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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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
NASA NTRS2014

Temperature and Humidity Profiles in the TqJoint Data Group of AIRS Version 6 Product for the Climate Model Evaluation

The Atmospheric Infrared Sounder (AIRS) mission is entering its 13th year of global observations of the atmospheric state, including temperature and humidity profiles, outgoing long-wave radiation, cloud properties, and trace gases. Thus AIRS data have been widely used, among other things, for short-term climate research and observational component for model evaluation. One instance is the fifth phase of the Coupled Model Intercomparison Project (CMIP5) which uses AIRS version 5 data in the climate model evaluation. The NASA Goddard Earth Sciences Data and Information Services Center (GES DISC) is the home of processing, archiving, and distribution services for data from the AIRS mission. The GES DISC, in collaboration with the AIRS Project, released data from the version 6 algorithm in early 2013. The new algorithm represents a significant improvement over previous versions in terms of greater stability, yield, and quality of products. The ongoing Earth System Grid for next generation climate model research project, a collaborative effort of GES DISC and NASA JPL, will bring temperature and humidity profiles from AIRS version 6. The AIRS version 6 product adds a new "TqJoint" data group, which contains data for a common set of observations across water vapor and temperature at all atmospheric levels and is suitable for climate process studies. How different may the monthly temperature and humidity profiles in "TqJoint" group be from the "Standard" group where temperature and water vapor are not always valid at the same time? This study aims to answer the question by comprehensively comparing the temperature and humidity profiles from the "TqJoint" group and the "Standard" group. The comparison includes mean differences at different levels globally and over land and ocean. We are also working on examining the sampling differences between the "TqJoint" and "Standard" group using MERRA data.

climate model evaluation↗
NASA NTRS2016

Rare Earth Element Partitioning in Lunar Minerals: An Experimental Study

The partitioning behavior of rare earth elements (REE) between minerals and melts is widely used to interpret the petrogenesis and geologic context of terrestrial and extra-terrestrial samples. REE are important tools for modelling the evolution of the lunar interior. The ubiquitous negative Eu anomaly in lunar basalts is one of the main lines of evidence to support the lunar magma ocean (LMO) hypothesis, by which the plagioclase-rich lunar highlands were formed as a flotation crust during differentiation of a global-scale magma ocean. The separation of plagioclase from the mafic cumulates is thought to be the source of the Eu depletion, as Eu is very compatible in plagioclase. Lunar basalts and volcanic glasses are commonly depleted in light REEs (LREE), and more enriched in heavy REEs (HREE). However, there is very little experimental data available on REE partitioning between lunar minerals and melts. In order to interpret the source of these distinctive REE patterns, and to model lunar petrogenetic processes, REE partition coefficients (D) between lunar minerals and melts are needed at conditions relevant to lunar processes. New data on D(sub REE) for plagioclase, and pyroxenes are now available, but there is limited available data for olivine/melt D(sub REE), particularly at pressures higher than 1 bar, and in Fe-rich and reduced compositions - all conditions relevant to the lunar mantle. Based on terrestrial data, REE are highly incompatible in olivine (i.e. D much less than 1), however olivine is the predominant mineral in the lunar interior, so it is important to understand whether it is capable of storing even small amounts of REE, and how the REEs might be fractionatied, in order to understand the trace element budget of the lunar interior. This abstract presents results from high-pressure and temperature experiments investigating REE partitioning between olivine and melt in a composition relevant to lunar magmatism.

McIntosh, E. C.↗
DOE OSTI2023

A data-driven method for modelling dissipation rates in stratified turbulence

We present a deep probabilistic convolutional neural network (PCNN) model for predicting local values of small-scale mixing properties in stratified turbulent flows, namely the dissipation rates of turbulent kinetic energy and density variance, $\varepsilon$ and $\chi$ . Inputs to the PCNN are vertical columns of velocity and density gradients, motivated by data typically available from microstructure profilers in the ocean. The architecture is designed to enable the model to capture several characteristic features of stratified turbulence, in particular the dependence of small-scale isotropy on the buoyancy Reynolds number $Re_b:=\varepsilon /(\nu N^2)$ , where $\nu$ is the kinematic viscosity and $N$ is the background buoyancy frequency, the correlation between suitably locally averaged density gradients and turbulence intensity and the importance of capturing the tails of the probability distribution functions of values of dissipation. Empirically modified versions of commonly used isotropic models for $\varepsilon$ and $\chi$ that depend only on vertical derivatives of density and velocity are proposed based on the asymptotic regimes $Re_b\ll 1$ and $Re_b\gg 1$ , and serve as an instructive benchmark for comparison with the data-driven approach. When trained and tested on a simulation of stratified decaying turbulence which accesses a range of turbulent regimes (associated with differing values of $Re_b$ ), the PCNN outperforms assumptions of isotropy significantly as $Re_b$ decreases, and additionally demonstrates improvements over the fitted empirical models. A differential sensitivity analysis of the PCNN facilitates a comparison with the theoretical models and provides a physical interpretation of the features enabling it to make improved predictions.

42 ENGINEERING↗
DOE OSTI2024

Short-Term Load Forecasting Considering EV Charging Loads with Prediction Interval Evaluation

Short-term load forecasting plays a critical role in power system planning and operation. Along with the electrification of various loads, electricity demands are becoming increasingly hard to predict. Notably, the recent rise in electric vehicles (EVs) has further contributed to this unpredictability. To address this issue, this paper proposes a probabilistic load forecasting strategy utilizing Gaussian process regression, structured in a day-ahead manner. While many works focus on deterministic prediction, probabilistic forecasting offers additional insights into variability and uncertainty, enabling more flexible and reliable operation for power systems. To enhance the accuracy of the load forecasting model, the inputs include features related to EV charging habits as well as commonly used weather information. The load forecasting results are evaluated using various metrics, including conventional ones that assess the accuracy of point forecasts, as well as additional metrics that test the reliability of prediction intervals. The proposed load forecasting method is finally tested on real residential power consumption data and EV charging data sampled from real-world sources. The results prove that the new features can greatly improve the performance of the load forecasting method.

electrical vehicle↗
NASA NTRS1992

Orbit determination by solving for gravity parameters with multiple arc data

The orbit of a satellite that repeats in the earth fixed coordinates is determined by combining GPS tracking data from multiple arcs. The satellite dynamics are modeled with the epoch state and a set of parameters, called the bin parameters, that account for the effect of the local gravitational field on the satellite current state. The epoch state is specific to each arc, and the bin parameters are common to all repeat arcs. The estimation algorithm is based on the Square Root Information Filter. It involves partitioning of the measurement matrix and use of the Householder transformation to combine multiple arc data and solve for the epoch states and the bin parameters. The bin parameters can then be converted into the earth's gravitational field with a modest amount of computation.

Wu, Jiun-Tsong↗
NASA NTRS2005

Possibilities and Challenges in Using Satellite Data for PM2.5 Forecasts

Satellite remote sensing has brought our observation of the earth's atmosphere into a new era, and remote sensing capability could lead to a quantum leap in our ability of air quality monitoring and prediction. In terms of aerosols, the most common quantity from satellite retrieval is the atmospheric column aerosol optical thickness (AOT), and the most common quantity indicating air quality at the surface is the concentration of PM2.5. w e present here the relationship between the column AOT and the surface PM2.5 from a global aerosol model GOCART and from satellite and surface measurements. We will discuss the possibilities and challenges in using satellite data for PM2.5 forecasts, and if model-satellite assimilation can improve the forecast quality.

Chin, Mian↗
NASA NTRS2015

Design of a Thermal Precipitator for the Characterization of Smoke Particles from Common Spacecraft Materials

A thermal precipitator (TP) was designed to collect smoke aerosol particles for microscopic analysis in fire characterization research. Information on particle morphology, size and agglomerate structure obtained from these tests supplements additional aerosol data collected. Modeling of the thermal precipitator throughout the design process was performed with the COMSOL Multiphysics finite element software package, including the Eulerian flow field and thermal gradients in the fluid. The COMSOL Particle Tracing Module was subsequently used to determine particle deposition. Modeling provided optimized design parameters such as geometry, flow rate and temperatures. The thermal precipitator was built and testing verified the performance of the first iteration of the device. The thermal precipitator was successfully operated and provided quality particle samples for microscopic analysis, which furthered the body of knowledge on smoke particulates. This information is a key element of smoke characterization and will be useful for future spacecraft fire detection research.

Smoke↗
DOE OSTI2023

Shock compression of poly(methyl methacrylate) PMMA in the 1000 GPa regime: Z machine experiments

Hydrocarbon polymers are used in a wide variety of practical applications. In the field of dynamic compression at extreme pressures, these polymers are used at several high energy density (HED) experimental facilities. One of the most common polymers is poly(methyl methacrylate) or PMMA, also called Plexiglass® or Lucite®. Here, we present high-fidelity, hundreds of GPa range experimental shock compression data measured on Sandia’s Z machine. We extend the principal shock Hugoniot for PMMA to more than threefold compression up to 650 GPa and re-shock Hugoniot states up to 1020 GPa in an off-Hugoniot regime, where experimental data are even sparser. These data can be used to put additional constraints on tabular equation of state (EOS) models. The present results provide clear evidence for the need to re-examine the existing tabular EOS models for PMMA above ~120 GPa as well as perhaps revisit EOSs of similar hydrocarbon polymers commonly used in HED experiments investigating dynamic compression, hydrodynamics, or inertial confinement fusion.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Nuclear Reaction Data Evaluations for Nuclear Applications Physics and modeling [Slides]

This presentation provides an evaluation workflow scheme for the project. It also discusses nuclear data libraries, including an overview of worldwide nuclear data libraries and current funding agencies. The presentation also discusses the theoretical framework commonly used in the evaluated work, including the R-matrix theory (≈ up to a few keVs for heavy nuclei) as well as pre-equilibrium (PE) and Hauser-Feshbach (HF) theory (⪆1 MeV for medium/heavy nuclei). Inclusion of experimental effects are also discussed. Examples of evaluated work, including theory, experiments, and applications are discussed, specifically n+ 28,29,30 Si evaluation (direct capture component), n+ 53 Cr evaluation (strong multiple scattering effects), n+ 239 Pu(n,γf) reaction (fluctuations in neutron multiplicities), and α+ 17,18 O evaluations (emerging neutron spectra).

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
NASA NTRS1991

On the estimation of climatological Z-R relationships

A statistical framework for climatological Z-R parameter estimation is developed and simulation experiments are conducted to examine sampling properties of the estimators. Both parametric and nonparametric models are considered. For parametric models, it is shown that Z-R parameters can be estimated by maximum likelihood, a procedure with optimal large sample properties. A general nonparametric framework for climatological Z-R estimation is also developed. Nonparametric procedures are attractive because of their flexibility in dealing with certain types of measurement errors common to radar data. Simulation experiments show that even under favorable assumptions on error characteristics of radar and raingages, large datasets are required to obtain accurate Z-R parameter estimates. Another important conclusion is that estimation results are generally quite sensitive to radar and raingage measurement thresholds. For fixed sample size, the simulation results can be used to provide quantitative assessments of the accuracy of Z-R model parameter estimates. These results are particularly useful for error analysis of precipitation products that are derived using climatological Z-R relations. One example is the large-area rainfall estimates derived using the height-area rainfall threshold (HART) technique.

Krajewski, Witold F.↗
DOE OSTI2025

Designing a Framework for Solving Multiobjective Simulation Optimization Problems

Multiobjective simulation optimization (MOSO) problems are optimization problems with multiple conflicting objectives, where evaluation of at least one of the objectives depends on a black-box numerical code or real-world experiment, which we refer to as a simulation. Whereas an extensive body of research is dedicated to developing new algorithms and methods for solving these and related problems, it is challenging and time-consuming to integrate these techniques into real-world production-ready solvers. This is partly because of the diversity and complexity of modern state-of-the-art MOSO algorithms and methods and partly because of the complexity and specificity of many real-world problems and their corresponding computing environments. The complexity of this problem is only compounded when introducing potentially complex and/or domain-specific surrogate-modeling techniques, problem formulations, design spaces, and data acquisition functions. Here, this paper carefully surveys the current state of the art in MOSO algorithms, techniques, and solvers, as well as problem types and computational environments where MOSO is commonly applied. We then present several key challenges in the design of a parallel multiobjective simulation optimization framework (ParMOO) and how they have been addressed. Finally, we provide two case studies demonstrating how customized ParMOO solvers can be quickly built and deployed to solve real-world MOSO problems.

engineering design optimization↗
NASA NTRS2020

Study of Antarctic Blowing Snow Storms Using MODIS and CALIOP Observations With a Machine Learning Model

As a common phenomenon over Antarctica, blowing snow (BLSN), especially the large BLSN storms, play an important role in the Antarctic surface mass balance, radiation budget, and planetary boundary layer processes. This study presents the work on BLSN storm identification and analysis with observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) onboard the Aqua satellite. Spectral analysis shows that BLSN identification is feasible with MODIS daytime data. A random forest machine learning model is developed and observations from the Cloud‐Aerosol Lidar with Orthogonal Polarization are used for training. Model performance results show that machine‐learning based classification can achieve over 90% overall accuracy when classifying MODIS pixels into cloud, clear, and BLSN categories. The machine learning model is applied to MODIS observations during the month of October 2009 for BLSN storm analysis. Results show that the size of BLSN storms has a large spectrum and can reach hundreds of thousands km2. The MODIS based BLSN storm frequency map extends the Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observations coverage limit from 82°S to the South Pole. A BLSN storm belt, which extends from the South Pole region to the coastal area between 130°E and 160°E along the Transantarctic Mountains, provides a potential pathway of snow transport. These results are important in improving the understanding of BLSN impact on Antarctic surface mass balance and boundary layer processes.

Antarctic↗
DOE OSTI2025

Prototype-Wise Sensitivity Analysis of Urban Building Energy Simulation Surrogate Modeling Accuracy

Urban Building Energy Modeling (UBEM) is an important reference for urban energy-related policymaking. Because of the significant impact of urban microclimates on the energy simulation, UBEM requires simulations of many microclimate-prototype pairs. Surrogate modeling is commonly used to reduce the cost of simulation computations. In UBEM surrogate modeling, it is important to determine the percentage of microclimates related to a prototype used for generating surrogate model training data. This study analyzes the prototype-wise variations and sensitivities of surrogate model estimation accuracy to the microclimate sampling ratios. The results of the study can help determine the number of simulations used for generating surrogate modeling data, avoid redundant simulations, and reduce the computational cost for UBEM surrogate modeling and its time.

Pan, Xiyu↗
DOE OSTI2024

Streamlining Ocean Dynamics Modeling with Fourier Neural Operators: A Multiobjective Hyperparameter and Architecture Optimization Approach

Training an effective deep learning model to learn ocean processes involves careful choices of various hyperparameters. We leverage DeepHyper’s advanced search algorithms for multiobjective optimization, streamlining the development of neural networks tailored for ocean modeling. The focus is on optimizing Fourier neural operators (FNOs), a data-driven model capable of simulating complex ocean behaviors. Selecting the correct model and tuning the hyperparameters are challenging tasks, requiring much effort to ensure model accuracy. DeepHyper allows efficient exploration of hyperparameters associated with data preprocessing, FNO architecture-related hyperparameters, and various model training strategies. We aim to obtain an optimal set of hyperparameters leading to the most performant model. Moreover, on top of the commonly used mean squared error for model training, we propose adopting the negative anomaly correlation coefficient as the additional loss term to improve model performance and investigate the potential trade-off between the two terms. The numerical experiments show that the optimal set of hyperparameters enhanced model performance in single timestepping forecasting and greatly exceeded the baseline configuration in the autoregressive rollout for long-horizon forecasting up to 30 days. Utilizing DeepHyper, we demonstrate an approach to enhance the use of FNO in ocean dynamics forecasting, offering a scalable solution with improved precision.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1976

Measurements of sound radiation from cavities at subsonic speeds

To better understand the physical mechanisms responsible for noise generated by flow over cavities with a range of dimensions common to commercial aircraft, a series of measurements of farfield sound pressure level, fluctuating surface pressure, and drag coefficient was made. An examination of the resultant data shows that two theoretical models are needed for this range of dimensions. The shallow cavity (width-to-depth ratio larger than 1.0) can be approximated by a dipole located near the downstream edge of the cavity and the deep cavity (ratio less than 1.0) can be approximated by a dipole located near the downstream edge with the superimposed effects of the upstream edge of the cavity.

Yu, Y. H.↗
NASA NTRS1986

Nonlinear sensitivity coefficients and corrections in system identification

The evaluation of sensitivity coefficients which are the changes in the eigencharacteristics with respect to changes in the structural parameters is important in the identification of structural systems. In such studies the commonly used linearized sensitivity coefficients for system identification have occasionally resulted in poor convergence or even divergence during the iteration process to update analytical models for better correlation with the test data. Nonlinear sensitivity coefficients and nonlinear correction terms, usually eliminated during the linearization process, have been developed to evaluate sensitivity coefficients of linear systems. Application to computer simulated example problems indicates a more rapid and stable convergence of the iteration processes to update the analytical model for the improvement of the correlation with test data. The successful application of combining the nonlinear sensitivity coefficients and nonlinear correction terms with the Multiple Boundary Condition Tests enhances the ability to validate large space structures by ground tests.

Kuo, C. P.↗
NASA NTRS1991

Application of Hapke photometric model to three geologic surfaces using PARABOLA bidirectional reflection data

The Geologic Remote Sensing Field Experiment (GRSFE) was conducted in July and September of 1989 to collect data with both ground and airborne instrumentation. A major objective of GRSFE was to collect data which could be used to test radiative transfer models for the extraction of composition and textural surface properties from remotely acquired data. Reported here are the initial results from an application of the Hapke photometric model, using data from the Portable Apparatus for Remote Acquisition of Bidirectional Observations of Land and Atmosphere (PARABOLA), a ground based radiometer with three spectral channels. PARABOLA data was collected in the Lunar Crater Volcanic Field in Nevada, specifically from the region of Lunar Lake, a playa. The Hapke model was found to be inadequate for three relatively common geologic surfaces (a clay-rich, hard packed surface with decimeter sized mudcracks; a cobble site, similar to a playa site, but strewn with basaltic cobbles and pebbles; and a surface mantled basalt lava flow). The model is not at fault; rather, the complexity of most geologic surfaces is not accounted for in the initial assumptions.

Shepard, Michael K.↗