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DOE OSTI2021

Artificial Intelligence for Multiphysics Nuclear Design Optimization with Additive Manufacturing

The geometric flexibility of additively manufactured metals and ceramics generates a very large and open design space that requires advanced modeling and simulation tools for physics simulations and the rigorous definition of design problems. This effort deploys artificial intelligence (AI) and machine learning (ML) algorithms to understand the design space, evaluate potential designs, and more efficiently generate optimized results. The Transformational Challenge Reactor (TCR) program is leveraging advances in several scientific areas—including materials, manufacturing, sensors and control systems, data analytics, and high-fidelity modeling and simulation—to accelerate the design, manufacturing, qualification, and deployment of advanced nuclear energy systems. Through a manufacturing-informed design approach, the TCR program seeks to integrate digital data for rapid nuclear innovation; accelerate the adoption of advances in manufacturing, materials, and computational sciences for nuclear applications; and dramatically reduce deployment costs and timelines for new nuclear reactor technologies. This report documents efforts under the TCR program to leverage advanced modeling and simulation techniques driven by AI/ML algorithms on high-performance computing (HPC) systems to yield more optimized TCR core designs. A multiphysics ML surrogate model was developed to run on the HPC architectures. The surrogate model is trained on high-fidelity simulation data of coupled neutronics and thermofluidics and is used to quickly evaluate thousands of candidate core designs in parallel, which drives the evolution of the cooling channel shapes to minimize temperature peaking and material stress. Outcomes from these activities provide design information and feedback into the core design efforts.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

NASA NTRS2008

Optical Properties of Astronomical Silicates in the Far-infrared

Correct interpretation of a vast array of astronomical data relies heavily on understanding the properties of silicate dust as a function of wavelength, temperature, and crystallinity. We introduce the QPASI-T (Optical Properties of Astronomical Silicates with Infrared Techniques) project to address the need for high fidelity optical characterization data on the various forms of astronomical dust. We use two spectrometers to provide extinction data for silicate samples across a wide wavelength range (from the near infrared to the millimeter). New experiments are in development that will provide complementary information on the emissivity of our samples, allowing us to complete the optical characterization of these dust materials. In this paper, we present initial results from several materials including amorphous iron silicate, magnesium silicate and silica smokes, over a wide range of temperatures, and discuss the design and operation of our new experiments.

Rinehart, Stephen A,↗

DOE OSTI2025

A Scalable Multi-Modal Framework for High-Fidelity Distributed Human Mobility Simulations

The development of data-driven models for human mobility in urban settings requires access to substantial and diverse real-world data. However, existing historical data often presents challenges such as limited volume, variety, and veracity, as well as missing data and privacy preservation concerns. Also, urban mobility modeling is inherently time-variant, complex, and multi-modal, encompassing everything from individual walking and running to private road travel and large-scale public transportation. These challenges call for innovative solutions to overcome data limitations and compute needs to model mobility behaviors accurately. To address these challenges, we propose a distributed, co-simulation-based architecture DURMOSim that integrates real-world data with scalable, high-fidelity simulations, demonstrating distributed co-simulation feasibility with existing mobility models. DURMOSim underpins a modular integration that would enable using any available mobility simulators for greater extensibility and scalability in performing various urban scenarios. In this paper, we present the design, implementation, and performance evaluation of DURMOSim, highlighting its capability to model population-scale mobility patterns. Our initial results show its ability to dynamically synchronize multiple simulation models at runtime with negligible computational overhead. We believe DURMOSim could be a robust tool for advancing urban mobility research and intelligent transportation systems.

Yoginath, Srikanth [ORNL] (ORCID:0000000184236050)↗

DOE OSTI2024

A study of the Rayleigh-Taylor Instability during deceleration in inertial confinement fusion relevant conditions [Slides]

Large Planar targets with significant room for RTI evolution could have the potential to reach regimes where magnetic field damping of RTI is observable. Hot-electron generation at 1 PW/cm 2 and greater can introduced substantial preheat to experimental targets. Manufacturing defects can create further 3D complexity in targets designed and modeled in 2D. Computational models used in experimental designing do not always account for all the physics present and must be reevaluated based on experimental data. 3D modeling and provide high fidelity estimates on what the experimental data will look like, and can be utilized for high fidelity bench-marking or complex 3D experimental design work.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

NASA NTRS2018

Objective Motion Cueing Criteria for Commercial Transport Simulators

This paper adds data to establish fidelity criteria for the simulator motion system diagnostic test now required during commercial aircraft simulator approval in the United States. Nineteen airline transport pilots flew three tasks under six different motion conditions in an experiment on the NASA Vertical Motion Simulator. The motion conditions allowed refinement of the initial fidelity criteria developed in previous experiments. In line with these previous experiments, the motion condition significantly affected (1) false motion cue pilot ratings, and sink rate and longitudinal deviation at touchdown in the approach and landing task, (2) false motion cue pilot ratings, roll deviations, and maximum pitch rate in the stall task, and (3) false motion cue pilot ratings, heading deviation, and pedal reaction time after an engine failure in the take-off task. Combining data from three experiments, significant differences in pilot-vehicle performance were used to define objective motion cueing criteria boundaries. These fidelity boundaries suggest that some hexapod simulators can possibly produce motion cues with improved fidelity in several degrees of freedom.

simulator motion↗

NASA NTRS2018

Objective Motion Cueing Criteria for Commercial Transport Simulators

pilot performance↗

DOE OSTI2026

Surrogate Distributed Radiological Sources—Part III: Quantitative Distributed Source Reconstructions

In this third part of a multi-paper series, we present quantitative image reconstruction results from aerial measurements of eight different surrogate distributed gamma-ray sources on flat terrain. Here, we show that our quantitative imaging methods can accurately reconstruct the expected shapes, and, after appropriate calibration, the absolute activity of the distributed sources. We conduct several studies of imaging performance versus various measurement and reconstruction parameters, including detector altitude and raster pass spacing, data and modeling fidelity, and regularization type and strength. The imaging quality performance is quantified using various quantitative image quality metrics. Our results confirm the utility of point source arrays as surrogates for truly distributed radiological sources, and advance the quantitative capabilities of Scene Data Fusion gamma-ray imaging methods.

Airborne survey↗

DOE OSTI2024

Assessing and benchmarking the fidelity of posterior inference methods for astrophysics data analysis

In this era of large and complex astronomical survey data, interpreting, validating, and comparing inference techniques becomes increasingly difficult. This is particularly critical for emerging inference methods like Simulation-Based Inference (SBI), which offer significant speedup potential and posterior modeling flexibility, especially when deep learning is incorporated. We present a study to assess and compare the performance and uncertainty prediction capability of Bayesian inference algorithms – from traditional MCMC sampling of analytic functions to deep learning-enabled SBI. We focus on testing the capacity of hierarchical inference modeling in those scenarios. Before we extend this study to cosmology, we first use astrophysical simulation data to ensure interpretability. We demonstrate a probabilistic programming implementation of hierarchical and non-hierarchical Bayesian inference using simulations derived from the DeepBench software library, a benchmarking tool developed by our group that generates simple and controllable astrophysical objects from first principles. This study will enable astronomers and physicists to harness the inference potential of these methods with confidence.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Leaf Wax Hydrogen Isotopes Reflect Storm Track Position Over Western North America

Proxy records of past climates have yielded powerful insights into regional hydroclimate dynamics. Novel insights may be gained by reconstructing spatiotemporal changes in precipitation isotopologues in past climate states that cannot be gleaned from individual site‐level studies. In this paper, we ask whether latitudinal gradients in the stable hydrogen isotopic composition of precipitation, as inferred from sedimentary leaf wax biomarkers, reflect aspects of large‐scale climate conditions in western North America (WNA). Modern coretop samples from offshore WNA show that leaf wax hydrogen isotopes broadly track the hydrogen isotopic composition of rainfall between 20 and 40N, but with an offset corresponding to a relatively constant “apparent fractionation” value. Poleward of 40N, the leaf wax signal may be complicated by fluvial transport of leaf waxes from the continental interior. Leaf wax‐inferred precipitation hydrogen isotopes show a shift to more negative values between 30 and 40N. We use modern observational data to show that this latitudinal range marks a major transition between the subtropical arid zone and the location of the midlatitude storm tracks over the northeast Pacific. Nudged water isotope‐enabled models capture the location of the arid to mesic climate transition with fidelity. This modern data set suggests that reconstructions of the latitudinal gradient of precipitation hydrogen isotopes can constrain the sensitivity of the storm tracks to shifts in climatic boundary conditions in past climate states. This approach can yield novel insights into past, present, and future hydroclimate variability in this arid region.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRS2023

Statistical Decomposition and Machine Learning to Clean In Situ Spaceflight Magnetic Field Measurements

Robust in situ magnetic field measurements are critical to understanding the various mechanisms that couple mass, momentum, and energy throughout our solar system. However, the spacecraft on which magnetometers are often deployed contaminate the magnetic field measurements via onboard subsystems including reaction wheels and magnetorquers. Two magnetometers can be deployed at different distances from the spacecraft to determine an approximation of the interfering field for subsequent removal, but constant data streams from both magnetometers can be impractical due to power and telemetry limitations. Here we propose a method to identify and remove time-varying magnetic interference from sources such as reaction wheels using statistical decomposition and convolutional neural networks, providing high-fidelity magnetic field data even in cases where dual-sensor measurements are not constantly available. For example, a measurement interval from the Parker Solar Probe outboard magnetometer experienced a 95.1% reduction in reaction wheel interference following application of the proposed technique.

magnetic field data↗

NASA NTRS2011

Metric Learning for Hyperspectral Image Segmentation

We present a metric learning approach to improve the performance of unsupervised hyperspectral image segmentation. Unsupervised spatial segmentation can assist both user visualization and automatic recognition of surface features. Analysts can use spatially-continuous segments to decrease noise levels and/or localize feature boundaries. However, existing segmentation methods use tasks-agnostic measures of similarity. Here we learn task-specific similarity measures from training data, improving segment fidelity to classes of interest. Multiclass Linear Discriminate Analysis produces a linear transform that optimally separates a labeled set of training classes. The defines a distance metric that generalized to a new scenes, enabling graph-based segmentation that emphasizes key spectral features. We describe tests based on data from the Compact Reconnaissance Imaging Spectrometer (CRISM) in which learned metrics improve segment homogeneity with respect to mineralogical classes.

Compact Reconnaissance Imaging Spectrometer (CRISM↗

DOE OSTI2023

High-Resolution Southeast Asia Wind Resource Data Set

Well informed decision-making is a key part of integrating variable renewable energy into the global energy marketplace. USAID and NREL, through the Advanced Energy Partnership for Asia, are expanding access to critical resource data by providing free, high-fidelity time-series wind resource data for Southeast Asia through the RE Data Explorer platform. This brief highlights the development of the Southeast Asia wind resource data set and discusses the impacts of this data.

Advanced Energy Partnership for Asia↗

DOE OSTI2023

Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design

Enhanced water management systems depend on accurate estimation of subsurface hydraulic properties. However, geologic formations can vary significantly, so information from a single source (e.g., widely spaced boreholes) is insufficient in characterizing subsurface aquifer properties. Therefore, multiple sources of information are needed to complement the hydrogeology understanding of a region. Here, this study presents a numerical framework in which information from different measurement sources is combined to characterize the 3D random field in a multi-fidelity prediction model. Coupled with the model, a Bayesian experimental design was used to determine the best future sampling locations. The Upper Sangamon watershed in east-central Illinois was selected as the case study site, where the multi-fidelity Gaussian process model was used to estimate the hydraulic conductivity in the region of interest. Multi-source observation data were obtained from electrical resistivity and borehole pumping tests. The accuracy of the model prediction is dependent on the locations and the distribution of both high- and low-fidelity data. Furthermore, the multi-fidelity model was compared with the single-fidelity model. The uncertainties and confidence in the measurements and parameter estimates were quantified and used to design future cycles of data collection to further improve the confidence intervals.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Hourly PM 2.5 Estimates across California from 2018 to 2023

This study presents a new data set of hourly PM 2.5 concentrations across California from 2018 to 2023 at a three-kilometer resolution. This data set was developed by assimilating observations from PurpleAir and the U.S. EPA Air Quality System monitors into wildfire smoke forecasts from the High-Resolution Rapid Refresh Smoke (HRRR-Smoke) model using the Gridpoint Statistical Interpolation (GSI) three-dimensional variational data assimilation framework. Archived forecasts of modeled wildfire smoke PM 2.5 from HRRR-Smoke create the background field for assimilation, which is then corrected using surface observations of total PM 2.5 . The resulting reanalysis from GSI provides an estimate of total PM 2.5 that minimizes error from both the observational and the model data. Validation results indicate strong performance, with monthly R 2 values ranging from 0.73 to 0.91 across the six-year data set, comparable to other PM 2.5 data sets. Case studies are presented for three major fire events, the 2018 Camp Fire, 2019 Kincade Fire, and 2020 Lightning Complex Fires to demonstrate the data set’s fidelity in resolving plume dynamics and local exposure patterns. Root-mean-squared error averaged over each month scales with average PM 2.5 concentrations, resulting in a low error under typical conditions but higher absolute errors during extreme smoke events. This is the first long-term, hourly PM 2.5 data set of its kind for California and enables the generation of subdaily exposure metrics, such as peak hourly concentrations, exceedance durations, and time-of-day exposure peaks. The novelty and strong validation of this data set make it a compelling resource for future studies on the impact and significance of subdaily PM 2.5 exposure.

PM2.5↗

NASA NTRS1983

Comparison of simulator fidelity model predictions with in-simulator evaluation data

A full factorial in simulator experiment of a single axis, multiloop, compensatory pitch tracking task is described. The experiment was conducted to provide data to validate extensions to an analytic, closed loop model of a real time digital simulation facility. The results of the experiment encompassing various simulation fidelity factors, such as visual delay, digital integration algorithms, computer iteration rates, control loading bandwidths and proprioceptive cues, and g-seat kinesthetic cues, are compared with predictions obtained from the analytic model incorporating an optimal control model of the human pilot. The in-simulator results demonstrate more sensitivity to the g-seat and to the control loader conditions than were predicted by the model. However, the model predictions are generally upheld, although the predicted magnitudes of the states and of the error terms are sometimes off considerably. Of particular concern is the large sensitivity difference for one control loader condition, as well as the model/in-simulator mismatch in the magnitude of the plant states when the other states match.

Parrish, R. V.↗

NASA NTRS1986

Advances in spaceborne synthetic aperture radar sensor technology

The evolution of space SARs for NASA projects since Seasat (1978) is surveyed, with an emphasis on hardware development. The fundamental principles of SAR are reviewed; the SIR-A and SIR-B instruments flown as Shuttle payloads are characterized; their antennas, transmitters, receivers, and data subsystems are described; the advantages offered by the SIR-C dual-frequency (L and C band) dual-polarization distributed SAR (being developed for a future Shuttle flight and as the basis of an SAR for the Earth Observing System) are explained; and a number of technical challenges are identified (including RF elements, structural fidelity, pointing accuracy, data handling, and dc power). Drawings, diagrams, sample images, photographs, and tables are provided.

Caro, E. R.↗

NASA NTRS2003

System and method for the adaptive mapping of matrix data to sets of polygons

A system and method for converting bitmapped data, for example, weather data or thermal imaging data, to polygons is disclosed. The conversion of the data into polygons creates smaller data files. The invention is adaptive in that it allows for a variable degree of fidelity of the polygons. Matrix data is obtained. A color value is obtained. The color value is a variable used in the creation of the polygons. A list of cells to check is determined based on the color value. The list of cells to check is examined in order to determine a boundary list. The boundary list is then examined to determine vertices. The determination of the vertices is based on a prescribed maximum distance. When drawn, the ordered list of vertices create polygons which depict the cell data. The data files which include the vertices for the polygons are much smaller than the corresponding cell data files. The fidelity of the polygon representation can be adjusted by repeating the logic with varying fidelity values to achieve a given maximum file size or a maximum number of vertices per polygon.

Burdon, David↗

NASA NTRS2018

Strategies to Maximize Science Data Availability for the GOES-R Series of Satellites

The Geostationary Operational Environmental Satellite-R Series (GOES-R) is the next generation of Untied States geostationary weather satellites. The GOES-R series significantly improves the detection and observation of environmental phenomena that directly affect public safety, protection of property and the economic health and prosperity of the United States and all countries within the western hemisphere. Given the real-time or ''now-casting'' nature of the GOES science gathering mission, any data outage or interruption can reduce warning times or scientific fidelity for critical weather data. GOES-R mission level requirements limit key performance product outages to a total of six hours per year to maximize science data availability. Lower level requirement only allow for 120 minutes of disruption between the spacecraft bus interface to the instruments. This requirement is met using both design features of the satellite and ground system, in addition to operational strategies.

Dudley, Renee↗