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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 235 records · Page 13
All sourcesNASA NTRSDOE OSTI
NASA NTRS1979

Stellar luminosity stability - Luminosity variations and light curve period changes in BY Draconis stars

The implications for convection in late type stars arising from the observations of BY Draconis type variability are examined. The possibility that the total luminosity of such a star is not constant is emphasized, and further observational tests to better define the variability are suggested. An alternative to standard spot models is explored in which the 'missing' flux from 'dark' spots is temporally redistributed; this model makes definite predictions about the correlation of mean light and color, and about the quiescent (i.e., unspotted) magnitudes of BY Draconis stars. The time scales of the long-period variability of these stars appear to require secular changes in convective energy transport. Consideration is given to the evidence for period changes in the optical light curves, and it is concluded that the reality of such changes is far less certain than previously claimed.

Hartmann, L.↗
NASA NTRS1987

Temporal and contextual knowledge in model-based expert systems

A basic paradigm that allows representation of physical systems with a focus on context and time is presented. Paragon provides the capability to quickly capture an expert's knowledge in a cognitively resonant manner. From that description, Paragon creates a simulation model in LISP, which when executed, verifies that the domain expert did not make any mistakes. The Achille's heel of rule-based systems has been the lack of a systematic methodology for testing, and Paragon's developers are certain that the model-based approach overcomes that problem. The reason this testing is now possible is that software, which is very difficult to test, has in essence been transformed into hardware.

Toth-Fejel, Tihamer↗
NASA NTRS2007

Effects of Rhenium Addition on the Temporal Evolution of the Nanostructure and Chemistry of a Model Ni-Cr-Al Superalloy: Analysis of the Coarsening Behavior - 2

The temporal evolution of the nanostructure and chemistry of a model Ni-8.5 at.% Cr-10 at.% Al alloy with the addition of 2 at.% Re was studied using transmission electron microscopy and atom-probe tomography in order to measure the number density and mean radius of the y' (LIZ) precipitates and the chemistry of the y'-precipitates and the y (fcc)-matrix. In this article, the coarsening behavior of the y'-precipitates is discussed in detail and compared with the Umantsev-Olson model for multi-component alloys. In addition, the experimental results are evaluated with PrecipiCalc(TradeMark) simulations. The results show that the diffusivities of the solute elements play a major role in the coarsening behavior of the y'-precipitates and that the addition of Re retards the coarsening kinetics and stabilizes the spheroidal morphology of the precipitates by reducing the interfacial energy.

Yoon, Kevin E.↗
DOE OSTI2023

Stream Temperature Prediction in a Shifting Environment: Explaining the Influence of Deep Learning Architecture

Stream temperature is a fundamental control on ecosystem health. Recent efforts incorporating process guidance into deep learning models for predicting stream temperature have been shown to outperform existing statistical and physical models. This performance is in part because deep learning architectures can actively learn spatiotemporal relationships that govern how water and energy propagate through a river network. However, exploration of how spatiotemporal awareness and process guidance influence a model's generalizability under shifting environmental conditions such as climate change is limited. Here, we use Explainable Artificial Intelligence (XAI) to interrogate how differing deep learning architectures affect a model's learned spatial and temporal dependencies, and how those learned dependencies affect a model's ability to maintain high accuracy when applied to unseen environmental conditions. Using the Delaware River Basin in the northeastern United States as a test case, we compare two spatiotemporally aware process–guided deep learning models for predicting stream temperature (a recurrent graph convolution network—RGCN, and a temporal convolution graph model—Graph WaveNet). Both models achieve equally high predictive performance when testing data are well represented in the training data (test root mean squared errors of 1.64°C and 1.65°C); however, Graph WaveNet significantly outperforms RGCN in 4 out of 5 experiments where test partitions represent different types of unseen environmental conditions. XAI results show that the architecture of Graph WaveNet leads to learned spatial relationships with greater fidelity to physical processes, and that this fidelity improves the generalizability of the model when applied to shifting and/or unseen environmental conditions.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2004

The Role of Rhenium on the Temporal Evolution of the Nanostructure of a Model Ni-Al-Cr-Re Superalloy

Rhenium (2 at.%) additions to a model Ni-8.5 at.% Cr-10 at.% Al alloy are studied with respect to its effects on the temporal evolution of the nanostructure and the partitioning behavior of the four elements between the gamma (fcc) and gamma' (L1(sub 2)) phases. Chemical evolution of this quaternary alloy aged at 1073 K from 0.25 to 264 h, is investigated by three-dimensional atom-probe (3DAP) microscopy. The morphology of gamma'-precipitates remains spheroidal, even at an aging time of 264 h. The results demonstrate that Re slows the coarsening of gamma'-precipitates, in comparison to the ternary Ni-10 at.% A1-8.5 at.% Cr alloy at 1073 K.

Yoon, Kevin E.↗
DOE OSTI2025

Spatiotemporal Downscaling Model for Solar Irradiance Forecast Using Nearest-Neighbor Random Forest and Gaussian Process

Accurate solar photovoltaic (PV) capacity estimation requires high-resolution, site-specific solar irradiance data to account for localized variability. However, global datasets, such as the National Solar Radiation Database (NSRDB), provide regional averages that fail to capture the fine-scale fluctuations critical for large-scale grid integration. This limitation is particularly relevant in the context of increasing distributed energy resources (DERs) penetration, such as rooftop PV. Additionally, it is critical to the implementation of the U.S. Federal Energy Regulatory Commission (FERC) Order 2222, which facilitates DER participation in U.S. bulk power markets. To address this challenge, this study evaluates Nearest-Neighbor Random Forest (NNRF) and Nearest-Neighbor Gaussian Process (NNGP) models for spatiotemporal downscaling of global solar irradiance data. By leveraging historical irradiance and meteorological data, these models incorporate spatial, temporal, and feature-based correlations to enhance local irradiance predictions. The NNRF model, a machine-learning approach, prioritizes computational efficiency and predictive accuracy, while the NNGP model offers a level of interpretability and prediction uncertainty by numerically quantifying correlations and dependencies in the data. Model validation was conducted using day-ahead predictions. The results showed that the average Goodness of Fit (GoF) of the NNRF model of 90.61% across all eight sites outperformed the GoF of the NNGP of 85.88%. Additionally, the computational speed of NNRF was 2.5 times faster than the NNGP. Finally, the NNGP displayed polynomial scaling while the NNRF scaled linearly with increasing number of nearest neighbors. Additional validation of the model on five sites in Puerto Rico further confirmed the superiority of the NNRF model over the NNGP model. These findings highlight the robustness and computational efficiency of NNRF for large-scale solar irradiance downscaling, making it a strong candidate for improving PV capacity estimation and real-time electricity market integration for DERs.

Asiedu, Shadrack (ORCID:0009000646004826)↗
NASA NTRS2011

Impact of January 2005 Solar Proton Events on Chlorine Species

Sudden changes in stratospheric chlorine species in the polar northern atmosphere, caused by the Solar Proton Events (SPEs) of 17 and 20 January 2005, have been investigated and compared with version 4 of the Whole Atmosphere Community Climate Model (WACCM4). We used Aura Microwave Limb Sounder (MLS) measurements to monitor the variability of ClO, HCl, HOCl and Michelson Interferometer for Passive Atmospheric Sounder (MIPAS) on ENVISAT to retrieve ClONO2. SPE-induced chlorine activation has been identified. HCl decrease occurred at nearly all the investigated altitudes with the lowest values (of less than 0.25 ppbv) on 21 January. HOCl was found to be the main active chlorine species under nighttime conditions (with increases of more than 0.2 ppbv) whereas both HOCL and CLO enhancements (about 0.1 ppbv) have been observed at the polar night terminator. Further, small ClO decreases (of less than 0.1 ppbv) and ClONO2 enhancements (~0.2 ppbv) have been observed at higher latitudes (i.e., at nighttime) roughly above 2hPa. While WACCM4 reproduces most of the SPE-induced variability in the chlorine species fairly well, in some particular regions discrepancies between the modeled and measured temporal evolution of the abundances of chlorine species were found. HOCl changes are modelled very well with respect to both magnitude and geographic distribution. ClO decreases are reproduced at high latitudes, whereas ClO enhancements in the terminator region are underestimated and attributed to background variations. WACCM4 also reproduces the HCl depletion in the mesosphere but it does not show the observed decrease below about 2 hPa. Finally, WACCM4 simulations indicate that the observed ClONO2 increase is dominated by background variability, although SPE-induced production might contribute by 0.1 ppbv.

Damiani, A.↗
NASA NTRS2012

Impact of January 2005 Solar Proton Events on Chlorine Species

Sudden changes in stratospheric chlorine species in the polar northern atmosphere, caused by the Solar Proton Events (SPEs) of 17 and 20 January 2005, have been investigated and compared with version 4 of the Whole Atmosphere Community Climate Model (WACCM4). We used Aura Microwave Limb Sounder (MLS) measurements to monitor the variability of ClO, HCl, HOCl and Michelson Interferometer for Passive Atmospheric Sounder (MIPAS) on ENVISAT to retrieve ClONO2. SPE-induced chlorine activation has been identified. HCl decrease occurred at nearly all the investigated altitudes (i.e., 10-0.5 hPa) with the strongest decrease (of about 0.25 ppbv) on 21 January. HOCl was found to be the main active chlorine species under nighttime conditions (with increases of more than 0.2 ppbv) whereas both HOCl and ClO enhancements (about 0.1 ppbv) have been observed at the polar night terminator. Further, small ClO decreases (of less than 0.1 ppbv) and ClONO2 enhancements (about 0.2 ppbv) have been observed at higher latitudes (i.e., at nighttime) roughly above 2 hPa. While WACCM4 reproduces most of the SPE-induced variability in the chlorine species fairly well, in some particular regions discrepancies between the modeled and measured temporal evolution of the abundances of chlorine species were found. HOCl changes are modelled very well with respect to both magnitude and geographic distribution. ClO decreases are reproduced at high latitudes, whereas ClO enhancements in the terminator region are underestimated and attributed to background variations. WACCM4 also reproduces the HCl depletion in the mesosphere but it does not show the observed decrease below about 2 hPa. Finally, WACCM4 simulations indicate that the observed ClONO2 increase is dominated by background variability, although SPE-induced production might contribute by 0.1 ppbv.

Damiani, A.↗
DOE OSTI2023

A general spatial-temporal framework for short-term building temperature forecasting at arbitrary locations with crowdsourcing weather data

Weather forecasting has been a critical component to predict and control building energy consumption for better building energy management. Without accessibility to other data sources, the onsite observed temperatures or the airport temperatures are used in forecast models. In this paper, we present a novel approach by utilizing the crowdsourcing weather data from neighboring personal weather stations (PWS) to improve the weather forecast accuracy around buildings using a general spatial-temporal modeling framework. The final forecast is based on the ensemble of local forecasts for the target location using neighboring PWSs. Our approach is distinguished from existing literature in various aspects. First, we leverage the crowdsourcing weather data from PWS in addition to public data sources. In this way, the data is at much finer time resolution (e.g., at 5-minute frequency) and spatial resolution (e.g., arbitrary location vs grid). Second, our proposed model incorporates spatial-temporal correlation information of weather variables between the target building and a set of neighboring PWSs so that underlying correlations can be effectively captured to improve forecasting performance. Here, we demonstrate the performance of the proposed framework by comparing to the benchmark models on temperature forecasting for a building located at an arbitrary location at San Antonio, Texas, USA. In general, the proposed model framework equipped with machine learning technique such as Random Forest can improve forecasting by 50% compares with persistent model and has 90% chance to outperform airport forecast in short-term forecasting. In a real-time setting, the proposed model framework can provide more accurate temperature forecasting results compared with using airport temperature forecast for most forecast horizon. Moreover, we analyze the sensitivity of model parameters to gain insights on how crowdsourcing data from the neighboring personal weather stations impacts forecasting performance. Finally, we implement our model in other cities such as Syracuse and Chicago to test the model's performance in different landforms and climate types.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Fluid for Thought: SLS Rocket Aerodynamics featuring Pressure Sensitive Paint

Combining computer vision algorithms, state-of-the-art high-speed cameras, pressure sensitive paint (PSP) and transonic wind tunnel facilities, teams at NASA and Boeing study unsteady pressure over the Space Launch System (SLS) rocket model with unprecedented temporal and spatial data resolution. Post-processing visualizes pressure fluctuations over a 3D model by converting intensity data from videos of the wind tunnel test. Additionally, data analysis creates predictions of buffet forcing and aeroacoustic activity over the rocket body with the accuracy of a CFD model! The development of the technique will revolutionize aerodynamics research and reveal uncharted insights into fluid behavior and vehicle performance, enhancing aerospace technology and design as they advance. The talk will focus on the impact of the PSP technique on the SLS design and other potential aerospace applications, as well as lay out the experimental process, from wind tunnel test to insights from the data analysis.

Lucy Zhonghui Tang↗
DOE OSTI2023

Spatial-Temporal Recurrent Graph Neural Networks for Fault Diagnostics in Power Distribution Systems

Fault diagnostics are extremely important to decide proper actions toward fault isolation and system restoration. The growing integration of inverter-based distributed energy resources imposes strong influences on fault detection using traditional overcurrent relays. This paper utilizes emerging graph learning techniques to build new temporal recurrent graph neural network models for fault diagnostics. The temporal recurrent graph neural network structures can extract the spatial-temporal features from data of voltage measurement units installed at the critical buses. From these features, fault event detection, fault type/phase classification, and fault location are performed. Compared with previous works, the proposed temporal recurrent graph neural networks provide a better generalization for fault diagnostics. Moreover, the proposed scheme retrieves the voltage signals instead of current signals so that there is no need to install relays at all lines of the distribution system. Therefore, the proposed scheme is generalizable and not limited by the number of relays installed. The effectiveness of the proposed method is comprehensively evaluated on the Potsdam microgrid and IEEE 123-node system in comparison with other neural network structures.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2024

Tethys: A Spatiotemporal Downscaling Model for Global Water Demand

Humans use water for many important tasks, such as drinking, growing food, and cooling power plants. Since future water demands depend on complex global interactions between economic sectors (e.g., demand for wheat in one country causing demand for water to grow that wheat in another country), it is often modeled at coarse spatial and temporal scales as part of models that account for complex, multi-sector system dynamics. However, models that project future water availability typically simulate physical processes at much finer scales. Tethys enables integration between these kinds of models by downscaling region-scale water demand projections using sector-specific proxies and formulas.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Acoustic Tomography of the Atmosphere: A Large-Eddy Simulation Sensitivity Study

Accurate measurement of atmospheric turbulent fluctuations is critical for understanding environmental dynamics and improving models in applications such as wind energy. Advanced remote sensing technologies are essential for capturing instantaneous velocity and temperature fluctuations. Acoustic tomography (AT) offers a promising approach that utilizes sound travel times between an array of transducers to reconstruct turbulence fields. This study presents a systematic evaluation of the time-dependent stochastic inversion (TDSI) algorithm for AT using synthetic travel-time measurements derived from large-eddy simulation (LES) fields under both neutral and convective atmospheric boundary-layer conditions. Unlike prior work that relied on field observations or idealized fields, the LES framework provides a ground-truth atmospheric state, enabling quantitative assessment of TDSI retrieval reliability, sensitivity to travel-time measurement noise, and dependence on covariance model parameters and temporal data integration. A detailed sensitivity analysis was conducted to determine the best-fit model parameters, identify the tolerance thresholds for parameter mismatch, and establish a maximum spatial resolution. The TDSI algorithm successfully reconstructed large-scale velocity and temperature fluctuations with root mean square errors ( RMSE s) below 0.35 m/s and 0.12 K, respectively. Spectral analysis established a maximum spatial resolution of approximately 1.4 m, and reconstructions remained robust for travel-time measurement uncertainties up to 0.002 s. These findings provide critical insights into the operational limits of TDSI and inform future applications of AT for atmospheric turbulence characterization and system design.

17 WIND ENERGY↗
DOE OSTI2021

A priori examination of reduced chemistry models derived from canonical stirred reactors using three-dimensional direct numerical simulation datasets

Data-driven approaches to construct reduced chemical kinetic models, that rely heavily on thermo-chemical datasets with full chemical kinetics, have been gaining popularity. Datasets from direct numerical simulations (DNS) under three-dimensional (3-D) realistic turbulent flow conditions are desirable but limited to carefully designed parametric conditions due to the computational cost. Constructing datasets from a large ensemble of zero-dimensional stirred reactors like perfectly stirred reactor (PSR) and partially stirred reactor (PaSR) is a computationally efficient solution to consider the turbulence-chemistry interactions and cover a broad range of parametric conditions. In this paper, we derive reduced chemistry models from solutions of a large number of PSR and PaSR reactors using autoencoder (AE) neural networks and principal component analysis (PCA), and conduct a priori examination of the reduced models in three temporally evolving 3-D DNS jet flames featuring local extinction and re-ignition. The results show that the reduced models derived from PaSR datasets, i.e., AE-PaSR and PCA-PaSR, generally show significant improvement over the ones derived from PSR datasets. Among all the reduced models, AE-PaSR shows the best agreement with DNS results on the reconstruction accuracy and the representation of temporally evolving local extinction and re-ignition events.

Zhang, Pei↗
DOE OSTI2021

pnnl/grid_prediction

Two datadriven predictive approaches, namely, {\em Koopman Operator Theoretic (KOT)-based} model and {\em Graph Neural Network (GNN)-based model}, to enable effective power system state predictions. The KOT-based approaches (Robust DMD, deepDMD) capture the power system evolution as a linear dynamical system on an abstract space. The GNNs model the spatio-temporal correlations using graph convolutional network and are called Spatio-Temporal Graph Convolutional Network (STGCN). These predictive models are trained, tested and compared rigorously based on their predictions of frequencies in the IEEE 68 bus system when subjected to a disturbance. GridSTAGE framework developed at Pacific Northwest National Laboratory is leveraged to generate multiple datasets (in the form of PMU measurements) for training and testing by strategically creating load changes across the spatial locations of the network

Nandanoor, Sai Pushpak↗
NASA NTRS1988

A comparative study of plasma expansion events in the polar wind

The temporal characteristics of the polar wind have been studied using a simplified set of collisionless equations and a full set of hydrodynamic equations which contained continuity, momentum, and energy equations for H(+), O(+), and electrons, including collisional conductivities and variable isotropic, ion and electron temperatures. The temporal evolution of the polar wind was modeled for initial, extended density depletions of factors of 3, 10, and 100 starting at several altitudes for each model. Comparison of the results from the two models showed the same temporal characteristics for the polar wind densities, flow velocities, and escape fluxes. The characteristics of plasma expansion are presented. It is found that the propagation velocities of the disturbance vary with the level of the depletion.

Gombosi, Tamas I.↗