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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

VERA Integration in the NEAMS Workbench

The Virtual Environment for Reactor Applications (VERA) is a cutting edge simulation code suite used to solve complex multiphysics problems for nuclear reactors. VERA makes use of high performance computing (HPC) systems to solve large problems that were not historically tractable. While extensive work has been put into the usability of VERA, it still takes significant effort to build reactor models, ensure their correctness, properly execute the calculations, and conduct the necessary analysis of the results. This difficulty is common for many complex codes such as VERA. As part of the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program, the NEAMS Workbench has been developed and maintained. The goal of the NEAMS Workbench is to provide a single interface with which to execute a wide variety of physics codes, from building the input models to analyzing the results. This document serves as a manual detailing the current integration of VERA with the NEAMS Workbench and a guide for configuring Workbench to allow remote execution of VERA.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2017

Existing Fortran interfaces to Trilinos in preparation for exascale ForTrilinos development

This report summarizes the current state of Fortran interfaces to the Trilinos library within several key applications of the Exascale Computing Program (ECP), with the aim of informing developers about strategies to develop ForTrilinos, an exascale-ready, Fortran interface software package within Trilinos. The two software projects assessed within are the DOE Office of Science's Accelerated Climate Model for Energy (ACME) atmosphere component, CAM, and the DOE Office of Nuclear Energy's core-simulator portion of VERA, a nuclear reactor simulation code. Trilinos is an object-oriented, C++ based software project, and spans a collection of algorithms and other enabling technologies such as uncertainty quantification and mesh generation. To date, Trilinos has enabled these codes to achieve large-scale simulation results, however the simulation needs of CAM and VERA-CS will approach exascale over the next five years. A Fortran interface to Trilinos that enables efficient use of programming models and more advanced algorithms is necessary. Where appropriate, the needs of the CAM and VERA-CS software to achieve their simulation goals are called out specifically. With this report, a design document and execution plan for ForTrilinos development can proceed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

NCSP Analytical Methods Subtask 3, AMPX Development and Maintenance, and NCSP Nuclear Data Subtask 6, SAMMY Modernization

SAMMY 1 is a production code that still contains a lot of old-style FORTRAN (F77) code. While the code is widely used and performs well, the legacy code base makes it difficult to maintain and to add new features. However, because it is a production code, it cannot lose any functionality while being modernized. In addition, new features need to be added to support the changing needs of the evaluators to analyze data obtained with new equipment and at higher resolution. In addition, as the data are analyzed with higher fidelity and new models within SAMMY, the processing codes and formats must keep pace. This will ensure that any new SAMMY evaluation can be processed by AMPX and can be tested and used in transport codes like SCALE 2 for maximum benefit of the Nuclear Criticality Safety Program (NCSP) mission.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2012

Interpretation of 3D void measurements with Tripoli4.6/JEFF3.1.1 Monte Carlo code

The present work details the first analysis of the 3D void phase conducted during the EPICURE/UM17x17/7% mixed UOX/MOX configuration. This configuration is composed of a homogeneous central 17x17 MOX-7% assembly, surrounded by portions of 17x17 1102 assemblies with guide-tubes. The void bubble is modelled by a small waterproof 5x5 fuel pin parallelepiped box of 11 cm height, placed in the centre of the MOX assembly. This bubble, initially placed at the core mid-plane, is then moved in different axial positions to study the evolution in the core of the axial perturbation. Then, to simulate the growing of this bubble in order to understand the effects of increased void fraction along the fuel pin, 3 and 5 bubbles have been stacked axially, from the core mid-plane. The C/E comparison obtained with the Monte Carlo code Tripoli4 for both radial and axial fission rate distributions, and in particular the reproduction of the very important flux gradients at the void/water interfaces, changing as the bubble is displaced along the z-axis are very satisfactory. It demonstrates both the capability of the code and its library to reproduce this kind of situation, as the very good quality of the experimental results, confirming the UM-17x17 as an excellent experimental benchmark for 3D code validation. This work has been performed within the frame of the V and V program for the future APOLL03 deterministic code of CEA starting in 2012, and its V and V benchmarking database. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Heat Pipe Modeling Capabilities in Sockeye

Presentation to be given in a meeting with stakeholders of the Nuclear Energy Advanced Modeling and Simulation program for modeling related to microreactors. This presentation gives an overview of the capabilities and status of the heat pipe code Sockeye.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Beam Propagator for Weather Radars, Modules 1 and 2

This program simulates the beam propagation of weather radar pulses under particular and realistic atmospheric conditions (without using the assumption of standard refraction conditions). It consists of two modules: radiosondings_refract_index_many.pro (MAIN MODULE) beam_propagation_function.pro(EXTERNAL FUNCTION) FOR THE MAIN MODULE, THE CODE DOES OUTPUT--INTO A FILE--THE BEAM HEIGHT AS A FUNCTION OF RANGE. THE RADIOSONDE INPUT FILES SHOULD BE ALREADY AVAILABLE BY THE USER. FOR EXAMPLE, RADIOSONDE OBSERVATION FILES CAN BE OBTAINED AT: RADIOSONDE OBSERVATIONS DOWNLOADED AT "http://weather.uwyo.edu/upperair/soounding.html" OR "http://jervis.pyr.ec.gc.ca" THE EXTERNAL FUNCTION DOES THE ACTUAL COMPUTATION OF BEAM PROPAGATION. IT INCLUDES CONDITIONS OF ANOMALOUS PROPAGATION AND NEGATIVE ELEVATION ANGLES. THE EQUATIONS USED HERE WERE DERIVED BY EDWIN CAMPOS, BASED ON THE SNELL-DESCARTES LAW OF REFRACTION, CONSIDERING THE EARTH CURVATURE. THE PROGRAM REQUIRES A COMPILER FOR THE INTERACTIVE DATA LANGUAGE (IDL). DESCRIPTION AND VALIDATION DETAILS HAVE BEEN PUBLISHED IN THE PEER-REVIEWED SCIENTIFIC LITERATURE, AS FOLLOWS: Campos E. 2012. Estimating weather radar coverage over complex terrain, pp.26-32, peer reviewed, in Weather Radar and Hydrology, edited by Moore RJ, Cole SJ and Illingworth AJ. International Association of Hydrological Sciences (IAHS) Press, IAHS Publ. 351. ISBN 978-1-907161-26-1.

Ortega, EdwinCampos↗
DOE OSTI2020

PyCDFT: A Python package for constrained density functional theory

In this paper, we present PyCDFT, a Python package to compute diabatic states using constrained density functional theory (CDFT). PyCDFT provides an object-oriented, customizable implementation of CDFT, and allows for both single-point self-consistent-field calculations and geometry optimizations. PyCDFT is designed to interface with existing density functional theory (DFT) codes to perform CDFT calculations where constraint potentials are added to the Kohn–Sham Hamiltonian. Here, we demonstrate the use of PyCDFT by performing calculations with a massively parallel first-principles molecular dynamics code, Qbox, and we benchmark its accuracy by computing the electronic coupling between diabatic states for a set of organic molecules. We show that PyCDFT yields results in agreement with existing implementations and is a robust and flexible package for performing CDFT calculations. The program is available at https://dx.doi.org/10.5281/zenodo.3821097.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

The Kokkos OpenMPTarget Backend: Implementation and Lessons Learned

As the supercomputing landscape diversifies, solutions such as Kokkos to write vendor agnostic applications and libraries have risen in popularity. Kokkos provides a programming model designed for performance portability, which allows developers to write a single source implementation that can run efficiently on various architectures. At its heart, Kokkos maps parallel algorithms to architecture and vendor specific backends written in lower level programming models such as CUDA and HIP. Another approach to writing vendor agnostic parallel code is using OpenMP’s directives based approach, which lets developers annotate code to express parallelism. It is implemented at the compiler level and is supported by all major high performance computing vendors, as well as the primary Open Source toolchains GNU and LLVM. Since its inception, Kokkos has used OpenMP to parallelize on CPU architectures. In this paper, we explore leveraging OpenMP for a GPU backend and discuss the challenges we encountered when mapping the Kokkos APIs and semantics to OpenMP target constructs. As an exemplar workload we chose a simple conjugate gradient solver for sparse matrices. We find that performance on NVIDIA and AMD GPUs varies widely based on details of the implementation strategy and the chosen compiler. Furthermore, the performance of the OpenMP implementations decreases with increasing complexity of the investigated algorithms.

Gayatri, Rahulkumar↗
DOE OSTI2019

Harnessing billions of tasks for a scalable portable hydrodynamic simulation of the merger of two stars

We propose a highly scalable demonstration of a portable asynchronous many-task programming model and runtime system applied to a grid-based adaptive mesh refinement hydrodynamic simulation of a double white dwarf merger with 14 levels of refinement that spans 17 orders of magnitude in astrophysical densities. The code uses the portable C++ parallel programming model that is embodied in the HPX library and being incorporated into the ISO C++ standard. The model reflects a significant shift from existing bulk synchronous parallel programming models under consideration for exascale systems. Through the use of the Futurization technique, seemingly sequential code is transformed into wait-free asynchronous tasks. We demonstrate the potential of our model by showing results from strong scaling runs on National Energy Research Scientific Computing Center’s Cori system (658,784 Intel Knight’s Landing cores) that achieve a parallel efficiency of 96.8% using billions of asynchronous tasks.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2017

Maxdose-SR and popdose-SR routine release atmospheric dose models used at SRS

MAXDOSE-SR and POPDOSE-SR are used to calculate dose to the offsite Reference Person and to the surrounding Savannah River Site (SRS) population respectively following routine releases of atmospheric radioactivity. These models are currently accessed through the Dose Model Version 2014 graphical user interface (GUI). MAXDOSE-SR and POPDOSE-SR are personal computer (PC) versions of MAXIGASP and POPGASP, which both resided on the SRS IBM Mainframe. These two codes follow U.S. Nuclear Regulatory Commission (USNRC) Regulatory Guides 1.109 and 1.111 (1977a, 1977b). The basis for MAXDOSE-SR and POPDOSE-SR are USNRC developed codes XOQDOQ (Sagendorf et. al 1982) and GASPAR (Eckerman et. al 1980). Both of these codes have previously been verified for use at SRS (Simpkins 1999 and 2000). The revisions incorporated into MAXDOSE-SR and POPDOSE-SR Version 2014 (hereafter referred to as MAXDOSE-SR and POPDOSE-SR unless otherwise noted) were made per Computer Program Modification Tracker (CPMT) number Q-CMT-A-00016 (Appendix D). Version 2014 was verified for use at SRS in Dixon (2014).

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

On the Green's function of the partially diffusion-controlled reversible ABCD reaction for radiation chemistry codes

Several computer codes simulating chemical reactions in particles systems are based on the Green's functions of the diffusion equation (GFDE). Indeed, many types of chemical systems have been simulated using the exact GFDE, which has also become the gold standard for validating other theoretical models. In this work, a simulation algorithm is presented to sample the interparticle distance for partially diffusion-controlled reversible ABCD reaction. This algorithm is considered exact for 2-particles systems, is faster than conventional look-up tables and uses only a few kilobytes of memory. The simulation results obtained with this method are compared with those obtained with the independent reaction times (IRT) method. This work is part of our effort in developing models to understand the role of chemical reactions in the radiation effects on cells and tissues and may eventually be included in event-based models of space radiation risks. However, as many reactions are of this type in biological systems, this algorithm might play a pivotal role in future simulation programs not only in radiation chemistry, but also in the simulation of biochemical networks in time and space as well.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Unified Language Frontend for Physic-Informed AI/ML

Artificial intelligence and machine learning (AI/ML) are becoming important tools for scientific modeling and simulation as in several other fields such as image analysis and natural language processing. ML techniques can leverage the computing power available in modern systems and reduce the human effort needed to configure experiments, interpret and visualize results, draw conclusions from huge quantities of raw data, and build surrogates for physics based models. Domain scientists in fields like fluid dynamics, microelectronics and chemistry can automate many of their most difficult and repetitive tasks or improve the design times by use of the faster ML-surrogates. However, modern ML and traditional scientific highperformance computing (HPC) tend to use completely different software ecosystems. While ML frameworks like PyTorch and TensorFlow provide Python APIs, most HPC applications and libraries are written in C++. Direct interoperability between the two languages is possible but is tedious and error-prone. In this work, we show that a compiler-based approach can bridge the gap between ML frameworks and scientific software with less developer effort and better efficiency. We use the MLIR (multi-level intermediate representation) ecosystem to compile a pre-trained convolutional neural network (CNN) in PyTorch to freestanding C++ source code in the Kokkos programming model. Kokkos is a programming model widely used in HPC to write portable, shared-memory parallel code that can natively target a variety of CPU and GPU architectures. Our compiler-generated source code can be directly integrated into any Kokkosbased application with no dependencies on Python or cross-language interfaces.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

SU-E-T-47: Application of the Repair-Misrepair-Fixation RBE Model to Describe the Results of High Resolution Proton Irradiation Cell Survival Experiments

Purpose: To develop a system to rapidly and accurately calculate RBE with the repair-misrepair-fixation (RMF) model for proton therapy data sets and to evaluate its effectiveness in modeling RBE for cell survival experiments performed with the H460 cell line for a range of proton LET. Methods: A system for using the Monte Carlo Damage Simulation (MCDS) software with high performance computing was developed. Input for the MCDS software for a range of proton energies in increments of 0.1 MeV was generated and simulated. The output data were then used to determine doseaveraged quantities for the RMF model based on equivalently binned proton energy spectra. The method was applied to calculate RBE at 50% survival for experimental cell survival data. Experimental data were obtained using a system which allowed for the accumulation of cell survival data at known values of dose-averaged proton LETs at a range of doses. RBE was calculated based directly on a Cs-137 reference experiment and, additionally, according to fitted values of the θ and κ terms of the RMF model. Results: Dose-averaged RMF model quantities were calculated using the HPC system. Compared to experimental RBE determined using a Cs-137 irradiation as a reference, the RBE from the model differed by at most 49%. RBE based on the fitted values of θ and κ differed by at most 18% for the highest LET. Conclusion: A system for rapidly generating data necessary to calculate RBE with the RMF model has been developed. For the H460 cell line, the RMF model could not reproduce the experimentally determined RBE based solely on the photon reference data. Fitting of the θ and κ terms of the RMF model indicates that their values increase for proton LET exceeding approximately 10 keV/µm. NIH Program Project Grant P01CA021239.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme

Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2012

Generation of Bondarenko F factors with MCNP5 for use in scale transport codes

Generally there are three methods of cross-section processing available when using the Scale computer code. They are NITAWL, BONAMI, and CENTRM, with CENTRM being the most common and accurate, but computationally expensive. In order to improve the accuracy of BONAMI (which uses the Bondarenko Method), new Bondarenko/F Factors were to be generated that will smooth out the current F Factors that are being generated using CENTRM. The case discussed here involves using a dedicated Monte Carlo code (MCNP5) to calculate the transport solution from which shielded cross-sections can be produced directly for the transport geometry/mesh. A simple program was created to parse and collect the tallied cross-sections from the MCNP output, which was fed into a modified CLAROL input (a module that replaces or adds data in an AMPX master library), which used the cross-sections obtained from MCNP to calculate new F-factors and update the SCALE library. This approach allows the use of other methods to generate the shielded cross-sections and for easy comparison to existing results. Initial proof-of-principle calculations were carried out for an various cases using various transport solvers, such as NEWT, KENO, and XSDRN, with BONAMI in SCALE. Poor results were obtained using cross-sections generated using infinite homogeneous cases, but good results were obtained by using pin cells in an infinite lattice. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

SU-E-J-253: The Radiomics Toolbox in the Computational Environment for Radiological Research (CERR)

Purpose: To present an open source and free platform to facilitate radiomics research — The “Radiomics toolbox” in CERR. Method: There is scarcity of open source tools that support end-to-end modeling of image features to predict patient outcomes. The “Radiomics toolbox” strives to fill the need for such a software platform. The platform supports (1) import of various kinds of image modalities like CT, PET, MR, SPECT, US. (2) Contouring tools to delineate structures of interest. (3) Extraction and storage of image based features like 1st order statistics, gray-scale co-occurrence and zonesize matrix based texture features and shape features and (4) Statistical Analysis. Statistical analysis of the extracted features is supported with basic functionality that includes univariate correlations, Kaplan-Meir curves and advanced functionality that includes feature reduction and multivariate modeling. The graphical user interface and the data management are performed with Matlab for the ease of development and readability of code and features for wide audience. Open-source software developed with other programming languages is integrated to enhance various components of this toolbox. For example: Java-based DCM4CHE for import of DICOM, R for statistical analysis. Results: The Radiomics toolbox will be distributed as an open source, GNU copyrighted software. The toolbox was prototyped for modeling Oropharyngeal PET dataset at MSKCC. The analysis will be presented in a separate paper. Conclusion: The Radiomics Toolbox provides an extensible platform for extracting and modeling image features. To emphasize new uses of CERR for radiomics and image-based research, we have changed the name from the “Computational Environment for Radiotherapy Research” to the “Computational Environment for Radiological Research”.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Equation-based languages – A new paradigm for building energy modeling, simulation and optimization

Most of the state-of-the-art building simulation programs implement models in imperative programming languages. This complicates modeling and excludes the use of certain efficient methods for simulation and optimization. In contrast, equation-based modeling languages declare relations among variables, thereby allowing the use of computer algebra to enable much simpler schematic modeling and to generate efficient code for simulation and optimization. We contrast the two approaches in this paper. We explain how such manipulations support new use cases. In the first of two examples, we couple models of the electrical grid, multiple buildings, HVAC systems and controllers to test a controller that adjusts building room temperatures and PV inverter reactive power to maintain power quality. In the second example, we contrast the computing time for solving an optimal control problem for a room-level model predictive controller with and without symbolic manipulations. As a result, exploiting the equation-based language led to 2, 200 times faster solution

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Library for Evolutionary Algorithms in Python (LEAP)

There are generally three types of scientific software users: users that solve problems using existing science software tools, researchers that explore new approaches by extending existing code, and educators that teach students scientific concepts. Python is a general-purpose programming language that is accessible to beginners, such as students, but also as a language that has a rich scientific programming ecosystem that facilitates writing research software. Additionally, as high-performance computing (HPC) resources become more readily available, software support for parallel processing becomes more relevant to scientific software.There currently are no Python-based evolutionary computation frameworks that support all three types of scientific software users. Moreover, some support synchronous concurrent fitness evaluation that do not efficiently use HPC resources. We pose here a new Python-based EC framework that uses an established generalized unified approach to EA concepts to provide an easy to use toolkit for users wishing to use an EA to solve a problem, for researchers to implement novel approaches, and for providing a low-bar to entry to EA concepts for students. Additionally, this toolkit provides a scalable asynchronous fitness evaluation implementation friendly to HPC that has been vetted on hardware ranging from laptops to the world’s fastest supercomputer, Summit.

Coletti, Mark↗