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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

SAS4A/SASSYS-1 Verification Testing for Sodium Fast Reactor Application: Acceptance Testing Report

AS4A/SASSYS-1 (SAS) is a simulation tool used to perform deterministic analyses of anticipated events as well as design basis and beyond design basis accidents for advanced liquid-metal-cooled nuclear reactors. With its origin as SAS1A in the late 1960s, the SAS series of codes has been under continuous use and development for over sixty years and represents a critical investment in safety analysis capabilities for the U.S. Department of Energy. To support the dedication effort, this report has been generated to provide a detailed description of the available verification testing. The verification testing presented in this report captures functionality testing, focusing mainly on the testing of specific functions and algorithms for accuracy and precision of output, and interface testing, focusing mainly on the testing of critical input parameters and their valid ranges. Although SAS was developed to support the analysis of any liquid-metal-cooled nuclear reactor, the testing described in this document primarily focuses on the verification of SAS capabilities as they relate to a generic pool-type Sodium Fast Reactor (SFR).

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

MCOR User Guide

User guide for the MCOR software package which is currently publicly hosted on Github (https://github.com/pnnl/MCOR). The Microgrid Component Optimization for Resilience (MCOR) tool simulates the operation of a renewable energy, battery, and back-up generator microgrid under a large range of outage conditions to understand how a potential system can meet the resilience goals of a particular site. It is an open-source, command line, Python-based tool that produces an output Excel spreadsheet as well as several types of plots to enable a user to compare different microgrid system sizes and costs. It is intended for high-level system planning and opportunity identification, and not for detailed electric system modeling and design. The tool includes a range of input parameters that can be adjusted or tuned to provide a more custom analysis as needed.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2016

Sensitivity Analysis of Passive Heat Removal Capability of FHR Based on Efficient Surrogate Model and GenFlow

Code Scaling, Applicability and Uncertainty Evaluation Methodology (CSAU) is used for quantifying reactor safety margins and works as an alternative method for licensing nuclear power plant. In the framework of CSAU, sensitivity and uncertainty analysis (SA and UA) should be performed. However, for large-scale complicated thermal hydraulics analysis simulated by code (e.g. RELAP5, CFD and subchannel analysis, the sensitivity and uncertainty analysis is really time-consuming. Furthermore, sometimes it is not feasible to perform SA and UA. Specifically, the computation time for Monte Carlo sampling or advanced LHS sampling can be neglected, but the computation time for thermal hydraulics code makes up main part of time during SA and UA process. In order to accelerate the speed of SA and UA, thermal hydraulics code can be improved based on advanced mathematic algorithm or using parallel method. However, the development for new mathematic method needs long time and strict verification and validation process is needed for a new code, which usually takes a couple of years or even longer time to have an approved code that can be used in nuclear industry. An alternative way to improve the speed of SA and UA is using surrogate model. The surrogate model is derived from the input as well as output data of the original code, assuming the code as a black box. The most common method is using response surface method (RSM) as a surrogate for the code, the input parameters are then sampled from their assumed distribution and the corresponding value of the response surface function is calculated. However, a couple of input parameters need hundreds or even thousands of original code calculations. The large number of runs is an obvious disadvantage of this method. Besides, the second disadvantage is an appropriate response surface function must be selected (e.g. quadratic, cubic, quartic, with or without cross product terms, or using a non-polynomial form) to avoid the overfit and underfit problem. This process is time consuming and expensive. Polynomial chaos expansion method is a reasonably fast and attractive alternative, stochastic model. The chaos expansion provides an efficient high-order method of including non-linear effects in stochastic analysis. Usually, the information about the model parameters and their probability distribution is limited and the dependence between them is not known. However, if arbitrary polynomial chaos is used, instead of assuming probability distribution for input parameters which may introduces additional subjectivity into the analysis, the available information can directly and most purely be used in stochastic analysis based on arbitrary data distribution. Thus, it is not necessary to know the exact probability density functions to perform SA and UA based on arbitrary polynomial chaos (aPC). The aPC has been applied for uncertainty quantification in many fields (e.g. in solid mechanics, fluid flows, thermal sciences), and become widely used in nuclear industry recently. Mk1 PB-FHR is a small modular fluoride-salt-cooled, high-temperature reactors (FHRs) that is a concept design by University of California, Berkeley (UCB). It has inherently safe features like natural-circulation-driven systems (e.g. direct reactor auxiliary cooling system (DRACS)) to passively remove decay heat from the reactor core during design basis and beyond design. It is necessary to perform SA and UA, since the licensing process of this new kind of reactor is under way and there are many design parameters that have not been fixed. Current nuclear commercial codes, such as RELAP5, have mainly been developed for light water reactors, which require the use of comprehensive two-phase flow models. This capability is not required to simulate single-phase flow phenomena in FHRs, whose baseline coolant, flibe, remains liquid up to 1430 deg. C at atmospheric pressure. The FHR advanced natural circulation analysis (FANCY) code has been developed specifically for FHR technology modeling and validation work has been performed by using CIET data in UCB. The code has been renamed General Flow (GenFlow) because it has been further developed to have the capability to model complicated pipework system based on object-oriented concept for user friendly purpose. Besides, it can be used to simulate both the force circulation as well as natural circulation. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2023

Mallat Scattering Transformation based surrogate for Magnetohydrodynamics

Abstract A Machine and Deep Learning (MLDL) methodology is developed and applied to give a high fidelity, fast surrogate for 2D resistive MagnetoHydroDynamic (MHD) simulations of Magnetic Liner Inertial Fusion (MagLIF) implosions. The resistive MHD code is used to generate an ensemble of implosions with different liner aspect ratios, initial gas preheat temperatures (that is, different adiabats), and different liner perturbations. The liner density and magnetic field as functions of x , y , and z were generated. The Mallat Scattering Transformation (MST) is taken of the logarithm of both fields and a Principal Components Analysis (PCA) is done on the logarithm of the MST of both fields. The fields are projected onto the PCA vectors and a small number of these PCA vector components are kept. Singular Value Decompositions of the cross correlation of the input parameters to the output logarithm of the MST of the fields, and of the cross correlation of the SVD vector components to the PCA vector components are done. This allows the identification of the PCA vectors vis-a-vis the input parameters. Finally, a Multi Layer Perceptron (MLP) neural network with ReLU activation and a simple three layer encoder/decoder architecture is trained on this dataset to predict the PCA vector components of the fields as a function of time. Details of the implosion, stagnation, and the disassembly are well captured. Examination of the PCA vectors and a permutation importance analysis of the MLP show definitive evidence of an inverse turbulent cascade into a dipole emergent behavior. The orientation of the dipole is set by the initial liner perturbation. The analysis is repeated with a version of the MST which includes phase, called Wavelet Phase Harmonics (WPH). While WPH do not give the physical insight of the MST, they can and are inverted to give field configurations as a function of time, including field-to-field correlations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

A science-driven approach to optimize the design for a biological small-angle neutron scattering instrument

Biological small-angle neutron scattering (SANS) instruments facilitate critical analysis of the structure and dynamics of complex biological systems. However, with the growth of experimental demands and the advances in optical systems design, a new neutron optical concept is necessary to overcome the limitations of current instruments. This work presents an approach to include experimental objectives ( i.e. the science to be supported by a specific neutron scattering instrument) in the optimization of the neutron optical concept. The approach for a proposed SANS instrument at the Second Target Station of the Spallation Neutron Source at Oak Ridge National Laboratory, USA, is presented here. Further, the instrument is simulated with the McStas software package. The optimization process is driven by an evolutionary algorithm using McStas output data, which are processed to calculate an objective function designed to quantify the expected performance of the simulated neutron optical configuration for the intended purpose. Each McStas simulation covers the complete instrument, from source to detector, including realistic sample scattering functions. This approach effectively navigates a high-dimensional parameter space that is otherwise intractable; it allows the design of next-generation SANS instruments to address specific scientific cases and has the potential to increase instrument performance compared with traditional design approaches.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

BD-CATS-IO

Modern cosmology and plasma physics codes are now capable of simulating trillions of particles on petascale systems. Each timestep output from such simulations is on the order of 10s of TBs. Summarizing and visualizing raw particle data is challenging, and scientists often focus on density structures, whether in the real 3D space, or a high-dimensional phase space. In this work, code was developed for reading 10s of terabytes of data and for writing the resultant cluster IDs for each particle. This code will be part of a highly scalable version of the clustering algorithm DBSCAN, and apply it to the largest datasets produced by state-of-the-art codes. The overall function of BD-CATS (Big Data Clustering at Trillion Particles Scale) is to perform end-to-end analysis: loading the data, dynamic partitioning, computing kdtrees, performing clustering analysis, and storing the results. BD-CATS-IO provides the functionality of loading the data and storing the results.

Byna, Surendra↗
DOE OSTI2017

Performance of Energy Storage with Nuclear and Renewable Power Systems

Nuclear power represents a traditional baseload power component to the grid, while renewables output variable amounts of power at unpredictable times. Nuclear and renewable energy integration (NREI) systems have previously been explored as valuable additions to the existing power grid. Energy storage systems can allow power plants to store energy mechanically using pumped hydro or compressed air, thermally using liquid salt or steam accumulators, or chemically with batteries. The addition of energy storage to NREI systems can allow for the discharge of electricity to adjust to demands placed on the grid. The allocation of energy to adjust to grid demand is explored through a historical 8,760-hour analysis including nuclear, wind, and solar NREI systems with energy storage. The operation of storage systems is compared through simulations of different storage and discharge parameters based off the maximum daily grid demands, and hourly electricity prices. The overall profitability of storage systems is determined by comparing simulation results for the overall net revenue increases exceeding storage efficiency losses. Several NREI systems have increased the value of electricity for both solar PV and wind energy, in combination with nuclear power. Optimization of these systems is developed using MATLAB code and the simulations of the most profitable results are explored.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Multioutput Convolutional Neural Network for Improved Parameter Extraction in Time-Resolved Electrostatic Force Microscopy Data

Time-resolved scanning probe microscopy methods, like time-resolved electrostatic force microscopy (trEFM), enable imaging of dynamic processes ranging from ion motion in batteries to electronic dynamics in microstructured thin film semiconductors for solar cells. Reconstructing the underlying physical dynamics from these techniques can be challenging due to the interplay of cantilever physics with the actual transient kinetics of interest in the resulting signal. Previously, quantitative trEFM used empirical calibration of the cantilever or feed-forward neural networks trained on simulated data to extract the physical dynamics of interest. Both these approaches are limited by interpreting the underlying signal as a single exponential function, which serves as an approximation but does not adequately reflect many realistic systems. Here, we present a multi-branched, multi-output convolutional neural network (CNN) that uses the trEFM signal in addition to the physical cantilever parameters as input. The trained CNN accurately extracts parameters describing both single-exponential and bi-exponential underlying functions, and more accurately reconstructs real experimental data in the presence of noise. This article demonstrates an application of physics-informed machine learning to complex signal processing tasks, enabling more efficient and accurate analysis of trEFM.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Peculiarities of Forming a Multiband Transmission Function Based on Multifrequency Acousto-Optic Diffraction

An analytical solution for the transmission function of noncoherent wideband radiation is obtained under acousto-optic (AO) filtering using a discrete set of monochromatic AO waves with a small spectral overlap. We studied characteristics of the AO transformation of a continuous spectrum of noncoherent radiation into a given set of discrete narrow bands of spectral transmission by excitation of a discrete set of sound frequencies. We carried out the analysis of transmission functions of individual channels taking into account a partial overlap of their spectra and possible intermodulation distortions. It is shown that a stationary value of the root-mean-square light power is found at the electronic output due to the photoelectric transformation and detecting diffracted light. Based on this, a necessary stationary, multiband, and nearly equidistant transmission function of a device can be formed by using a relevant spectrum of acoustic excitation. Peculiarities of this way of forming the multiband transmission function are revealed: the limitation of diffraction efficiency for an individual channel, the possibility of decoupling side lobes of adjacent channels, etc. A multiband acousto-optic filter (MAOF) was simulated that was based on a paratellurite monocrystal (TeO{sub 2}), which was previously used for experimental optical encoding. The theoretical and experimental results are in gratifying agreement.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Hybrid Natural Gas Geothermal Combined Cycle Power Plant Analysis

Low temperature geothermal resources, including those associated with oil and gas production, are an underutilized source of low carbon energy. The present work investigates coupling of low-temperature geothermal resources with concentrating solar and/or natural gas energy sources to increase the number of locations at which power generation from low temperature geothermal resources would be technically and economically viable. Stand-alone and hybrid geothermal power cycles are simulated using SimTech IPSEpro process modeling software. Design point strategies for a hybrid power cycle that may operate with either a single heat source or two simultaneous heat input sources are considered. Additionally, off-design power plant operation is investigated to consider the impacts of the heat source availability and ambient temperature variations. The off-design analysis utilizes a modeling tool that predicts power plant performance at each time step as a function of topping cycle heat input (from gas or solar), bottoming cycle heat input (from geothermal), and ambient temperature. Addition of a steam topping cycle to an organic Rankine cycle geothermal power plant provides opportunities to increase the efficiency and power output relative to a stand-alone geothermal power plant. Additionally, use of the waste heat from gas turbine power generation in a geothermal bottoming cycle provides opportunities to increase the amount of power generation associated with each unit of carbon dioxide emitted. This paper will describe the hybrid plant configuration evaluated, discuss the predicted power cycle performance, and compare with stand-alone natural gas and geothermal power generation cases. The power cycle investigated is expected to be applicable for use with conventional hydrothermal resources as well as with geologic thermal energy storage applications and/or enhanced geothermal systems. The steam topping cycle could use a concentrated solar heat source for fully renewable hybrid plant configuration. A plant initially constructed with a natural gas topping cycle heat source could be converted to a solar heat source part way though the power cycle operational life to achieve life cycle carbon emission reductions.

concentrating solar power↗
NASA NTRSDate not supplied

Web-based Visualization and Analytics of Petascale Data: Equity as a Tide that Lifts All Boats

Scientists generate petabytes of data daily to help uncover environmental trends or behaviors that are hard to predict. For example, understanding climate simulations based on the long-term average of temperature, precipitation, and other environmental variables is essential to predicting and establishing root causes of future undesirable scenarios and assessing possible mitigation strategies. While supercomputer centers provide a powerful infrastructure for generating petabytes of simulation output, accessing and analyzing these datasets interactively remains challenging on multiple fronts. This paper presents an approach to managing, visualizing, and analyzing petabytes of data within a browser on equipment ranging from the top NASA supercomputer to commodity hardware like a laptop. Our novel data fabric abstraction layer allows user-friendly querying of scientific information while hid-ing the complexities of dealing with file systems or cloud services.We also optimize network utilization while streaming from petas-cale repositories through state-of-the-art progressive compression algorithms. Based on this abstraction, we provide customizable dashboards that can be accessed from any device with any inter-net connection, enabling interactive visual analysis of vast amounts of data to a wide range of users - from top scientists with access to leadership-class computing environments to undergraduate students of disadvantaged backgrounds from minority-serving institutions. We focus on NASA’s use of petascale climate datasets as an example of particular societal impact and, therefore, a case where achieving equity in science participation is critical. We validate our approach by improving the ability of climate scientists to visually explore their data via two fully interactive dashboards. We further validate our approach by deploying the dashboards and simplified training materials in the classroom at a minority-serving institution.These dashboards, released in simplified form to the general public, contribute significantly to a broader push to democratize the access and use of climate data.

Data visualization↗
DOE OSTI2019

Relating Convection to GCM Grid-Scale Fields Using Cloud-Resolving Model Simulation of a Squall Line Observed during MC3E Field Experiment

In this study, a WRF (Weather Research and Forecasting) model is used as a cloud-resolving model to simulate a squall line observed on 20 May 2011 in the Southern Great Plains (SGP) of the United States. The model output is then used to examine the relationships between convective precipitation and coarse-grained variables averaged over a range of subdomain sizes equivalent to various global climate model horizontal resolutions. The objective is to determine to what extent convection within the subdomains can be related to these “large-scale” variables, thus that they can potentially serve as closure in convective parameterization. Results show that convective precipitation is well correlated with the vertical velocity at 500 hPa, column integrated moisture convergence and CAPE change due to large-scale advective forcing (dCAPE) for various subdomain sizes, but the correlation decreases with decreasing subdomain size. dCAPE leads convective precipitation for all subdomain sizes examined; however, the lead time decreases with decreasing subdomain size. Moisture convergence leads convective precipitation for subdomain sizes greater than 32 km but has no lead time for smaller subdomain sizes. Mid-tropospheric vertical velocity has no lead time or slightly lags convective precipitation. The lead/lag composite analysis with respect to maximum precipitation time indicates that peaks of large-scale variables increase with decreasing subdomain size. The peaks of 500 hPa vertical velocity and column integrated moisture convergence occur at the same time as maximum precipitation, but maximum dCAPE leads maximum precipitation by twelve minutes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Residential Vehicle-to-Home Backup Power Capabilities: Key Findings from a ComEd Beneficial Electrification R&D Pilot

This report summarizes key findings from a collaborative technical study of residential, non-grid-tied vehicle-to-home (V2H) backup power systems in Commonwealth Edison’s (ComEd’s) service territory. The work integrates (1) a feeder-level technoeconomic analysis (TEA) using historical outage-event data and simulated electric-vehicle (EV) driving/charging profiles to estimate potential reliability and customer interruption-cost impacts under V2H and vehicle-to-grid (V2G) adoption scenarios; (2) controlled laboratory performance testing of a representative V2H backup ecosystem to characterize transfer-to-backup behavior, sustained power delivery, efficiency trends, and repeatable reliability limitations; and (3) a cybersecurity assessment aligned with NIST Cybersecurity Framework (CSF) 2.0 and ISO/SAE 21434 to evaluate interface-level risk drivers and identify program-relevant mitigations. Results indicate that V2H can provide measurable resilience value, but outcomes are strongly context dependent on outage patterns and the share of events that are “V2H-applicable.” Typical transfer-to-backup behavior clustered on the order of minutes, but rare long-delay edge cases were observed (including an event approaching 30 minutes) and should be treated as a reliability risk. High-power testing showed that peak-rated output is not necessarily continuously deliverable; stable operation may require operation below nameplate ratings and attention to thermal and installation constraints. The cybersecurity assessment highlights a broad attack surface spanning commissioning, home networks, embedded services, and cloud/OTA pathways, motivating minimum controls for secure onboarding, signed updates, patch cadence, and coordinated vulnerability response for any scaled deployment.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2021

Estimating Spatial Distribution Impacts of Rooftops Solar PV on Dynamic Hosting Capacity Evaluation for a Real Distribution Feeder

This paper models the sensitivity of dynamic hosting capacity analysis to the spatial distribution of distributed PV scenarios, random, close and far from substation deployments, at various penetration levels on a real distribution feeder, in a quasi-static time series (QSTS) power flow simulation using high resolution time-aware metrics. These spatial distribution scenarios were chosen to capture a wide range of potential operation impacts of these deployments using a set of thermaland voltage-based metrics such as instantaneous violations and moving averages for both thermal and voltage constraints. This study uses actual load and PV data up-scaled from 1-hour time step to 1-minute resolution to fully characterize the interaction between the daily changes in load and PV output, and their impacts on distribution system operations.

distribution system↗
DOE OSTI2023

Mitigating the noise of DESI mocks using analytic control variates

In order to address fundamental questions related to the expansion history of the Universe and its primordial nature with the next generation of galaxy experiments, we need to model reliably large-scale structure observables such as the correlation function and the power spectrum. Cosmological N-body simulations provide a reference through which we can test our models, but their output suffers from sample variance on large scales. Fortunately, this is the regime where accurate analytic approximations exist. To reduce the variance, which is key to making optimal use of these simulations, we can leverage the accuracy and precision of such analytic descriptions using Control Variates (CV). The power of control variates stems from utilizing inexpensive but highly correlated surrogates of the statistics one wishes to measure. The stronger the correlation between the surrogate and the statistic of interest, the larger the variance reduction delivered by the method. We apply two control variate formulations to mock catalogs generated in anticipation of upcoming data from the Dark Energy Spectroscopic Instrument (DESI) to test the robustness of its analysis pipeline. Our CV-reduced measurements offer a factor of 5-10 improvement in the measurement error compared with the raw measurements. We explore the relevant properties of the galaxy samples that dictate this reduction and comment on the improvements we find on some of the derived quantities relevant to Baryon Acoustic Oscillation (BAO) analysis.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Requirements Description of EvaluateFlux

This report presents the modeling and simulation capabilities of Argonne National Laboratory’s EvaluateFlux code [1] that is used in present reactor analysis activities. These capabilities will then be used to establish the set of EvaluateFlux verification tasks necessary to verify EvaluateFlux for usage on commercial projects. A similar approach was taken for the PERSENT, REBUS and DIF3D software packages. The EvaluateFlux program is a post-processing utility program for DIF3D, specifically DIF3D-VARIANT. As covered in [1], the EvaluateFlux program was built to allow users to obtain flux and power traverses through the domain. Its primary purpose was to facilitate foil analysis by evaluating the flux solution from DIF3D-VARIANT and combining it with foil cross section data. The outputs from EvaluateFlux are contained in four possible output files: FluxAndRegionRR.out, IsotopeMicroRR.out, LabeledRegionRR.out, and IsotopeMacroRR.out. Each file is focused on using the flux evaluate and cross section data in a different manner and all are covered in this verification process.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS1993

Automated propellant leak detection

An automated hydrogen leak detection system is being developed for earth-to-orbit rocket engine applications. The system consists of three elements, a sensor array, a signal processing unit, and a diagnostic processor. The sensor array consists of discrete solid state sensors which are located at specific potential leak sites and in potential leak zones. The signal processing unit provides excitations power for the sensors and provides analog to digital data conversion of the sensor signals. The diagnostic computer analyzes the sensor outputs to determine leak sources and magnitude. Leak data from the sensor network is interpreted using knowledge based software and displayed on-line through a graphical user interface including 3-D leak visualization. The system requirements have been developed assuming eventual application to the Space Shuttle Main Engine which requires approximately 72 measurement locations. A prototype system has been constructed to demonstrate the operational features of the system. This system includes both prototype electronics and data processing software algorithms. Experiments are in progress to evaluate system operation at conditions which simulate prelaunch and flight. The prototype system consists of a network using 16 sensors within a testbed which simulates engine components. Sensor response, orientation, and data analysis algorithms are being evaluated using calibrated leaks produced within the testbed. A prototype flight system is also under development consisting of 8 sensors and flight capable electronics with autonomous control and data recording.

Makel, D. B.↗
NASA NTRS1994

Investigating and implementing enhancements to the simulation of short-term collision hazards

A software tool, the Relative Collision Matrix (RCM), has been developed to provide a quick-look representation of the shortterm collision hazard to space systems from a fragmentation event in Earth orbit. The software performs multiple fragmentation simulations of space objects to quantify the probability of collision for a satellite or a constellation of satellites nearby. Previously, the results were displayed in a color matrix format which showed the relative hazard of each constellation. The RCM can be used for scientific research and operational assessments even though it was designed for test and evaluation applications. Because of its successful use as an analytical tool, the capabilities of RCM are being extended by enhancing the orbital hazard analysis routines, developing ballistic trajectory hazard analysis routines, and expanding the breakup modeling. Improvements are also being made to the RCM's usability and presentation quality by developing a graphical user interface and by providing graphical animated and nonanimated output.

Huth, Jeffrey S.↗