mitchell, Silling, and Littlewood describes a new material model within peridynamics that helps avoid difficulties due to the nonlocal nature of the theory in treating free surfaces. With the goal of applying peridynamics only within a small subregion of a large structure where damage is expected, Silling, Littlewood, and Seleson investigate techniques for varying the peridynamic horizon within a model, including application to local-nonlocal coupling. In a related paper elsewhere in this journal (

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