1st International Conference Faculty Development in the Health Professions. Toronto (Canada) May, 10th-13th 2011

A new asymptotic analysis of slender vortices in three dimensions, based solely on the vorticity transport equation and the non-local vorticity-velocity relation gives new insight into the structure of slender vortex filaments. The approach is quite different from earlier analyses using matched asymptotic solutions for the velocity field and it yields additional information. This insight is used to derive three different modifications of the thin-tube version of a numerical vortex element method. Our modifications remove an O(1) error from the node velocities of the standard thin-tube model and allow us to properly account for any prescribed physical vortex core structure independent of the numerical vorticity smoothing function. We demonstrate the performance of the improved models by comparison with asymptotic solutions for slender vortex rings and for perturbed slender vortex filaments in the Klein-Majda regime, in which the filament geometry is characterized by small-amplitude-short-wavelength displacements from a straight line. These comparisons represent a stringent mutual test for both the proposed modified thin-tube schemes and for the Klein-Majda theory. Importantly, we find a convincing agreement of numerical and asymptotic predictions for values of the Klein-Majda expansion parameter E as large as 1/2. Thus, our results support their findings in earlier publications for realistic physical vortex core sizes

Coordinate Transformation and Polynomial Chaos for the Bayesian Inference of a Gaussian Process with Parametrized Prior Covariance Function

This paper addresses model dimensionality reduction for Bayesian inference based on prior Gaussian fields with uncertainty in the covariance function hyper-parameters. The dimensionality reduction is traditionally achieved using the Karhunen-\Loeve expansion of a prior Gaussian process assuming covariance function with fixed hyper-parameters, despite the fact that these are uncertain in nature. The posterior distribution of the Karhunen-Lo\`{e}ve coordinates is then inferred using available observations. The resulting inferred field is therefore dependent on the assumed hyper-parameters. Here, we seek to efficiently estimate both the field and covariance hyper-parameters using Bayesian inference. To this end, a generalized Karhunen-Lo\`{e}ve expansion is derived using a coordinate transformation to account for the dependence with respect to the covariance hyper-parameters. Polynomial Chaos expansions are employed for the acceleration of the Bayesian inference using similar coordinate transformations, enabling us to avoid expanding explicitly the solution dependence on the uncertain hyper-parameters. We demonstrate the feasibility of the proposed method on a transient diffusion equation by inferring spatially-varying log-diffusivity fields from noisy data. The inferred profiles were found closer to the true profiles when including the hyper-parameters' uncertainty in the inference formulation.Comment: 34 pages, 17 figure

Optimal projection of observations in a Bayesian setting

Optimal dimensionality reduction methods are proposed for the Bayesian inference of a Gaussian linear model with additive noise in presence of overabundant data. Three different optimal projections of the observations are proposed based on information theory: the projection that minimizes the Kullback-Leibler divergence between the posterior distributions of the original and the projected models, the one that minimizes the expected Kullback-Leibler divergence between the same distributions, and the one that maximizes the mutual information between the parameter of interest and the projected observations. The first two optimization problems are formulated as the determination of an optimal subspace and therefore the solution is computed using Riemannian optimization algorithms on the Grassmann manifold. Regarding the maximization of the mutual information, it is shown that there exists an optimal subspace that minimizes the entropy of the posterior distribution of the reduced model; a basis of the subspace can be computed as the solution to a generalized eigenvalue problem; an a priori error estimate on the mutual information is available for this particular solution; and that the dimensionality of the subspace to exactly conserve the mutual information between the input and the output of the models is less than the number of parameters to be inferred. Numerical applications to linear and nonlinear models are used to assess the efficiency of the proposed approaches, and to highlight their advantages compared to standard approaches based on the principal component analysis of the observations

Self-Propagating Reactive Fronts in Compacts of Multilayered Particles

Reactive multilayered foils in the form of thin films have gained interest in various applications such as joining, welding, and ignition. Typically, thin film multilayers support self-propagating reaction fronts with speeds ranging from 1 to 20 m/s. In some applications, however, reaction fronts with much smaller velocities are required. This recently motivated Fritz et al. (2011) to fabricate compacts of regular sized/shaped multilayered particles and demonstrate self-sustained reaction fronts having much smaller velocities than thin films with similar layering. In this work, we develop a simplified numerical model to simulate the self-propagation of reactive fronts in an idealized compact, comprising identical Ni/Al multilayered particles in thermal contact. The evolution of the reaction in the compact is simulated using a two-dimensional transient model, based on a reduced description of mixing, heat release, and thermal transport. Computed results reveal that an advancing reaction front can be substantially delayed as it crosses from one particle to a neighboring particle, which results in a reduced mean propagation velocity. A quantitative analysis is thus conducted on the dependence of these phenomena on the contact area between the particles, the thermal contact resistance, and the arrangement of the multilayered particles

A priori testing of sparse adaptive polynomial chaos expansions using an ocean general circulation model database

This work explores the implementation of an adaptive strategy to design sparse ensembles of oceanic simulations suitable for constructing polynomial chaos surrogates. We use a recently developed pseudo-spectral algorithm that is based on a direct application of the Smolyak sparse grid formula and that allows the use of arbitrary admissible sparse grids. The adaptive algorithm is tested using an existing simulation database of the oceanic response to Hurricane Ivan in the Gulf of Mexico. The a priori tests demonstrate that sparse and adaptive pseudo-spectral constructions lead to substantial savings over isotropic sparse sampling in the present setting.United States. Office of Naval Research (award N00014-101-0498)United States. Dept. of Energy. Office of Advanced Scientific Computing Research (award numbers DE-SC0007020, DE-SC0008789, and DE-SC0007099)Gulf of Mexico Research Initiative (contract numbers SA1207GOMRI005 (CARTHE) and SA12GOMRI008 (DEEP-C)

Final Technical Report

This is a collaborative proposal that aims to establish theoretical foundations and computational tools that enable uncertainty quantification (UQ) in tightly coupled atomistic-to-continuum multiscale simulations. The program emphasizes the following three research thrusts: 1. UQ and its propagation in atomistic simulations, whether through intrusive or nonintrusive approaches; 2. Extraction of macroscale observables from atomistic simulations and propagation across scales; and 3. Uncertainty quantification and propagation in continuum simulations for macroscale properties tightly coupled with instantaneous states of the atomistic systems. Thus, the project offers to enable the use of multiscale multiphysics simulations as predictive design tools for complex systems