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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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This software implements a data-driven framework to uncover mechanistic models of dynamics directly from time-resolved coherent X-ray scattering measurements. It employs neural differential equations to parameterize unknown real-space dynamics and incorporates a computational scattering forward model to relate real-space predictions to reciprocal-space observations.
Flavor remains one of the most successful yet least understood structures of the Standard Model. The discovery of the Higgs boson completed the electroweak account of mass generation, but did not explain the origin of fermion families, mass hierarchies, or mixing patterns. In this sense, flavor can be regarded as an empirically successful but conceptually incomplete structure. Neutrinos occupy a particularly sensitive place within this problem: their masses are tiny, their mixing is large, and their mass-generation mechanism may differ from that of charged fermions. In this article, we discuss flavor as an open conceptual problem and argue that DUNE, as a phased program spanning precision oscillation measurements and sensitivity to BSM and dark-sector phenomena, provides a powerful framework for testing the self-consistency and possible limits of the present three-flavor description. In particular, the complementarity between the long-baseline program and the Phase I near-detector complex, together with the DUNE-PRISM strategy for controlling interaction-model systematics and enabling data-driven near-to-far predictions, makes DUNE especially well-suited to search for small, correlated departures from the minimal flavor framework.
We present a comprehensive reevaluation of the ft values in superallowed nuclear β decays crucial for the precise determination of V ud and low-energy tests of the electroweak standard model. It consists of the first, fully data-driven analysis of the nuclear β decay form factor, that utilizes isospin relations to connect the nuclear charged weak distribution to the measurable charge distributions. This prescription supersedes previous shell-model estimations, and allows for a rigorous quantification of theory uncertainties in f which is absent in the existing literature. Furthermore, our new evaluation shows an overall downward shift of the central values of f at the level of 0.01%.
Focal Area: Focal Area 2. Predictive modeling through the use of AI techniques and AI-derived model components; the use of AI and other tools to design a prediction system comprising of a hierarchy of models (e.g., AI driven model/component/parameterization selection). Science Challenge: A major challenge exists in the lack of reliable predictive modeling of water-cycle extremes using macro-scale hydrologic models that are driven by atmospheric climate data. This causes a critical knowledge gap in understanding the magnitudes, probabilities, and severity of droughts, rainfall, and flooding. Improved understanding of these extreme events requires more accurate modeling at high spatial and temporal resolutions. This shortcoming is especially apparent when complex couplings between atmospheric quantities and engineered systems emerge, such as during extreme precipitation or drought events and at intersections of atmospheric-land-fluvial-ocean systems. Added complexity around representing operations of engineered infrastructure leads to a limited capability to analyze the temporal and spatial implications of compounding extreme events across inland and coastal regions. This results in profound challenges for water and power systems operators, agriculture production, and regional infrastructure planning. The science challenge can be summarized as How can we use AI/ML to improve short term predictability of extreme water-cycle events and associated risk, especially compounding events and extreme rainfall patterns that result in flooding, to inland and coastal communities under different climate scenarios?
Multiphase flow phenomena have been widely observed in the industrial applications while it remains a challenging yet unsolved problems. Three-dimensional computational fluid dynamics (CFD) approaches resolve the flow fields on a finer special and temporal scales which can complement the dedicated experimental study. However, closures have to be introduced to reflect the underlying physics in multiphase flow. Among them, the interfacial forces, including drag, lift, turbulent dispersion and wall lubrication forces, play in important role on the bubble’s distribution and migration in liquid-vapor two-phase flow. Development of those closures traditionally rely on the experimental data and analytical derivation with simplified assumptions which usually cannot deliver a universal solution across wide range of flow conditions. In this paper, a data-driven approach, named as Feature Similarity Measurement (FSM), is developed and applied to improve the simulation capability of two-phase flow with coarse-mesh CFD approach. Interfacial momentum transfer in adiabatic bubbly flow serves as the focus of the present study. Both a mature and a simplified set of interfacial closures are taken as the low fidelity data. Experimental data and fine mesh CFD simulations results are adopted as high-fidelity data. Qualitative and quantitative analysis are performed in this paper which reveals that FSM can substantially improve the prediction of coarse mesh CFD model regardless of the choice of interfacial closures and it provides scalability and consistency across discontinuous flow regimes. Furthermore, it demonstrates that data-driven method can aid the multiphase flow modeling by exploring the connections between local physical features and simulation errors.
Project Objective Execute intelligent analytics via an advanced analytical framework, to assess the current state of offshore infrastructure, evaluate infrastructure life, and identify technologies to reduce infrastructure hazards, costs, and extend infrastructure life. Approach & Results thus Far• Build comprehensive dataset• Perform data-driven analytics to evaluate infrastructure integrity<p> 1. Remaining lifespan</p><p> 2. Likelihood of future risk</p><p>• Apply data-driven advanced spatial, statistical, and Machine Learning (ML) models to quantify existing infrastructure integrity</p><p>• Release data and models through a smart, online platform hosted by Energy Data eXchange (EDX)</p>
A data-driven projection-based reduced-order model (ROM) for nonlinear thermal radiative transfer (TRT) problems is presented. The TRT ROM is formulated by (i) a hierarchy of low-order quasidiffusion (aka variable Eddington factor) equations for moments of the radiation intensity and (ii) the normalized Boltzmann transport equation (BTE). The multilevel system of moment equations is derived by projection of the BTE onto a sequence of subspaces which represent elements of the phase space of the problem. Exact closure for the moment equations is provided by the Eddington tensor. A Petrov-Galerkin (PG) projection of the normalized BTE is formulated using a proper orthogonal decomposition (POD) basis representing the normalized radiation intensity over the whole phase space and time. The Eddington tensor linearly depends on the solution of the normalized BTE. By linear superposition of the POD basis functions, a low-rank expansion of the Eddington tensor is constructed with coefficients defined by the PG projected normalized BTE. The material energy balance (MEB) equation is coupled with the effective gray low-order equations which exist on the same dimensional scale as the MEB equation. The resulting TRT ROM is structure and asymptotic preserving. A detailed analysis of the ROM is performed on the classical Fleck-Cummings (F-C) TRT multigroup test problem in 2D geometry. Numerical results are presented to demonstrate the ROM's effectiveness in the simulation of radiation wave phenomena. Importantly, the ROM is shown to produce solutions with sufficiently high accuracy while using low-rank approximation of the normalized BTE solution. Essential physical characteristics of supersonic radiation wave are preserved in the ROM solutions.
Accurate net-load forecasting is vital for energy planning, aiding decisions on trade and load distribution. However, assessing the performance of forecasting models across diverse input variables, like temperature and humidity, remains challenging, particularly for eliciting a high degree of trust in the model outcomes. In this context, there is a growing need for data-driven technological interventions to aid scientists in comprehending how models react to both noisy and clean input variables, thus shedding light on complex behaviors and fostering confidence in the outcomes. In this paper, we present Forte, a visual analytics-based application to explore deep probabilistic net-load forecasting models across various input variables and understand the error rates for different scenarios. With carefully designed visual interventions, this web-based interface empowers scientists to derive insights about model performance by simulating diverse scenarios, facilitating an informed decision-making process. We discuss observations made using Forte and demonstrate the effectiveness of visualization techniques to provide valuable insights into the correlation between weather inputs and net-load forecasts, ultimately advancing grid capabilities by improving trust in forecasting models.
Recent advances in the field of data-driven dynamics allow for the discovery of ODE systems using state measurements. One approach, known as Sparse Identification of Nonlinear Dynamics (SINDy), assumes the dynamics are sparse within a predetermined basis in the states and finds the expansion coefficients through linear regression with sparsity constraints. This approach requires an accurate estimation of the state time derivatives, which is not necessarily possible in the high-noise regime without additional constraints. We present an approach called Derivative-based SINDy (DSINDy) that combines two novel methods to improve ODE recovery at high-noise levels. First, we denoise the state variables by applying a projection operator that leverages the assumed basis for the system dynamics. Second, we use a second order cone program (SOCP) to find the derivative and governing equations simultaneously. We derive theoretical results for the projection-based denoising step, which allow us to estimate the values of hyperparameters used in the SOCP formulation. This underlying theory helps limit the number of required user-specified parameters. Finally, we present results demonstrating that our approach leads to improved system recovery for the Van der Pol oscillator, the Duffing oscillator, the Rössler attractor, and the Lorenz 96 model.
Stochastic collocation (SC) is a well-known non-intrusive method of constructing surrogate models for uncertainty quantification. In dynamical systems, SC is especially suited for full-field uncertainty propagation that characterizes the distributions of the high-dimensional solution fields of a model with stochastic input parameters. However, due to the highly nonlinear nature of the parameter-to-solution map in even the simplest dynamical systems, the constructed SC surrogates are often inaccurate. Here, this work presents an alternative approach, where we apply the SC approximation over the dynamics of the model, rather than the solution. By combining the data-driven sparse identification of nonlinear dynamics framework with SC, we construct dynamics surrogates and integrate them through time to construct the surrogate solutions. We demonstrate that the SC-over-dynamics framework leads to smaller errors, both in terms of the approximated system trajectories as well as the model state distributions, when compared against full-field SC applied to the solutions directly. We present numerical evidence of this improvement using three test problems: a chaotic ordinary differential equation, and two partial differential equations from solid mechanics.
Mountainous headwater streams are sustained by both snowmelt‐driven streamflow and groundwater discharge in the Upper Colorado River Basin. However, predicting headwater stream discharge magnitude and peak flow timing is challenging in mountainous terrains, where snowmelt rates vary with vegetation type and elevation, and heterogeneous subsurface physical properties influence groundwater storage and its release. We used a model‐data integration approach to investigate the roles of snowmelt and subsurface structure in stream discharge and groundwater level. We ran an ensemble of 100 integrated surface‐subsurface hydrologic models for a mountainous headwater catchment near Crested Butte, Colorado, USA. We also evaluated and calibrated these models against observed data sets, including snow depth measurements using distributed temperature probes, stream discharge, and groundwater levels. Calibration with multiple data sources using neural density estimators has further constrained uncertainty in subsurface properties and snowmelt rates. Results indicated that observed slower snowmelt rates in evergreen forests delayed the peak flow and baseflow onset. In upstream areas with lower subsurface permeability, water was stored within the subsurface but was not released as interflow or shallow groundwater flow, and thereby not contributing to downstream streamflow during recession limb periods. Double peaks in groundwater occurred in areas with spatial subsurface heterogeneity, in our case due to the contrast between granodiorite and Mancos shale. These process‐based insights into groundwater and snowmelt dynamics in mountainous headwaters will help improve predictions of headwater hydrology.
Methods for downstream river flow prediction can be categorized into physics-based and empirical approaches. Although based on well-studied physical relationships, physics-based models rely on numerous hydrologic variables characteristic of the specific river system that can be costly to acquire. Moreover, simulation is often computationally intensive. Conversely, empirical models require less information about the system being modeled and can capture a system’s interactions based on a smaller set of observed data. This article introduces two empirical methods to predict downstream hydraulic variables based on observed stream data: a linear programming (LP) model, and a convolutional neural network (CNN). We apply both empirical models within the Colorado River system to a site located on the Green River, downstream of the Yampa River confluence and Flaming Gorge Dam, and compare it to the physics-based model Streamflow Synthesis and Reservoir Regulation (SSARR) currently used by federal agencies. Results show that both proposed models significantly outperform the SSARR model. Moreover, the CNN model outperforms the LP model for hourly predictions whereas both perform similarly for daily predictions. Although less accurate than the CNN model at finer temporal resolution, the LP model is ideal for linear water scheduling tools.
The project investigated biogeochemical processes at terrestrial-aquatic interfaces (TAIs), focusing on soil microsite heterogeneity and its impact on greenhouse gas (GHG) fluxes. Using laboratory experiments, modeling, and data integration, researchers explored redox-driven microbial processes under fluctuating hydrological conditions. Key advancements included modifying the DAMM-GHG model to incorporateelectron acceptor availability and enhancing the AquaMEND model for improved microbial metabolism representation. Results highlighted microsite redox variability as a key driver of GHG fluxes, informing Earth system models. The project fostered interdisciplinary collaborations, student training, and the development of novel modeling frameworks to improve Earth'senergy budget.
As part of the ongoing effort to transition from preventive maintenance strategies to condition-based maintenance strategies in nuclear power plants, there is significant reliance on using machine learning techniques. To develop a robust machine learning model that can diagnose all the fault modes of a vertical motor-driven pump, data capturing the unique signature of each fault mode is required. In practice, it is difficult to collect or capture data that captures all the fault modes from a single plant site. So to address this situation, a computational model of a vertical motor-driven pump is developed using the multipurpose finite element software COMSOL Multiphysics. The developed model is used to generate simulated data under normal operation and is compared with the vibration data collected using vibration sensors. Once the simulation model is verified under normal operating condition, simulated data for the fault mode for which minimal or no evidence is available in historical plant process data is developed. This simulated data is used to develop fault signatures to achieve robust predictive models. This paper presents modeling details and verification of the model that can used to generate data for fault modes that are not available at a plant site for condition monitoring purpose.
As part of the ongoing effort to transition from preventive maintenance strategies to condition-based maintenance strategies in nuclear power plants, there is significant reliance on using machine learning techniques. To develop a robust machine learning model that can diagnose all the fault modes of a vertical motor-driven pump, data capturing the unique signature of each fault mode is required. In practice, it is difficult to collect or capture data that captures all the fault modes from a single plant site. So to address this situation, a computational model of a vertical motor-driven pump is developed using the multipurpose finite element software COMSOL Multiphysics. The developed model is used to generate simulated data under normal operation and is compared with the vibration data collected using vibration sensors. Once the simulation model is verified under normal operating condition, simulated data for the fault mode for which minimal or no evidence is available in historical plant process data is developed. This simulated data is used to develop fault signatures to achieve robust predictive models. This paper presents modeling details and verification of the model that can used to generate data for fault modes that are not available at a plant site for condition monitoring purpose.
The design space for inertial confinement fusion (ICF) experiments is vast, and experiments are extremely expensive. Researchers rely heavily on computer simulations to explore the design space in search of high-performing implosions. However, ICF multiphysics codes must make simplifying assumptions, and thus deviate from experimental measurements for complex implosions. For more effective design and investigation, simulations require input from past experimental data to better predict future performance. In this work, we describe a cognitive simulation method for combining simulation and experimental data into a common, predictive model. This method leverages a machine learning technique called “transfer learning,” the process of taking a model trained to solve one task, and partially retraining it on a sparse dataset to solve a different, but related task. In the context of ICF design, neural network models are trained on large simulation databases and partially retrained on experimental data, producing models that are far more accurate than simulations alone. Here, we demonstrate improved model performance for a range of ICF experiments at the National Ignition Facility and predict the outcome of recent experiments with less than 10% error for several key observables. We discuss how the methods might be used to carry out a data-driven experimental campaign to optimize performance, illustrating the key product—models that become increasingly accurate as data are acquired.
Smart corridor digital twins are often created for the development and evaluation of emerging intelligent transportation systems and Connected and Autonomous Vehicle (CAV) technologies. However, limited guidance exists for data quality assessment for digital twin development. To address this, this paper discusses the data quality assessment utilized to develop data-driven real-time microscopic simulation models, i.e., digital twins, for two separate smart corridors: the North Avenue Smart Corridor in Atlanta, GA, and the Martin Luther King Smart Corridor in Chattanooga, Tennessee. This paper provides a summary of the author’s investigations of data requirements and data characteristics for the given smart corridor digital twin development efforts. With a focus on data, this summary includes a description of the data investigation process, key data issues observed, and strategies to address observed issues. Discussion is provided to help expand the lessons from these studies to other digital twin development efforts.