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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 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Global Data-Driven Determination of Baryon Transition Form Factors

Hadronic resonances emerge from strong interactions encoding the dynamics of quarks and gluons. The structure of these resonances can be probed by virtual photons parametrized in transition form factors. Here, in this study, twelve N* and Δ transition form factors at the pole are extracted from data with the center-of-mass energy from πN threshold to 1.8 GeV, and the photon virtuality 0 ≤ Q 2 /GeV 2 ≤ 8. For the first time, these results are determined from a simultaneous analysis of more than one state, i.e., ~10 5 π⁢N, η⁢N, and K⁢Λ electroproduction data. In addition, about 5×10 4 data in the hadronic sector as well as photoproduction serve as boundary conditions. For the Δ⁡(1232) and N⁡(1440) states our results are in qualitative agreement with previous studies, while the transition form factors at the poles of some higher excited states are estimated for the first time. Realistic uncertainties are determined by further exploring the parameter space.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2019

A Game-Theoretic Data-Driven Approach for Pseudo-Measurement Generation in Distribution System State Estimation

In this paper, we present an efficient computational framework with the purpose of generating weighted pseudo-measurements to improve the quality of distribution system state estimation (DSSE) and provide observability with advanced metering infrastructure (AMI) against unobservable customers and missing data. The proposed technique is based on a game-theoretic expansion of relevance vector machines (RVMs). This platform is able to estimate the nodal power consumption and quantify its uncertainty while reducing the prohibitive computational burden of model training for large AMI datasets. To achieve this objective, the large training set is decomposed and distributed among multiple parallel learning entities. The resulting estimations from the parallel RVMs are then combined using a game-theoretic model based on the idea of repeated games with vector payoff. It is observed that through this approach and by exploiting the seasonal changes in customers’ behavior the accuracy of pseudo-measurements can be considerably improved, while introducing robustness against bad training data samples. The proposed pseudo-measurement generation model is integrated into a DSSE using a closed-loop information system, which takes advantage of a branch current state estimator (BCSE) to further improve the performance of the designed machine learning framework. This method has been tested on a practical distribution feeder model with smart meter data for verification.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2020

Data Driven User Emulator

This software generates realistic network traffic to test intrusion detection systems. The most realistic traffic is generated when software is used to drive actual applications, thereby behaving like a real user. Tools that generate such realistic user behavior are called user emulators. However, no existing user emulators use models based on data science and real user data, and they suffer from a decrease in the fidelity of the generated traffic. We develop a user emulator that uses real user data and data science to generate higher fidelity emulation and increase the accuracy of our experimental results.

Oesch, TimothyS.↗
DOE OSTI2026

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2026

Improving Detector Systematic Uncertainties Through Data-Driven Machine Learning

Detector simulation in liquid argon time projection chambers (LArTPCs) is a constant challenge. In particular the modeling of electrons response on wires is highly nontrivial. However, new machine learning techniques exist which can be leveraged to ameliorate these concerns. We present a novel methodology to attempt to learn from cosmic muon data in the ICARUS detector how reconstructed wire signals are influenced by features of the hits such as location, particle direction, angle relative to the wire plane, etc. A model can then be generated which can apply the learned mapping to Monte Carlo events to create a more data-like simulation sample. By creating such a sample we expect to reduce the systematic uncertainties at ICARUS due to our detector modeling.

Hausner, Harry [Fermilab] (ORCID:0000000188932280)↗
DOE OSTI2026

Data driven investigation to understand the influence of total solids on biological biogas upgrading

In situ biogas upgrading achieves CO 2 conversion to CH 4 via hydrogenotrophic methanogenesis; however, gas-liquid mass transfer constraints limit the upgrading performance. Recognizing that optimization studies often underrepresent the effects of total solids (TS) and organic loading rate (OLR), this study undertook a holistic, statistics driven assessment of operating conditions for in situ H 2 assisted biogas upgrading, centering the analysis on TS and OLR. A dataset of 31 studies was compiled and comprised 99 observations. A rigorous analytical framework was employed, combining data standardization, fixed- and random-effects (REML) weighted regressions with cluster-robust errors, stratified analyses, and machine learning. Mixed-effects meta regression indicated that TS was the main factor explaining differences of methane fraction (CH 4 %) when considering the between studies heterogeneity. Focusing on a near-stoichiometric subset (H 2 /CO 2 ≈ 4:1), TS remained significant. Stratified results showed a stronger negative relationship between TS and CH 4 % in UASB reactors than in CSTRs, with a negative effect under mesophilic conditions and no significant effect under thermophilic conditions. A Random Forest model corroborated the statistical findings, consistently ranking H 2 /CO 2 ratio, OLR, TS, and hydrogen injection rate (HIR) as the most influential predictors. These findings delineate trends across increasing TS levels, particularly between 1% and 10%, and provide preliminary insights for TS above 15% in in situ biogas upgrading. They further provide insights for the influence of TS by reactor type and temperature, thereby advancing the evidence base for implementing biological CO 2 conversion to CH 4 in practice.

In situ biogas upgrading↗
DOE OSTI2025

Evaluating system responses to electric vehicle charging infrastructure expansion through data-driven simulation

Understanding the system responses to electric vehicle (EV) charging infrastructure expansion, including vehicle charging needs, station utilization, and energy consumption, is critical for effective planning to meet growing charging demand without unnecessary resource investment. This study evaluates the system responses to EV charging infrastructure expansion, focusing on charging needs, station utilization, and energy consumption. Using trip data from the National Household Travel Survey and origin–destination patterns, we simulated trip chains in downtown Atlanta with 10 % EV penetration. We assessed 32 scenarios involving different charging port power levels and siting strategies. Furthermore, we found that higher-power ports were more sensitive to placement, with concentrated expansion boosting station utilization more than uniform expansion. Adding high-power ports did not always increase peak energy consumption; in some cases, a few 400 kW ports reduced overall consumption compared to 150 kW ports by enabling faster charging and higher vehicle turnover.

Electric vehicle↗
DOE OSTI2019

Data-Driven Load Modeling and Forecasting of Residential Appliances

The expansion of residential demand side management programs and increased deployment of controllable loads require accurate appliance-level load modeling and forecasting. This paper proposes a conditional hidden semi-Markov model to describe the probabilistic nature of residential appliance demand. Model parameters are estimated directly from power consumption data using scalable statistical learning methods. We also propose an algorithm for short-term load forecasting as a key application for appliance-level load models. Case studies performed using granular sub-metered power measurements from various types of appliances demonstrate the effectiveness of the proposed load model for short-term prediction.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Realizing the data-driven, computational discovery of metal-organic framework catalysts

Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally suited for a computational approach towards catalyst design and discovery. Nonetheless, the widespread use of data science and machine learning to accelerate the discovery of MOF catalysts has yet to be substantially realized. In this review, we provide an overview of recent work that sets the stage for future high-throughput computational screening and machine learning studies involving MOF catalysts. This is followed by a discussion of several challenges currently facing the broad adoption of data-centric approaches in MOF computational catalysis, and we share possible solutions that can help propel the field forward.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Machine learning for data-driven discovery in solid Earth geoscience

Understanding the behavior of Earth through the diverse fields of the solid Earth geosciences is an increasingly important task. It is made challenging by the complex, interacting, and multiscale processes needed to understand Earth’s behavior and by the inaccessibility of nearly all of Earth’s subsurface to direct observation. Substantial increases in data availability and in the increasingly realistic character of computer simulations hold promise for accelerating progress, but developing a deeper understanding based on these capabilities is itself challenging. Machine learning will play a key role in this effort. We review the state of the field and make recommendations for how progress might be broadened and accelerated.

Bergen, Karianne J.↗
DOE OSTI2022

Data–driven and constrained optimization of semi–local exchange and nonlocal correlation functionals for materials and surface chemistry

Reliable predictions of surface chemical reaction energetics require an accurate description of both chemisorption and physisorption. Herein, we present an empirical approach to simultaneously optimize semi-local exchange and nonlocal correlation of a density functional approximation to improve these energetics. A combination of reference data for solid bulk, surface, and gas-phase chemistry and physical exchange-correlation model constraints leads to the VCML-rVV10 exchange-correlation functional. Owing to the variety of training data, the applicability of VCML-rVV10 extends beyond surface chemistry simulations. It provides optimized gas phase reaction energetics and an accurate description of bulk lattice constants and elastic properties.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Data-driven Mapping of the Mouse Connectome: The utility of transfer learning to improve the performance of deep learning models performing axon segmentation on light-sheet microscopy images

Light sheet microscopy has made possible the high temporal and spatial 3D imaging of both fixed and live biological tissue, with samples as large as the entire mouse brain. However, segmentation and quantification of that data remains a time-consuming manual process. Machine learning methods promise the possibility of automating this process. This study seeks to advance the performance of prior models through the application of refinements such as transfer learning.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗
DOE OSTI2017

Data driven modeling of plastic deformation

In this paper the application of machine learning techniques for the development of constitutive material models is being investigated. A flow stress model, for strain rates ranging from 10 –4 to 10 12 (quasi-static to highly dynamic), and temperatures ranging from room temperature to over 1000 K, is obtained by beginning directly with experimental stress-strain data for Copper. An incrementally objective and fully implicit time integration scheme is employed to integrate the hypo-elastic constitutive model, which is then implemented into a finite element code for evaluation. Accuracy and performance of the flow stress models derived from symbolic regression are assessed by comparison to Taylor anvil impact data. The results obtained with the free-form constitutive material model are compared to well-established strength models such as the Preston-Tonks-Wallace (PTW) model and the Mechanical Threshold Stress (MTS) model. Here, preliminary results show candidate free-form models comparing well with data in regions of stress-strain space with sufficient experimental data, pointing to a potential means for both rapid prototyping in future model development, as well as the use of machine learning in capturing more data as a guide for more advanced model development.

36 MATERIALS SCIENCE↗
DOE OSTI2026

A Physical Model Enhanced Data Driven Method for High-Resolution Residential Load Profile Generation

Residential buildings account for significant energy consumption, creating opportunities to offer grid services. As electric utilities seek to implement effective system operation strategies, understanding residential energy consumption patterns becomes essential; However, the time intervals of load profiles measured by utilities' smart meters are typically from 15 minutes to 60 minutes. The low-resolution data make it hard to extract appliance-level load information, which is critical for providing grid services. This paper presents a load profile generator designed to produce synthetic load profiles for residential buildings that emphasizes the importance of accurate representations of realistic energy consumption patterns. The generator takes realistic low-resolution residential load measurements and weather data as inputs, producing 1-minute interval profiles that match the characteristics of the original profiles. Further, this generator can be used to populate load profiles in areas where actual measurements are limited to improve the ability of utilities to analyze their distribution systems. By providing more high-resolution residential building load profiles, this tool supports electric utilities to enhance their residential building load control strategies and improve overall grid stability.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

A Data-driven Phytotechnology Framework for Identification and Remediation of Leached-Metals-Contaminated Soil Near Coal Ash Impoundments

This project developed and evaluated advanced remote sensing and machine learning approaches to identify, monitor, and address environmental contamination associated with coal combustion residual (CCR) impoundments and landfills at coal-fired power plants. In Phase I, historical and multi-temporal Sentinel-2 satellite imagery, groundwater monitoring data, and environmental variables were integrated to detect vegetation stress potentially caused by toxic metal leaching from coal ash disposal sites. Multiple vegetation and biophysical indices were analyzed to determine their effectiveness in identifying abnormal vegetation growth patterns linked to contamination. Results from case studies conducted at coal ash–impacted power plant sites in North Carolina and Virginia demonstrated that satellite-based vegetation monitoring can serve as an effective early indicator of environmental stress associated with metals such as arsenic, cadmium, cobalt, lead, lithium, radium, and thallium.

01 COAL, LIGNITE, AND PEAT↗