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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Phenomena Identification and Ranking Tables for High Temperature Gas-Cooled Microreactors

Micro-scaled high-temperature gas-cooled reactors (micro-HTGRs) offer a promising option for reliable power in remote or off-grid locations. While the safety characteristics of modular HTGRs have been widely studied, a micro-HTGR configuration alters several key thermal-fluid phenomena that govern both normal operation and passive decay-heat removal. In many proposed concepts, the reactor vessel is oriented horizontally and integrated into an ISO shipping container to enhance transportability and modular deployment. This report documents a Phenomena Identification and Ranking Table (PIRT) exercise focused on the thermal hydraulic safety phenomena relevant to all micro-HTGRs. The objective is to systematically identify, describe, and rank the importance, uncertainty, and modeling complexity of the key phenomena that control core and vessel temperatures during normal operation, pressurized conduction cooldown (PCC), and depressurized conduction cooldown (with air ingress) conditions.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

Giant kelp genetic monitoring before and after disturbance reveals stable genetic diversity in Southern California

Given the impacts of climate change and other anthropogenic stressors on marine systems, there is a need to accurately predict how species respond to changing environments and disturbance regimes. The use of genetic tools to monitor temporal trends in populations gives ecologists the ability to estimate changes in genetic diversity and effective population size that may be undetectable by traditional census methods. Although multiple studies have used temporal genetic analysis, they usually involve commercially important species, and rarely sample before and after disturbance. In this study, we run a temporal analysis of giant kelp, Macrocystis pyrifera , genetic diversity over the scope of 10 years (2008-2018) using the same microsatellite marker panel to assess the genetic consequences of disturbance in several populations of giant kelp ( Macrocystis pyrifera ) in the Southern California Bight. The study is a rare pre- and post-disturbance microsatellite analysis that included declines to giant kelp caused by the 2015/16 El Nino Southern Oscillation event. We used canopy biomass estimated by remote sensing (Landsat) to quantify the extent of disturbance to kelp beds, and sea surface temperature data to understand how kelp was pushed towards its temperature limits during this period. Despite prolonged periods with decreased canopy at several sites, no changes in genetic structure and allelic richness were observed. We argue that giant kelp in the region is best described as a “patchy population” system where true extinctions are rare. We discuss how deep refugia of subsurface sporophytes and cryptic microscopic life stages could have kept genetic diversity through disturbance. Given the increasing effects of climate change and uncertainty in modeling impacts of species with cryptic life history stages, we suggest further investigation to reveal the role such stages play in species resilience. Genetic monitoring studies of sites selected by remote census demographic and climate surveys should be continued in the future given the predicted impacts of climate change.

Klingbeil, III, William H.↗
DOE OSTI2023

Series FACTS Devices for Increasing Resiliency in Severe Weather Conditions

Severe weather conditions are low-probability, high-impact events that affect grid operations. The majority of power outages are caused by severe weather conditions. Grid resiliency to weather events can be enhanced by decreasing the reliance on its affected sections. One way to do this is to reduce the power flow through lines vulnerable to severe weather. If a line is disconnected, its initial power flow is distributed through the neighbor lines, which may cause congestion in the grid. FACTS devices can be used to control the power flow of lines that have a higher chance of power outages. Most previous works do not consider weather events in power flow control. In this work, a linearized optimal power flow (OPF)–based algorithm is developed to minimize the real power flow of vulnerable lines considering the thermal limits of lines to prevent infeasible solutions; the simulation is fast, making it suitable for large-scale systems. The proposed optimization problem is presented as a mixed-integer linear program (MILP), making it capable of using short-term load forecasting due to its high solution speed. The proposed optimization problem considers multiple lines with different outage probabilities and the uncertainties of the weather forecast. Moreover, it estimates the power reduction in vulnerable lines due to changes in the series FACTS devices. The performance of the proposed optimization problem is tested on IEEE 14-, 30-, and 118-bus systems for several scenarios. The results are validated with the AC power flow results from MATPOWER.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints

Type Ia supernovae (SNe Ia) are standardizable candles that must be modeled empirically to yield cosmological constraints. To understand the robustness of this modeling to variations in the model-training procedure, we build an end-to-end pipeline to test the recently developed SALT3 model. We explore the consequences of removing pre-2000s low-z or poorly calibrated U-band data, adjusting the amount and fidelity of SN Ia spectra, and using a model-independent framework to simulate the training data. We find that the SALT3 model surfaces are improved by having additional spectra and U-band data, and can be shifted by ~5% if host-galaxy contamination is not sufficiently removed from SN spectra. We find that resulting measurements of w are consistent to within 2.5% for all of the training variants explored in this work, with the largest shifts coming from variants that add color-dependent calibration offsets or host-galaxy contamination to the training spectra and those that remove pre-2000s low-z data. These results demonstrate that the SALT3 model-training procedure is largely robust to reasonable variations in the training data, but that additional attention must be paid to the treatment of spectroscopic data in the training process. We also find that the training procedure is sensitive to the color distributions of the input data—the resulting w measurement can be biased by ~2% if the color distribution is not sufficiently wide. Future low-z data, particularly u-band observations and high signal-to-noise ratio SN Ia spectra, will help to significantly improve SN Ia modeling in the coming years.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Differences in fine particle chemical composition on clear and cloudy days

Clouds are prevalent and alter fine particulate matter (PM 2.5 ) mass and chemical composition. Cloud-affected satellite retrievals are subject to higher uncertainty and are often removed from data products, hindering quantitative estimates of tropospheric chemical composition during cloudy times. We examine surface PM 2.5 chemical constituent concentrations in the Interagency Monitoring of PROtected Visual Environments (IMPROVE) network in the United States during cloudy and clear-sky times defined using Moderate Resolution Imaging Spectroradiometer (MODIS) cloud flags from 2010 to 2014 with a focus on differences in particle species that affect hygroscopicity and aerosol liquid water (ALW). Cloudy and clear-sky periods exhibit significant differences in PM 2.5 mass and chemical composition that vary regionally and seasonally. In the eastern US, relative humidity alone cannot explain differences in ALW, suggesting that emissions and in situ chemistry related to anthropogenic sources exert determining impacts. An implicit clear-sky bias may hinder efforts to quantitatively understand and improve representation of aerosol–cloud interactions, which remain dominant uncertainties in models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Analysis and Validation of Spectral Irradiance Simulations for 75 kW Bifacial Solar Test Field

This work investigates how the spectrum of irradiance incident on the rear of bifacial modules impacts their performance. Light incident on the rear of bifacial modules can be reflected from the ground, mounting structures, and other nearby objects, causing both the intensity and spectrum to be modified. As a result, PV cells and sensors with different spectral responses report different values for the rear irradiance, leading to increased uncertainty when modeling performance. Here, we calculate the spectral irradiance incident on the rear of modules through raytrace simulations and validate with measured field data collected from a 75kW single-axis-tracked bifacial test site. Spectral responses for different sensors and PV cells are applied and compared with field measurements for four days. Based on these results, a method for improving non-spectral simulations is proposed. In addition, a linear regression is applied to the modeled spectral results as a method to gain insight into the contributions of ground reflected light to total rear irradiance.

bifacial photovoltaics↗
DOE OSTI2020

BeyondPlanck I. Global Bayesian analysis of the Planck Low Frequency Instrument data

We describe the BeyondPlanck project in terms of motivation, methodology and main products, and provide a guide to a set of companion papers that describe each result in fuller detail. Building directly on experience from ESA's Planck mission, we implement a complete end-to-end Bayesian analysis framework for the Planck Low Frequency Instrument (LFI) observations. The primary product is a joint posterior distribution P(omega|d), where omega represents the set of all free instrumental (gain, correlated noise, bandpass etc.), astrophysical (synchrotron, free-free, thermal dust emission etc.), and cosmological (CMB map, power spectrum etc.) parameters. Some notable advantages of this approach are seamless end-to-end propagation of uncertainties; accurate modeling of both astrophysical and instrumental effects in the most natural basis for each uncertain quantity; optimized computational costs with little or no need for intermediate human interaction between various analysis steps; and a complete overview of the entire analysis process within one single framework. As a practical demonstration of this framework, we focus in particular on low-l CMB polarization reconstruction, paying special attention to the LFI 44 GHz channel. We find evidence of significant residual systematic effects that are still not accounted for in the current processing, but must be addressed in future work. These include a break-down of the 1/f correlated noise model at 30 and 44 GHz, and scan-aligned stripes in the Southern Galactic hemisphere at 44 GHz. On the Northern hemisphere, however, we find that all results are consistent with the LCDM model, and we constrain the reionization optical depth to tau = 0.067 +/- 0.016, with a low-resolution chi-squared probability-to-exceed of 16%. The marginal CMB dipole amplitude is 3359.5 +/- 1.9 uK. (Abridged.)

Andersen, KJ↗
DOE OSTI2021

A measurement of proton-carbon forward scattering in a proof-of-principle test of the EMPHATIC spectrometer

The next generation of long-baseline neutrino experiments will be capable of precision measurements of neutrino oscillation parameters, precision neutrino-nucleus scattering, and unprecedented sensitivity to physics beyond the Standard Model. Reduced uncertainties in neutrino fluxes are necessary to achieve high precision and sensitivity in these future precise neutrino measurements. New measurements of hadron-nucleus interaction cross sections are needed to reduce uncertainties of neutrino fluxes. We report measurements of the differential cross-section as a function of scattering angle for proton-carbon interactions with a single charged particle in the final state at beam momenta of 20, 30, and 120 GeV/c. These measurements are the result of a beam test for EMPHATIC, a hadron-scattering and hadron-production experiment. The total, elastic and inelastic cross-sections are also extracted from the data and compared to previous measurements. These results can be used in current and future long-baseline neutrino experiments, and demonstrate the feasibility of future measurements by an upgraded EMPHATIC spectrometer.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2023

Passive Safety System Reliability Analysis: Lessons Learned and Open Items

Passive safety systems have multiple benefits, largely stemming from their high functional reliability due to their simplicity and lack of dependencies. However, accurately assessing the reliability of passive systems can be challenging due in large part to the possibility of functional failures. Argonne National Laboratory (Argonne) has been involved in several recent efforts concerning the reliability assessment of passive safety systems for advanced, non-light water reactors (non-LWRs). These efforts have focused on the development and application of mechanistic methods for the evaluation of passive system reliability, which rely on system modelling and uncertainty analyses to derive reliability values for use in probabilistic safety assessments (PSAs). Further information on these efforts is provided in ref [1]. The current paper reviews key lessons learned through these projects, along with outstanding open items regarding the reliability assessment of passive systems.

Grabaskas, David↗
DOE OSTI2024

Sound at Scale: Characterizing Impacts of Noise Ordinances on the Onshore Wind Energy Technical Potential for the United States

Recent surveys have documented the rapid rise of sound ordinances across state and county jurisdictions, which has become crucial for wind energy siting. However, the lack of information on ordinances and computational challenges in turbine sound modeling create uncertainties regarding how evolving policies may affect resource potential and clean energy objectives. Therefore, we develop an approach to evaluate wind turbine sound profiles at millions of locations across the U.S. and translate them into setback distances for every residential structure. Compared to a baseline reference scenario, we find a 7% reduction in the national wind energy capacity potential when accounting for counties with existing sound ordinances. Additionally, when expanding the surveyed sound ordinances nationwide, we observe a potential loss of 53% of the national wind capacity under the most stringent ordinances, with a disproportionate share of this lost capacity coming from high-quality and low-cost wind resource. This work reveals that neglecting sound ordinances results in a significant overestimation of wind resource potential and highlights the important trade-offs between increased wind energy deployment to meet target decarbonization goals and the social/environmental impacts of this deployment that must be considered.

aeroacoustics↗
DOE OSTI2024

A Bayesian Learning Approach to Wireless Outdoor Heatmap Construction using Deep Gaussian Process

We present a novel Bayesian learning approach to outdoor radio heatmap construction utilizing deep Gaussian process (GP). The proposed approach employs a two-layer hierarchy which consists of two cascaded Gaussian processes that are capable of modeling more complex input-output relations than standard single-layer Gaussian processes. Since deriving the exact model likelihood is challenging, a lower bound is optimized instead so that gradient descent-based methods can be performed to find out the optimal model parameters. Typically, inducing points are used in GPs to facilitate low-rank approximation of covariance (kernel) matrices for computation speedup. However, the inaccuracy induced by inducing points can accumulate when stacking multiple layers of GP which may hinder the performance of deep GP. Moreover, since inducing points need to be learned, having them at all layers of deep GP also incurs computational burden. To overcome the above challenges, in contrast to the canonical deep GP model, we use a modified architecture where a full standard GP resides in the first layer and inducing points are only introduced for the second layer. This modified architecture strikes a balance between model accuracy and training complexity. In the proposed model, the noise parameter of the first GP layer is also eliminated to improve the training efficiency as the noise parameter at the output of the second layer suffices to model the uncertainty in the output. The proposed approach is evaluated on real-world datasets, in the form of location-Received Signal Strength (RSS) pairs, collected from the Platform for Open Wireless Data-driven Experimental Research (POWDER) located at the campus of the University of Utah. Experiment results show that the proposed approach can achieve smaller prediction errors on various training and testing data configurations than DNN-based and GP-based methods.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2022

Li-ion Battery Material phase prediction through Hierarchical Curriculum Learning

Li-ion Batteries (LIB), one of the most efficient energy storage devices, are widely adopted in many industrial applications. Imaging data of these battery electrodes obtained from X-ray tomography can explain the distribution of material constituents and allow reconstructions to study electron transport pathways. Therefore, it can eventually help quantify various associated properties of electrodes (e.g., volume-specific surface area, porosity) which determine the performance of batteries. However, these images often suffer from low image contrast between multiple material constituents , making it difficult for humans to distinguish and characterize these constituents through visualization. A minor error in detecting distributions among the material constituents can lead to a high error in the calculated parameters of material properties.We present a novel hierarchical curriculum learning framework to address the complex task of estimating material constituent distribution in battery electrodes. To provide spatially smooth prediction, our framework comprises three modules: (i) an uncertainty-aware model trained to yield inferences conditioned upon global knowledge of material distribution, (ii) a technique to capture relatively more fine-grained (local) distributional signals, (iii) an aggregator to appropriately fuse the local and global effects towards obtaining the final distribution.

Tabassum, Anika↗