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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 433 records · Page 24
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

NEML2: A High Performance Library for Constitutive Modeling

NEML2, the New Engineering Material model Library, version 2, is an offshoot of NEML, an earlier material modeling code developed at Argonne National Laboratory. NEML2 extends the key philosophy of its predecessor, i.e., material models are flexible, modular, and can be built from smaller blocks. It also provides modern features that do not exist in the framework of its predecessor such as material model vectorization, automatic differentiation, device-portable just-in-time compilation, operator fusion, lazy tensor evaluation, etc. Moreover, NEML2 can seamlessly integrate with the popular machine learning package PyTorch to take advantage of modern and fast-growing machine learning techniques. In this fiscal year, the development of core library features and capabilities are complete. The purpose of this report is not to serve as a verbatim copy of the software API reference (which is available online at https://reverendbedford.github.io/neml2/). Instead, this report documents the motivation, implementation, design choices, and usage of each core capability as well as their applications in solving practical engineering problems. This report is compiled based on the NEML2 major release 2.0.0.

36 MATERIALS SCIENCE↗
DOE OSTI2026

plexosdb: A Modular Library for Programmatic PLEXOS Model Construction

plexosdb is a lightweight Python library for constructing PLEXOS models using a SQLite-backed data structure. It provides a clear, modular interface that maps relational data directly to model components. By leveraging SQLite and idiomatic Python, it enables fast iteration and reproducible workflows. The result is a performant, composable foundation for scalable PLEXOS model development.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

An Open-Source Parallel EMT Simulation Framework

As the integration level of inverter-based resources (IBRs) increases, ensuring the reliable operation of the bulk power systems requires the use of electromagnetic transient (EMT) simulation tools to identify and mitigate system-wide stability risks. Conducting EMT studies for large-scale, IBR-rich grids, however, is challenging due to the inherent computational bottleneck caused by the underlying high-fidelity models and required small time steps. This paper introduces ParaEMT: an open-source, generic EMT simulation framework designed to accelerate simulations by leveraging advanced parallel computational technologies, such as high-performance computers. This paper presents a comprehensive exposition of ParaEMT, covering its modeling library, simulation strategy, framework structure, operational procedures, and auxiliary features, alongside its extensible parallel computational architecture. Notably, ParaEMT is a publicly accessible and modularized framework written in Python, thereby facilitating future development and the integration of new models and algorithms. The accuracy and efficiency of ParaEMT are demonstrated by rigorous validations via multiple case studies.

electromagnetic transient simulation↗
DOE OSTI2024

Current State of Neutron Source Term Calculations

SOURCES-4C has some significant deficiencies that are mostly due to problems with the default data libraries that it uses. Some research groups have modified these libraries to fix most of the problems, but not all of them. Adding modern data libraries to SOURCES-4C is one of the most promising approaches to resolving these issues. Other promising options include using either NEDIS or a GEANT4 tool. Both of these codes approximately match the accuracy of the modified SOURCES-4C code. The NEDIS code is Russian. The GEANT4 tool might be computationally slow and it might also no longer be actively maintained. There are a handful of other (α,n) codes, but they are not accurate.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

A Parametric Reduced-Order Model for Inverter Short-Circuit Response in Protection Studies

This paper presents a reduced-order model (ROM) for grid-following (GFL) inverters that reproduces inverter fault current trajectories, including sub transients, transient, and steady-state phases, across a range of fault types, locations, and pre-fault operating points. . The proposed model is developed by: Constructing the positive- and negative-sequence current with parameterization fitted by large data training and fitting Validating using EMT simulation against EMT full model and demonstrating the ROM's capability to capture fault current magnitude, phase angle, and oscillatory transients. Building a standard EMT simulation platform library component for easy configuration and application.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Preparation of Nuclear Data Libraries for Web Release [Slides] and Tutorial for Generating Correlated Random Samples and Propagation of Uncertainty [Slides]

The first presentation discuses moving distribution of nuclear data to an online platform which allows ore frequent nuclear data updates, greater ease of acquiring nuclear data, and user flexibility in what nuclear data to download. The second presentation illustrates uncertainties without correlation, uncertainties without correlation with negative samples, uncertainties with correlation, uncertainties with a χ-like constraint, uncertainties with a Σ tot -like constraint, sampling using different distributions, and dealing with negative eigenvalues in a covariance matrix.

42 ENGINEERING↗
DOE OSTI2022

PyAlbany: A Python interface to the C++ multiphysics solver Albany

Albany is a parallel C++ finite element library for solving forward and inverse problems involving partial differential equations (PDEs). In this paper we introduce PyAlbany, a newly developed Python interface to the Albany library. PyAlbany can be used to effectively drive Albany enabling fast and easy analysis and post-processing of applications based on PDEs that are pre-implemented in Albany. PyAlbany relies on the library PyBind11 to bind Python with C++ Albany code. Here we detail the implementation of PyAlbany and showcase its capabilities through a number of examples targeting a heat-diffusion problem. In particular we consider the following: (1) the generation of samples for a Monte Carlo application, (2) a scalability study, (3) a study of parameters on the performance of a linear solver, and finally (4) a tool for performing eigenvalue decompositions of matrix-free operators for a Bayesian inference application.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Where did the tumor start? An inverse solver with sparse localization for tumor growth models

In this work, we present a numerical scheme for solving an inverse problem for parameter estimation in tumor growth models for glioblastomas, a form of aggressive primary brain tumor. The growth model is a reaction–diffusion partial differential equation (PDE) for the tumor concentration. We use a PDE-constrained optimization formulation for the inverse problem. The unknown parameters are the reaction coefficient (proliferation), the diffusion coefficient (infiltration), and the initial condition field for the tumor PDE. Segmentation of magnetic resonance imaging (MRI) scans drive the inverse problem where segmented tumor regions serve as partial observations of the tumor concentration. Like most cases in clinical practice, we use data from a single time snapshot. Moreover, the precise time relative to the initiation of the tumor is unknown, which poses an additional difficulty for inversion. We perform a frozen-coefficient spectral analysis and show that the inverse problem is severely ill-posed. We introduce a biophysically motivated regularization on the structure and magnitude of the tumor initial condition. In particular, we assume that the tumor starts at a few locations (enforced with a sparsity constraint on the initial condition of the tumor) and that the initial condition magnitude in the maximum norm is equal to one. We solve the resulting optimization problem using an inexact quasi-Newton method combined with a compressive sampling algorithm for the sparsity constraint. Our implementation uses PETSc and AccFFT libraries. We conduct numerical experiments on synthetic and clinical images to highlight the improved performance of our solver over a previously existing solver that uses standard two-norm regularization for the calibration parameters. The existing solver is unable to localize the initial condition. Our new solver can localize the initial condition and recover infiltration and proliferation. In clinical datasets (for which the ground truth is unknown), our solver results in qualitatively different solutions compared to the two-norm regularized solver.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

NRC Multiphysics Analysis Capability Deployment FY2020 - Part 4

This report details progress and activities of Idaho National Laboratory (INL) on the Nuclear Regulatory Commission (NRC) project “Development and Modeling Sup- port for Advanced Non-Light Water Reactors.” The tasks completed for this report are: • Task 3a: This task demonstrates the effectiveness of Griffin’s methods for interpolating cross sections and SPH correction factors as a function of control drum rotation. The Monte Carlo code Serpent is used to generate reference results. The accuracy of Griffin-calculated eigenvalues and reaction rates are studied as a function of control drum rotation for cross section and SPH factor libraries of varying fidelity. Higher-fidelity libraries require less interpolation and are therefore inherently more accurate; however, they require more computational resources to create. Overall, Griffin is able to accurately model eigenvalue and reaction rates over the full range of drum rotation using a library generated from three to five discrete drum rotations for a particular microreactor design. • Task 3c: This preliminary task successfully demonstrates the performance of a coupled neutron-photon transport calculation with Griffin using the spherical harmonics approximation. The Griffin solutions are compared to reference solutions from the continuous energy Monte Carlo codes MCNP and Serpent. Furthermore, this work also demonstrates how to prepare the neutron and photon libraries using NJOY, MCNP, and Serpent with the latest ENDF/B-VIII data for 235 U and graphite. The Griffin total energy deposition in the active core region is calculated within 0.3% of the reference, but it degrades in the reflector regions with a maximum difference of -1.5%. Overall, the results look promising and future analysis should use an improved data stream and focus on determining the potential cancellation of error within each energy group.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Spiner: Performance Portable Routines for Generic, Tabulated, Multi-Dimensional Data

We present Spiner, a new, performance-portable library for working with tabulated data. Spiner provides efficient routines for multi-dimensional interpolation and indexing on both CPUs and GPUs—as well as more exotic hardware—including interwoven interpolation and indexing access patterns, as needed for radiation transport. Importantly, Spiner defines a data format, based on HDF5, that couples the tabulated data to the information required to interpolate it, which Spiner can read and move to GPU.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Sigma: Uncertainty Propagation for C++

Sigma is a header-only C++-17 library for uncertainty propagation, inspired by uncertainties for Python and Measurements.jl for Julia. The library tracks the functional correlation between dependent and independent variables, ensuring that the uncertainty of the independent variables is properly considered in the calculation of the dependent variables’ uncertainties. It is intended as a near drop-in replacement for the standard floating point types (aside from uncertainty specification), and aims to be easily interoperable with the existing standard types.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Spiner

Spiner is a library for storing, indexing, and interpolating multidimensional data in a performance-portable way. It’s intended to run on CPUs, GPUs and everything in-between. You can create a table on a CPU, copy it to a GPU, and interpolate on it in a GPU kernel, for example. Spiner also defines (via hdf5) a file format that bundles data together with instructions for interpolating it. This means you don’t have to specify anything to start interpolating, simple load the file and evaluate where you want.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Towards elastic in situ analysis for high-performance computing simulations

In situ analysis and visualization have grown increasingly popular for enabling direct access to data from high-performance computing (HPC) simulations. As a simulation progresses and interesting physical phenomena emerge, however, the data produced may become increasingly complex, and users may need to dynamically change the type and scale of in situ analysis tasks being carried out and consequently adapt the amount of resources allocated to such tasks. To date, none of the production in situ analysis frameworks offer such an elasticity feature, and for good reason: the assumption that the number of processes could vary during run time would force developers to rethink software and algorithms at every level of the in situ analysis stack. In this paper we present Colza, a data staging service with elastic in situ visualization capabilities. We demonstrate the use of Colza with the Deep Water Impact and the AMR-Wind simulations, coupling them with the ParaView Catalyst and Ascent in situ libraries, and show that Colza enables dynamic rescaling of these widely-used frameworks with no interruption to the simulation or staging service. Here, we highlight the challenges of enabling such elasticity, which requires overcoming these frameworks' reliance on MPI, using distinct engineering approaches, namely dependency injection and dependency overload. To the best of our knowledge, this work is the first to enable elastic in situ visualization capabilities for HPC applications on top of existing production analysis tools.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Adiabatic quantum linear regression

Abstract A major challenge in machine learning is the computational expense of training these models. Model training can be viewed as a form of optimization used to fit a machine learning model to a set of data, which can take up significant amount of time on classical computers. Adiabatic quantum computers have been shown to excel at solving optimization problems, and therefore, we believe, present a promising alternative to improve machine learning training times. In this paper, we present an adiabatic quantum computing approach for training a linear regression model. In order to do this, we formulate the regression problem as a quadratic unconstrained binary optimization (QUBO) problem. We analyze our quantum approach theoretically, test it on the D-Wave adiabatic quantum computer and compare its performance to a classical approach that uses the Scikit-learn library in Python. Our analysis shows that the quantum approach attains up to $${2.8 \times }$$ 2.8 × speedup over the classical approach on larger datasets, and performs at par with the classical approach on the regression error metric. The quantum approach used the D-Wave 2000Q adiabatic quantum computer, whereas the classical approach used a desktop workstation with an 8-core Intel i9 processor. As such, the results obtained in this work must be interpreted within the context of the specific hardware and software implementations of these machines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

EZFF: Python library for multi-objective parameterization and uncertainty quantification of interatomic forcefields for molecular dynamics

Parameterization of interatomic forcefields is a necessary first step in performing molecular dynamics simulations. This is a non-trivial global optimization problem involving quantification of multiple empirical variables against one or more properties. We present EZFF, a lightweight Python library for parameterization of several types of interatomic forcefields implemented in several molecular dynamics engines against multiple objectives using genetic-algorithm-based global optimization methods. The EZFF scheme provides unique functionality such as the parameterization of hybrid forcefields composed of multiple forcefield interactions as well as built-in quantification of uncertainty in forcefield parameters and can be easily extended to other forcefield functional forms as well as MD engines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

HYPPO: A Surrogate-Based Multi-Level Parallelism Tool for Hyperparameter Optimization

We present a new software, HYPPO, that enables the automatic tuning of hyperparameters of various deep learning (DL) models. Unlike other hyperparameter optimization (HPO) methods, HYPPO uses adaptive surrogate models and directly accounts for uncertainty in model predictions to find accurate and reliable models that make robust predictions. Using asynchronous nested parallelism, we are able to significantly alleviate the computational burden of training complex architectures and quantifying the uncertainty. HYPPO is implemented in Python and can be used with both TensorFlow and PyTorch libraries. We demonstrate various software features on time-series prediction and image classification problems as well as a scientific application in computed tomography image reconstruction. Finally, we show that (1) we can reduce by an order of magnitude the number of evaluations necessary to find the most optimal region in the hyperparameter space and (2) we can reduce by two orders of magnitude the throughput for such HPO process to complete.

adaptation models↗
DOE OSTI2022

Verification and Validation Tests of the Gamma Library of the ARC Software Package (Rev.1)

As part of the verification and validation (V&V) efforts of the Argonne Reactor Code (ARC) software system to support the Versatile Test Reactor (VTR) project of the U.S. Department of Energy (DOE), work has been performed to verify and validate the gamma (or photon) library of the multigroup cross section generation code MC 2 -3 of the ARC system, which is the essential part of the coupled neutron and gamma heating calculations to determine the heat generation rate or power distribution in the core. This V&V study was carried out in three steps: 1) review of the procedures and utility programs for generating the MC 2 -3 gamma library, 2) verification tests of cross sections and KERMA factors by comparing the total heat generation rate of coupled neutron and gamma calculation with that obtained with reaction Q values under the assumption of local gamma energy deposition and by comparing the prompt heat generation rate with that calculated with the MCNP6.2 code, and 3) validation tests of cross sections and KERMA factors by analyzing the ZPPR-15D gamma dose data measured with thermoluminescent dosimeters (TLD).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2024

Initial Development of Fusion Magnet Simulation Capabilities for Performance and Safety Evaluation Using the MOOSE Framework

Fusion energy holds the promise of being a transformative technology as a carbon-neutral, sustainable source of energy. Whole device modeling and the development of fusion digital twins will be increasingly important for emerging fusion device concepts at both national laboratories and within the commercial fusion industry. However, meeting the challenge of whole device modeling of fusion energy devices requires robust, multiphysics, multiscale modeling and simulation technologies capable of running on large-scale supercomputers. Detailed analysis of individual systems at-scale is also required to ensure safe and efficient operation as well as provide the safety basis for future device designs and licensing activities. In a tokamak, toroidal and poloidal magnets confine and shape the fusion plasma to promote the fusion reaction. High plasma temperatures and high magnetic field requirements in modern design concepts (leading to high amounts of energy stored within each magnet) impose electrical, thermal, and mechanical loads on the magnet components, which in turn impacts the safety considerations of the magnet and their supporting systems. Idaho National Laboratory (INL) has a history of working in this space, including development and benchmarking of the Magnetic System Circuitry Analysis Program (MSCAP) and Magnet Arcing (MAGARC) codes to study magnet quench events; notably, MAGARC was used to study quenching during the ITER Engineering Design Activity. However, these legacy codes and capabilities are not parallel and scalable, and new tools are required for future advances in this area, which leads to the INL-developed Multiphysics Object-Oriented Simulation Environment (MOOSE) framework. Developed originally for fission reactor systems under United States Department of Energy, Office of Nuclear Energy modeling and simulation programs, the MOOSE framework has been well-suited to multiscale, multiphysics modeling and simulation needs for nuclear systems. The framework is open-source, well-tested, under continuous development and deployment, and developed to a Nuclear Quality Assurance, Level 1 software quality standard. MOOSE has also been used in the fusion space previously in several projects: INL’s Tritium Migration Analysis Program, Version 8 (TMAP8) for tritium migration, UK Atomic Energy Authority’s A Unified Resource for OpenMC (fusion) Reactor Applications (AURORA) code for fusion thermo-mechanical and neutronics analysis, and Argonne National Laboratory’s Cardinal for high-fidelity computational fluid dynamics and neutronics. However, to model superconducting magnets, several MOOSE enhancements are required: additions to the current MOOSE electromagnetic capabilities, new material libraries for superconductors of interest (such as YBCO), as well as fusion-specific models for thermo-mechanics. This talk will discuss initial development activities to build these capabilities in MOOSE, focusing on initial validation and benchmarking activities. Proposed coupling workflows and future work to support the simulation of fusion magnets and magnet structural assemblies for performance and safety evaluation in MOOSE will also be discussed.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗