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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 667 records · Page 37
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Nonlinear response of hail precipitation rate to environmental moisture content: A real case modeling study of an episodic midlatitude severe convective event: Hail and Environmental Moisture

The dependence of hail production on initial moisture content in a simulated midlatitude episodic convective event occurred in northeast China on 10–11 June 2005 was investigated using the Weather Research and Forecasting (WRF) model with a double-moment microphysics scheme where both graupel and hail are considered. Three sensitivity experiments were performed by modifying the initial water vapor mixing ratio profile to 90% (“Q-10%”), 105% (“Q+5%”), and 110% (“Q+10%”) of the initial conditions used for the control simulation. It was found that increasing the initial water vapor content caused the hail and total precipitation rates to increase during the first 5 h. The precipitation response to increasing water vapor content was monotonic for this first episode; however, for the event's second episode, the hail precipitation rate responds to the initial water vapor profile nonlinearly, while the total precipitation rate responds mostly monotonically. In particular, simulation Q+5% achieves the largest hail production rate while simulation Q+10% has the largest total precipitation rate. In contrast, during the second episode simulation Q-10% has the strongest vertical motion, produces the most cloud ice and snow, but has the lowest hail production. Analysis shows that increasing the initial moisture content directly increases the precipitation during the first episode, which subsequently induces a stronger, longer-asting cold pool that limits the development of deep convection during the second episode.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

High-Resolution Simulation of Snowfall over the Korean Eastern Coastal Region Using WRF Model: Sensitivity to Domain Nesting-Down Strategy

The eastern coastal area of the Korean Peninsula, Yeongdong region, often experiences severe snowfall during the winter season. We studied here the development mechanism of a snowfall case occurred in February 2014 using the Weather Research and Forecasting (WRF) model at the convection-permitting resolution of 1 km. The model reasonably captured the observed snowfall, and three factors were discovered as important contributors to this snowfall case: a temperature contrast between land and ocean, a high-pressure system over the northeastern part of the Korean Peninsula, and the mountainous terrain. Cold and dry air blown from the high-pressure system gains heat and moisture when it passes over the East Sea enhancing the low-level instability. When the air mass reaches the coastline, updraft is generated by the density difference with the continental atmosphere and by the mountain slope. Further sensitivity experiments were conducted by adjusting strategies of nesting down (offline and online). Sensitivity experiments consist of three offline nesting experiments with different boundary condition update frequencies in 6, 3 and 1 h and one online nesting experiment that updates the boundary condition every integration time step. The major snowfall along the mountain range located in the middle of domain does not change significantly regardless of the nesting frequency. The offline nesting causes unphysical discontinuities of precipitation along the north and east sides of lateral boundary where wind blows in, which are caused by the absence of condensed hydrometeor variables in the lateral boundary condition. The domain nesting strategy tested in this study has dramatically impacted on the upper troposphere structure in the model: artificial fluctuations at the upper troposphere in the offline nesting simulations were significantly alleviated in the online nesting simulation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Understanding Subseasonal Moisture Recycling from Extreme MCSs Using a Land–Atmosphere Coupled Water Tracer Model

Extreme precipitation events often produce severe flooding and pose significant threats to our society. However, their subseasonal impacts on the terrestrial and atmospheric systems are not well understood. Here, we use a new land–atmosphere coupled water tracer tool embedded in the Weather Research and Forecasting (WRF) Model to “tag” precipitation produced by a series of extreme mesoscale convective systems (MCSs) in May 2015 over the southern Great Plains to reveal their contributions to different terrestrial water storages and moisture recycling during May–July. During May, we find that precipitation from earlier MCSs can contribute to 20%–50% of total evapotranspiration (ET) in later May and contribute ∼10% of local precipitation for later MCSs. After May, the water “tagged” to preceding May MCS events has the most active contribution to moisture recycling during the first 5 days in June, but this contribution diminishes quickly afterward. Both periods indicate a quick turnover of tagged precipitation, which may be representative of the southern Great Plains region. The quick turnover is closely associated with the direct evaporation from the soil surface due to the predominant shrubland and grassland coverage over this region. Over some forested areas, however, the tracer-ET flux is dominated by transpiration that stably supplies a small fraction (<1%) of moisture to the atmosphere throughout June and July. In conclusion, the tracer-contributed plume (through evaporation and transpiration) in atmospheric moisture can extend 500–700 km downstream between May and July, highlighting the far-reaching contribution to moisture recycling and precipitation from the tagged MCS events in the southern Great Plains.

Atmosphere-land interaction↗
DOE OSTI2025

Self-Supervised T-GCN for Detection of Disturbance and Propagation in Power Grid

Urban power systems increasingly rely on dense sensing to monitor grid reliability, yet disturbance labels are scarce and events are rare. We present a self-supervised spatio-temporal method that detects, localizes, and characterizes grid frequency disturbances across urban areas using only unlabeled data. Our approach trains a tiny Temporal Graph Convolutional Network (T-GCN) to forecast per-site frequency residuals (deviation from 60 Hz). The sensor graph is constructed directly from signals using pre-event Pearson correlation with a cross-correlation lag penalty without geocoding. At inference, node-level anomalies are the model's forecast errors; region-level alarms arise from connected components of high-score nodes. We estimate disturbance propagation by computing per-node arrival times (first persistent exceedance), then fit a planar or time-of-arrival model to obtain direction, speed, and an epicenter proxy. With only three real events collected at decisecond resolution across U.S. cities, we evaluate the T-GCN and report time-to-detect, footprint size, and propagation consistency. We further show that short-window embeddings from the T-GCN's hidden states enable few-shot event-vs-background recognition via a simple prototypical classifier. Despite minimal data and no labels, our system yields fast, spatially coherent detection and interpretable propagation maps, offering a practical, lightweight pathway to city-scale grid resilience analytics.

Niu, Haoran [ORNL] (ORCID:0000000155228297)↗
DOE OSTI2022

Simulated wind speed and initial conditions over the WFIP2 region: Cold-front (D01)

The purpose of this work is to assess the sensitivity of the forecast for turbine height wind speed to initial condition (IC) uncertainties over the Columbia River Gorge and Columbia River Basin for two typical weather phenomena: a local thermal gradient induced by a marine air intrusion and passage of a cold front. The Weather Research and Forecasting (WRF) model data assimilation system (WRFDA) was used to generate ensemble ICs from the North American Regional Analysis (NARR) for the WRF model initialization. The simulated turbine-height wind speeds were categorized into four types using the self-organizing map (SOM) technique. This work advances understanding of IC uncertainties impacts on wind speed forecasts and locates the high-impact regions.

17 WIND ENERGY↗
DOE OSTI2022

Simulated wind speed and initial conditions over the WFIP2 region: Cold-front (D02)

The purpose of this work is to assess the sensitivity of the forecast for turbine height wind speed to initial condition (IC) uncertainties over the Columbia River Gorge and Columbia River Basin for two typical weather phenomena: a local thermal gradient induced by a marine air intrusion and passage of a cold front. The Weather Research and Forecasting (WRF) model data assimilation system (WRFDA) was used to generate ensemble ICs from the North American Regional Analysis (NARR) for the WRF model initialization. The simulated turbine-height wind speeds were categorized into four types using the self-organizing map (SOM) technique. This work advances understanding of IC uncertainties impacts on wind speed forecasts and locates the high-impact regions.

17 WIND ENERGY↗
DOE OSTI2022

Simulated wind speed and initial conditions over the WFIP2 region: Sea-breeze (D01)

The purpose of this work is to assess the sensitivity of the forecast for turbine height wind speed to initial condition (IC) uncertainties over the Columbia River Gorge and Columbia River Basin for two typical weather phenomena: a local thermal gradient induced by a marine air intrusion and passage of a cold front. The Weather Research and Forecasting (WRF) model data assimilation system (WRFDA) was used to generate ensemble ICs from the North American Regional Analysis (NARR) for the WRF model initialization. The simulated turbine-height wind speeds were categorized into four types using the self-organizing map (SOM) technique. This work advances understanding of IC uncertainties impacts on wind speed forecasts and locates the high-impact regions.

17 WIND ENERGY↗
DOE OSTI2022

Simulated wind speed and initial conditions over the WFIP2 region: Sea-breeze (D02)

The purpose of this work is to assess the sensitivity of the forecast for turbine height wind speed to initial condition (IC) uncertainties over the Columbia River Gorge and Columbia River Basin for two typical weather phenomena: a local thermal gradient induced by a marine air intrusion and passage of a cold front. The Weather Research and Forecasting (WRF) model data assimilation system (WRFDA) was used to generate ensemble ICs from the North American Regional Analysis (NARR) for the WRF model initialization. The simulated turbine-height wind speeds were categorized into four types using the self-organizing map (SOM) technique. This work advances understanding of IC uncertainties impacts on wind speed forecasts and locates the high-impact regions.

17 WIND ENERGY↗
DOE OSTI2020

Modeling and Detection of Future Cyber-Enabled DSM Data Attacks

Demand-Side Management (DSM) is an essential tool to ensure power system reliability and stability. In future smart grids, certain portions of a customer’s load usage could be under the automatic control of a cyber-enabled DSM program, which selectively schedules loads as a function of electricity prices to improve power balance and grid stability. In this scenario, the security of DSM cyberinfrastructure will be critical as advanced metering infrastructure and communication systems are susceptible to cyber-attacks. Such attacks, in the form of false data injections, can manipulate customer load profiles and cause metering chaos and energy losses in the grid. The feedback mechanism between load management on the consumer side and dynamic price schemes employed by independent system operators can further exacerbate attacks. To study how this feedback mechanism may worsen attacks in future cyber-enabled DSM programs, we propose a novel mathematical framework for (i) modeling the nonlinear relationship between load management and real-time pricing, (ii) simulating residential load data and prices, (iii) creating cyber-attacks, and (iv) detecting said attacks. In this framework, we first develop time-series forecasts to model load demand and use them as inputs to an elasticity model for the price-demand relationship in the DSM loop. This work then investigates the behavior of such a feedback loop under intentional cyber-attacks. We simulate and examine load-price data under different DSM-participation levels with three types of random additive attacks: ramp, sudden, and point attacks. We conduct two investigations for the detection of DSM attacks. The first studies a supervised learning approach, with various classification models, and the second studies the performance of parametric and nonparametric change point detectors. Results conclude that higher amounts of DSM participation can exacerbate ramp and sudden attacks leading to better detection of such attacks, especially with supervised learning classifiers. We also find that nonparametric detection outperforms parametric for smaller user pools, and random point attacks are the hardest to detect with any method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Closing the loop: model-predictive control for a closed-circuit reverse osmosis system

This article presents a model-predictive controller (MPC) for the maximization of the energy efficiency of a closed-circuit desalination reverse osmosis (CCRO) system. CCRO is a process for producing drinking water that is based on a cyclic operation with the following two phases: (a) filtration and (b) drain. In this article, we test model predictive control for optimal control of this process. The most important features of our approach are as follows: (a) the selection of a model structure that enables reliable forecasts of the filtration phase (up to 3 h), (b) an on-line model calibration strategy that ensures model forecast reliability, and (c) the satisfaction of equipment safety and operational constraints on the selected setpoints. We challenge this through deliberate introduction of changes in the unmeasured feed concentration and the applied constraints. Our results indicate that frequent model parameter updates are critical to maintain model reliability for MPC purposes. In addition, we illustrate that parameter identifiability is not guaranteed and that deliberate variation in flow rates is necessary even though the process never operates in steady state. Finally, MPC can compute flow rate setpoints that maximize the energy efficiency of the CCRO process while satisfying the applicable equipment and safety constraints.

closed-circuit reverse osmosis↗
DOE OSTI2019

A two-step short-term probabilistic wind forecasting methodology based on predictive distribution optimization

With increasing wind penetrations into electric power systems, probabilistic wind forecasting becomes more critical to power system operations because of its capability of quantifying wind uncertainties. In this paper, a two-step probabilistic wind forecasting approach based on pinball loss optimization is developed. First, a multimodel machine learning-based ensemble deterministic forecasting framework is adopted to generate deterministic forecasts. The deterministic forecast is assumed to be the mean value of the predictive distribution at each forecasting time stamp. Then, the optimal unknown parameter (i.e., standard deviation) of the predictive distribution is estimated by a support vector regression surrogate model based on the deterministic forecasts. Lastly, probabilistic forecasts are generated from the predictive distribution. Numerical results of case studies at eight locations show that the developed two-step probabilistic forecasting methodology has improved the pinball loss metric score by up to 35% compared to a baseline quantile regression forecasting model.

17 WIND ENERGY↗
DOE OSTI2022

Evaluation of coupled wind-wave model simulations of offshore winds in the Mid-Atlantic Bight using lidar-equipped buoys.

From 2014 to 2017, two Department of Energy buoys equipped with Doppler lidar were deployed off the U.S. East Coast to provide long term measurements of hub-height wind speed in the marine environment. In this study, we performed simulations of selected cases from the deployment using a 5-km configuration of the Weather Research and Forecasting (WRF) model, to see if simulated hub height speeds could produce closer agreement with the observations than existing reanalysis products. For each case we performed two additional simulations: one in which marine surface roughness height was one-way coupled to forecast wave parameters from a standalone WaveWatch III (WW3) simulation, and another in which WRF and WW3 were two-way coupled using the Coupled-Ocean-Atmosphere-Wave-Sediment-Transport (COAWST) framework. It was found that all the 5-km WRF simulations improved 90-m wind speed statistics for the tropical cyclone case of 08 May 2015 and the cold frontal case of 25 Mar 2016, but not the nor-easter of 18 Jan 2016. The impact of wave coupling on buoy-level (4 m) wind speed was modest and case dependent, but when present, the impact was typically seen at 90 m as well, being as large as 10% in stable conditions. One-way wave coupling consistently reduced wind speeds, improving biases for 25 Mar 2016 but worsening them for 08 May 2015. Two-way wave coupling mitigated these negative biases, improved wave field representation and statistics, and mostly improved 4-m wind field correlation coefficients, at least at the VA buoy, largely due to greater self-consistency between wind and wave fields.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Disentangling magnification in combined shear-clustering analyses

ABSTRACT We investigate the sensitivity to the effects of lensing magnification on large-scale structure analyses combining photometric cosmic shear and galaxy clustering data (i.e. the now commonly called ‘3 × 2-point’ analysis). Using a Fisher matrix bias formalism, we disentangle the contribution to the bias on cosmological parameters caused by ignoring the effects of magnification in a theory fit from individual elements in the data vector, for Stage-III and Stage-IV surveys. We show that the removal of elements of the data vectors that are dominated by magnification does not guarantee a reduction in the cosmological bias due to the magnification signal, but can instead increase the sensitivity to magnification. We find that the most sensitive elements of the data vector come from the shear-clustering cross-correlations, particularly between the highest redshift shear bin and any lower redshift lens sample, and that the parameters ΩM, $S_8=\sigma _8\sqrt{\Omega _\mathrm{ M}/0.3}$, and w0 show the most significant biases for both survey models. Our forecasts predict that current analyses are not significantly biased by magnification, but this bias will become highly significant with the continued increase of statistical power in the near future. We therefore conclude that future surveys should measure and model the magnification as part of their flagship ‘3 × 2-point’ analysis.

Thiele, Leander↗
DOE OSTI2025

BatteryPro: A Python Toolkit for Battery Data Analysis and Machine Learning Predictions

Analyzing battery test data for research & development can be time-consuming since battery tests often run on the order of months to years, generating large volumes of data. BatteryPro is a comprehensive Python package and software designed to facilitate advanced analysis and performance predictions for battery test data. Developed for battery researchers, it supports data types from widely used battery testing instruments, including MACCOR and Biologic cycling systems. The software provides a variety of tools for extracting and plotting key battery parameters such as time, voltage, capacity, current, and pressure. In addition to its extensive data analysis capabilities, BatteryPro features a dedicated machine learning module that employs a Bayesian Gaussian Mixture Model (GMM) to predict battery performance and degradation. Users can generate synthetic capacity fade data, calculate fade metrics, and leverage predictive models to forecast long-term battery behavior. The software's graphical user interface (GUI) enhances usability, allowing researchers to upload, merge, and analyze multiple data files with full customizability. The GUI also supports machine learning predictions, enabling users to fit models and make predictions based on selected data and parameters. BatteryPro is built using QtDesigner, scikit-learn, matplotlib, and pandas, ensuring a high level of customization, flexibility, and accuracy in battery data analysis. This tool aims to empower researchers with the ability to perform detailed battery analysis and make informed predictions, ultimately advancing the field of battery research.

25 - ENERGY STORAGE↗
DOE OSTI2024

Structural Uncertainty in the Sensitivity of Urban Temperatures to Anthropogenic Heat Flux

One key source of uncertainty for weather and climate models is structural uncertainty arising from the fact that these models must simplify or approximate complex physical, chemical, and biological processes that occur in the real world. However, structural uncertainty is rarely examined in the context of simulated effects of anthropogenic heat flux in cities. Using the Weather Research and Forecasting (WRF) model coupled with a single-layer urban canopy model, it is found that the sensitivity of urban canopy air temperature to anthropogenic heat flux can differ by an order of magnitude depending on how anthropogenic heat flux is released to the urban environment. Moreover, varying model structures through changing the treatment of roof-air interaction and the parameterization of convective heat transfer between the canopy air and the atmosphere can affect the sensitivity of urban canopy air temperature by a factor of 4. Urban surface temperature and 2-m air temperature are less sensitive to the methods of anthropogenic heat flux release and the examined model structural variants than urban canopy air temperature, but their sensitivities to anthropogenic heat flux can still vary by as much as a factor of 4 for surface temperature and 2 for 2-m air temperature. Our study recommends using temperature sensitivity instead of temperature response to understand how various physical processes (and their representations in numerical models) modulate the simulated effects of anthropogenic heat flux.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

A Parameterization of the Cloud Scattering Polarization Signal Derived From GPM Observations for Microwave Fast Radative Transfer Models

Microwave cloud polarized observations have shown the potential to improve precipitation retrievals since they are linked to the orientation and shape of ice habits. Stratiform clouds show larger brightness temperature (TB) polarization differences (PDs), defined as the vertically polarized TB (TBV) minus the horizontally polarized TB (TBH), with ~10 K PD values at 89 GHz due to the presence of horizontally aligned snowflakes, while convective regions show smaller PD signals, as graupel and/or hail in the updraft tend to become randomly oriented. The launch of the global precipitation measurement (GPM) microwave imager (GMI) has extended the availability of microwave polarized observations to higher frequencies (166 GHz) in the tropics and midlatitudes, previously only available up to 89 GHz. This study analyzes one year of GMI observations to explore further the previously reported stable relationship between the PD and the observed TBs at 89 and 166 GHz, respectively. The latitudinal and seasonal variability is analyzed to propose a cloud scattering polarization parameterization of the PD-TB relationship, capable of reconstructing the PD signal from simulated TBs. Given that operational radiative transfer (RT) models do not currently simulate the cloud polarized signals, this is an alternative and simple solution to exploit the large number of cloud polarized observations available. Finally, the atmospheric radiative transfer simulator (ARTS) is coupled with the weather research and forecasting (WRF) model, in order to apply the proposed parameterization to the RT simulated TBs and hence infer the corresponding PD values, which show to reproduce the observed GMI PDs well.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Large-Scale Visualization of 3D Unstructured Groundwater Model Using Cave Automated Virtual Environment

The immersive three-dimensional (3D) virtual reality (VR) visualization of groundwater models allows us to deepen our understanding of aquifer systems and provide better solutions to present groundwater-related problems, such as groundwater recharge, water quality, and sustainability. Visualization assists in accurately developing groundwater models and revealing important subsurface features, including faulting, folding, and unconformity. However, assessing model accuracy poses challenges due to the complexity of geology and groundwater systems. This research demonstrates a workflow to visualize and analyze raw 3D unstructured groundwater model data using an immersive Cave Automated Virtual Environment (CAVE). To visualize the unstructured groundwater model data, the raw dataset is converted into interactive CAVE-compatible formats utilizing a set of tools: ParaView, Blender, and Unity. This enables researchers to immerse themselves in the data, identifying influential patterns and relationships. e resulting insights can inform the development of sophisticated machine-learning models for groundwater level prediction. The CAVE’s immersive capabilities allow intuitive exploration from various perspectives, providing a more holistic understanding of the factors affecting groundwater levels. These insights are crucial to improve predictive models. The CAVE results also facilitate collaborative analysis and have potential applications in training and education. is research demonstrates the value of immersive VR tools such as the CAVE for unraveling intricacies within high-dimensional scientific data to drive real-world forecasting and modeling applications.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Cosmological forecasts for future galaxy surveys with the linear point standard ruler: Toward consistent BAO analyses far from a fiducial cosmology

The linear point (LP) standard ruler was identified as the basis of a purely geometric method for exploiting the baryon acoustic oscillations (BAO). The LP exploits the BAO feature imprinted in the galaxy two-point correlation function to measure cosmological distances independent of any specific cosmological model. We forecast the expected precision of future and ongoing spectroscopic galaxy surveys to measure distances leveraging the linear point. We investigate the cosmological implications of our forecasted results. We focus in particular on a relevant working example: the detection of the late-time cosmic acceleration independent of other cosmological probes. Further, our findings show that, even within the Λ⁢CDM standard cosmological paradigm, estimated distances need to be reliable over a very wide parameter range in order to realize their maximum utility. This is particularly relevant if we aim to properly characterize cosmological tensions. The LP is a promising candidate approach to achieve this reliability. In contrast, widely employed procedures in BAO analysis estimate distances keeping fixed cosmological parameters to fiducial values close to cosmic-microwave-background constraints in flat-Λ⁢CDM. It is unclear whether they are purely geometric methods. Moreover, they rely on untested extrapolations to explore the parameter space away from those fiducial flat-Λ⁢CDM values. We recommend that all BAO methodologies be validated across the full range of models and parameters over which their results are quoted, first by means of linear predictions and then N -body simulations.

79 ASTRONOMY AND ASTROPHYSICS↗