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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Implicit, manifold-preserving numerical representations and solvers for multiscale kinetic simulations of plasmas

Presentation Conclusions: We have derived manifold-preserving algorithms for kinetic plasma simulation; Collisionless (PIC): we have solved the 40-year-old algorithmic challenge of developing accurate implicit PIC algorithms; Collisional (VFP): we have demonstrated a truly multiscale algorithm that has enabled routine simulation of ICF spherical capsule implosions with a few hundred cores for a couple of days.; In both cases: Strict conservation properties have been shown to be critical for long-term accuracy.; Significant algorithmic acceleration has been achieved by using nested asymptotic models.; We have seen similar benefits in other applications

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Learned implicit representations of aerosol chemistry and physics for enhancing the predictability of water cycle extreme events

Focal Area(s): Focal area 2: Predictive modeling through the use of AI-derived model components; and Focal area 3: physics-guided AI. Science Challenge: Comprehensive models of many geophysical processes require a large number of state variables, disqualifying them for inclusion in Earth System Models (ESMs) for the foreseeable future. This white paper proposes the paradigm shift from the use of state variables representing explicit physical quantities to the use of machine-learning to create and integrate a compact, implicit representation of a physical system with a smaller number of state variables. As an initial application, we propose to create parsimonious machine-learned surrogate models of gas- and aerosol-phase chemistry and physics with the purpose of improving the accuracy of the representation of cloud formation and cloud-aerosol interaction in the E3SM model. This type of improved representation of cloud microphysics is critical for enhancing the predictability of water cycle extremes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

THE IMPLICIT CONTRIBUTION OF SLAB MODES TO THE PERPENDICULAR DIFFUSION COEFFICIENT OF PARTICLES INTERACTING WITH TWO-COMPONENT TURBULENCE

We explore the transport of energetic particles in two-component turbulence in which the stochastic magnetic field is assumed to be a superposition of slab and two-dimensional modes. It is known that in magnetostatic slab turbulence, the motion of particles across the mean magnetic field is subdiffusive. If a two-dimensional component is added, diffusion is recovered. It was also shown before that in two-component turbulence, the slab modes do not explicitly contribute to the perpendicular diffusion coefficient. In the current paper, the implicit contribution of slab modes is explored and it is shown that this contribution leads to a reduction of the perpendicular diffusion coefficient. This effect improves the agreement between simulations and analytical theory. Furthermore, the obtained results are relevant for investigations of diffusive shock acceleration.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2010

Tuple spaces in hardware for accelerated implicit routing

Organizing and optimizing data objects on networks with support for data migration and failing nodes is a complicated problem to handle as systems grow. The goal of this work is to demonstrate that high levels of speedup can be achieved by moving responsibility for finding, fetching, and staging data into an FPGA-based network card. We present a system for implicit routing of data via FPGA-based network cards. In this system, data structures are requested by name, and the network of FPGAs finds the data within the network and relays the structure to the requester. This is acheived through successive examination of hardware hash tables implemented in the FPGA. By avoiding software stacks between nodes, the data is quickly fetched entirely through FPGA-FPGA interaction. The performance of this system is orders of magnitude faster than software implementations due to the improved speed of the hash tables and lowered latency between the network nodes.

42 ENGINEERING↗
DOE OSTI2010

Toward a discrete maximum principle for the implicit Monte Carlo equations

It has long been known that temperature solutions of the Implicit Monte Carlo (IMC) equations can exceed the external boundary temperatures, a so-called violation of the 'maximum principle.' Previous attempts at prescribing a maximum value of the time step size {Delta}{sub t} that is sufficient to eliminate these violations have recommended a {Delta}{sub t} that is typically too small to be used in practice and that appeared to be much too conservative when compared to numerical solutions of the IMC equations for practical problems. In this paper, we derive a new estimator for the maximum time step size that includes the spatial grid size {Delta}{sub x}. This explicitly demonstrates that the effect of coarsening {Delta}{sub x} is to increase the limit on {Delta}{sub t}, which helps explain the overly conservative nature of the earlier, grid-independent results. As {Delta}{sub x} is reduced to zero, the original result due to Larsen and Mercier is almost reproduced (the discrepancy is also explained). We demonstrate that our new time step restriction is a much more accurate means of producing violations of the maximum principle. We discuss how the implications of the new time step restriction can impact IMC codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2010

Towards a frequency-dependent discrete maximum principle for the implicit Monte Carlo equations

It has long been known that temperature solutions of the Implicit Monte Carlo (IMC) equations can exceed the external boundary temperatures, a so-called violation of the 'maximum principle.' Previous attempts at prescribing a maximum value of the time-step size {Delta}{sub t} that is sufficient to eliminate these violations have recommended a {Delta}{sub t} that is typically too small to be used in practice and that appeared to be much too conservative when compared to numerical solutions of the IMC equations for practical problems. In this paper, we derive a new estimator for the maximum time-step size that includes the spatial-grid size {Delta}{sub x}. This explicitly demonstrates that the effect of coarsening {Delta}{sub x} is to reduce the limitation on {Delta}{sub t}, which helps explain the overly conservative nature of the earlier, grid-independent results. We demonstrate that our new time-step restriction is a much more accurate means of predicting violations of the maximum principle. We discuss how the implications of the new, grid-dependent timestep restriction can impact IMC solution algorithms.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2013

A Parallel, Fully Coupled, Fully Implicit Solution to Reactive Transport in Porous Media Using the Preconditioned Jacobian-Free Newton-Krylov Method

Modeling large multicomponent reactive transport systems in porous media is particularly challenging when the governing partial differential algebraic equations (PDAEs) are highly nonlinear and tightly coupled due to complex nonlinear reactions and strong solution-media interactions. Here we present a preconditioned Jacobian-Free Newton-Krylov (JFNK) solution approach to solve the governing PDAEs in a fully coupled and fully implicit manner. A well-known advantage of the JFNK method is that it does not require explicitly computing and storing the Jacobian matrix during Newton nonlinear iterations. Our approach further enhances the JFNK method by utilizing physics-based, block preconditioning and a multigrid algorithm for efficient inversion of the preconditioner. This preconditioning strategy accounts for self- and optionally, cross-coupling between primary variables using diagonal and off-diagonal blocks of an approximate Jacobian, respectively. Numerical results are presented demonstrating the efficiency and massive scalability of the solution strategy for reactive transport problems involving strong solution-mineral interactions and fast kinetics. We found that the physics-based, block preconditioner significantly decreases the number of linear iterations, directly reducing computational cost; and the strongly scalable algebraic multigrid algorithm for approximate inversion of the preconditioner leads to excellent parallel scaling performance.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Adaptive Implicit Non-Equilibrium Radiation Diffusion

We describe methods for accurate and efficient long term time integra- tion of non-equilibrium radiation diffusion systems: implicit time integration for effi- cient long term time integration of stiff multiphysics systems, local control theory based step size control to minimize the required global number of time steps while control- ling accuracy, dynamic 3D adaptive mesh refinement (AMR) to minimize memory and computational costs, Jacobian Free Newton-Krylov methods on AMR grids for efficient nonlinear solution, and optimal multilevel preconditioner components that provide level independent solver convergence.

Philip, Bobby↗
DOE OSTI2014

A projection preconditioner for solving the implicit immersed boundary equations

This study presents a method for solving the linear semi-implicit immersed boundary equations which avoids the severe time step restriction presented by explicit-time methods. The Lagrangian variables are eliminated via a Schur complement to form a purely Eulerian saddle point system, which is preconditioned by a projection operator and then solved by a Krylov subspace method. From the viewpoint of projection methods, we derive an ideal preconditioner for the saddle point problem and compare the efficiency of a number of simpler preconditioners that approximate this perfect one. For low Reynolds number and high stiffness, one particular projection preconditioner yields an efficiency improvement of the explicit IB method by a factor around thirty. Substantial speed-ups over explicit-time method are achieved for Reynolds number below 100. In conclusion, this speedup increases as the Eulerian grid size and/or the Reynolds number are further reduced.

42 ENGINEERING↗
DOE OSTI2016

IMPLEMENTATION AND VALIDATION OF A FULLY IMPLICIT ACCUMULATOR MODEL IN RELAP-7

This paper presents the implementation and validation of an accumulator model in RELAP-7 under the framework of preconditioned Jacobian free Newton Krylov (JFNK) method, based on the similar model used in RELAP5. RELAP-7 is a new nuclear reactor system safety analysis code being developed at the Idaho National Laboratory (INL). RELAP-7 is a fully implicit system code. The JFNK and preconditioning methods used in RELAP-7 is briefly discussed. The slightly modified accumulator model is summarized for completeness. The implemented model was validated with LOFT L3-1 test and benchmarked with RELAP5 results. RELAP-7 and RELAP5 had almost identical results for the accumulator gas pressure and water level, although there were some minor difference in other parameters such as accumulator gas temperature and tank wall temperature. One advantage of the JFNK method is its easiness to maintain and modify models due to fully separation of numerical methods from physical models. It would be straightforward to extend the current RELAP-7 accumulator model to simulate the advanced accumulator design.

Accumulator↗
DOE OSTI2016

Developing A Fully Implicit Compressible Flow Valve Model in RELAP-7 and Its Application in Short Term BWR Station Black-Out Analyses

This paper presents a set of dynamical models for SRV simulation and their implementation in the RELAP-7 code, which is a new reactor system analysis code being developed at INL. RELAP-7 is based on the MOOSE (the Multi-Physics Object-Oriented Simulation Environment) framework. The compressible flow SRV model covers three different sets of models: fully closed case, subsonic case, and choking case. The RELAP-7 is a fully implicit code and the preconditioned Jacobian-free Newton-Krylov (JFNK) method is used to solve the discretized nonlinear problem. The JFNK method generally needs a good preconditioning scheme to be effective and efficient. There are different ways to precondition the JFNK method. In RELAP-7, two main options are used to obtain the preconditioning matrix: (1) FDP (Finite Difference Preconditioning) option which uses the default finite differencing method provided in the PETSc package. PETSc is the numerical solver software developed by Argonne National Laboratory and used by MOOSE as the numerical solution engine; (2) SMP (Single Matrix Preconditioning) option which requires code developers to provide approximate analytical Jacobian inputs for the important diagonal and off-diagonal terms. Due to the strong discontinuity in the residual vector space when the compressible flow valve model switches from one set of model to another, FDP option cannot guarantee convergence. Instead, accurate analytical Jacobain terms are provided for the compressible flow valve model. With the SMP option selected, the compressible flow valve model can be solved efficiently under different cases. The developed compressible flow valve model has been used for SRV component simulation in several simplified BWR SBO (Station Black-Out) simulations with RELAP-7. This paper only presents cases for short-term SBO in which all the safety systems fail to start and function properly when SBO occurs. Consequently, only the SRVs automatically open and close periodically to control the primary system pressure and discharge the high temperature and high pressure steam into the suppression pool. Although simplified, this case is quite similar to what happened in Fukushima Daiichi Unit 1. Unit 1 had no RCIC system, while the isolation condenser system was believed to be non-functioning, or only available for a very short period of time during the accident. Therefore, the Unit 1 reactor core reached a fuel failure temperature only a few hours into the accident. Two short-term SBO cases will be compared: one with single SRV discharge line which represents all the SRV lines and another case with multi-group SRVs which represent each group of SRV line with different setting points.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2019

Generating an implicit voronoi mesh to decompose a domain of arbitrarily many dimensions

Described herein are technologies pertaining to generating a Voronoi mesh for an enclosed domain of arbitrarily many dimensions. An implicit Voronoi mesh is received that comprises locations of seeds in the domain. Neighbors of a seed are identified by extending a segment from the seed to the boundary of the domain and performing successive hyperplane trimming of the segment to identify a point on the facet of the Voronoi cell of the seed. A vertex of the cell can be identified by successively extending and trimming one or more additional spokes to identify points on a facet of the cell that are constrained to lie in a (d−n) space, where d is a number of dimensions of the domain and n is a number of iterations. A numerical simulation is performed based on the Voronoi mesh to generate simulation results in applications including surrogate modeling, global optimization, calibration, dimension reduction.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Temperature-extrapolation method for Implicit Monte Carlo - Radiation hydrodynamics calculations

We present a method for implementing temperature extrapolation in Implicit Monte Carlo solutions to radiation hydrodynamics problems. The method is based on a BDF-2 type integration to estimate a change in material temperature over a time step. We present results for radiation only problems in an infinite medium and for a 2-D Cartesian hohlraum problem. Additionally, radiation hydrodynamics simulations are presented for an RZ hohlraum problem and a related 3D problem. Our results indicate that improvements in noise and general behavior are possible. We present considerations for future investigations and implementations. (authors)

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Solving implicit multi-mesh flow and conjugate heat transfer problems with RELAP-7

The fully implicit simulation capability of RELAP-7 to solve multi-mesh flow and conjugate heat transfer problems for reactor system safety analysis is presented. Compared to general single-mesh simulations, the reactor system safety analysis-type of code has unique challenges due to its highly simplified, interconnected, one-dimensional, and zero-dimensional flow network describing multiple physics with significantly different time and length scales. To use the Jacobian-free Newton Krylov-type of solver, preconditioning is generally required for the Krylov method. The uniqueness of the reactor safety analysis-type of code in treating the interconnected flow network and conjugate heat transfer also introduces challenges in providing preconditioning matrix. Typical flow and conjugate heat transfer problems involved in reactor safety analysis using RELAP-7, as well as the special treatment on the preconditioning matrix are presented in detail. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2016

Implementation and validation of a fully implicit accumulator model in RELAP-7

This paper presents the implementation and validation of an accumulator model in RELAP-7 under the framework of preconditioned Jacobian free Newton Krylov (JFNK) method, based on the similar model used in RELAP5. RELAP-7 is a new nuclear reactor system safety analysis code being developed at the Idaho National Laboratory (INL). RELAP-7 is a fully implicit system code. The JFNK and preconditioning methods used in RELAP-7 is briefly discussed. The slightly modified accumulator model is summarized for completeness. The implemented model was validated with LOFT L3-1 test and benchmarked with RELAP5 results. RELAP-7 and RELAP5 had almost identical results for the accumulator gas pressure and water level, although there were some minor difference in other parameters such as accumulator gas temperature and tank wall temperature. One advantage of the JFNK method is its easiness to maintain and modify models due to fully separation of numerical methods from physical models. It would be straightforward to extend the current RELAP-7 accumulator model to simulate the advanced accumulator design. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Uncertainty-aware Continuous Implicit Neural Representations for Remote Sensing Object Counting

Many existing object counting methods rely on density map estimation (DME) of the discrete grid representation by decoding extracted image semantic features from designed convolutional neural networks (CNNs). Relying on discrete density maps not only leads to information loss dependent on the original image resolution, but also has a scalability issue when analyzing high-resolution images with cubically increasing memory complexity. Furthermore, none of the existing methods can offer reliable uncertainty quantification (UQ) for the derived count estimates. To overcome these limitations, we design UNcertainty-aware, hypernetwork-based Implicit neural representations for Counting (UNIC) to assign probabilities and the corresponding counting confidence over continuous spatial coordinates. We derive a sampling-based Bayesian counting loss function and develop the corresponding model training algorithm. UNIC outperforms existing methods on the Remote Sensing Object Counting (RSOC) dataset with reliable UQ and improved interpretability of the derived count estimates. Our code is available at https://github.com/SiyuanXu-tamu/UNIC.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Forecasting Dark Matter Subhalo Constraints from Stellar Streams using Implicit Likelihood Inference

The evidence for dark matter (DM) remains compelling, although attempts to understand its particle nature remain inconclusive. One promising method to study DM is detecting DM subhalos through their gravitational interactions with stellar streams. In this study, we apply Neural Posterior Estimation (NPE) to constrain subhalo interaction parameters, including mass, scale radius, velocity, and encounter geometry, from stellar stream kinematics. We generate particle spray simulations based on the Lagrange Cloud stripping technique, focusing on the ATLAS-Aliqa Uma stream as a test case. We train multiple NPE models across multiple observational scenarios, quantifying how kinematic completeness affects inference and forecasting constraints from upcoming surveys including LSST, 4MOST, and 10-year Gaia data. Our results demonstrate that NPE can produce accurate and well-calibrated posteriors. In the idealized case with full 6D coordinates, we achieve subhalo mass uncertainties of 15-20% for a $10^7 \, \mathrm{M_\odot}$ subhalo, with 5D coordinates (excluding radial velocities) achieving similar performance. Under realistic observational conditions, mass uncertainties range from 50% (present-day) to 20-40% (future scenarios), with comparable performance between the photometric-only LSST sample and a smaller sample that includes Gaia proper motions and 4MOST radial velocities. Most notably, we find that velocity bimodality emerges when phase space is poorly sampled, whether due to missing kinematic information or limited stellar tracers. Combining large photometric samples with targeted spectroscopic follow-up can effectively resolves this degeneracy. These results demonstrate the power of implicit likelihood inference for optimizing stellar stream observational strategies and forecasting DM subhalo constraints from upcoming surveys.

Nguyen, Tri [Northwestern U. (main); SkAI, Chicago↗