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At least 1,693 records · Page 94
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

User Guide to the Facility Cybersecurity Capability Maturity Model (F-C2M2) Assessment

The Facility Cybersecurity Capability Maturity Model (F-C2M2) assessment was designed to help facilities address their unique cybersecurity challenges. Building upon the foundational principles of the U.S. Department of Energy (DOE) Cybersecurity Capability Maturity Model (C2M2), the F-C2M2 was explicitly tailored for federal facilities to help evaluate, benchmark, and enhance the cybersecurity capabilities of both their information technology (IT) and operational technology (OT) environments. This user guide was developed to facilitate a thorough understanding of the tool. Whether users are familiar with the original C2M2 or are new to the maturity model concept, this guide was designed to offer clarity, direction, and support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Combining protein sequences and structures with transformers and equivariant graph neural networks to predict protein function

Abstract Motivation Millions of protein sequences have been generated by numerous genome and transcriptome sequencing projects. However, experimentally determining the function of the proteins is still a time consuming, low-throughput, and expensive process, leading to a large protein sequence-function gap. Therefore, it is important to develop computational methods to accurately predict protein function to fill the gap. Even though many methods have been developed to use protein sequences as input to predict function, much fewer methods leverage protein structures in protein function prediction because there was lack of accurate protein structures for most proteins until recently. Results We developed TransFun—a method using a transformer-based protein language model and 3D-equivariant graph neural networks to distill information from both protein sequences and structures to predict protein function. It extracts feature embeddings from protein sequences using a pre-trained protein language model (ESM) via transfer learning and combines them with 3D structures of proteins predicted by AlphaFold2 through equivariant graph neural networks. Benchmarked on the CAFA3 test dataset and a new test dataset, TransFun outperforms several state-of-the-art methods, indicating that the language model and 3D-equivariant graph neural networks are effective methods to leverage protein sequences and structures to improve protein function prediction. Combining TransFun predictions and sequence similarity-based predictions can further increase prediction accuracy. Availability and implementation The source code of TransFun is available at https://github.com/jianlin-cheng/TransFun.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Bootstrap current modeling in M3D-C1

Bootstrap current plays a crucial role in the equilibrium of magnetically confined plasmas, particularly in quasi-symmetric stellarators and in tokamaks, where it can represent bulk of the electric current density. Accurate modeling of this current is essential for understanding the magnetohydrodynamic (MHD) equilibrium and stability of these configurations. This study expands the modeling capabilities of M3D-C1, an extended-MHD code, by implementing self-consistent physics models for bootstrap current. It employs two analytical frameworks: a generalized Sauter model (Sauter et al. 1999 Phys. Plasmas vol. 6, no. 7, pp. 2834–2839), and a revised Sauter-like model (Redl et al. 2021 Phys. Plasmas vol. 28, no. 2, pp. 022502). The isomorphism described by Landreman et al. (2022 Phys. Rev. Lett. vol. 128, pp. 035001) is employed to apply these models to quasi-symmetric stellarators. The implementation in M3D-C1 is benchmarked against neoclassical codes, including NEO, XGCa and SFINCS, showing excellent agreement. These improvements allow M3D-C1 to self-consistently calculate the neoclassical contributions to plasma current in axisymmetric and quasi-symmetric configurations, providing a more accurate representation of the plasma behavior in these configurations. A workflow for evaluating the neoclassical transport using SFINCS with arbitrary toroidal equilibria calculated using M3D-C1 is also presented. This workflow enables a quantitative evaluation of the error in the Sauter-like model in cases that deviate from axi- or quasi-symmetry (e.g. through the development of an MHD instability).

fusion plasma↗
DOE OSTI2026

GRIDAPPSD/distopf (33583-E)

DistOPF is an open-source Python package providing a three-phase, asymmetric optimal power flow (OPF) tool specifically designed for distribution systems. The key inventive features include: - Asymmetrical 3-phase OPF modeling for distribution systems with unbalanced phases - Comprehensive control optimization supporting both active (P) and reactive (Q) power control variables - Built-in visualization and validation tools - Standard test system benchmarking platform for algorithm development and comparison - Modular CSV-based input system using Pandas DataFrames for flexible model specification - Standard power distribution model importer enabling direct conversion from CIM and OpenDSS format to optimization-ready models - Multiple solve interface compatibility (PYOMO, CVXPY, SciPy) with automatic solver selection based on problem type

Gray, Nathan [Pacific Northwest National Laborator↗
NASA NTRS2010

Development of a Benchmark Example for Delamination Fatigue Growth Prediction

The development of a benchmark example for cyclic delamination growth prediction is presented and demonstrated for a commercial code. The example is based on a finite element model of a Double Cantilever Beam (DCB) specimen, which is independent of the analysis software used and allows the assessment of the delamination growth prediction capabilities in commercial finite element codes. First, the benchmark result was created for the specimen. Second, starting from an initially straight front, the delamination was allowed to grow under cyclic loading in a finite element model of a commercial code. The number of cycles to delamination onset and the number of cycles during stable delamination growth for each growth increment were obtained from the analysis. In general, good agreement between the results obtained from the growth analysis and the benchmark results could be achieved by selecting the appropriate input parameters. Overall, the results are encouraging but further assessment for mixed-mode delamination is required

Krueger, Ronald↗
NASA NTRS2010

Development of a Benchmark Example for Delamination Fatigue Growth Prediction

The development of a benchmark example for cyclic delamination growth prediction is presented and demonstrated for a commercial code. The example is based on a finite element model of a Double Cantilever Beam (DCB) specimen, which is independent of the analysis software used and allows the assessment of the delamination growth prediction capabilities in commercial finite element codes. First, the benchmark result was created for the specimen. Second, starting from an initially straight front, the delamination was allowed to grow under cyclic loading in a finite element model of a commercial code. The number of cycles to delamination onset and the number of cycles during stable delamination growth for each growth increment were obtained from the analysis. In general, good agreement between the results obtained from the growth analysis and the benchmark results could be achieved by selecting the appropriate input parameters. Overall, the results are encouraging but further assessment for mixed-mode delamination is required.

Krueger, Ronald↗
DOE OSTI2022

Deep learning methods for obtaining photometric redshift estimations from images

ABSTRACT Knowing the redshift of galaxies is one of the first requirements of many cosmological experiments, and as it is impossible to perform spectroscopy for every galaxy being observed, photometric redshift (photo-z) estimations are still of particular interest. Here, we investigate different deep learning methods for obtaining photo-z estimates directly from images, comparing these with ‘traditional’ machine learning algorithms which make use of magnitudes retrieved through photometry. As well as testing a convolutional neural network (CNN) and inception-module CNN, we introduce a novel mixed-input model that allows for both images and magnitude data to be used in the same model as a way of further improving the estimated redshifts. We also perform benchmarking as a way of demonstrating the performance and scalability of the different algorithms. The data used in the study comes entirely from the Sloan Digital Sky Survey (SDSS) from which 1 million galaxies were used, each having 5-filtre (ugriz) images with complete photometry and a spectroscopic redshift which was taken as the ground truth. The mixed-input inception CNN achieved a mean squared error (MSE) =0.009, which was a significant improvement ($30{{\ \rm per\ cent}}$) over the traditional random forest (RF), and the model performed even better at lower redshifts achieving a MSE = 0.0007 (a $50{{\ \rm per\ cent}}$ improvement over the RF) in the range of z < 0.3. This method could be hugely beneficial to upcoming surveys, such as Euclid and the Vera C. Rubin Observatory’s Legacy Survey of Space and Time (LSST), which will require vast numbers of photo-z estimates produced as quickly and accurately as possible.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Assessing the WRF-Solar Model Performance Using Satellite-Derived Irradiance from the National Solar Radiation Database

Abstract WRF-Solar is a numerical weather prediction model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol–cloud–radiation processes. This flexibility can be argued to make it more difficult to improve the model’s performance because of the necessity of inspecting different configurations. To alleviate this situation, WRF-Solar has a reference configuration to use as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the contiguous United States (CONUS). Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, and sensitivities to the grid spacing and the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute to increasing the WRF-Solar performance.

14 SOLAR ENERGY↗
DOE OSTI2023

First principles simulation of reacting hypersonic flow over a blunt wedge

This article presents molecular-level analysis of a reactive, near-continuum, Mach 21 nitrogen flow over a blunt wedge using the direct molecular simulation (DMS) method. The flow conditions lead to internal energy excitation and dissociation in the flow field, resulting in thermal and chemical nonequilibrium in the flow. Thermal nonequilibrium in the vibrational mode is observed to extend to the molecular level, where the vibrational energy distributions at various points in the flow field are observed to be non-Boltzmann. Furthermore, this is the first reactive DMS calculation where the wall is assumed to be isothermal and full momentum accommodation of the particles is enforced, hence incorporating viscous wall effects. Since the DMS method uses a quantum mechanically generated interaction potential as its only modeling input, all thermochemical and transport properties of the flow field can directly be attributed to the ab initio potential energy surface. Using the DMS solution as a benchmark, this article assesses the performance of Navier–Stokes computational fluid dynamics solutions using lower fidelity two-temperature models. Two models are chosen as points of comparison: the well-known Park two-temperature model and the recently developed modified Marrone and Treanor model.

Mechanics↗
DOE OSTI2024

Transmission Interconnection Roadmap: Transforming Bulk Transmission Interconnection by 2035

The U.S. electricity system is amid a rapidly occurring and widespread energy transition. Regional, Tribal, state, and customer demand for new energy resources, combined with favorable policies, is driving a rapid rise of interconnection requests. Interconnection processes will need to evolve to handle this larger number of requests today and into the future, as policy and economic drivers continue to motivate significant resource development. This roadmap identifies and organizes nearer- and longer-term solutions to enable transmission interconnection processes to meet this expected demand, and it is intended for a diverse audience of stakeholders participating within transmission interconnection processes. The roadmap is a result of the Interconnection Innovation e-Xchange (i2X) program launched by the U.S. Department of Energy (DOE) in June 2022 to convene stakeholders and address interconnection challenges. The roadmap is organized into four primary goal areas, each important to the overall i2X mission to enable a simpler, faster, and fairer interconnection of clean energy resources while enhancing the reliability, resiliency, and security of our electric grid. The first goal aims to improve interconnection data transparency, to aid interconnection customers’ ability to screen and site potential projects, better enable third-party modeling, facilitate more process automation, enhance competition while ensuring equitable outcomes, and enable benchmarking, tracking, and auditing of interconnection processes and reforms. The second goal covers solutions to improve queue management practices, affected system studies, fair processes, and workforce development. The third goal incorporates solutions that aim to improve cost allocation, reduce costs to electricity consumers, enhance the coordination between transmission planning and the interconnection process, and optimize the rightsizing of transmission investment through improvements in interconnection studies. The fourth and final goal aims to reduce the performance issues not identified during interconnection studies by updating technical requirements within interconnection studies, models, and tools while also improving industry interconnection standards.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

XPlacer Machine Learning Guided Data Placement

XPlacer Machine Learning Guided Data Placement uses machine learning model to assist programmers to select the data placement advises for application running on HPC system with Nvidia GPUs. XPlacer Machine Learning Guided Data Placement consists three components: (1) 4 revised benchmarks from Rodinia benchmarks; (2) compiler script and execution script to generate variants, and collect training data for the machine learning model; (3) the scripts to post-process the collected data and generate the machine leaning model.

Liao, Chunhua↗
DOE OSTI2020

Assessment of Grizzly Capabilities for Reactor Pressure Vessels and Reinforced Concrete Structures

Over the last several years, capabilities to simulate the progression and effects of degradation in critical structures in light water reactor (LWR) nuclear power plants have been under development in the Grizzly code. Age-related material degradation is important for a number of systems in LWRs, but the main focus for Grizzly development has been on reactor pressure vessels (RPVs) and concrete structures because of their central role and the difficulty of replacement of these structures if they are found to be degraded to an unacceptable degree. The capabilities for analyzing both RPVs and concrete structures in Grizzly have reached a point where the feature sets are sufficiently complete to perform credible analyses of those types of structures. Because of this, the emphasis has shifted from foundational development to assessing the accuracy of these modeling capabilities on representative problems of interest and on improving the physical basis of those models to improve their ability to predict the actual response of those structural systems. This report documents a first set of test cases that have been developed to assess the Grizzly code on real-world problems for these two types of structures. For RPVs, these test cases consist of a set of benchmark problems where Grizzly is compared to another code. For concrete structures, a set of models of experimental specimens designed to characterize the multidimensional swelling response of reinforced concrete members due to alkali-silica reaction (ASR) has been developed and compared with experimental results. Both the RPV and concrete test cases developed here are much more computationally intensive than the small regression tests that are used in the testing that Grizzly undergoes every time a proposed set of changes is made to ensure that those changes do not adversely affect previously established behavior. A system for regularly running these large problems to monitor the behavior of the code as it is developed has been instituted. While these test cases generally indicate good comparison with the benchmark results, they also indicate areas where further development is warranted.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Benchmarking collective effects of electron interactions in a wiggler with OPAL-FEL

OPAL-FEL is a recently developed tool for the modeling of particle accelerators containing wigglers or undulators. It extends the well established 3D electrostatic particle-tracking code OPAL, by merging it with the finite-difference time-domain electromagnetic solver MITHRA. We present results of two benchmark cases where OPAL-FEL simulations are compared to experimental results. Both experiments concern electron beamlines where the longitudinal phase space is modulated with a short magnetic wiggler. Good agreement was found in both the space charge and radiation dominated regimes.

43 PARTICLE ACCELERATORS↗
NASA NTRSDate not supplied

Latest Development in Radiative Transfer Models and Retrieval Algorithms Using Principal Components

The radiative transfer model (RTMs) has a wide range of applications in satellite remote sensing and atmospheric radiation applications. However, millions of line-by-line (LBL) radiative transfer calculations at fine monochromatic frequencies are needed in order to properly calculate spectral contributions of water vapor and trace gases in the atmosphere in infrared and solar spectral regions. Therefore, fast and accurate RTMs are needed to efficiently process large amount of satellite data. A Principal Component-based Radiative Transfer Model (PCRTM) was first developed in 2004 at NASA Langley Research Centre to fulfil this need. By using PC-compression, one can reduce the data dimension significantly while maintaining original information content. The PCRTM can directly compute PC-scores and their derivatives with respect to retrieved parameters. The PCRTM can simulate the top-of-atmosphere (TOA) radiance or reflectance spectra from 0.250 µm (400000 cm-1) to 2000 µm (50 cm-1) with several orders of magnitude faster speed as compared to a LBL RTM. It is also extremely accurate compared to LBL RTM benchmarks (0.03 K RMS error in IR and 0.05% in solar). The PCRTM model has been developed for hyperspectral sensors such as AIRS, CrIS, IASI, NAST-I, SHIS, FIRST, and CLARREO-IR in thermal IR spectral region and CLARREO-Solar, CPF, TEMPO, EMIT, OMI, and SCIAMACHY in solar spectral region. The PCRTM accuracy has been demonstrated via RTM intercomparisons and with real satellite observations from AIRS, CrIS, IASI, SCHIAMACHY, and EMIT etc. In this presentation, we will describe two PCRTM-based inversion algorithms to retrieve atmospheric temperature, water vapor, and trace gas profiles, as well as cloud and surface properties from hyperspectral sounders such as AIRS, CrIS, IASI, and NAST-I. The first one is called Single Fieldof-view Sounder Atmospheric Product (SiFSAP) algorithm. It provides L2 products with 9-times higher area spatial resolution as compared to current cloud-clearing sounder algorithms. The SiFSAP L2 and L3 products are available at NASA's Goddard Earth Sciences Data and Information Services Center (GES DISC) for public access. The second inversion algorithm is called Climate Fingerprinting Atmospheric Product (ClimFiSP) algorithm. It is designed to produce high quality climate products (trends, anomalies, daily, and monthly profiles of temperature, water vapor, traces, clouds, and surface properties) from multiple satellite sensors such AIRS on Aqua and CrIS on multiple satellites. This product will be available at NASA GES DISC later this year. The PCRTMbased high fidelity simulators for CLARREO and CPF have been used for sensor performance trade studies, algorithm development, and inter-satellite calibrations. We have also used PCRTM generated TOA radiance spectra to train an AI-based algorithm and successfully retrieved cloud properties from EMIT solar hyperspectral imagers.

PCRTM↗
DOE OSTI2023

Applications of physics informed neural operators

Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code , an interactive website to visualize the predictions of our PINOs, and a tutorial for their use at the Data and Learning Hub for Science .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Bay Area Regional Energy: Network Integrated Commercial Retrofits (BRICR) Project. Final Report

The BRICR project applied large-scale building energy modeling concepts with the aim of reducing the cost of energy efficiency targeting, design, and project development, and measurement of energy savings for energy efficiency programs implemented by local governments that serve small and medium commercial buildings (SMB). The project leveraged the services and resources of existing local government energy programs serving disadvantaged and hard-to-reach SMB customers. In contrast to programs run by utilities, local government programs generally do not have direct access to energy billing records for an entire class of customers in a geographic area, which prior research demonstrated useful for large-scale building energy model baseline development and calibration. , However, local governments are rich in public records that offer important clues about physical attributes and uses that, along with behavior, determine energy use. Relying only on public records, BRICR demonstrated development of credible baseline energy models for 3,792 office, retail, and hotel buildings. Publicly disclosed annual energy use data from a local energy benchmarking program and anonymized data from the Building Performance Database, the nation’s largest dataset about energy-related characteristics of buildings, were utilized to validate and calibrate energy models via an innovative method comparing distributions of energy intensity by fuel type for portfolios of buildings of similar size, vintage, and use. Portfolio calibration does not provide certainty that an energy model fits an individual building; the method is useful when billing data is not accessible – a common situation for researchers, energy service providers and ESCOs, local governments, and any party other than a utility. A software component was developed, the BRICR gem, which automates simulation when relevant data is added or edited by the user to a file saved in the standardized BuildingSync XML schema for energy audit data. The component was demonstrated as a simplified means to generate a mass of energy models corresponding to public records containing basic attributes such as building scale, location, use, year built, and aspect ratio in combination with building energy code prototype data corresponding to use and vintage. The component was also demonstrated as a simplified means to automate energy simulation when attributes are revised; the intention was to enable iterative improvement of the baseline model and energy savings estimates for common energy conservation measures as users revise relevant attributes based on their observations. In the context of institutional change and uncertainty for the participating local government energy programs, 13 whole building retrofits were completed. Impacts were measured by applying the CalTRACK2.0 methods to standardize measurement of normalized metered energy consumption. The GRIDMeter methods of stratified sampling and individual load shape analysis were applied to adjust for impacts of the effect of COVID-19 on retrofitted buildings in the context of all local buildings of similar size and use. Excluding impacts of the pandemic, retrofitted buildings demonstrated between 1.6% and 25.1% reduction in energy use. The project contributed use cases and feedback that helped inform evolution of the software tools and data formats that were combined for the first time in the BRICR project.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2023

Emergent Behaviors and the Limits of Large Language Model Generalization

Over the past couple years, large language models (LLMs) have rapidly grown in size and performance across wide numbers of tasks, leading some to herald them as machine learning models with truly “general” capabilities. We address some of the ways that the language used to describe these models can be both deceptive and a poor framing for making progress towards understanding their capabilities, and also review arguments on to what extent these models can represent and interact with the meaning of language. Emergent behaviors in these highly complex models are unpredictable and research into understanding them is still in early phases, which limits the strength of claims of generality about them. Moreover, while these models demonstrate surprising emergent reasoning capabilities on many tasks, there are still many limitations in generalization that overall high benchmark performance can hide. We review the literature showcasing limitations of LLMs as well as techniques to mitigate or overcome these challenges, while also highlighting how fundamental problems in model evaluation may prevent true claims of generality. We conclude with a practical section on using LLMs on real world problems while appropriately evaluating and validating these tasks to prevent or mitigate the downstream impacts of incorrect results.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1997

Numerical Study of Magnetic Damping During Unidirectional Solidification

A fully 3-D numerical model is developed to represent magnetic damping of complex fluid flow, heat transfer and electromagnetic field distributions in a melt cavity. The model is developed based on our in-house finite element code for the fluid flow, heat transfer and electromagnetic field calculations. The computer code has been tested against benchmark test problems that are solved by other commercial codes as well as analytical solutions whenever available. The numerical model is tested against numerical and experimental results for water reported in literature. With the model so tested, various numerical simulations are carried out for the Sn-35.5% Pb melt convection and temperature distribution in a cylindrical cavity with and without the presence of a transverse magnetic field. Numerical results show that magnetic damping can be effectively applied to reduce turbulence and flow levels in the melt undergoing solidification and over a certain threshold value a higher magnetic field resulted in a higher velocity reduction. It is found also that for a fully 3-D representation of the magnetic damping effects, the electric field induced in the melt by the applied DC magnetic field does not vanish, as some researchers suggested, and must be included even for molten metal and semiconductors. Also, for the study of the melt flow instability, a long enough time has to be applied to ensure the final fluid flow recirculation pattern. Moreover, our numerical results suggested that there seems to exist a threshold value of applied magnetic field, above which magnetic damping becomes possible and below which the convection in the melt is actually enhanced. Because of the limited financial resource allocated for the project, we are unable to carry out extensive study on this effect, which should warrant further theoretical and experimental study. In that endeavor, the developed numerical model should be very useful; and the model should serve as a useful tool for exploring necessary design parameters for planning magnetic damping experiments and interpreting the experimental results.

Li, Ben Q.↗