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At least 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Survey of Dynamic Mode Decomposition Methods

Dynamic mode decomposition (DMD) is a data-driven reduced order modeling (ROM) technique used for dynamic systems. The widely adopted algorithm was first introduced and demonstrated on fluid flow data by Schmid. In recent years, various other fields, such as nuclear engineering, have begun to adopt this method. For example, DMD has been used for estimating α-eigenvalues, as an ROM for pulsed neutron problems, for predicting isotopic composition in burnup calculations, as acceleration techniques for iterative methods, and in capturing dynamic behaviors in molten salt reactor transients. This report seeks to demonstrate the capabilities and limits of the standard DMD algorithm, and identify problem spaces where variants may be better suited. The primary variant this report considers is Multi-Resolution DMD (mrDMD). Because this serves as a survey, synthetically produced data is used in lieu of simulation results. The remainder of this report will go into detail on the DMD theory, outline the standard DMD and mrDMD algorithms, present test cases highlighting the applicability of each, and finally present a discussion on how to determine the best suited algorithm for a given problem. All calculations performed in this report are carried out using the open source DMD library, PyDMD.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Enabling Efficient Surveillance, Control, and Automation of Geothermal Operations with Advanced Predictive Analytics

Automation and control of geothermal energy production and operations require reliable and efficient predictive tools. While physics-based simulation offers a comprehensive tool for predicting energy production performance in geothermal systems, predicting the behavior of geothermal reservoirs involves complex multi-physics processes with coupling effects, highly uncertain input parameters and subsurface descriptions. Moreover, building, running, and integrating simulation models into standard model calibration and optimization workflows entail significant technical and computational efforts. An emerging alternative to physics-based simulation is data-driven predictive analytics models that have gained popularity in energy industry. In this report, we develop novel predictive models for integration into real-time fault diagnosis and model predictive control algorithms to improve the efficiency of energy production operations in geothermal reservoirs. The report includes two major research Thrust Areas, that is, the surface power plant and the subsurface reservoir.

15 GEOTHERMAL ENERGY↗
DOE OSTI2021

Development and Application of a Data-Driven Methodology for Validation of Risk-Informed Safety Margin Characterization Models

The document is the Final Technical Progress Report for the Nuclear Energy University Program’s Integrated Research Project (IRP) on “Development and Application of a Data-Driven Methodology for Validation of Risk-Informed Safety Margin Characterization Models” that supports the LWR Sustainability Program’s RISMC R&D Pathway. The project goal is to develop and demonstrate a data-driven methodology for validation of advanced computer models used in nuclear power plant safety analysis. Specifically, the advanced computer models are those in the toolkit developed to support risk-informed safety margin characterization (RISMC), an integrated deterministic/probabilistic safety analysis methodology developed in the Department of Energy’s Light Water Reactor Sustainability (LWR-S) program. The report reflects the progress made towards the project’s stated goal by contributions by researchers and graduate students from universities, with support from researchers from national laboratories and industry companies. The project organization, effort coordination and technical implementation are summarized, followed by discussion of main findings, issues, and path forward. Selected chapters provide a more detailed description of tasks, approaches and respective findings and recommendations. Noteworthy are contributions that serve as guidelines for methodology development. It is also noted that this report is complemented by other milestone reports (as stand-alone deliverables) that provide detailed discussion of the technical developments. The project results have been documented in a number (12) dissertations and these, 50+ peer-reviewed publications in technical journals and conference proceedings.

42 ENGINEERING↗
DOE OSTI2024

A Data-driven approach to Core Power distribution reconstruction in a Nuclear Reactor

This report presents the initial development of a data-driven approach for reconstructing the core power distribution in a nuclear reactor (power shape synthesis) using ex-core sensors. Traditional techniques rely on deploying a large number of detectors throughout the reactor core. However, this approach is not feasible for innovative reactor concepts like Advanced Reactors and Microreactors. First, the tight lattice pitch, designed to maximize power density, limits the space available for sensors. Secondly, the harsh operating conditions are not compatible with commercially available detectors. The method proposed in this work integrates high-fidelity modeling with data-driven techniques to accurately reconstruct power distribution across various reactor types, thereby reducing the reliance on in-core sensors. Purdue University Reactor One (PUR-1) was selected as the test case. The CAD model representing the latest configuration of the PUR-1 core was imported into the OpenMC simulation framework, and the model was built. Additionally, the previously developed MCNP6 model was updated. The two models were assessed against the data collected during an experimental campaign conducted in July 2024. Thirty gold foils were placed in three Irradiation Assemblies in PUR-1 core. Using the measured activity of the irradiated foils, the neutron flux at different core locations was estimated.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2020

Object-Based Comparison of Data-Driven and Physics-Driven Satellite Estimates of Extreme Rainfall

The Global Precipitation Measurement (GPM) constellation of spaceborne sensors provides a variety of direct and indirect measurements of precipitation processes. Such observations can be employed to derive spatially and temporally consistent gridded precipitation estimates either via data-driven retrieval algorithms or by assimilation into physically based numerical weather models. We compare the data-driven Integrated Multisatellite Retrievals for GPM (IMERG) and the assimilation-enabled NASA-Unified Weather Research and Forecasting (NU-WRF) model against Stage IV reference precipitation for four major extreme rainfall events in the southeastern United States using an object-based analysis framework that decomposes gridded precipitation fields into storm objects. As an alternative to conventional ‘‘grid-by-grid analysis,’’ the object-based approach provides a promising way to diagnose spatial properties of storms, trace them through space and time, and connect their accuracy to storm types and input data sources. The evolution of two tropical cyclones are generally captured by IMERG and NU-WRF, while the less organized spatial patterns of two mesoscale convective systems pose challenges for both. NU-WRF rain rates are generally more accurate, while IMERG better captures storm location and shape. Both show higher skill in detecting large, intense storms compared to smaller, weaker storms. IMERG’s accuracy depends on the input microwave and infrared data sources; NU-WRF does not appear to exhibit this dependence. Findings highlight that an object-oriented view can provide deeper insights into satellite precipitation performance and that the satellite precipitation community should further explore the potential for ‘‘hybrid’’ data-driven and physics-driven estimates in order to make optimal usage of satellite observations.

extreme events↗
DOE OSTI2025

Data-driven high-dimensional statistical inference with generative models

Crucial to many measurements at the LHC is the use of correlated multi-dimensional information to distinguish rare processes from large backgrounds, which is complicated by the poor modeling of many of the crucial backgrounds in Monte Carlo simulations. In this work, we introduce HI-SIGMA, a method to perform unbinned high-dimensional statistical inference with data-driven background distributions. In contradistinction to many applications of Simulation Based Inference in High Energy Physics, HI-SIGMA relies on generative ML models, rather than classifiers, to learn the signal and background distributions in the high-dimensional space. These ML models allow for interpretable inference while also incorporating model errors and other sources of systematic uncertainties. We showcase this methodology on a simplified version of a di-Higgs measurement in the bbγγ final state, where the di-photon resonance allows for background interpolation from sidebands into the signal region. We demonstrate that HI-SIGMA provides improved sensitivity as compared to standard classifier-based methods, and that systematic uncertainties can be straightforwardly incorporated by extending methods which have been used for histogram based analyses.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2020

A priori analysis on deep learning of subgrid-scale parameterizations for Kraichnan turbulence

In the present study, we investigate different data-driven parameterizations for large eddy simulation of two-dimensional turbulence in the a priori settings. These models utilize resolved flow field variables on the coarser grid to estimate the subgrid-scale stresses. We use data-driven closure models based on localized learning that employs a multilayer feedforward artificial neural network with point-to-point mapping and neighboring stencil data mapping, and convolutional neural network fed by data snapshots of the whole domain. The performance of these data-driven closure models is measured through a probability density function and is compared with the dynamic Smagorinksy model (DSM). The quantitative performance is evaluated using the cross-correlation coefficient between the true and predicted stresses. We analyze different frameworks in terms of the amount of training data, selection of input and output features, their characteristics in modeling with accuracy, and training and deployment computational time. We also demonstrate computational gain that can be achieved using the intelligent eddy viscosity model that learns eddy viscosity computed by the DSM instead of subgrid-scale stresses. Here, we detail the hyperparameters optimization of these models using the grid search algorithm.

42 ENGINEERING↗
DOE OSTI2023

Efficient and Scalable Time-Stepping Algorithms and Reduced-Order Modeling for Ocean System Simulations (Scientific/Technical Report)

This report provides a description of major accomplishments and results obtained by the University of South Carolina/Florida State University/Los Alamos National Laboratory team participating in the project "Efficient and Scalable Time-Stepping Algorithms and Reduced-Order Modeling for Ocean System Simulations" and the list of publications produced from the project.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

The data-driven future of high-energy-density physics

High-energy-density physics is the field of physics concerned with studying matter at extremely high temperatures and densities. Such conditions produce highly nonlinear plasmas, in which several phenomena that can normally be treated independently of one another become strongly coupled. The study of these plasmas is important for our understanding of astrophysics, nuclear fusion and fundamental physics—however, the nonlinearities and strong couplings present in these extreme physical systems makes them very difficult to understand theoretically or to optimize experimentally. Here we argue that machine learning models and data-driven methods are in the process of reshaping our exploration of these extreme systems that have hitherto proved far too nonlinear for human researchers. Furthermore, from a fundamental perspective, our understanding can be improved by the way in which machine learning models can rapidly discover complex interactions in large datasets. From a practical point of view, the newest generation of extreme physics facilities can perform experiments multiple times a second (as opposed to approximately daily), thus moving away from human-based control towards automatic control based on real-time interpretation of diagnostic data and updates of the physics model. To make the most of these emerging opportunities, we suggest proposals for the community in terms of research design, training, best practice and support for synthetic diagnostics and data analysis.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2024

Physics-informed hybrid modeling methodology for building infiltration

Infiltration is responsible for one-third to one-half of the space conditioning load of a typical residential home, but the modeling of infiltration for building energy modeling is either represented by over-simplified equations or dependent on over-generalized rules of thumb. Here, this paper develops a physics-informed data-driven methodology for modeling infiltration using building-specific empirical measurements. The developed hybrid methodology combines machine-learning categorization and grey-box sub-modeling to improve the accuracy and generalization of commonly used grey-box infiltration models. The developed methodology excels at predicting infiltration by improving the ability to predict infiltration under unseen environmental conditions using machine learning algorithms with physical significance. In a case study conducted using the iUnit, a modular studio apartment experimental test facility located at the National Renewable Energy Laboratory, we use empirical airtightness measurements to fit an infiltration model using the developed methodology. We find that the developed methodology can improve the overall model accuracy by 43% and improve extrapolation by 38%, compared with the model based on the common grey-box infiltration equation. We also notice that the selected features can improve the performance of a pure machine-learning model, indicating that our methodology identifies the features with the most physical significance to infiltration modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Efficient and generalizable nested Fourier-DeepONet for three-dimensional geological carbon sequestration

Geological carbon sequestration (GCS) involves injecting CO2 into subsurface geological formationsfor permanent storage. Numerical simulations could guide decisions in GCS projects by predictingCO 2 migration pathways and the pressure distribution in storage formation. However, these simula-tions are often computationally expensive due to highly coupled physics and large spatial-temporalsimulation domains. Surrogate modelling with data-driven machine learning has become a promis-ing alternative to accelerate physics-based simulations. Among these, the Fourier neural operator(FNO) has been applied to three-dimensional synthetic subsurface models. Despite its good accuracyin simulating CO 2 plume migration, it requires large computational resources in training and alsolacks generalizability. Here, to further improve performance, we have developed a nested Fourier-DeepONet by combining the expressiveness of the FNO with the modularity of a deep operatornetwork (DeepONet). This new framework is twice as efficient as a nested FNO for training and has atleast 80% lower GPU memory requirement due to its flexibility to treat temporal coordinates sepa-rately. These performance improvements are achieved without compromising prediction accuracy.In addition, the generalization and extrapolation ability of nested Fourier-DeepONet beyond thetraining range has been thoroughly evaluated. Nested Fourier-DeepONet outperformed the nestedFNO for extrapolation in time with more than 50% reduced error. It also exhibited good extrapolationaccuracy beyond the training range in terms of reservoir properties, number of wells, and injectionrate.

Lee, Jonathan E. [Department of Chemical and Envir↗
DOE OSTI2021

Building an AI-enhanced modeling framework to address multiscale predictability challenges

Focal Area(s): Build an AI-enhanced modeling framework that integrates the three focus areas in the solicitation. Science Challenge: The 4M-2N complexities (Multiscale, Multiphysics, Multibody, Multidimension, Non-linearity, and Non-Gaussianality) of atmospheric aerosol-cloud-precipitation-turbulence-radiation system poses physical and computational challenges to further advance predictive models; We plan to address the challenges by developing an AI-enhanced modeling framework that facilitates automated calibration and improvement of subgrid parameterizations, enhance data assimilation of measurements to improve initial and boundary conditions used to drive the physical model, and optimally blends data-driven and physics-based forecasting models.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Multi-Model Monte Carlo Estimators for Trajectory Simulation

Predicting landing radius and other quantities of interest (QoI) for entry, descent, andlanding (EDL) applications requires a viable uncertainty propagation method for quantifying the impact of uncertainties in aerodynamics, atmosphere, mass properties, etc. While standard Monte Carlo (MC) simulation is the de facto standard for producing robust and unbiasedstatistical estimators, it is often infeasible for expensive, high-fidelity models. Low-fidelity models are commonly constructed to replace the high-fidelity model in MC simulation for computational speedup, but at the expense of accuracy and unbiasedness. Emerging multi-model MC methods are bridging this gap by combining predictions from two or more modelsof varying fidelity and computational cost for efficient and unbiased uncertainty propagation.This works establishes a proof of concept for using multi-model MC to increase the speed and precision of trajectory simulation for EDL. It is shown that combining a high-fidelity EDL model with low-fidelity models (e.g., data-driven, reduced physics) in this manner has the potential to yield significant efficiency and accuracy gains for certain EDL QoIs versusa standard MC approach. Moreover, the unbiasedness of multi-model MC predictions ishighlighted by showing increased accuracy versus an approach that leverages a low-fidelity model alone.

James E Warner↗
DOE OSTI2023

Data Driven Approach to Dislocation-Based Plasticity Models of Face-Centered Cubic Metals

Dislocation dynamics controls plastic deformation, mechanical strength, and failure of crystalline materials. It also governs fatigue resistance under cyclic loading, creep resistance at elevated-temperature, and radiation resistance for reactor applications. There is a compelling need for understanding fundamental dislocation mechanisms for deformation because virtually all structural metals used in energy systems are fabricated to desired forms and shapes by deformation processes. To date, the most outstanding problem in a physics-based multiscale model of crystal plasticity is the lack of quantitative connections between continuum plasticity (CP) models with the lower scale dislocation models. As a result, existing CP models used in engineering applications are still phenomenological, while evidence continues to mount that they can make inaccurate predictions under realistically complex scenarios. This project takes advantage of the recent advances in high-performance discrete dislocation dynamics (DDD) simulations and data science approaches to establish the first fully connected multiscale plasticity model for pure face-centered cubic (FCC) single crystals.

36 MATERIALS SCIENCE↗
DOE OSTI2025

High-Temperature Gas Sensor Materials with Properties Predicted via First-Principles Calculations with Machine Learning Modeling and Experimental Corroboration

Understanding the temperature dependence of functional properties of sensing materials is vital for their applications in combustion environments. The electron-phonon coupling that derives the electronic structure change with temperatures is a key property of interest as it affects other sensing responses. Herein, we first assess the temperature dependence of band gap renormalization in sensing materials by employing Allen-Heine-Cardona (AHC) theory with density functional theory (DFT) simulations corroborated with experimental observation. As the AHC calculations are impractical for high-throughput screening of materials, we employ data-driven Gaussian process regression to predict the parameters employed in the O’Donnell empirical model from a set of physical features. To mitigate the reliability issues arising from the small size of the dataset, we apply a Bayesian technique to improve the generalizability of the data-driven models as well as to quantify the uncertainty associated with theoretical predictions. These models capture well the overall trend of the O’Donnell parameters with respect to a reduced feature set obtained by transforming the available physical features. Quantifying the associated uncertainty helps us understand the reliability of the predictions and, therefore, the variation of bandgap as a function of temperature for other novel materials. The predicted candidates from machine learning models are further validated by experiments and DFT calculations.

bandgap renormalization↗
DOE OSTI2025

Autonomous phase mapping of gold nanoparticles synthesis with differentiable models of spectral shape

Autonomous experimentation–or self-driving labs–offers a systematic approach to accelerate materials discovery by integrating automated synthesis, characterization, and data-driven decision-making. We present a closed-loop workflow for the on-demand synthesis and structural characterization of colloidal gold nanoparticles, enabling direct mapping from composition to nanoscale structure. Our framework leverages differentiable models of spectral shape to address two central tasks in self-driving labs: (a) phase mapping, or identifying compositional regions with distinct structural behavior; and (b) material retrosynthesis, or optimizing compositions for target structure. Using functional data analysis, we develop a data-driven model with generative pre-training, active learning, and high-throughput experiments to predict spectral responses across composition space. We demonstrate the approach on seed-mediated growth of gold nanoparticles, showcasing its ability to extract design rules, reveal secondary interactions, and efficiently navigate morphology space. Gradient-based optimization of the models enables inverse design, making this a unified platform.

36 MATERIALS SCIENCE↗
NASA NTRS2003

Flight-Test Evaluation of Flutter-Prediction Methods

The flight-test community routinely spends considerable time and money to determine a range of flight conditions, called a flight envelope, within which an aircraft is safe to fly. The cost of determining a flight envelope could be greatly reduced if there were a method of safely and accurately predicting the speed associated with the onset of an instability called flutter. Several methods have been developed with the goal of predicting flutter speeds to improve the efficiency of flight testing. These methods include (1) data-based methods, in which one relies entirely on information obtained from the flight tests and (2) model-based approaches, in which one relies on a combination of flight data and theoretical models. The data-driven methods include one based on extrapolation of damping trends, one that involves an envelope function, one that involves the Zimmerman-Weissenburger flutter margin, and one that involves a discrete-time auto-regressive model. An example of a model-based approach is that of the flutterometer. These methods have all been shown to be theoretically valid and have been demonstrated on simple test cases; however, until now, they have not been thoroughly evaluated in flight tests. An experimental apparatus called the Aerostructures Test Wing (ATW) was developed to test these prediction methods.

Lind, RIck↗
NASA NTRS2014

Using Historical Data to Automatically Identify Air-Traffic Control Behavior

This project seeks to develop statistical-based machine learning models to characterize the types of errors present when using current systems to predict future aircraft states. These models will be data-driven - based on large quantities of historical data. Once these models are developed, they will be used to infer situations in the historical data where an air-traffic controller intervened on an aircraft's route, even when there is no direct recording of this action.

trajectory generation↗