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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 775 records · Page 43
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Behavioral and Population Data-Driven Distribution System Load Modeling

Distribution system residential load modeling and analysis for different geographic areas within a utility or an independent system operator territory are critical for enabling small-scale, aggregated distributed energy resources to participate in grid services under Federal Energy Regulatory Commission Order No. 2222 [1]. In this study, we develop a methodology of modeling residential load profiles in different geographic areas with a focus on human behavior impact. First, we construct a behavior-based load profile model leveraging state-of-the-art appliance models. We simulate human activity and occupancy using Markov chain Monte Carlo methods calibrated with the American Time Use Survey data set. Second, we link our model with cleaned Current Population Survey data from the U.S. Census Bureau. Finally, we populate two sets of 500 households using California and Texas census data, respectively, to perform an initial analysis of the load in different geographic areas with various group features (e.g., different income levels). To distinguish the effect of population behavior differences on aggregated load, we simulate load profiles for both sets assuming fixed physical household parameters and weather data. Analysis shows that average daily load profiles vary significantly by income and income dependency varies by locality.

American Time Use Survey↗
DOE OSTI2021

Dark Energy Survey Y3 results: blending shear and redshift biases in image simulations

ABSTRACT As the statistical power of galaxy weak lensing reaches per cent level precision, large, realistic, and robust simulations are required to calibrate observational systematics, especially given the increased importance of object blending as survey depths increase. To capture the coupled effects of blending in both shear and photometric redshift calibration, we define the effective redshift distribution for lensing, nγ(z), and describe how to estimate it using image simulations. We use an extensive suite of tailored image simulations to characterize the performance of the shear estimation pipeline applied to the Dark Energy Survey (DES) Year 3 data set. We describe the multiband, multi-epoch simulations, and demonstrate their high level of realism through comparisons to the real DES data. We isolate the effects that generate shear calibration biases by running variations on our fiducial simulation, and find that blending-related effects are the dominant contribution to the mean multiplicative bias of approximately $-2{{\ \rm per\ cent}}$. By generating simulations with input shear signals that vary with redshift, we calibrate biases in our estimation of the effective redshift distribution, and demonstrate the importance of this approach when blending is present. We provide corrected effective redshift distributions that incorporate statistical and systematic uncertainties, ready for use in DES Year 3 weak lensing analyses.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Federated Machine Learning-Based Anomaly Detection System for Synchrophasor Network Using Heterogeneous Data Sets: Preprint

Synchrophasor technology is widely deployed in the energy management system to monitor the grid health at micro level and perform necessary corrective actions in real time; however, integrated phasor devices and data aggregators are exposed to several cybersecurity threats. This paper proposes a federated ML(FML)-based ADS to detect several data integrity attacks in the synchrophasor network. The proposed approach integrates the horizontal FML technique and consists of substation-based local models and a control center-based global model. The proposed methodology includes training local models using heterogeneous data sets that include network and grid information and updating the global model through multiple iterations by sharing model gradients. Finally, the trained global model is applied to identify cyberattacks, normal operation, and physical events. To validate the proof of concept, we used synthetic data sets generated by Mississippi State University and Oak Ridge National Laboratory for training and testing the classification models using the National Renewable Energy Laboratory's high performance computing resources. Our experimental results, computed through several performance measures, reveal that the proposed approach shows consistent performance during the binary, three-class, and multiclass classifications while ensuring privacy of synchrophasor data.

anomaly detection system↗
DOE OSTI2020

Paradox of sustainability in tobacco control in Thailand: A comprehensive assessment of three-decade experiences

Background: Over the past three decades of tobacco control, Thailand has gained international recognition as a middle-income country with sustained achievement of declining smoking prevalence. However, the number of key FCTC measures implementation is still far away from the highest-level implementation. As a result, we aim to explore explanatory factors for the paradoxical phenomenon of sustainability in tobacco control in Thailand, to understand what the paradox means, why it happens, and how to take further steps in minimizing the paradox. Methods: We used a mixed-method approach comprising qualitative (review of literature and documents plus Program Sustainability Assessment Tool (PSAT) guided key informant interviews) and semi-quantitative methods (PSAT scoring, Theory of change (TOC), and causal-loop diagram (CLD)) to synthesize all the findings from the qualitative data. Results: Across all eight domains, sustainability scores at the local level are lower than the national level. The highest total score was in three domains: political support, partnership, and organizational capacity. The lowest total score was for the strategic planning domain. We propose a set of key strategic elements and drivers for future strategic planning. Discussion: Using CLD, we capture a high-level view of tobacco control with dynamic interactions between contexts, mechanisms, interventions, and outcomes. Finally, we believe the deep understanding of tobacco control and the proposed strategy to counteract transnational tobacco companies in Thailand will guide future sustainable actions to reduce the prevalence of smoking, especially in the strategic planning domain that has the lowest PSAT score.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Physics basis for the reference flat-top plasma scenario in the ST–E1 fusion power plant

As part of the U.S. Department of Energy’s Milestone-Based Fusion Energy Development Program, Tokamak Energy has completed the pre-concept design of the ST–E1 fusion power plant. ST–E1 is envisaged to operate in two phases: a pilot plant phase, targeting sustained net power production of 300 - 500 MWe for a duration >1 hr, followed by a commercial power plant phase targeting steady-state operations and a normalised overnight capital cost of ⩽12 000 $\$$/kWe. The design process adopted was highly iterative, integrating all major plant systems and progressing in a phased fidelity approach. At the pre-conceptual stage, the emphasis has been on exploring the design space, identifying the main system-level trade-offs, and making the key decisions that define the overall plant concept, rather than optimising a single operating point. This paper, part of a focused collection detailing the ST–E1 pre-concept design, addresses the development of a series of reference flat-top plasma operating points for the pilot plant phase. A modelling workflow was established to develop and assess candidate plasma design points and explore key dependencies. The workflow includes integrated core plasma modelling, magnetohydrodynamic (MHD) stability assessment, equilibrium generation, scrape-off-layer and exhaust modelling, heating & current drive design and optimisation, and turbulent transport modelling. Using this framework, the impact of several key parameters on the flat-top operating space was investigated, including the density limit, core radiation fraction and divertor power loading, level of external heating and curent drive power and assumed pedestal characteristics. The MHD stability, controllability and micro-stability characteristics of these plasmas were also analysed. These investigations informed the definition of a set of fully non-inductive, flat-top reference operating points that satisfy the high-level ST–E1 mission, including a low and high density case, a case that is stable to resistive wall modes and a case with reduced divertor power loading.

ST–E1↗
DOE OSTI2024

Delayed coastal inundations caused by ocean dynamics post-Hurricane Matthew

Post Hurricane Abnormal Water Level (PHAWL) poses a persistent inundation threat to coastal communities, yet unresolved knowledge gaps exist regarding its spatiotemporal impacts and causal mechanisms. Using a high-resolution coastal model with a set of observations, we find that the PHAWLs are up to 50 cm higher than the normal water levels for several weeks and cause delayed inundations around residential areas of the U.S. Southeast Coast (USSC). Numerical experiments reveal that while atmospheric forcing modulates the coastal PHAWLs, ocean dynamics primarily driven by the Gulf Stream control the mean component and duration of the shelf-scale PHAWLs. Because of the large spatial impact of the post-hurricane oceanic forcing, the coastal hazards are not limited to a direct hit from a hurricane but can be detected throughout the USSC where the oceanic processes reach.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Topical Group on Application and Industry Community Engagement Frontier Snowmass 2021 (Summary Report)

HEP community leads and operates cutting-edge experiments for the DOE Office of Science which have challenging sensing, data processing, and computing requirements that far surpass typical industrial applications. To make necessary progress in the energy, material, and fundamental sciences, development of novel technologies is often required to enable these advanced detector and accelerator programs. Our capabilities include efficient co-design, which is a prerequisite to enable the deployment of advanced techniques in a scientific setting where development spans from rapid prototyping to robust and reliable production scale. This applies across the design spectrum from the low level fabrication techniques to the high level software development. It underpins the requirement for a holistic approach of innovation that accelerates the cycle of technology development and deployment. The challenges set by the next generation of experiments requires a collaborative approach between academia, industry and national labs. Just a single stakeholder will be unable to deliver the technologies required for the success of the scientific goals. Tools and techniques developed for High Energy Physics (HEP) research can accelerate scientific discovery more broadly across DOE Office of Science and other federal initiatives and also benefit industry applications.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Evaluating the Capabilities of Behind-the-Meter Solar-plus-Storage for Providing Backup Power during Long-Duration Power Interruptions [Slides]

The study estimates the performance of behind-the-meter solar PV-plus-energy-storage-systems (PVESS) in providing critical-load or whole-building backup across a wide range of geographies, building types, and power interruption conditions. The study also considers a set of 10 historical long-duration power outage events and evaluates how PVESS could have performed in providing backup power during those specific events. The analysis is the first in what will be a series of studies by Berkeley Lab, in collaboration with the National Renewable Energy Laboratory, on the use of PVESS for backup power. This initial study, which relies on simulated end-use level building loads, solar generation, and storage dispatch, is intended to provide a baseline set of performance estimates and to illustrate key performance drivers.

14 SOLAR ENERGY↗
DOE OSTI2020

Athena - Shuttle Bus Optimization and Event Driven Simulator [SWR 20-105]

The primary purpose of the software is to optimize shuttle routes to and from terminals, given some airport campus location like the rental car center. This code was developed as part of the DOE funded Athena project. One aspect of the Athena project was to investigate if the optimization of shuttle routes that move passengers to and from the five DFW terminals and the rental car center could result in lower energy consumption from the shuttle fleet while still maintaining an acceptable level of service to passengers. Hence, the optimization done by the code determines a set of shuttle routes, the type of shuttle on each route, and the number of shuttles servicing each route. The optimization aims to make these choices in a way that the energy consumption per hour by the fleet is minimized. Additionally, the software can fast simulate airport shuttle operations with collected data. The simulation inputs are passenger arrival rates, shuttle routes and frequencies, and simulation configurations. The outputs include: time dependent shuttle energy level, time dependent charging station usage, time dependent number of passengers on each bus, time dependent number of passengers at each bus stop, history of number of passengers left at each bus stop, route history of fleet shuttle buses and on-demand buses, and time dependent bus distance traveled.

Kelly, Kenneth↗
DOE OSTI2021

Iterative self-organizing SCEne-LEvel sampling (ISOSCELES) for large-scale building extraction

Convolutional neural networks (CNN) provide state-of-the-art performance in many computer vision tasks, including those related to remote-sensing image analysis. Successfully training a CNN to generalize well to unseen data, however, requires training on samples that represent the full distribution of variation of both the target classes and their surrounding contexts. With remote sensing data, acquiring a sufficiently representative training set is a challenge due to both the inherent multi-modal variability of satellite or aerial imagery and the general high cost of labeling data. To address this challenge, we have developed ISOSCELES, an Iterative Self-Organizing SCEne LEvel Sampling method for hierarchical sampling of large image sets. Using affinity propagation, ISOSCELES automates the selection of highly representative training images. Compared to random sampling or using available reference data, the distribution of the training is principally data driven, reducing the chance of oversampling uninformative areas or undersampling informative ones. In comparison to manual sample selection by an analyst, ISOSCELES exploits descriptive features, spectral and/or textural, and eliminates human bias in sample selection. Using a hierarchical sampling approach, ISOSCELES can obtain a training set that reflects both between-scene variability, such as in viewing angle and time of day, and within-scene variability at the level of individual training samples. We verify the method by demonstrating its superiority to stratified random sampling in the challenging task of adapting a pre-trained model to a new image and spatial domain for country-scale building extraction. Using a pair of hand-labeled training sets comprising 1,987 sample image chips, a total of 496,000,000 individually labeled pixels, we show, across three distinct model architectures, an increase in accuracy, as measured by F1-score, of 2.2–4.2%.

42 ENGINEERING↗
DOE OSTI2025

Estimating soybean yields from high-temporal-resolution multi-source data using deep learning

Accurate and timely crop yield prediction is crucial for ensuring food security and maintaining stable agricultural markets. In recent years, there has been a surge in interest in leveraging high-temporal-resolution, multi-source data for effective crop growth monitoring and yield estimation. A notable challenge arises from the difficulty in capturing the intricate interactions between variables across different time steps within these high-temporal-resolution time series datasets. This complexity hinders the reliable extraction of yield information from voluminous and often noisy datasets, especially during periods of extreme weather events. Here, in this study, we propose an Attention and Graph Isomorphism Network-enhanced Bi-directional Long Short-Term Memory network (AGB-LSTM) for estimating county-level soybean yield in the United States. This model integrates a diverse set of remote sensing data, including Near-Infrared Reflectance of Vegetation (NIRv), Sun-Induced chlorophyll Fluorescence (SIF), and Gross Primary Productivity (GPP), along with environmental covariates. The AGB-LSTM effectively leverages information related to crop yield from high-temporal-resolution time series data (5-days), achieving an accuracy of R²= 0.67 and rRMSE = 14.46%. This approach significantly outperforms traditional machine learning methods such as Random Forest (RF) (R²= 0.52, rRMSE = 17.36%) and Bi-LSTM (R²= 0.58, rRMSE = 16.17%). Sensitivity experiments with different time steps and ranges demonstrated that our model could accurately and stably predict yields 1 to 2 months before harvest. Moreover, data with a finer temporal resolution consistently improved prediction performance, resulting in an approximately 20% increase in and an approximately 20% decrease in rRMSE compared to using monthly composites. We also evaluated the robustness of the model under extreme climate events and observed strong performance (R²= 0.50, rRMSE = 21.32%). Finally, yield mapping for major soybean-producing regions in North America in 2023 revealed spatial patterns that closely matched USDA yield reports. Our findings suggest that the AGB-LSTM model is a promising and effective method for estimating yield and has notable potential for global crop yield forecasting.

Deep learning↗
DOE OSTI2022

Diffusion of cesium in oxidized and unoxidized IG-110 nuclear graphite

Time-release diffusion measurements of cesium have been conducted over the temperature range 1073 K – 1973 K on oxidized and unoxidized IG-110 graphite. Four cesium concentrations were tested to investigate the concentration dependence of the diffusion coefficient. Two levels of oxidation were tested and compared to unoxidized concentration-matched sets to explore the effects of graphite oxidation. Here, the results demonstrate that cesium diffusion coefficient in unoxidized IG-110 graphite is independent of concentration within the range 34 – 163 µg Cs/g graphite . Above this, the effective cesium diffusion coefficient changes with concentration. The diffusion coefficient was increased by a factor of 2–12 in the oxidized set with 7.8% mass loss. These results can be used to aid predictive modeling of cesium diffusion in HTGR cores.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Analysis and mitigation of parasitic resistance effects for analog in-memory neural network acceleration

To support the increasing demands for efficient deep neural network processing, accelerators based on analog in-memory computation of matrix multiplication have recently gained significant attention for reducing the energy of neural network inference. However, analog processing within memory arrays must contend with the issue of parasitic voltage drops across the metal interconnects, which distort the results of the computation and limit the array size. This work analyzes how parasitic resistance affects the end-to-end inference accuracy of state-of-the-art convolutional neural networks, and comprehensively studies how various design decisions at the device, circuit, architecture, and algorithm levels affect the system's sensitivity to parasitic resistance effects. Here, a set of guidelines are provided for how to design analog accelerator hardware that is intrinsically robust to parasitic resistance, without any explicit compensation or re-training of the network parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Rare multinucleon decays with the full data sets of the M AJORANA D EMONSTRATOR

The M AJORANA D EMONSTRATOR was an ultra-low-background experiment designed for neutrinoless double-beta decay (0⁢𝜈⁢𝛽⁢𝛽) investigation in 76 Ge . Located at the Sanford Underground Research Facility in Lead, South Dakota, the D EMONSTRATOR utilized modular high-purity Ge detector arrays within shielded vacuum cryostats, operating deep underground. The arrays, with a capacity of up to 40.4 kg (27.2 kg enriched to ∼88% in 76 Ge ), have accumulated the full data set, totaling 64.5 kg yr of enriched active exposure and 27.4 kg yr of exposure for natural detectors. Here, our updated search improves previously explored three-nucleon decay modes in Ge isotopes, setting new partial lifetime limits of 1.83 × 10 26 yr (90% confidence level) for 76 Ge (𝑝⁢𝑝⁢𝑝) → 73 Cu 𝑒 + ⁢𝜋 + ⁢𝜋 + and 76 Ge (𝑝⁢𝑝⁢𝑛) → 73 Zn 𝑒 + ⁢𝜋 + . The partial lifetime limit for the fully inclusive triproton decay mode of 76 Ge is found to be 2.1×10 25 yr. Furthermore, we have updated limits for corresponding multinucleon decays.

baryon & lepton number symmetries↗
DOE OSTI2024

Revisiting leptonic nonunitarity

In the presence of extra neutrino states at high scales, the low-energy effective 3 × 3 leptonic mixing matrix (LMM) is in general nonunitary. We revisit the question of what is our current knowledge of individual LMM matrix elements without assuming unitarity. We first demonstrate that a minimal set of experimental constraints suffices in bounding LMM nonunitarity parameters to the level of O ( 10 − 3 ) , without the use of neutrino oscillation data. We then revisit oscillation results as a complementary cross-check, using different physics and different experimental techniques to probe a similar parameter space. We correct some common misconceptions in the neutrino nonunitarity literature resulting from an incautious treatment of input parameters. We find that neutrino oscillation experiments can constrain LMM nonclosure, but, contrary to claims in the literature, are completely insensitive to the overall normalization of the LMM. Thus, we conclude that oscillation experiments, including the future DUNE experiment, have no power in excluding nonunitarity altogether. Published by the American Physical Society 2024

Astronomy & Astrophysics↗
DOE OSTI2025

Learning with Adaptive Conservativeness for Distributionally Robust Optimization: Incentive Design for Voltage Regulation

Information asymmetry between the Distribution System Operator (DSO) and Distributed Energy Resource Aggregators (DERAs) obstructs designing effective incentives for voltage regulation. To capture this effect, we employ a Stackelberg game-theoretic framework, where the DSO seeks to overcome the information asymmetry and refine its incentive strategies by learning from DERA behavior over multiple iterations. We introduce a model-based online learning algorithm for the DSO, aimed at inferring the relationship between incentives and DERA responses. Given the uncertain nature of these responses, we also propose a distributionally robust incentive design model to control the probability of voltage regulation failure and then reformulate it into a convex problem. This model allows the DSO to periodically revise distribution assumptions on uncertain parameters in the decision model of the DERA. Finally, we present a gradient-based method that permits the DSO to adaptively modify its conservativeness level, measured by the size of a Wasserstein metric-based ambiguity set, according to historical voltage regulation performance. The effectiveness of our proposed method is demonstrated through numerical experiments.

adaptation models↗
DOE OSTI2022

Optimizing Sensor Count and Placement to Detect Bond Wire Lift-Offs and Surface Defects in High-Power IGBT Modules Using Low-Cost Piezo-Electric Resonators

This manuscript presents the most recent results and findings to identify bond wire lift-offs and surface defects in high-power isolated gate bipolar junction transistor (IGBT) modules. The authors of this manuscript formerly proposed a low-cost, piezoelectric resonator-based measurement unit to detect bond wire related degradation in larger IGBT modules. Since high-power IGBT modules are expensive, it was apparent that evaluating our proposed method by inducing controlled damage to fresh (new) IGBTs may not be cost-effective especially when multiple sets of data need to be captured by inducing damage to different levels. In order to overcome this limitation, IGBT bond wires have been mimicked using a 3D printed enclosure, a PCB, and copper wires with dimensions very closely resembling a real IGBT. Using this method, multiple test devices can be built at the cost of a real IGBT, and the proposed technique could be fine-tuned without damaging expensive, real IGBTs. Our recent findings can be used to determine real IGBT degradation and bond wire lift-offs using only two sensors, as opposed to six transducers used in the first iteration of the setup. In addition to optimizing the sensor count, we have also identified the best possible locations of these sensors by attempting multiple placements inside the IGBT casing.

condition monitoring↗
DOE OSTI2022

Modeling pre-Exascale AMR Parallel I/O Workloads via Proxy Applications

The present work investigates the modeling of preexascale input/output (I/O) workloads of Adaptive Mesh Refinement (AMR) simulations through a simple proxy application. We collect data from the AMReX Castro framework running on the Summit supercomputer for a wide range of scales and mesh partitions for the hydrodynamic Sedov case as a baseline to provide sufficient coverage to the formulated proxy model. The non-linear analysis data production rates are quantified as a function of a set of input parameters such as output frequency, grid size, number of levels, and the Courant-Friedrichs-Lewy (CFL) condition number for each rank, mesh level and simulation time step. Linear regression is then applied to formulate a simple analytical model which allows to translate AMReX inputs into MACSio proxy I/O application parameters, resulting in a simple “kernel” approximation for data production at each time step. Results show that MACSio can simulate actual AMReX nonlinear “static” I/O workloads to a certain degree of confidence on the Summit supercomputer using the present methodology. The goal is to provide an initial level of understanding of AMR I/O workloads via lightweight proxy applications models to facilitate autotune data management strategies in anticipation of exascale systems.

Godoy, William↗