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Ryan G. McClarren

Publications and source records attributed to Ryan G. McClarren.

At least 37 records · Page 2Linked to original sources

A Hybrid Monte Carlo, Discontinuous Galerkin method for linear kinetic transport equations

We present a hybrid method for time-dependent particle transport problems that combines Monte Carlo (MC) estimation with deterministic solutions based on discrete ordinates. For spatial discretizations, the MC algorithm computes a piecewise constant solution and the discrete ordinates uses bilinear discontinuous finite elements. From the hybridization of the problem, the resulting problem solved by Monte Carlo is scattering free, resulting in a simple, efficient solution procedure. Between time steps, we use a projection approach to ``relabel'' collided particles as uncollided particles. From a series of standard 2-D Cartesian test problems we observe that our hybrid method has improved accuracy and reduction in computational complexity of approximately an order of magnitude relative to standard discrete ordinates solutions.

math.NA↗

Reducing Spatial Discretization Error with Linear Discontinuous Source Tilting in Iterative Quasi-Monte Carlo for Neutron Transport

Recently, iterative Quasi-Monte Carlo (iQMC) was introduced as a new method of neutron transport which combines deterministic iterative methods and quasi-Monte Carlo simulation for more efficient solutions to the neutron transport equation. Previous iQMC results utilized a uniform Cartesian grid with a piecewise-constant source. Similar to "teleportation error" in Implicit Monte Carlo (IMC) methods, the spatial discretization and piecewise-constant source can lead to a significant spatial error that limits convergence of the overall method. Taking concepts from IMC, we have developed a history-based discontinuous piecewise-linear source tilting scheme to reduce spatial error in iQMC. The source tilting method is described below and afterward we present results from a fixed-source 2D reactor-like problem adapted from the Takeda-1 Benchmark problem.

physics.comp-ph↗

A Collision-Based Hybrid Method for the BGK Equation

We apply the collision-based hybrid introduced in \cite{hauck} to the Boltzmann equation with the BGK operator and a hyperbolic scaling. An implicit treatment of the source term is used to handle stiffness associated with the BGK operator. Although it helps the numerical scheme become stable with a large time step size, it is still not obvious to achieve the desired order of accuracy due to the relationship between the size of the spatial cell and the mean free path. Without asymptotic preserving property, a very restricted grid size is required to resolve the mean free path, which is not practical. Our approaches are based on the noncollision-collision decomposition of the BGK equation. We introduce the arbitrary order of nodal discontinuous Galerkin (DG) discretization in space with a semi-implicit time-stepping method; we employ the backward Euler time integration for the uncollided equation and the 2nd order predictor-corrector scheme for the collided equation, i.e., both source terms in uncollided and collided equations are treated implicitly and only streaming term in the collided equation is solved explicitly. This improves the computational efficiency without the complexity of the numerical implementation. Numerical results are presented for various Knudsen numbers to present the effectiveness and accuracy of our hybrid method. Also, we compare the solutions of the hybrid and non-hybrid schemes.

math.NA↗

iQMC: Iterative Quasi-Monte Carlo for k-Eigenvalue Neutron Transport Simulations

The Iterative Quasi-Monte Carlo method, or iQMC, replaces standard quadrature techniques used in deterministic linear solvers with Quasi-Monte Carlo simulation for more accurate and efficient solutions to the neutron transport equation. This work explores employing iQMC in the Monte-Carlo Dynamic Code (MCDC) to solve k-eigenvalue problems for neutron transport with both the standard power iteration and the generalized Davidson method, a Krylov Subspace method. Results are verified with the 3-D, 2-group, Takeda-1 Benchmark problem.

physics.comp-ph↗

Development of MC/DC: a performant, scalable, and portable Python-based Monte Carlo neutron transport code

We discuss the current development of MC/DC (Monte Carlo Dynamic Code). MC/DC is primarily designed to serve as an exploratory Python-based MC transport code. However, it seeks to offer improved performance, massive scalability, and backend portability by leveraging Python code-generation libraries and implementing an innovative abstraction strategy and compilation scheme. Here, we verify MC/DC capabilities and perform an initial performance assessment. We found that MC/DC can run hundreds of times faster than its pure Python mode and about 2.5 times slower, but with comparable parallel scaling, than the high-performance MC code Shift for simple problems. Finally, to further exercise MC/DC's time-dependent MC transport capabilities, we propose a challenge problem based on the C5G7-TD benchmark model.

physics.comp-ph↗

High-fidelity treatment for object movement in time-dependent Monte Carlo transport simulations

We investigate the use of time-dependent surfaces in Monte Carlo transport simulation to accurately model prescribed, continuous object movements. The performance of the continuous time-dependent surface technique, relative to the typical stepping approximations and the recently proposed at-source geometry adjustment technique, is assessed by running a simple test problem involving continuous movements of an absorbing object. A figure of merit analysis, measured from the method's accuracy and total runtime, shows that the time-dependent surface is more efficient than the stepping approximations. We also demonstrate that the time-dependent surface technique offers robustness, as it produces accurate solutions even in problems where the at-source geometry technique fails. Finally, we verify the time-dependent surface technique against one of the multigroup 3D C5G7-TD benchmark problems.

physics.comp-ph↗

An effective initial particle sampling technique for Monte Carlo reactor transient simulations

We propose a technique to effectively sample initial neutron and delayed neutron precursor particles for Monte Carlo (MC) simulations of typical off-critical reactor transients. The technique can be seen as an improvement, or alternative, to the existing ones. Similar to some existing techniques, the proposed sampling technique uses the standard MC criticality calculation. However, different from the others, the technique effectively produces uniform-weight particles around user-specified target sizes. The technique is implemented into the open-source Python-based MC code MC/DC and verified against an infinite homogeneous 361-group medium problem and the 3D C5G7-TD benchmark model.

physics.comp-ph↗

Benchmark solutions for radiative transfer with a moving mesh and exact uncollided source treatments

The set of benchmark solutions used in the thermal radiative transfer community suffer some coverage gaps, in particular nonlinear, non-equilibrium problems. Also, there are no non-equilibrium, optically thick benchmarks. These shortcomings motivated the development of a numerical method free from the requirement of linearity and easily able to converge on smooth optically thick problems, a moving mesh Discontinuous Galerkin (DG) framework that utilizes an uncollided source treatment. Having already proven this method on time dependent scattering transport problems, we present here solutions to non-equilibrium thermal radiative transfer problems for familiar linearized systems together with more physical nonlinear systems in both optically thin and thick regimes, including both the full transport and the $S_2$/$P_1$ solution. Geometric convergence is observed for smooth sources at all times and some nonsmooth sources at late times when there is local equilibrium. Also, accurate solutions are achieved for step sources when the solution is not smooth.

cs.CE↗

Multigroup Neutron Transport using a Collision-Based Hybrid Method

A collision-based hybrid algorithm for the discrete ordinates approximation of the neutron transport equation is extended to the multigroup setting. The algorithm uses discrete energy and angle grids at two different resolutions and approximates the fission and scattering sources on the coarser grids. The coupling of a collided transport equation, discretized on the coarse grid, with an uncollided transport equation, discretized on the fine grid, yields an algorithm that, in most cases, is more efficient than the traditional multigroup approach. The improvement over existing techniques is demonstrated for time-dependent problems with different materials, geometries, and energy groups.

physics.comp-ph↗

Accurate solutions to time dependent transport problems with a moving mesh and exact uncollided source treatment

For the purpose of finding benchmark quality solutions to time dependent Sn transport problems, we develop a numerical method in a Discontinuous Galerkin (DG) framework that utilizes time dependent cell edges, which we call a moving mesh, and an uncollided source treatment. The DG method for discretizing space is a powerful solution technique on smooth problems and is robust on non-smooth problems. In order to realize the potential of the DG method to spectrally resolve smooth problems, our moving mesh and uncollided source treatment is devised to circumvent discontinuities in the solution or the first derivative of the solutions that are admitted in transport calculations. The resulting method achieves spectral convergence on smooth problems, like a standard DG implementation. When applied to problems with nonsmooth sources that induce discontinuities, our moving mesh, uncollided source method returns a significantly more accurate solution than the standard DG method. On problems with smooth sources, we observe spectral convergence even in problems with wave fronts. In problems where the angular flux is inherently non-smooth, as in Ganapol's (2001) well known plane pulse benchmark, we do not observe an elevated order of accuracy when compared with static meshes, but there is a reduction in error that is nearly three orders of magnitude.

cs.CE↗

Transfer Learning of High-Fidelity Opacity Spectra in Autoencoders and Surrogate Models

Simulations of high energy density physics are expensive, largely in part for the need to produce non-local thermodynamic equilibrium opacities. High-fidelity spectra may reveal new physics in the simulations not seen with low-fidelity spectra, but the cost of these simulations also scale with the level of fidelity of the opacities being used. Neural networks are capable of reproducing these spectra, but neural networks need data to to train them which limits the level of fidelity of the training data. This paper demonstrates that it is possible to reproduce high-fidelity spectra with median errors in the realm of 3\% to 4\% using as few as 50 samples of high-fidelity Krypton data by performing transfer learning on a neural network trained on many times more low-fidelity data.

physics.plasm-ph↗

A sweep-based low-rank method for the discrete ordinate transport equation

The dynamical low-rank (DLR) approximation is an efficient technique to approximate the solution to matrix differential equations. Recently, the DLR method was applied to radiation transport calculations to reduce memory requirements and computational costs. This work extends the low-rank scheme for the time-dependent radiation transport equation in 2-D and 3-D Cartesian geometries with discrete ordinates discretization in angle (SN method). The reduced system that evolves on a low-rank manifold is constructed via an unconventional basis update and Galerkin integrator to avoid a substep that is backward in time, which could be unstable for dissipative problems. The resulting system preserves the information on angular direction by applying separate low-rank decompositions in each octant where angular intensity has the same sign as the direction cosines. Then, transport sweeps and source iteration can efficiently solve this low-rank-SN system. The numerical results in 2-D and 3-D Cartesian geometries demonstrate that the low-rank solution requires less memory and computational time than solving the full rank equations using transport sweeps without losing accuracy.

physics.comp-ph↗

Analysis of Population Control Techniques for Time-Dependent and Eigenvalue Monte Carlo Neutron Transport Calculations

An extensive study of population control techniques (PCTs) for time-dependent and eigenvalue Monte Carlo (MC) neutron transport calculations is presented. We define PCT as a technique that takes a censused population and returns a controlled, unbiased population. A new perspective based on an abstraction of particle census and population control is explored, paving the way to improved understanding and application of the concepts. Five distinct PCTs identified from the literature are reviewed: Simple Sampling (SS), Splitting-Roulette (SR), Combing (CO), modified Combing (COX), and Duplicate-Discard (DD). A theoretical analysis of how much uncertainty is introduced to a population by each PCT is presented. Parallel algorithms for the PCTs applicable for both time-dependent and eigenvalue MC simulations are proposed. The relative performances of the PCTs based on runtime and tally mean error or standard deviation are assessed by solving time-dependent and eigenvalue test problems. It is found that SR and CO are equally the most performant techniques, closely followed by DD.

physics.comp-ph↗

Benchmarks for infinite medium, time dependent transport problems with isotropic scattering

The widely used AZURV1 transport benchmarks package provides a suite of solutions to isotropic scattering transport problems with a variety of initial conditions (Ganapol 2001). Most of these solutions have an initial condition that is a Dirac delta function in space; as a result these benchmarks are challenging problems to use for verification tests in computer codes. Nevertheless, approximating a delta function in simulation often leads to low orders of convergence and the inability to test the convergence of high-order numerical methods. While there are examples in the literature of integration of these solutions as Green's functions for the transport operator to produce results for more easily simulated sources, they are limited in scope and briefly explained. For a sampling of initial conditions and sources, we present solutions for the uncollided and collided scalar flux to facilitate accurate testing of source treatment in numerical solvers. The solution for the uncollided scalar flux is found in analytic form for some sources. Since integrating the Green's functions is often nontrivial, discussion of integration difficulty and workarounds to find convergent integrals is included. Additionally, our uncollided solutions can be used as source terms in verification studies, in a similar way to the method of manufactured solutions.

cs.CE↗

Transfer Learning as a Method to Reproduce High-Fidelity NLTE Opacities in Simulations

Simulations of high-energy density physics often need non-local thermodynamic equilibrium (NLTE) opacity data. This data, however, is expensive to produce at relatively low-fidelity. It is even more so at high-fidelity such that the opacity calculations can contribute ninety-five percent of the total computation time. This proportion can even reach large proportions. Neural networks can be used to replace the standard calculations of low-fidelity data, and the neural networks can be trained to reproduce artificial, high-fidelity opacity spectra. In this work, it is demonstrated that a novel neural network architecture trained to reproduce high-fidelity krypton spectra through transfer learning can be used in simulations. Further, it is demonstrated that this can be done while achieving a relative percent error of the peak radiative temperature of the hohlraum of approximately 1\% to 4\% while achieving a 19.4x speed up.

physics.comp-ph↗

Data Reduction in Deterministic Neutron Transport Calculations Using Machine Learning

Neutron cross section matrices for fission and scattering data are required for each material, temperature, and enrichment level to calculate the neutron transport equation accurately. This information can be a limiting factor when using the multigroup discrete ordinates (SN) method when the number of energy groups is large. Machine Learning (ML) can be used to replace the need for the cross section matrices by reproducing the function that maps the scalar flux to the scattering and fission sources. Through the use of autoencoders and Deep Jointly-Informed Neural Networks (DJINN), the data storage requirements are reduced by 94% of the original data for a 618 group problem. This is accomplished while preserving the scalar flux, maintaining generality, and decreasing wall clock times.

physics.comp-ph↗

Semi-implicit Hybrid Discrete $\left(\text{H}^T_N\right)$ Approximation of Thermal Radiative Transfer

The thermal radiative transfer (TRT) equations form an integro-differential system that describes the propagation and collisional interactions of photons. Computing accurate and efficient numerical solutions TRT are challenging for several reasons, the first of which is that TRT is defined on a high-dimensional phase. In order to reduce the dimensionality of the phase space, classical approaches such as the P$_N$ (spherical harmonics) or the S$_N$ (discrete ordinates) ansatz are often used in the literature. In this work, we introduce a novel approach: the hybrid discrete (H$^T_N$) approximation to the radiative thermal transfer equations. This approach acquires desirable properties of both P$_N$ and S$_N$, and indeed reduces to each of these approximations in various limits: H$^1_N$ $\equiv$ P$_N$ and H$^T_0$ $\equiv$ S$_T$. We prove that H$^T_N$ results in a system of hyperbolic equations for all $T\ge 1$ and $N\ge 0$. Another challenge in solving the TRT system is the inherent stiffness due to the large timescale separation between propagation and collisions, especially in the diffusive (i.e., highly collisional) regime. This stiffness challenge can be partially overcome via implicit time integration, although fully implicit methods may become computationally expensive due to the strong nonlinearity and system size. On the other hand, explicit time-stepping schemes that are not also asymptotic-preserving in the highly collisional limit require resolving the mean-free path between collisions, making such schemes prohibitively expensive. In this work we develop a numerical method that is based on a nodal discontinuous Galerkin discretization in space, coupled with a semi-implicit discretization in time. We conduct several numerical experiments to verify the accuracy, efficiency, and robustness of the H$^T_N$ ansatz and the numerical discretizations.

math.NA↗

Neural Network Surrogate Models for Absorptivity and Emissivity Spectra of Multiple Elements

Simulations of high energy density physics are expensive in terms of computational resources. In particular, the computation of opacities of plasmas in the non-local thermal equilibrium (NLTE) regime can consume as much as 90\% of the total computational time of radiation hydrodynamics simulations for high energy density physics applications. Previous work has demonstrated that a combination of fully-connected autoencoders and a deep jointly-informed neural network (DJINN) can successfully replace the standard NLTE calculations for the opacity of krypton. This work expands this idea to combining multiple elements into a single surrogate model with the focus here being on the autoencoder.

physics.plasm-ph↗