CO2 Rock Physics Code with Compliant Porosity and CO2 Weakening
An NRAP funded code development for CO2 storage. This code converts reservoir simulations to seismic properties for underground CO2 storage.
Creasy, Neala↗
SEARCH · Engineering Papers
Results for “Coding”
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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Code Coverage Reporter
The code-coverage-reporter is a JavaScript GitHub Action that may be called from within GitHub workflows. Its role is to parse a code coverage report file in the LCOV format and translate the contents into a summary that gets added to individual Pull Requests. It is fully reusable and repository-agnostic.
Curley, Jacob [Fermilab]↗
Multidimensional Fuel Performance Code: BISON
BISON is a finite element based nuclear fuel performance code applicable to a variety of fuel forms including light water reactor fuel rods, TRISO fuel particles, and metallic rod and plate fuel (Refs. [a, b, c]). It solves the fully-coupled equations of thermo-mechanics and species diffusion and includes important fuel physics such as fission gas release and material property degradation with burnup. BISON is based on the MOOSE framework (Ref. [d]) and can therefore efficiently solve problems on 1-, 2- or 3-D meshes using standard workstations or large high performance computers. BISON is also coupled to a MOOSE-based mesoscale phase field material property simulation capability (Refs. [e, f]). As described here, BISON includes the code library named FOX, which was developed concurrent with BISON. FOX contains material and behavioral models that are specific to oxide fuels.
Hales, Jason↗
Code artifact for "C.A.T.S.: Memory and Control Flow Tracing for Whole-Program Performance Analysis"
This software contains the code artifacts for the Supercomputing 2025 paper titled "C.A.T.S.: Memory and Control Flow Tracing for Whole-Program Performance Analysis". The included code contains analysis passes, dynamic program instrumentation, runtime, examples, and tests.
Tal, Ben Nun [Lawrence Livermore National Laborato↗
Jupyter Notebook Code for “Data-Driven Insights to Accelerate Advanced Biomanufacturing”
This page contains the datasets and code #O5097 Jupyter Notebook Code for “Data-Driven Insights to Accelerate Advanced Biomanufacturing”. Data literature-derived cultivation experiments for polyhydroxybutyrate (PHB) production in Synechocystis sp. PCC 6803 and were used for ML model development, interpretation, and experimental validation.
Lalonde, Jessica N. [Los Alamos National Laborator↗
Multi-core performance studies of a Monte Carlo neutron transport code
Performance results are presented for a multi-threaded version of the OpenMC Monte Carlo neutronics code using OpenMP in the context of nuclear reactor criticality calculations. Our main interest is production computing, and thus we limit our approach to threading strategies that both require reasonable levels of development effort and preserve the code features necessary for robust application to real-world reactor problems. Several approaches are developed and the results compared on several multi-core platforms using a popular reactor physics benchmark. A broad range of performance studies are distilled into a simple, consistent picture of the empirical performance characteristics of reactor Monte Carlo algorithms on current multi-core architectures.
97 MATHEMATICS AND COMPUTING↗
A survey of software implementations used by application codes in the Exascale Computing Project
The US Department of Energy Office of Science and the National Nuclear Security Administration initiated the Exascale Computing Project (ECP) in 2016 to prepare mission-relevant applications and scientific software for the delivery of the exascale computers starting in 2023. The ECP currently supports 24 efforts directed at specific applications and six supporting co-design projects. These 24 application projects contain 62 application codes that are implemented in three high-level languages—C, C++, and Fortran—and use 22 combinations of graphical processing unit programming models. The most common implementation language is C++, which is used in 53 different application codes. The most common programming models across ECP applications are CUDA and Kokkos, which are employed in 15 and 14 applications, respectively. This article provides a survey of the programming languages and models used in the ECP applications codebase that will be used to achieve performance on the future exascale hardware platforms.
97 MATHEMATICS AND COMPUTING↗
Understanding power and energy utilization in large scale production physics simulation codes
Power is an often-cited reason for the move to advanced architectures on the path to Exascale computing. Here, this is due to practical considerations related to delivering enough power to successfully site and operate these machines, as well as concerns about energy usage while running large simulations. Since obtaining accurate power measurements can be challenging, it may be tempting to use the processor thermal design power (TDP) as a surrogate due to its simplicity and availability. However, TDP is not indicative of typical power usage while running simulations. Using commodity and advanced technology systems at Lawrence Livermore and Sandia National Labs, we performed a series of experiments to measure power and energy usage in running simulation codes. These experiments indicate that large scale Lawrence Livermore simulation codes are significantly more efficient than a simple processor TDP model might suggest.
HPC↗
Sparse coding of pathology slides compared to transfer learning with deep neural networks
Background Histopathology images of tumor biopsies present unique challenges for applying machine learning to the diagnosis and treatment of cancer. The pathology slides are high resolution, often exceeding 1GB, have non-uniform dimensions, and often contain multiple tissue slices of varying sizes surrounded by large empty regions. The locations of abnormal or cancerous cells, which may constitute a small portion of any given tissue sample, are not annotated. Cancer image datasets are also extremely imbalanced, with most slides being associated with relatively common cancers. Since deep representations trained on natural photographs are unlikely to be optimal for classifying pathology slide images, which have different spectral ranges and spatial structure, we here describe an approach for learning features and inferring representations of cancer pathology slides based on sparse coding. Results We show that conventional transfer learning using a state-of-the-art deep learning architecture pre-trained on ImageNet (RESNET) and fine tuned for a binary tumor/no-tumor classification task achieved between 85% and 86% accuracy. However, when all layers up to the last convolutional layer in RESNET are replaced with a single feature map inferred via a sparse coding using a dictionary optimized for sparse reconstruction of unlabeled pathology slides, classification performance improves to over 93%, corresponding to a 54% error reduction. Conclusions We conclude that a feature dictionary optimized for biomedical imagery may in general support better classification performance than does conventional transfer learning using a dictionary pre-trained on natural images.
59 BASIC BIOLOGICAL SCIENCES↗
Code and Data for "Carbon Mitigation Payments Can Reduce the Riskiness of Bioenergy Crop Production"
A stochastic domination analysis model was developed to examine the effect that emerging carbon markets can have on the spatially varying returns and risk profiles of bioenergy crops relative to conventional crops. The code is written in MATLAB, and includes the calculated output. See the README file for instructions to run the code.
bioenergy crops↗
Model Code and Data for "Assessing the Efficiency Implications of Renewable Fuel Policy Design in the United States"
This dataset contains model code (including input data) to replicate the outcomes for "Assessing the Efficiency Implications of Renewable Fuel Policy Design in the United States". The model consists of: (1) The replication codes and data for the model. To run the model, using GAMS to run the "Models.gms" file.
cellulosic waiver credit↗
Optimization of the deep neural network parameters for generating homogenized fuel assembly data for nodal codes
Homogenized fuel assembly (FA) data is a typical input data for nodal codes. Generating that data, however, could be time-consuming. One of promising ways to mitigate the computational burden of generating macroscopic cross-sections is to use trained artificial neural network (ANN) models for predicting nuclear data. However, there is a challenge to make the model support variable FA geometry. In this work, two most common types of FA were combined in one ANN model. Since there could be multiple ways of converting 2-dimensional FA data into 1-dimensional input vector for ANN, three different approaches of data flattening were evaluated. The input parameters included each fuel pin enrichment, fuel temperature, moderator temperature and boron concentration. The output parameters were 2-group macroscopic cross-sections (XS) and pin power distribution (HFF). A fully connected deep neural network (DNN) model was trained and tested using pre-generated data obtained with lattice physics code STREAM. The results of this study showed no statistically significant difference in the accuracy of XS and HFF generation for all 3 tested input vector orders. This means that fully connected DNN for XS generation demonstrated input sequence invariance. Results of comparing predicted XS data with reference solutions were found sufficiently close considering the reduction of computation time offered by ANN. Mean relative difference (MRD) for all output XS parameters was found below 0.7%, while HFF MRD was found higher compared to XS values, in some cases slightly exceeding 1%, mostly near guide tube locations. (authors)
22 GENERAL STUDIES OF NUCLEAR REACTORS↗
Performance of the Initial Implementation of the Shift Monte Carlo Code in SCALE 6.3
The Shift Monte Carlo code will be introduced in SCALE 6.3 as an alternative to the KENO V.a, KENO-VI, and Monaco codes. Calculations were performed to establish the performance of Shift for criticality safety analyses within the criticality safety analyses sequence (CSAS) based on models in the Verified, Archived Library of Inputs and Data (VALID). This test suite contains over 600 critical experiment models covering a broad range of fissile materials and neutron energy spectra. The comparisons presented include calculated k eff values and runtime performance for serial calculations and a selection of parallel calculations. Comparisons are presented for multigroup (MG) and continuous-energy (CE) calculations for KENO V.a and KENO-VI models. Results generated with a beta version of SCALE 6.3 indicate excellent agreement in k eff values between KENO and Shift. The largest differences in the average k eff value calculated for the 15 categories of KENO V.a models are 0.00020 ± 0.00011 Δ k for MG calculations and 0.00011 ± 0.00005 Δ k for CE calculations. Similar comparisons in three categories using KENO-VI result in the largest differences for MG calculations: as 0.00004 ± 0.00003 Δ k , and -0.00002 ± 0.00003 Δ k for CE. The preliminary results also indicate that Shift is faster than KENO on a per particle basis, especially for fast spectrum systems. The uncertainty per history is also higher, however, so the Monte Carlo figure of merit is higher for KENO for thermal and intermediate spectrum systems. As expected, Shift generally has better speedup than KENO for parallel calculations, regardless of neutron energy spectrum.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
Neural correlates of sparse coding and dimensionality reduction
Supported by recent computational studies, there is increasing evidence that a wide range of neuronal responses can be understood as an emergent property of nonnegative sparse coding (NSC), an efficient population coding scheme based on dimensionality reduction and sparsity constraints. We review evidence that NSC might be employed by sensory areas to efficiently encode external stimulus spaces, by some associative areas to conjunctively represent multiple behaviorally relevant variables, and possibly by the basal ganglia to coordinate movement. In addition, NSC might provide a useful theoretical framework under which to understand the often complex and nonintuitive response properties of neurons in other brain areas. Although NSC might not apply to all brain areas (for example, motor or executive function areas) the success of NSC-based models, especially in sensory areas, warrants further investigation for neural correlates in other regions.
59 BASIC BIOLOGICAL SCIENCES↗
Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability: Supporting Data and Code
This repository contains R code and associated datasets for reproducing the analysis described in the manuscript titled “Disentangling the Impacts of Microtopography and Shrub Distribution on Snow Depth in a Subarctic Watershed: Toward a Predictive Understanding of Snow Spatial Variability” (DOI: 10.1029/2024JG008604). The provided scripts facilitate a comprehensive analysis of snow depth variability influenced by microtopography and vegetation distribution in a subarctic watershed. Included datasets are high-resolution spatial maps of snow depth, terrain elevation, vegetation height, and distance from shrubs taller than 1 meter, all formatted as text files (.txt). These data are fully describe in doi:10.15485/2316038. Users can adapt the provided R scripts to accommodate different data formats or larger spatial domains, noting that some output files may require modification due to their size.The code includes implementations for boosted regression tree analysis adapted from methods outlined in Elith et al. (2008). Users interested in understanding or modeling landscape-scale snow distribution patterns, particularly in Arctic or subarctic ecosystems, will find this package useful. The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic), was a research effort to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy's Office of Biological and Environmental Research.The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska and 2) multiple areas on the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska.Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy's Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).
54 ENVIRONMENTAL SCIENCES↗
T-BMT Spin Resonance Tracker Code for He3 with Six Snakes
Polarization lifetime for He3 using two and six snakes are studied using the T-BMT Spin Resonance Tracker code. This code integrates a reduced spinor form of the T-BMT equation including only several spin resonances and the kinematics of synchrotron motion. It was previously benchmarked against RHIC polarization lifetime under the two snake system *. * Phys. Rev.Accel. Beams 22 (2019) 9, 091001
Ranjbar, V. H.↗
Scientific Computational Imaging Code (SCICO)
Scientific Computational Imaging Code (SCICO) is a Python package for solving the inverse problems that arise in scientific imaging applications. Its primary focus is providing methods for solving ill-posed inverse problems by using an appropriate prior model of the reconstruction space. SCICO includes a growing suite of operators, cost functionals, regularizers, and optimization routines that may be combined to solve a wide range of problems, and is designed so that it is easy to add new building blocks. SCICO is built on top of JAX rather than NumPy, enabling GPU/TPU acceleration, just-in-time compilation, and automatic gradient functionality, which is used to automatically compute the adjoints of linear operators. An example of how to solve a multi-channel tomography problem with SCICO is shown in Figure 1. The SCICO source code is available from GitHub, and pre-built packages are available from PyPI. It has extensive online documentation, including API documentation and usage examples, which can be run online at Google Colab and binder.
97 MATHEMATICS AND COMPUTING↗
PeleLMeX: an AMR Low Mach Number Reactive Flow Simulation Code without level sub-cycling
PeleLMeX simulates chemically reacting low Mach number flows with block-structured adaptive mesh refinement (AMR). The code is built upon the AMReX library, which provides the underlying data structures and tools to manage and operate on them across massively parallel computing architectures. PeleLMeX algorithmic features are inherited from its predecessor PeleLM but key improvements allow representation of more complex physical processes. Together with its compressible flow counterpart PeleC, the thermo-chemistry library PelePhysics and the multi-physics library PeleMP, it forms the Pele suite of open-source reactive flow simulation codes.
97 MATHEMATICS AND COMPUTING↗