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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Spatiotemporal Coherence of Mean and Extreme Aerosol Particle Events over Eastern North America as Observed from Satellite

Quantifying spatiotemporal scales of coherence and variability of aerosol particle properties provides (i) metrics for evaluating observational data sets and model simulations, (ii) inferences regarding dominant causes of variability, and (iii) information that is necessary to determine their climate forcing and air quality impacts. Accordingly, analyses of columnar aerosol optical depth (AOD) and Ångstrom exponent (AE) from the MODerate resolution Imaging Spectroradiometer (MODIS) on the Aqua and Terra satellites, and AErosol RObotic NETwork (AERONET) stations are used to evaluate the spatially averaged (Level-3) MODIS products, and to characterize the spatiotemporal scales of variability (and coherence) of aerosol particles over eastern North America (2000-2013). Results show a high degree of consistency in AOD retrievals from the different data sets but larger discrepancies in AE estimates, highest mean AOD and lowest day-to-day variability during the summer, and largest scales of spatial coherence in summer and fall. Consistent with the scales of spatial coherence from MODIS data, in both data sets power spectra indicate AOD variability is manifest primarily on synoptic and annual time scales. Conversely, AE variability in MODIS data is primarily focused on seasonal, semiannual, and annual time scales, and there is an additional mode of AE variability at approx. 30 days in the AERONET measurements. The frequency of cooccurrence of extreme AOD values (>local 90th percentile) decreases to below 50% at approx. 150 km from a central grid cell, but is above that expected by random chance over almost all of eastern North America, indicating supra-regional scale extreme events.

MODIS↗
DOE OSTI2022

Inference-Optimized AI and High Performance Computing for Gravitational Wave Detection at Scale

We introduce an ensemble of artificial intelligence models for gravitational wave detection that we trained in the Summit supercomputer using 32 nodes, equivalent to 192 NVIDIA V100 GPUs, within 2 h. Once fully trained, we optimized these models for accelerated inference using NVIDIA TensorRT. We deployed our inference-optimized AI ensemble in the ThetaGPU supercomputer at Argonne Leadership Computer Facility to conduct distributed inference. Using the entire ThetaGPU supercomputer, consisting of 20 nodes each of which has 8 NVIDIA A100 Tensor Core GPUs and 2 AMD Rome CPUs, our NVIDIA TensorRT-optimized AI ensemble processed an entire month of advanced LIGO data (including Hanford and Livingston data streams) within 50 s. Our inference-optimized AI ensemble retains the same sensitivity of traditional AI models, namely, it identifies all known binary black hole mergers previously identified in this advanced LIGO dataset and reports no misclassifications, while also providing a 3X inference speedup compared to traditional artificial intelligence models. We used time slides to quantify the performance of our AI ensemble to process up to 5 years worth of advanced LIGO data. In this synthetically enhanced dataset, our AI ensemble reports an average of one misclassification for every month of searched advanced LIGO data. We also present the receiver operating characteristic curve of our AI ensemble using this 5 year long advanced LIGO dataset. This approach provides the required tools to conduct accelerated, AI-driven gravitational wave detection at scale.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Linear Mixed-Effects Models for Human-in-the-Loop Tracking Experiment Data

Linear mixed-effects models provide several benefits over more traditional statistical inference tests that are particularly useful for most human-in-the-loop tracking experiment data. However, surprisingly, mixed models are virtually not used for the analysis of tracking experiment data. This paper uses of linear mixed-effects models to analyze combined tracking data from two previous human-in-the-loop roll tracking experiments that compared control behavior metrics collected in both a research aircraft and a motion-base simulator. In the experiments, pilots' behavior under 10 different motion configurations with varying motion filter gains and break frequencies was evaluated and compared to that in the real aircraft. The linear mixed-effects model analysis on the combined dataset confirmed the main statistical outcomes of the individual experiments. The main benefits of mixed models for this type of data were demonstrated by successfully combining data from two experiments that used different experimental conditions and of which one had an additional apparatus and the other a missing participant. Finally, the mixed-model analysis was able to explicitly test for scientifically relevant statistical differences in the dependent measures between the aircraft and simulator, as well as both experiments.

manual control↗
DOE OSTI2021

A Multifidelity Ensemble Kalman Filter with Reduced Order Control Variates

This work develops a new multi delity ensemble Kalman lter (MFEnKF) algorithm based on linear control variate framework. The approach allows for rigorous multi delity extensions of the EnKF, where the uncertainty in coarser delities in the hierarchy of models represent control variates for the uncertainty in ner delities. Small ensembles of high delity model runs are com- plemented by larger ensembles of cheaper, lower delity runs, to obtain much improved analyses at only small additional computational costs. We investigate the use of reduced order models as coarse delity control variates in the MFEnKF, and provide analyses to quantify the improvements over the traditional ensemble Kalman lters. We apply these ideas to perform data assimilation with a quasi-geostrophic test problem, using direct numerical simulation and a corresponding POD-Galerkin reduced order model. Numerical results show that the two- delity MFEnKF provides better analyses than existing EnKF algorithms at comparable or reduced computational costs.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Temperature of the atmosphere of Jupiter from Pioneer 10/11 radio occultations

Radio-occultation data from the Pioneer 10 and 11 flybys are analyzed using an integral inversion technique combined with a model for Jupiter's shape based on gravity data in order to account for the oblateness of the planet's atmosphere. The inversion technique defines the center of refraction in terms of the radius of curvature and the normal to the equipotential surface at the closest approach point of the ray. An Abelian inversion of the data obtained during the Pioneer 10 entry, the Pioneer 10 exit, and the Pioneer 11 exit is performed by fixing the center of refraction at some average value for each occultation event. The results for all three measurements are found to be consistent and to show a temperature inversion between the 10-mb and the 100-mb levels, with temperatures of 130 to 170 K at 10 mb and 80 to 100 K at 100 mb. Major sources of error are discussed, and it is concluded that the range of reasonable temperature uncertainties at these levels does not exceed 40 K. It is noted that the temperature inversion was also inferred from Pioneer 10 IR radiometric data.

Kliore, A. J.↗
DOE OSTI2022

Computer Model Calibration with Time Series Data Using Deep Learning and Quantile Regression

Computer models play a key role in many scientific and engineering problems. One major source of uncertainty in computer model experiments is input parameter uncertainty. Computer model calibration is a formal statistical procedure to infer input parameters by combining information from model runs and observational data. The existing standard calibration framework suffers from inferential issues when the model output and observational data are high-dimensional dependent data, such as large time series, due to the difficulty in building an emulator and the nonidentifiability between effects from input parameters and data-model discrepancy. To overcome these challenges, we, in this work, propose a new calibration framework based on a deep neural network (DNN) with long short-term memory layers that directly emulates the inverse relationship between the model output and input parameters. Adopting the “learning with noise” idea, we train our DNN model to filter out the effects from data-model discrepancy on input parameter inference. We also formulate a new way to construct interval predictions for DNN using quantile regression to quantify the uncertainty in input parameter estimates. Through a simulation study and real data application with the Weather Research and Forecasting Model Hydrological modeling system (WRF-Hydro), we show our approach can yield accurate point estimates and well-calibrated interval estimates for input parameters.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Machine-Learning Accelerated Studies of Materials with High Performance and Edge Computing

In the studies of materials, experimental measurements often serve as the reference to verify physics theory and modeling; while theory and modeling provide a fundamental understanding of the physics and principles behind. However, the interactions and cross validation between them have long been a challenge even to-date. Not only that inferring a physics model from experimental data is itself a difficult inverse problem, another major challenge is the orders-of-magnitude longer wall-clock time required to carry out high-fidelity computer modeling to match the timescale of experiments. We envisage that by combining high performance computing, data science, and edge computing technology, the current predicament can be alleviated, and a new paradigm of data-driven physics research will open up. For example, we can accelerate computer simulations by first performing the large-scale modeling on high performance computers and train a machine-learned surrogate model. This computationally inexpensive surrogate model can then be transferred to the computing units residing closely to the experimental facilities to perform high-fidelity simulations at a much higher throughout. The model will also be more amenable to analyzing and validating experimental observations in comparable time scales at a much lower computational cost. Further integration of these accelerated computer simulations with an outer machine learning loop can also inform and direct future experiments, while making the inverse problem of physics model inference more tractable. We will demonstrate a proof-of-concept by using a quantum Monte Carlo application, Dynamical Cluster Approximation (DCA++), to machine-learn a surrogate model and accelerate the study of quantum correlated materials.

Li, Ying Wai↗
DOE OSTI2026

A score-based diffusion model approach for adaptive learning of stochastic partial differential equation solutions

In this paper, we propose a novel framework for adaptively learning the time-evolving solutions of stochastic partial differential equations (SPDEs) using score-based diffusion models within a recursive Bayesian inference setting. SPDEs play a central role in modeling complex physical systems under uncertainty, but their numerical solutions often suffer from model errors and reduced accuracy due to incomplete physical knowledge and environmental variability. To address these challenges, we encode the governing physics into the score function of a diffusion model using simulation data and incorporate observational information via a likelihood-based correction in a reverse-time stochastic differential equation. This enables adaptive learning through iterative refinement of the solution as new data becomes available. To improve computational efficiency in high-dimensional settings, we introduce the ensemble score filter, a training-free approximation of the score function designed for real-time inference. Numerical experiments on benchmark SPDEs demonstrate the accuracy and robustness of the proposed method under sparse and noisy observations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1991

Structure of an internal bore and dissipating gravity current as revealed by Raman lidar

The Raman lidar observations of a weak gravity current and an internal bore associated with a thunderstorm gust front are presented. These observations have been complemented by conventional surface meteorologial analyses, special radiosonde data, spectral and bandpass filter analysis of barograph data, and infrared satellite imagery. Results obtained reveal the time-space continuity and dynamic nature of two boundary-layer disturbances seen in the lidar data. A comparison of the lidar display with the rawinsonde data makes it possible to determine the thermal fields associated with these disturbances at high temporal resolution (2 min) and an altitude of 6 km. The airflow associated with the disturbances was inferred by synthesizing the lidar and rawinsonde data. One of the two disturbances represents a dissipating outflow boundary (gust front) and can be characterized as a gravity current. The second disturbance represents an internal bore propagating ahead of the gravity current on a surface-based stable layer, which acted as a waveguide. The lidar revealed a mean bore depth of 1.9 km, observed and calculated speeds were in good agreement (about + or - 20 percent).

Koch, Steven E.↗
NASA NTRS2016

Secure Mass Measurements From Transit Timing: 10 Kepler Exoplanets Between 3 and 8 M⊕ With Diverse Densities and Incident Fluxes

We infer dynamical masses in eight multiplanet systems using transit times measured from Keplerʼs complete data set, including short-cadence data where available. Of the 18 dynamical masses that we infer, 10 pass multiple tests for robustness. These are in systems Kepler-26 (KOI-250), Kepler-29 (KOI-738), Kepler-60 (KOI-2086), Kepler-105 (KOI-115), and Kepler-307 (KOI-1576). Kepler-105 c has a radius of 1.3 R(sub ⨁) and a density consistent with an Earth-like composition. Strong transit timing variation (TTV) signals were detected from additional planets, but their inferred masses were sensitive to outliers or consistent solutions could not be found with independently measured transit times, including planets orbiting Kepler-49 (KOI-248), Kepler-57 (KOI-1270), Kepler-105 (KOI-115), and Kepler-177 (KOI-523). Nonetheless, strong upper limits on the mass of Kepler-177 c imply an extremely low density of ∽0.1 g cm(exp −3). In most cases, individual orbital eccentricities were poorly constrained owing to degeneracies in TTV inversion. For five planet pairs in our sample, strong secular interactions imply a moderate to high likelihood of apsidal alignment over a wide range of possible eccentricities. We also find solutions for the three planets known to orbit Kepler-60 in a Laplace-like resonance chain. However, nonlibrating solutions also match the transit timing data. For six systems, we calculate more precise stellar parameters than previously known, enabling useful constraints on planetary densities where we have secure mass measurements. Placing these exoplanets on the mass–radius diagram, we find that a wide range of densities is observed among sub-Neptune-mass planets and that the range in observed densities is anticorrelated with incident flux.

Individual stars↗
DOE OSTI2022

Probabilistic Modeling of Commercial Building Occupancy Patterns Using Location-Based Map Data: Preprint

Considering occupancy patterns is crucial to simulate buildings' energy use. Current energy models use inputs that simplify the actual diversity in occupancy into static occupancy patterns and are not able to represent the numerous variations in occupancy patterns between buildings and across different locations. Recently, inferring occupancy schedules from metered electricity consumption data was used to model occupancy in commercial buildings. However, the translation from metered data to occupancy schedules requires many assumptions that might not capture the reality, and the process is hindered by the availability of data from advanced metering infrastructure. With the development of information technologies, occupancy modeling should not be limited to traditional approaches. The prevalence of social networks and location services with real-time user feedback provides publicly accessible data via Maps Application Programming Interfaces (APIs) such as Google Maps, SafeGraph, Mapbox, Foursquare, etc. This paper presents an automated framework for modeling parametric occupancy patterns using such APIs to calibrate commercial district buildings' energy models. This process includes three main steps: data extraction and processing, parametric schedules generation, and schedules integration. We demonstrated this framework in districts where we used maps API to generate more accurate behavioral patterns for operations and electric vehicle charging events. We used these patterns to determine differences in energy use across key sociodemographic and spatial parameters. The presented method has the potential for worldwide applications. Users can utilize this framework to extract data for selected locations of interest to create more realistic behavioral patterns for commercial facilities across different districts.

building energy modeling↗
NASA NTRS1995

Making intelligent systems team players. A guide to developing intelligent monitoring systems

This reference guide for developers of intelligent monitoring systems is based on lessons learned by developers of the DEcision Support SYstem (DESSY), an expert system that monitors Space Shuttle telemetry data in real time. DESSY makes inferences about commands, state transitions, and simple failures. It performs failure detection rather than in-depth failure diagnostics. A listing of rules from DESSY and cue cards from DESSY subsystems are included to give the development community a better understanding of the selected model system. The G-2 programming tool used in developing DESSY provides an object-oriented, rule-based environment, but many of the principles in use here can be applied to any type of monitoring intelligent system. The step-by-step instructions and examples given for each stage of development are in G-2, but can be used with other development tools. This guide first defines the authors' concept of real-time monitoring systems, then tells prospective developers how to determine system requirements, how to build the system through a combined design/development process, and how to solve problems involved in working with real-time data. It explains the relationships among operational prototyping, software evolution, and the user interface. It also explains methods of testing, verification, and validation. It includes suggestions for preparing reference documentation and training users.

Land, Sherry A.↗
DOE OSTI2025

Unraveling emission line galaxy conformity at z ∼ 1 with DESI early data

Emission line galaxies (ELGs) are now the preeminent tracers of large-scale structure at z > 0.8 due to their high density and strong emission lines, which enable accurate redshift measurements. However, relatively little is known about ELG evolution and the ELG–halo connection, exposing us to potential modelling systematics in cosmology inference using these sources. In this paper, we use a variety of observations and simulated galaxy models to propose a physical picture of ELGs and improve ELG–halo connection modelling in a halo occupation distribution framework. We investigate Dark Energy Spectroscopic Instrument (DESI)-selected ELGs in COSMOS data, and infer that ELGs are rapidly star-forming galaxies with a large fraction exhibiting disturbed morphology, implying that many of them are likely to be merger-driven starbursts. We further postulate that the tidal interactions from mergers lead to correlated star formation in central–satellite ELG pairs, a phenomenon dubbed ‘conformity’. We argue for the need to include conformity in the ELG–halo connection using galaxy models such as IllustrisTNG, and by combining observations such as the DESI ELG autocorrelation, ELG cross-correlation with luminous red galaxies, and ELG–cluster cross-correlation. We also explore the origin of conformity using the UniverseMachine model and elucidate the difference between conformity and the well-known galaxy assembly bias effect.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2020

Veridical data science

Building and expanding on principles of statistics, machine learning, and scientific inquiry, we propose the predictability, computability, and stability (PCS) framework for veridical data science. Our framework, composed of both a workflow and documentation, aims to provide responsible, reliable, reproducible, and transparent results across the data science life cycle. The PCS workflow uses predictability as a reality check and considers the importance of computation in data collection/storage and algorithm design. It augments predictability and computability with an overarching stability principle. Stability expands on statistical uncertainty considerations to assess how human judgment calls impact data results through data and model/algorithm perturbations. As part of the PCS workflow, we develop PCS inference procedures, namely PCS perturbation intervals and PCS hypothesis testing, to investigate the stability of data results relative to problem formulation, data cleaning, modeling decisions, and interpretations. We illustrate PCS inference through neuroscience and genomics projects of our own and others. Moreover, we demonstrate its favorable performance over existing methods in terms of receiver operating characteristic (ROC) curves in high-dimensional, sparse linear model simulations, including a wide range of misspecified models. Finally, we propose PCS documentation based on R Markdown or Jupyter Notebook, with publicly available, reproducible codes and narratives to back up human choices made throughout an analysis. The PCS workflow and documentation are demonstrated in a genomics case study available on Zenodo.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Elemental and isotopic signatures of Asteroid Ryugu support three early Solar System reservoirs

Understanding the number and locations of different reservoirs present in the early Solar System is crucial to understanding the Solar System’s origin and evolution. Previous work has suggested that three unique isotopic reservoirs existed in the early Solar System but subsequent works have challenged that idea. Here we present elemental abundances along with Ca, Ti, Cr, Fe, Ni, and Zn isotopic data from primitive material returned by the Japan Aerospace Exploration Agency’s (JAXA) Hayabusa2 mission to asteroid (162173) Ryugu to make inferences on the Solar System’s early architecture. Data from Ryugu particle A0208 are consistent with a close genetic heritage between Ryugu and CI chondrites. Here, we employ principal component analysis (PCA) on these Ryugu and published meteorite data to demonstrate that Ryugu and CI chondrites are distinct from other known astromaterials, strongly supporting the existence of a third major isotopic reservoir in the early Solar System.

Isotopes↗
DOE OSTI2024

Subsurface electrical conductivity across the BEO site inferred using a capacitively coupled resistivity survey in May 2013, Utqiagvik, Alaska

Multiple transects across and around the Barrow Environmental Observatory (BEO) site were surveyed using a capacitively coupled resistivity tool to infer the subsurface electrical conductivity in the top ~6 m in order to improve the understanding of heterogeneity in ice and salinity content. This work was led by the environmental geophysics team from Lawrence Berkeley National Laboratory as part of the Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic). The acquisition was performed in May 2013 using a capacitively coupled resistivity tool (OhmMapper from Geometrics, Inc) mounted behind a sled hooked to a snowmobile. Twenty-eight ~750 m long transects cover the NGEE intensive site (polygon areas A, B, C, D) in the BEO with a measurement (apparent resistivity for 5 different geometries) taken every 1.5 to 2 m. The survey also includes other transects that have been acquired along Electrical Resistivity Tomography (ERT) transects previously acquired at the site (Site 0, AB, and BD), and several long transects (up to 7 km each) around the BEO site crossing several Drained Thawed Lake Basins (DTLB). The various transects acquired during this survey cover a distance of about 100 km. All the datasets are provided with the acquisition geometry and the associated subsurface apparent electrical conductivity. In addition, the 28 transects at the BEO intensive site were inverted to infer the subsurface electrical conductivity. Unprocessed and processed data products are included in this package. This metadata document contains a description of the survey and processing steps and the inferred products.The Next-Generation Ecosystem Experiments: Arctic (NGEE Arctic) was a 15-year research effort (2012-2027) to reduce uncertainty in Earth System Models by developing a predictive understanding of carbon-rich Arctic ecosystems and feedbacks to climate. NGEE Arctic was supported by the Department of Energy’s Office of Biological and Environmental Research. The NGEE Arctic project had two field research sites: 1) located within the Arctic polygonal tundra coastal region on the Barrow Environmental Observatory (BEO) and the North Slope near Utqiagvik (Barrow), Alaska, and 2) multiple areas in the discontinuous permafrost region of the Seward Peninsula north of Nome, Alaska. Through observations, experiments, and synthesis with existing datasets, NGEE Arctic provided an enhanced knowledge base for multi-scale modeling and contributed to improved process representation at global pan-Arctic scales within the Department of Energy’s Earth system Model (the Energy Exascale Earth System Model, or E3SM), and specifically within the E3SM Land Model component (ELM).

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

An Investigation Into Possible Systematic Effects on Neutron Star Radius Estimates using NICER-like Synthetic Data

Neutron star cores contain the densest matter in the observable universe. The state of this matter is of interest in numerous fields, but laboratory experiments cannot explore this matter. Although the composition of the matter would be of great interest, macroscopic observables such as the neutron star mass-radius relation depend primarily on the equation of state (EOS). As a result, precise and reliable radius measurements would be valuable in constraining the EOS. However, most attempts at radius measurements are susceptible to systematic errors, meaning the inferred radius can be significantly biased even though the fit to the data appears to be statistically good. Previous studies suggested that radii inferred using X-ray data provided by NASA’s NICER mission may be more immune from such systematic errors. This is in part because, compared with previous measurements that obtained averaged spectra and fluxes, NICER observes millisecond pulsars by timing the photons so precisely, it is possible to obtain the spectrum as a function of rotational phase and see variations such as heated regions on the star rotate into and out of view hundreds of times per second. This extra information seems promising to break degeneracies and mitigate systematic errors, but a more in-depth study is necessary. We report the first steps of that study, in which we generate NICER-like synthetic data and determine the quality of fit and bias in radius obtained when we fit the data using a model different from the model used to generate the data.

Isiah Holt↗
NASA NTRS2005

Modeling Atmospheric CO2 Processes to Constrain the Missing Sink

We report on a NASA supported modeling effort to reduce uncertainty in carbon cycle processes that create the so-called missing sink of atmospheric CO2. Our overall objective is to improve characterization of CO2 source/sink processes globally with improved formulations for atmospheric transport, terrestrial uptake and release, biomass and fossil fuel burning, and observational data analysis. The motivation for this study follows from the perspective that progress in determining CO2 sources and sinks beyond the current state of the art will rely on utilization of more extensive and intensive CO2 and related observations including those from satellite remote sensing. The major components of this effort are: 1) Continued development of the chemistry and transport model using analyzed meteorological fields from the Goddard Global Modeling and Assimilation Office, with comparison to real time data in both forward and inverse modes; 2) An advanced biosphere model, constrained by remote sensing data, coupled to the global transport model to produce distributions of CO2 fluxes and concentrations that are consistent with actual meteorological variability; 3) Improved remote sensing estimates for biomass burning emission fluxes to better characterize interannual variability in the atmospheric CO2 budget and to better constrain the land use change source; 4) Evaluating the impact of temporally resolved fossil fuel emission distributions on atmospheric CO2 gradients and variability. 5) Testing the impact of existing and planned remote sensing data sources (e.g., AIRS, MODIS, OCO) on inference of CO2 sources and sinks, and use the model to help establish measurement requirements for future remote sensing instruments. The results will help to prepare for the use of OCO and other satellite data in a multi-disciplinary carbon data assimilation system for analysis and prediction of carbon cycle changes and carbodclimate interactions.

Kawa, S. R.↗