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Correcting implicit solvation at metal/water interfaces through the incorporation of competitive water adsorption
Conventional continuum solvation models are ubiquitous in computational catalysis, including for describing metal/water interfaces, which are relevant to both solution-phase heterogeneous catalysis and electrocatalysis. Nonetheless, we find that such continuum models qualitatively fail to describe both the adsorption free energy and conformational preference for many organic molecules at such interfaces, largely due to the failure of continuum models to incorporate the role of competitive water adsorption. We develop a simple phenomenological model that accounts for competitive water adsorption and show that the model, when used in conjunction with continuum solvation, provides a dramatic improvement in the description of both adsorption and conformational preference. The model is also extended to additionally incorporate the influence of applied potential at the electrode surface, thus facilitating computationally efficient applications to scenarios including electrocatalysis.
Dissipative magnetohydrodynamics for nonresistive relativistic plasmas: An implicit second-order flux-conservative formulation with stiff relaxation
Based on a 14-moment closure for nonresistive (general-) relativistic viscous plasmas, here we describe a new numerical scheme that is able to handle all first-order dissipative effects (heat conduction, bulk and shear viscosities), as well the anisotropies induced by the presence of magnetic fields. The latter is parametrized in terms of a thermal gyrofrequency or, equivalently, a thermal Larmor radius and allows to correctly capture the thermal Hall effect. By solving an extended Israel-Stewart-like system for the dissipative quantities that enforces algebraic constraints via stiff-relaxation, we are able to cast all first-order dissipative terms in flux-divergence form. This allows us to apply traditional high-resolution shock capturing methods to the equations, making the system suitable for the numerical study of highly turbulent flows. We present several numerical tests to assess the robustness of our numerical scheme in flat spacetime. The 14-moment closure can seamlessly interpolate between the highly collisional limit found in neutron star mergers, and the highly anisotropic limit of relativistic Braginskii magnetohydrodynamics appropriate for weakly collisional plasmas in black-hole accretion problems. We believe that this new formulation and numerical scheme will be useful for a broad class of relativistic magnetized flows.
Implicit Neural Representations for Simultaneous Reduction and Continuous Reconstruction of Multi-Altitude Climate Data
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sputniPIC: an Implicit Particle-in-Cell Code for Multi-GPU Systems
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Attack Power System State Estimation by Implicitly Learning the Underlying Models
Not Available
Deep Reinforcement Learning in Buildings: Implicit Assumptions and their Impact
As deep reinforcement learning (DRL) continues to gain interest in the smart building research community, there is a transition from simulation-based evaluations to deploying DRL control strategies in actual buildings. While the efficacy of a solution could depend on a particular implementation, there are common obstacles that developers have to overcome to deliver an effective controller. Additionally, a deployment in a physical building can invalidate some of the assumptions made during the controller development. Assumptions on the sensor placement or on the equipment behavior can quickly come undone. This paper presents some of the significant assumptions made during the development of DRL based controllers that could affect their operations in a physical building. Furthermore, a preliminary evaluation revealed that controllers developed with some of these assumptions can incur twice the expected costs when they are deployed in a building.
Semi-implicit relaxed Douglas-Rachford algorithm (sDR) for ptychography
Not Available
The next frontier in predictive simulation: implicit kinetic simulation at the system scale [Slides]
Abstract not provided.
Benchmark Solutions in Solid Mechanics and Implicit-Explicit Schemes in Radiation Hydrodynamics
Abstract not provided.
Scalable Nonlinear Solvers for Fully Implicit Coupled Nuclear Fuel Modeling. Final Report
The focus of the project is on the development and customization of some highly scalable domain decomposition based preconditioning techniques for the numerical solution of nonlinear, coupled systems of partial differential equations (PDEs) arising from nuclear fuel simulations. These high-order PDEs represent multiple interacting physical fields (for example, heat conduction, oxygen transport, solid deformation), each is modeled by a certain type of Cahn-Hilliard and/or Allen-Cahn equations. Most existing approaches involve a careful splitting of the fields and the use of field-by-field iterations to obtain a solution of the coupled problem. Such approaches have many advantages such as ease of implementation since only single field solvers are needed, but also exhibit disadvantages. For example, certain nonlinear interactions between the fields may not be fully captured, and for unsteady problems, stable time integration schemes are difficult to design. In addition, when implemented on large scale parallel computers, the sequential nature of the field-by-field iterations substantially reduces the parallel efficiency. To overcome the disadvantages, fully coupled approaches have been investigated in order to obtain full physics simulations.
A Multiscale, Conservative, Implicit 1D-2V Multispecies Vlasov-Fokker-Planck Solver for ICF Capsule Implosion Simulations
No abstract provided.
Implicit Large Eddy Simulations(ILES) and Turbulence modeling
No abstract provided.
High Order Implicit Residual-Based Spatial Discretization Error Estimation for S N Neutron Transport
This work demonstrates our novel residual source spatial discretization error estimator (LeR/TEAD) for a DGFEM-1 discretization and assesses it along with two contemporary estimators, Ragusa and Wang's h -refinement estimator (RW) and Duo, Azmy, and Zikatanov's explicit residual-based estimator (DAZ), on a suite of Method of Manufactured Solutions (MMS) 2D problems and three realistic problem geometries. LeR/TE-AD is attractive because it directly estimates the local error in the angular flux, as opposed to a mere indicator of the error's behavior, on the same mesh and method order as the original numerical solution, thus typically being less computationally intensive than a refinement-based method. On the MMS suite, LeR/TE-AD consistently displayed a reduced performance versus its DGFEM-0 results in terms of accuracy and precision metrics, though it was not typically grossly inaccurate. This is attributed to the irregularities in the true solution across singular characteristics limiting the local accuracy of the numerical flux solution, leading to poor derivative approximations used in the residual approximations. The error transport problem then spreads the error in the residual to nearby cells, causing a greater degree of imprecision that did not afflict DAZ or RW. In testing the estimators on realistic problem geometries, however, LeR/TE-AD fared better. In practice, the true error is much larger in non-idealized geometries like in MMS, and a superlinear true solution means that RW and DAZ are not beneficially biased for DGFEM-1 error estimation. LeR/TE-AD was typically first or second in accuracy, primarily competing with RW, but the latter usually consumed 2-4 times the computational time as LeR/TE-AD, and requires a solution with four times as many unknowns. Furthermore, RW and LeR/TE-AD can be used to compute direct estimates of the error in any quantity of interest that is based on the angular ux solution, such as the fission rate density in a fuel pin, whereas DAZ requires a heuristic extension due to its norm-based nature.