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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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At least 505 records · Page 28
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Machine-learned closure of URANS for stably stratified turbulence: connecting physical timescales & data hyperparameters of deep time-series models

Stably stratified turbulence (SST), a model that is representative of the turbulence found in the oceans and atmosphere, is strongly affected by fine balances between forces and becomes more anisotropic in time for decaying scenarios. Moreover, there is a limited understanding of the physical phenomena described by some of the terms in the Unsteady Reynolds-Averaged Navier–Stokes (URANS) equations—used to numerically simulate approximate solutions for such turbulent flows. Rather than attempting to model each term in URANS separately, it is attractive to explore the capability of machine learning (ML) to model groups of terms, i.e. to directly model the force balances. We develop deep time-series ML for closure modeling of the URANS equations applied to SST. We consider decaying SST which are homogeneous and stably stratified by a uniform density gradient, enabling dimensionality reduction. We consider two time-series ML models: long short-term memory and neural ordinary differential equation. Both models perform accurately and are numerically stable in a posteriori (online) tests. Furthermore, we explore the data requirements of the time-series ML models by extracting physically relevant timescales of the complex system. We find that the ratio of the timescales of the minimum information required by the ML models to accurately capture the dynamics of the SST corresponds to the Reynolds number of the flow. The current framework provides the backbone to explore the capability of such models to capture the dynamics of high-dimensional complex dynamical system like SST flows.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

TEMPI: An Interposed MPI Library with Canonical Representation of MPI Datatypes [Poster]

TEMPI provides a transparent non-contiguous data-handling layer compatible with various MPIs. MPI Datatypes are a powerful abstraction for allowing an MPI implementation to operate on non-contiguous data. CUDA-aware MPI implementations must also manage transfer of such data between the host system and GPU. The non-unique and recursive nature of MPI datatypes mean that providing fast GPU handling is a challenge. The same noncontiguous pattern may be described in a variety of ways, all of which should be treated equivalently by an implementation. This work introduces a novel technique to do this for strided datatypes. Methods for transferring non-contiguous data between the CPU and GPU depends on the properties of the data layout. This work shows that a simple performance model can accurately select the fastest method. Unfortunately, the combination of MPI software and system hardware available may not provide sufficient performance. The contributions of this work are deployed on OLCF Summit through an interposer library which does not require privileged access to the system to use

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evaluation and projection of long period return values of extreme daily precipitation in the CMIP5 and CMIP6 models

Using a non-stationary Generalized Extreme Value statistical method, we calculate selected extreme daily precipitation indices and their 20 year return values from the CMIP5 and CMIP6 climate models over the historical and future periods. We evaluate model performance of these indices and their return values in replicating similar quantities calculated from multiple gridded observational products. Difficulties in interpreting model quality in the context of observational uncertainties are discussed. Projections are framed in terms of specified global warming target temperatures rather than at specific times and under specific emissions scenarios. The change in framing shifts projection uncertainty due to differences in model climate sensitivity from the values of the projections to the timing of the global warming target. At their standard resolutions, we find there are no meaningful differences between the two generations of models in their quality or projections of simulated extreme daily precipitation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Automated Shift Detection in Sensor-Based PV Power and Irradiance Time Series

PV power and irradiance sensor-based measurements are prone to error, resulting in issues such as time series data shifts. In this research, a changepoint detection (CPD) algorithm that automatically detects data shifts in sensor-based time series is introduced. Data shift periods in 101 daily PV power and irradiance time series were labeled manually by two solar experts. These data streams represent sensor-based measurements, and display a variety of data shift behaviors. A changepoint detection algorithm was tuned using the 101 labeled data streams, with each model configuration's ability to detect labeled changepoints benchmarked using metrics such as F1-score, recall, and Rand Index. Best performing models on seasonality-corrected data streams include the Pruned Exact Linear (PELT) method, the Binary Segmentation method, and the Bottom-Up method, all scoring an average F1-score of 0.76 or greater at detecting labeled changepoints within a 30-day window across the labeled data sets. Pending approval, we plan to release the labeled data sets for this research on NREL's DuraMAT Data Hub, and the associated algorithm in the Python PVAnalytics package. By supplying the training sets and algorithm, we hope to encourage further development in this research space.

data shift↗
DOE OSTI2025

Large-scale tearing-mode hazard function analysis with standard matched equilibrium reconstructions

The association between features from standard tokamak equilibrium reconstructions and the onset of n = 1 tearing modes (TMs) is analyzed at scale. The TM onset rate is directly modeled with a ‘hazard’ function which gives the expected number of onsets (per unit time spent) in a given equilibrium parameter region. In particular the different statistical modeling performance achieved for magnetics-only reconstructions and motional Stark effect (MSE) enhanced reconstructions is studied. It is observed that a better hazard model for the TM onset rate can be built with the MSE-enhanced equilibria compared to the matched magnetics-only situation. This advantage disappears if internal profile details are withheld from the matched analysis. Plausibility of the hazard function is further demonstrated with visualizations of global trends in the operational space, and time-traces from specific tokamak discharges. As a result, TMs typically degrade tokamak plasma performance and may lead to plasma termination, motivating this statistical study.

equilibrium↗
DOE OSTI2021

Oilwell Conversion (Well API 121913310501) to Geothermal Heat Storage Well for Flexible Electricity Storage

Geothermal growth is limited by a lack of geographically dispersed high-temperature thermal resources and high initial upfront investment in characterization and well construction. This project intended to address the challenges of energy supply intermittency and enhance grid resilience, reliability, and energy security by storing energy provided from currently available renewable resources in the subsurface to harvest it a later time during at-peak energy demand. This project intended to improve geothermal adoption, reduce initial project risk, and improve price competitiveness through utilizing existing oil and gas infrastructure such as non-productive wells, non-economic fields, dry holes, and orphaned wells. The project also intended to address the lack of geographically dispersed thermal resources and enhance grid resilience, reliability, and energy security by introducing an economical method for storing energy from currently available renewable resources in the subsurface for usage during at-peak energy demand. During this research, the project furthered the understanding of the feasibility of utilizing abandoned oil and gas wells as geothermal heat storage wells. The project team investigated the heat storage and hydrogeological characteristics of subsurface reservoirs in the Illinois Basin to evaluate their response to heat injection for determining the evolution of temperature profiles and heat losses over time using existing and available data sets. The project team then performed modeling and simulation to evaluate the heat losses of returning fluids during heat extraction. The outputs were used to select an optimal candidate reservoir and location in Southern Illinois. The team designed and performed a small-scale field test in an existing oil well to refine the model and to demonstrate the permitting and regulatory pathways necessary for the conversion of oil and gas assets to geothermal use. The field test also serves as a proof of concept and can guide the procedures for future research and implementation. Additionally, the project team, conducted initial market research and customer discovery to develop a go to market strategy for an Advanced Geothermal Energy Storage (AGES) system. The project team in this research also identified the parameters to be refined in future research, to improve the current go to market strategy economic model. To this end several subject matter experts were also identified to assist in future research with geothermal infrastructure setup, energy storage policy and law, energy storage market demand, potential siting based on demand etc. Future research will involve further sophistication of the site commercial modeling, implementing a larger-scale test, and further refinement of the thermodynamic modeling/simulation process. The output will be lifecycle costs and economics suitable for comparison to alternative approaches from a validated full-scale demonstration for venture capital investment into this technology. The project successfully demonstrated the ability to leverage existing oilfield infrastructure, permits, and land access and leasing agreements, to enable geothermal storage projects to come online faster and cheaper than a greenfield development could. This technology could allow for greater energy independence and security through long-term energy storage solutions. The longer duration allows for greater storage for renewables currently limited by hours-long storage durations of lithium-ion. The AGES system would support the growth of renewable energy farms, and provide greater opportunities for a cleaner energy infrastructure.

15 GEOTHERMAL ENERGY↗
DOE OSTI2026

Machine Learning for Mapping Multipactor Susceptibility in RF Systems: Capabilities and Generalization Constraints

Multipactor is a surface-driven electron avalanche phenomenon that degrades the performance and reliability of radio-frequency (RF) systems in particle accelerator and vacuum electronics applications. Multipactor behavior in a given device structure is conventionally assessed through susceptibility charts, which provide a parameter-space characterization of the instability. In this work, we assess the capabilities of machine-learning (ML) models to learn and predict such susceptibility charts and analyze the constraints governing their generalization across materials. Using a simulation-derived dataset spanning six distinct secondary-electron-yield material profiles in a canonical two-surface planar geometry, we train supervised regression models and artificial neural networks to predict the time-averaged electron growth rate, δavg, across the relevant parameter space. Model performance is evaluated using metrics that explicitly probe the structure of susceptibility charts, including Intersection over Union, Structural Similarity Index, and correlation analysis. Tree-based ensemble models outperform neural-network models in reconstructing susceptibility regions and in generalizing across material domains. Principal-component analysis reveals disjoint material feature distributions, indicating that the piecewise mode structure of multipactor susceptibility is difficult to represent with a single global model and that generalization is constrained by data coverage rather than by model complexity. An exhaustive reduced-coverage study further shows that sparse material-space coverage can yield mean performance in the same general range but producing large variability in the susceptibility-region overlap. These results clarify the capabilities of ML-based surrogate models for parameter-space characterization of multipactor discharge. They also provide guidance for their appropriate use in RF system design.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Overview of interpretive modelling of fusion performance in JET DTE2 discharges with TRANSP

In the paper we present an overview of interpretive modelling of a database of JET-ILW 2021 D-T discharges using the TRANSP code. The main aim is to assess our capability of computationally reproducing the fusion performance of various D-T plasma scenarios using different external heating and D-T mixtures, and to understand the performance driving mechanisms. We find that interpretive simulations confirm a general power-law relationship between increasing external heating power and fusion output, which is supported by absolutely calibrated neutron yield measurements. A comparison of measured and computed D-T neutron rates shows that the calculations' discrepancy depends on the absolute neutron yield. The calculations are found to agree well with measurements for higher performing discharges with external heating power above ~20 MW, while low-neutron shots display an average discrepancy of around +40% compared to measured neutron yields. A similar trend is found for the ratio between thermal and beam-target fusion, where larger discrepancies are seen in shots with dominant beam-driven performance. We compare the observations to studies of JET-ILW D discharges, to find that on average the fusion performance is well modelled over a range of heating power, although an increased unsystematic deviation for lower-performing shots is observed. The ratio between thermal and beam-induced D-T fusion is found to be increasing weakly with growing external heating power, with a maximum value of ≳1 achieved in a baseline scenario experiment. An evaluation of the fusion power computational uncertainty shows a strong dependence on the plasma scenario type and fusion drive characteristics, varying between ±25% and 35%. D-T fusion alpha simulations show that the ratio between volume-integrated electron and ion heating from alphas is ≲10 for the majority of analysed discharges. Alphas are computed to contribute between ~15% and 40% to the total electron heating in the core of highest performing D-T discharges. An alternative workflow to TRANSP was employed to model JET D-T plasmas with the highest fusion yield and dominant non-thermal fusion component because of the use of fundamental radio-frequency heating of a large minority in the scenario, which is calculated to have provided ~10% to the total fusion power.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

Phase-field simulations of intergranular fission gas bubble behavior in U 3 Si 2 nuclear fuel

U 3 Si 2 is a potential accident-tolerant fuel that shows promise due to its high thermal conductivity and higher uranium density relative to UO 2 . However, its swelling and fission gas release behavior in light water reactor (LWR) conditions is relatively unknown. To provide mechanistic insight and determine parameters for engineering-scale fuel performance modeling of pellet-form U 3 Si 2 , phase-field simulations of the growth, interconnection, and venting of intergranular fission gas bubbles were performed. The fractional coverage of the grain boundary and the fraction of bubble area that is vented were calculated as a function of time. From the simulation data, the fractional grain boundary coverage at saturation, an important parameter needed in engineering-scale modeling of swelling and fission gas release, was determined. Multiple simulations were run to determine the uncertainty in the calculated value. Finally, the effect of model assumptions and input parameters that are not well known was evaluated. Simulation results are compared to related theoretical and computational work. Based on the simulation results, a value of 0.60 for the fractional grain boundary coverage at saturation is recommended for U 3 Si 2 fuel.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Identifying schools at high-risk for elevated lead in drinking water using only publicly available data

Estimating the risk of lead contamination of schools' drinking water at the State level is a complex, important, and unexplored challenge. Variable water quality among water systems and changes in water chemistry during distribution affect lead dissolution rates from pipes and fittings. In addition, the locations of lead-bearing plumbing materials are uncertain. We tested the capability of six machine learning models to predict the likelihood of lead contamination of drinking water at the schools' taps using only publicly available datasets. The predictive features used in the models correspond to those with a proven correlation to the dominant, but commonly unavailable, factors that govern lead leaching: the presence of lead-bearing plumbing materials and water quality conducive to lead corrosion. By combining water chemistry data from public reports, socioeconomic information from the US census, and spatial features using Geographic Information Systems, we trained and tested models to estimate the likelihood of lead contaminated tap water in over 8,000 schools across California and Massachusetts. Our best-performing model was a Random Forest, with a 10-fold cross validation score of 0.88 for Massachusetts and 0.78 for California using the average Area Under the Receiver Operating Characteristic Curve (ROC AUC) metric. The model was then used to assign a lead leaching risk category to half of the schools across California (the other half was used for training). There was good agreement between the modeled risk categories and the actual lead leaching outcomes for every school; however, the model overestimated the lead leaching risk in up to 17% of the schools. This model is the first of its kind to offer a tool to predict the risk of lead leaching in schools at the State level. Further use of this model can help deploy limited resources more effectively to prevent childhood lead exposure from school drinking water.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

Automated defect identification in electroluminescence images of solar modules

Solar photovoltaic (PV) modules are susceptible to manufacturing defects, mishandling problems or extreme weather events that can limit energy production or cause early device failure. Trained professionals use electroluminescence (EL) images to identify defects in modules, however, field surveys or inline image acquisition can generate millions of EL images, which are infeasible to analyze by rote inspection. Here, we develop a rapid automatic computer vision pipeline (~0.5 seconds/module) to analyze EL images and identify defects including cracks, intra-cell defects, oxygen-induced defects, and solder disconnections. Defect identification is achieved with a machine learning model (Random Forest, ResNet models and YOLO) trained on 762 manually-labeled EL images of PV modules. We compare model performance on an imbalanced real-world validation set containing 134 EL images and determine that ResNet18 and YOLO are the optimal models; we next evaluated these models on a dedicated testing set (129 module images) with resulting macro F1 scores of 0.83 (ResNet18) and 0.78 (YOLO). Using a field EL survey of a PV power plant damaged in a vegetation fire, we analyze 18,954 EL images (2.4 million cells) and inspect the spatial distribution of defects on the solar modules. The results find increased frequency of ‘crack’, ‘solder’ and ‘intra-cell’ defects on the edges of the solar module closest to the ground after fire. We also find an abnormal increase of striation rings on cells which were assumed to be caused mainly in fabrication process. Our methods are published as open-source software. It can also be used to identify other kinds of defects or process different types of solar cells with minor modification on models by transfer learning.

14 SOLAR ENERGY↗
DOE OSTI2025

Irradiation Vehicles for Evaluating SiC/SiC Cladding Bowing Under Neutron Flux Gradients

Silicon carbide fiber–reinforced silicon carbide matrix (SiC/SiC) composites are among the most promising candidates for long term accident-tolerant nuclear fuel cladding. A key challenge related to their deployment is lateral bowing caused by differential radiation-induced swelling under dose or temperature gradients, which could obstruct coolant flow or interfere with control rod/blade movements. Although previous modeling efforts have predicted bowing behavior in light-water reactor (LWR) environments, experimental validation remains limited, especially at prototypic LWR temperatures. This study addresses that gap by irradiating six reduced-length SiC/SiC cladding tubes (~600 mm) in the High Flux Isotope Reactor (HFIR) at ~300°C, which is representative of LWR conditions. The tubes will be housed in a sealed vessel with an inert gas gap to maintain target temperatures and prevent direct coolant contact. Arranged in three pairs, each set will receive a different radiation dose (low, medium, high), with the central pair receiving ~0.1 displacements per atom (dpa)—the expected dose for peak bowing. The experiment will determine the dose-dependent bowing behavior and validate predictive finite element models. In this work, the tubes are freely suspended from pins to allow for unconstrained bowing; however, we present a concept for introducing localized constraints to represent grid spacer effects. Post-irradiation examination will include swelling measurements and profilometry to assess bowing and compare the results with model predictions. This work aims to confirm the conditions under which maximum bowing occurs so as to improve the reliability of SiC/SiC performance models in nuclear applications.

Russell, Nick [ORNL] (ORCID:0000000296099820)↗
DOE OSTI2022

Uncertainty in Annual Energy Resulting from Uncertain Irradiance Measurements

We report an analysis quantifying the contribution to uncertainty in annual energy projections from uncertainty in ground-measured irradiance. Uncertainty in measured irradiance is quantified for eight instruments by the difference from a well maintained, secondary standard pyranometer which is regarded as truthful. We construct a statistical model of irradiance uncertainty and apply the model to generate a sample of 100 annual time series of irradiance for each instrument. The sample is propagated through a common performance model for a reference photovoltaic system to quantify variation in annual energy. Although the measured irradiance varies from the reference by a few percent (standard deviation of 1-2%) the uncertainty in annual energy is on the order of a fraction of one percent. We propose a model for a factor that represents uncertainty in modeled annual energy that arises from uncertainty in ground-measured irradiance.

14 SOLAR ENERGY↗
DOE OSTI2020

A Tidal Hydrodynamic Model for Cook Inlet, Alaska, to Support Tidal Energy Resource Characterization

Cook Inlet in Alaska has been identified as a prime site in the U.S. for potential tidal energy development, because of its enormous tidal power potential that accounts for nearly one-third of the national total. As one important step to facilitate tidal energy development, a tidal hydrodynamic model based on the unstructured-grid, finite-volume community ocean model (FVCOM) was developed for Cook Inlet to characterize the tidal stream energy resource. The model has a grid resolution that varies from about 1000 m at the open boundary to 100–300 m inside the Inlet. Extensive model validation was achieved by comparing model predictions with field observations for tidal elevation and velocity at various locations in Cook Inlet. The error statistics confirmed the model performs reasonably well in capturing the tidal dynamics in the system, e.g., R2 > 0.98 for tidal elevation and generally > 0.9 for velocity. Model results suggest that tides in Cook Inlet evolve from progressive waves at the entrance to standing waves at the upper Inlet, and that semi-diurnal tidal constituents are amplified more rapidly than diurnal constituents. The model output was used to identify hotspots that have high energy potential and warrant additional velocity and turbulence measurements such as East Foreland, where averaged power density exceeds 5 kw/m2. Lastly, a tidal energy extraction simulation was conducted for a hypothetical turbine farm configuration at the Forelands cross section to evaluate tidal energy extraction and resulting changes in far-field hydrodynamics.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

KGML-ag: a modeling framework of knowledge-guided machine learning to simulate agroecosystems: a case study of estimating N<sub>2</sub>O emission using data from mesocosm experiments

Abstract. Agricultural nitrous oxide (N2O) emission accounts for a non-trivial fraction of global greenhouse gas (GHG) budget. To date, estimating N2O fluxes from cropland remains a challenging task because the related microbial processes (e.g., nitrification and denitrification) are controlled by complex interactions among climate, soil, plant and human activities. Existing approaches such as process-based (PB) models have well-known limitations due to insufficient representations of the processes or uncertainties of model parameters, and due to leverage recent advances in machine learning (ML) a new method is needed to unlock the “black box” to overcome its limitations such as low interpretability, out-of-sample failure and massive data demand. In this study, we developed a first-of-its-kind knowledge-guided machine learning model for agroecosystems (KGML-ag) by incorporating biogeophysical and chemical domain knowledge from an advanced PB model, ecosys, and tested it by comparing simulating daily N2O fluxes with real observed data from mesocosm experiments. The gated recurrent unit (GRU) was used as the basis to build the model structure. To optimize the model performance, we have investigated a range of ideas, including (1) using initial values of intermediate variables (IMVs) instead of time series as model input to reduce data demand; (2) building hierarchical structures to explicitly estimate IMVs for further N2O prediction; (3) using multi-task learning to balance the simultaneous training on multiple variables; and (4) pre-training with millions of synthetic data generated from ecosys and fine-tuning with mesocosm observations. Six other pure ML models were developed using the same mesocosm data to serve as the benchmark for the KGML-ag model. Results show that KGML-ag did an excellent job in reproducing the mesocosm N2O fluxes (overall r2=0.81, and RMSE=3.6 mgNm-2d-1 from cross validation). Importantly, KGML-ag always outperforms the PB model and ML models in predicting N2O fluxes, especially for complex temporal dynamics and emission peaks. Besides, KGML-ag goes beyond the pure ML models by providing more interpretable predictions as well as pinpointing desired new knowledge and data to further empower the current KGML-ag. We believe the KGML-ag development in this study will stimulate a new body of research on interpretable ML for biogeochemistry and other related geoscience processes.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Powered By SAM [Slides]

The System Advisor Model(TM) (SAM) is a free, open-source desktop application for techno-economic analysis of energy technologies. By combining detailed performance modeling with financial analysis, SAM allows users to assess technology trade-offs, explore future scenarios, and make informed decisions about energy investments. Users also have access to model details and the ability to embed SAM's core models in their own applications. This webinar, hosted by National Laboratory of the Rockies researchers Janine Keith and Matt Prilliman, highlights how this widely used modeling tool supports data-driven decision-making for energy systems.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

A Review of Machine Learning Classification Using Quantum Annealing for Real-World Applications

Optimizing the training of a machine learning pipeline helps in reducing training costs and improving model performance. One such optimizing strategy is quantum annealing, which is an emerging computing paradigm that has shown potential in optimizing the training of a machine learning model. The implementation of a physical quantum annealer has been realized by D-wave systems and is available to the research community for experiments. Recent experimental results on a variety of machine learning applications using quantum annealing have shown interesting results where the performance of classical machine learning techniques is limited by limited training data and high dimensional features. This article explores the application of D-wave’s quantum annealer for optimizing machine learning pipelines for real-world classification problems. Finally, we review the application domains on which a physical quantum annealer has been used to train machine learning classifiers. We discuss and analyze the experiments performed on the D-Wave quantum annealer for applications such as image recognition, remote sensing imagery, computational biology, and particle physics. We discuss the possible advantages and the problems for which quantum annealing is likely to be advantageous over classical computation.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Performance Evaluation of a Two-Dimensional Flood Model on Heterogeneous High-Performance Computing Architectures

This paper describes the implementation of a two-dimensional hydrodynamic flood model with two different numerical schemes on heterogeneous high-performance computing architectures. Both schemes were able to solve the nonlinear hyperbolic shallow water equations using an explicit upwind first-order approach on finite differences and finite volumes, respectively, and were conducted using MPI and CUDA. Four different test cases were simulated on the Summit supercomputer at Oak Ridge National Laboratory. Both numerical schemes scaled up to 128 nodes (768 GPUs) with a maximum 98.2x speedup of over 1 GPU. The lowest run time for the 10 day Hurricane Harvey event simulation at 5 meter resolution (272 million grid cells) was 50 minutes. GPUDirect communication proved to be more convenient than the standard communication strategy. Both strong and weak scaling are shown.

Sharif, Md Bulbul↗