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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

A nudged hybrid analysis and modeling approach for realtime wake-vortex transport and decay prediction

Here, we put forth a long short-term memory (LSTM) nudging framework for the enhancement of reduced order models (ROMs) of fluid flows utilizing noisy measurements for air traffic improvements. Toward emerging applications of digital twins in aviation, the proposed approach allows for constructing a realtime predictive tool for wake-vortex transport and decay systems. We build on the fact that in realistic application, there are uncertainties in initial and boundary conditions, model parameters, as well as measurements. Moreover, conventional nonlinear ROMs based on Galerkin projection (GROMs) suffer from imperfection and solution instabilities, especially for advection-dominated flows with slow decay in the Kolmogorov n-width. In the presented LSTM nudging (LSTM-N) approach, we fuse forecasts from a combination of imperfect GROM and uncertain state estimates, with sparse Eulerian sensor measurements to provide more reliable predictions in a dynamical data assimilation framework. We illustrate our concept by solving the two-dimensional vorticity transport equation. We investigate the effects of measurements noise and state estimate uncertainty on the performance of the LSTM-N behavior. We also demonstrate that it can sufficiently handle different levels of temporal and spatial measurement sparsity, and offer a huge potential in developing next-generation digital twin technologies for aerospace applications.

42 ENGINEERING↗
DOE OSTI2023

A Robust Parallel Distributed State Estimation for Large Scale Distribution Systems

The growing need and interest in real-time monitoring of large distribution networks motivated by the rapid population of renewable sources, EVs and etc. demand a computationally efficient state estimation framework. Furthermore, this paper presents an improved computational framework for implementing a robust state estimator using a multi-core processor. The main contribution of the paper is the proposed computational framework along with two partitioning strategies which enable fast and robust state estimation for large scale radial and/or meshed distribution systems. Formulation of the proposed method and its implementation are described in detail. Performance of the estimator is tested by simulations first using a small 84-bus radial distribution system. Then the method’s scalability is demonstrated by simulations on two very large scale distribution networks one configured radially and the other meshed each containing over 12,500 buses.

42 ENGINEERING↗
NASA NTRS1994

On the use of radiative surface temperature to estimate sensible heat flux over sparse shrubs in Nevada

The accurate partitioning of available energy into sensible and latent heat flux is crucial to the understanding of surface atmosphere interactions. This issue is more complicated in arid and semi arid regions where the relative contribution to surface fluxes from the soil and vegetation may vary significantly throughout the day and throughout the season. A three component model to estimate sensible heat flux over heterogeneous surfaces is presented. The surface was represented with two adjacent compartments. The first compartment is made up of two components, shrubs and shaded soil, the second of open 'illuminated' soil. Data collected at two different sites in Nevada (U.S.) during the Summers of 1991 and 1992 were used to evaluate model performance. The results show that the present model is sufficiently general to yield satisfactory results for both sites.

Chehbouni, A.↗
NASA NTRS1992

LAI inversion from optical reflectance using a neural network trained with a multiple scattering model

The inversion of the leaf area index (LAI) canopy parameter from optical spectral reflectance measurements is obtained using a backpropagation artificial neural network trained using input-output pairs generated by a multiple scattering reflectance model. The problem of LAI estimation over sparse canopies (LAI < 1.0) with varying soil reflectance backgrounds is particularly difficult. Standard multiple regression methods applied to canopies within a single homogeneous soil type yield good results but perform unacceptably when applied across soil boundaries, resulting in absolute percentage errors of >1000 percent for low LAI. Minimization methods applied to merit functions constructed from differences between measured reflectances and predicted reflectances using multiple-scattering models are unacceptably sensitive to a good initial guess for the desired parameter. In contrast, the neural network reported generally yields absolute percentage errors of <30 percent when weighting coefficients trained on one soil type were applied to predicted canopy reflectance at a different soil background.

Smith, James A.↗
NASA NTRS2001

Validation of Ocean Color Satellite Data Products in Under Sampled Marine Areas

The planktonic marine cyanobacterium, Trichodesmium sp., is broadly distributed throughout the oligotrophic marine tropical and sub-tropical oceans. Trichodesmium, which typically occurs in macroscopic bundles or colonies, is noteworthy for its ability to form large surface aggregations and to fix dinitrogen gas. The latter is important because primary production supported by N2 fixation can result in a net export of carbon from the surface waters to deep ocean and may therefore play a significant role in the global carbon cycle. However, information on the distribution and density of Trichodesmium from shipboard measurements through the oligotrophic oceans is very sparse. Such estimates are required to quantitatively estimate total global rates of N2 fixation. As a result current global rate estimates are highly uncertain. Thus in order to understand the broader biogeochemical importance of Trichodesmium and N2 fixation in the oceans, we need better methods to estimate the global temporal and spatial variability of this organism. One approach that holds great promise is satellite remote sensing. Satellite ocean color sensors are ideal instruments for estimating global phytoplankton biomass, especially that due to episodic blooms, because they provide relatively high frequency synoptic information over large areas. Trichodesmium has a combination of specific ultrastructural and biochemical features that lend themselves to identification of this organism by remote sensing. Specifically, these features are high backscatter due to the presence of gas vesicles, and absorption and fluorescence of phycoerythrin. The resulting optical signature is relatively unique and should be detectable with satellite ocean color sensors such as the Sea-Viewing Wide Field-of-view Sensor (SeaWiFS).

Subramaniam, Ajit↗
NASA NTRS2020

Regional Tropical Aboveground Biomass Mapping with L-Band Repeat-Pass Interferometric Radar, Sparse Lidar, and Multiscale Superpixels

We introduce a multiscale superpixel approach that leverages repeat-pass interferometric coherence and sparse AGB estimates from a simulated spaceborne lidar in order to extend the NISAR mission’s applicable range of aboveground biomass (AGB) in tropical forests. Airborne and spaceborne L-band radar and full-waveform airborne lidar data are used to simulate the NISAR and GEDI mission, respectively. In addition to UAVSAR data, we use spaceborneALOS-2/PALSAR-2 imagery with 14-day temporal baseline, which is comparable to NISAR’s 12-daybaseline. Our reference AGB maps are derived from the airborne LVIS data during the AfriSAR campaign for three sites (Mondah, Ogooue, and Lope). Each tropical site has mean AGB of at least 125 Mg/ha in addition to areas with AGB exceeding 700 Mg/ha. Spatially sampling from these LVIS-derived AGB reference maps, we approximate GEDI AGB estimates. To evaluate our methodology, we perform several different analyses. First, we partition each study site into low(≤100 Mg/ha) and high (>100 Mg/ha) AGB areas, in conformity with the NISAR mission requirement to provide AGB estimates for forests between 0 and 100 Mg/ha with a RMSE below 20 Mg/ha. In the low AGB areas, this RMSE requirement is satisfied in Lope and Mondah and it fell short of the requirement in Ogooue by less 3 Mg/ha with UAVSAR and 6 Mg/ha with PALSAR-2. We note that our maps have finer spatial resolution (50 m) than NISAR requires (1 hectare). In the high AGB areas, the normalized RMSE increases to 51% (i.e.,<90 Mg/ha), but with negligible bias for all three sites. Second, we train a single model to estimate AGB across both high and low AGB regimes simultaneously and obtain a normalized RMSE that is<60% (or<100 Mg/ha). Lastly, we show the use of both (a) multiscale superpixels and (b) interferometric coherence significantly improves the accuracy of the AGB estimates. The InSAR coherence improved the RMSE by approximately 8% at Mondah with both sensors, lowering the RMSE from 59 Mg/ha to 47.4 Mg/h with UAVSAR and from 57.1 Mg/ha to 46 Mg/ha. This work illustrates one of the numerous synergistic relationships between the spaceborne lidars, such as GEDI, with L-band SAR, such as PALSAR-2 and NISAR, in order to produce robust regional AGB in high biomass tropical regions.

NISAR↗
DOE OSTI2025

Data-Driven Closures and Assimilation for Stiff Multiscale Random Dynamics

Here, we introduce a data-driven and physics-informed framework for propagating uncertainty in stiff, multiscale random ordinary differential equations (RODEs) driven by correlated (colored) noise. Unlike systems subjected to Gaussian white noise, a deterministic equation for the joint probability density function (PDF) of RODE state variables does not exist in closed form. Moreover, such an equation would require as many phase-space variables as there are states in the RODE system. To alleviate this curse of dimensionality, we instead derive exact, albeit unclosed, reduced-order PDF (RoPDF) equations for low-dimensional observables/quantities of interest. The unclosed terms take the form of state-dependent conditional expectations, which are directly estimated from data at sparse observation times. However, for systems exhibiting stiff, multiscale dynamics, data sparsity introduces regression discrepancies that compound during RoPDF evolution. This is overcome by introducing a kinetic-like defect term to the RoPDF equation, which is learned by assimilating in sparse, low-fidelity RoPDF estimates. Two assimilation methods are considered, namely nudging and deep neural networks, which are successfully tested against Monte Carlo simulations.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2017

Coupled Data Assimilation for Integrated Earth System Analysis and Prediction: Goals, Challenges, and Recommendations

The purpose of this report is to identify fundamental issues for coupled data assimilation (CDA), such as gaps in science and limitations in forecasting systems, in order to provide guidance to the World Meteorological Organization (WMO) on how to facilitate more rapid progress internationally. Coupled Earth system modeling provides the opportunity to extend skillful atmospheric forecasts beyond the traditional two-week barrier by extracting skill from low-frequency state components such as the land, ocean, and sea ice. More generally, coupled models are needed to support seamless prediction systems that span timescales from weather, subseasonal to seasonal (S2S), multiyear, and decadal. Therefore, initialization methods are needed for coupled Earth system models, either applied to each individual component (called Weakly Coupled Data Assimilation - WCDA) or applied the coupled Earth system model as a whole (called Strongly Coupled Data Assimilation - SCDA). Using CDA, in which model forecasts and potentially the state estimation are performed jointly, each model domain benefits from observations in other domains either directly using error covariance information known at the time of the analysis (SCDA), or indirectly through flux interactions at the model boundaries (WCDA). Because the non-atmospheric domains are generally under-observed compared to the atmosphere, CDA provides a significant advantage over single-domain analyses. Next, we provide a synopsis of goals, challenges, and recommendations to advance CDA: Goals: (a) Extend predictive skill beyond the current capability of NWP (e.g. as demonstrated by improving forecast skill scores), (b) produce physically consistent initial conditions for coupled numerical prediction systems and reanalyses (including consistent fluxes at the domain interfaces), (c) make best use of existing observations by allowing observations from each domain to influence and improve the full earth system analysis, (d) develop a robust observation-based identification and understanding of mechanisms that determine the variability of weather and climate, (e) identify critical weaknesses in coupled models and the earth observing system, (f) generate full-field estimates of unobserved or sparsely observed variables, (g) improve the estimation of the external forcings causing changes to climate, (h) transition successes from idealized CDA experiments to real-world applications. Challenges: (a) Modeling at the interfaces between interacting components of coupled Earth system models may be inadequate for estimating uncertainty or error covariances between domains, (b) current data assimilation methods may be insufficient to simultaneously analyze domains containing multiple spatiotemporal scales of interest, (c) there is no standardization of observation data or their delivery systems across domains, (d) the size and complexity of many large-scale coupled Earth system models makes it is difficult to accurately represent uncertainty due to model parameters and coupling parameters, (e) model errors lead to local biases that can transfer between the different Earth system components and lead to coupled model biases and long-term model drift, (e) information propagation across model components with different spatiotemporal scales is extremely complicated, and must be improved in current coupled modeling frameworks, (h) there is insufficient knowledge on how to represent evolving errors in non-atmospheric model components (e.g. as sea ice, land and ocean) on the timescales of NWP.

CDA↗
NASA NTRS1999

TRMM Field Campaigns: Objectives and Status Report

The Tropical Rainfall Measuring Mission (TRMM) satellite has been sending valuable data since launch in November 1997. Some of the key goals of the joint NASA (US) and NASDA (Japan) mission are: (1) to estimate the four-dimensional diabatic heating in the tropical and subtropical atmosphere, (2) understand the role of latent heating in driving tropical and extratropical circulations, (3) obtain monthly area-averaged estimates of rainfall over the data-sparse oceans, and (4) estimate the relative contribution of convective and stratiform precipitation over different regions during different seasons. The overarching scientific objective is to understand and improve estimates of rainfall and latent heating profiles throughout the global tropics. This requires observations for fundamental understanding of cloud dynamics and microphysics, as well as for validation, testing assumptions and error estimates of cloud-resolving models, forward radiative transfer models, algorithms used to estimate rainfall statistics and vertical structure of precipitation from surface-based radar, and from satellites. Field experiments designed to contribute to this understanding have been conducted in Texas and the South China Sea in spring of 1998, Florida in summer of 1998, and interior Brazil in (boreal) winter 1999. In summer 1999, a major oceanic campaign will be based at Kwajalein Atoll. Some early results will be highlighted, noting some significant contrasts between oceanic and continental convective systems.

Zipser, Edward I.↗
DOE OSTI2023

Estimation of hydraulic conductivity in a watershed using sparse multi-source data via Gaussian process regression and Bayesian experimental design

Enhanced water management systems depend on accurate estimation of subsurface hydraulic properties. However, geologic formations can vary significantly, so information from a single source (e.g., widely spaced boreholes) is insufficient in characterizing subsurface aquifer properties. Therefore, multiple sources of information are needed to complement the hydrogeology understanding of a region. Here, this study presents a numerical framework in which information from different measurement sources is combined to characterize the 3D random field in a multi-fidelity prediction model. Coupled with the model, a Bayesian experimental design was used to determine the best future sampling locations. The Upper Sangamon watershed in east-central Illinois was selected as the case study site, where the multi-fidelity Gaussian process model was used to estimate the hydraulic conductivity in the region of interest. Multi-source observation data were obtained from electrical resistivity and borehole pumping tests. The accuracy of the model prediction is dependent on the locations and the distribution of both high- and low-fidelity data. Furthermore, the multi-fidelity model was compared with the single-fidelity model. The uncertainties and confidence in the measurements and parameter estimates were quantified and used to design future cycles of data collection to further improve the confidence intervals.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1988

Optimal post-experiment estimation of poorly modeled dynamic systems

Recently, a novel strategy for post-experiment state estimation of discretely-measured dynamic systems has been developed. The method accounts for errors in the system dynamic model equations in a more general and rigorous manner than do filter-smoother algorithms. The dynamic model error terms do not require the usual process noise assumptions of zero-mean, symmetrically distributed random disturbances. Instead, the model error terms require no prior assumptions other than piecewise continuity. The resulting state estimates are more accurate than filters for applications in which the dynamic model error clearly violates the typical process noise assumptions, and the available measurements are sparse and/or noisy. Estimates of the dynamic model error, in addition to the states, are obtained as part of the solution of a two-point boundary value problem, and may be exploited for numerous reasons. In this paper, the basic technique is explained, and several example applications are given. Included among the examples are both state estimation and exploitation of the model error estimates.

Mook, D. Joseph↗
DOE OSTI2020

Sensing analytical instrument parameters, specimen characteristics, or both from sparse datasets

Disclosed are methods for sensing conditions of an electron microscope system and/or a specimen analyzed thereby. Also disclosed are sensor systems and electron microscope systems able to sense system conditions, and/or conditions of the specimen being analyzed by such systems. In one embodiment, a sparse dataset can be acquired from a random sub-sampling of the specimen by an electron beam probe of the electron microscope system. Instrument parameters, specimen characteristics, or both can be estimated from the sparse dataset.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1981

The effects of missing data on global ozone estimates

The effects of missing data and model truncation on estimates of the global mean, zonal distribution, and global distribution of ozone are considered. It is shown that missing data can introduce biased estimates with errors that are not accounted for in the accuracy calculations of empirical modeling techniques. Data-fill techniques are introduced and used for evaluating error bounds and constraining the estimate in areas of sparse and missing data. It is found that the accuracy of the global mean estimate is more dependent on data distribution than model size. Zonal features can be accurately described by 7th order models over regions of adequate data distribution. Data variance accounted for by higher order models appears to represent climatological features of columnar ozone rather than pure error. Data-fill techniques can prevent artificial feature generation in regions of sparse or missing data without degrading high order estimates over dense data regions.

Drewry, J. W.↗
DOE OSTI2026

Deep Learning Reconstruction of Daily Soil CO 2 Efflux Reveals Biogeochemical Insights and Reduces Annual Estimate Uncertainty Despite Limited Daily Predictability

Soil CO 2 efflux is commonly measured monthly or seasonally, leaving daily dynamics poorly resolved and contributing to global estimation uncertainty. We trained a single Long Short-Term Memory (LSTM) model to predict daily soil CO 2 efflux across 82 globally distributed sites in COSORE, with 0.2%–46.9% daily data coverage from 2003 to 2020. Despite using far fewer sites than are typically used to train a single deep learning model, with observations biased toward temperate mesic sites, the LSTM model performed well at approximately one-third of sites, reconstructed nearly 2 decades of daily efflux, and outperformed commonly used approaches for estimating daily efflux when applied to the same data set. Performance was weakest at pronounced peaks and troughs and at non-temperate sites with <1.5 years of observations and irregular data patterns. Nevertheless, annual efflux from reconstructed daily data had <40% error even at underperforming sites, substantially improving estimates derived from monthly and seasonal sampling (maximum errors of 95% and 136%, respectively). Temperature sensitivity (Q 10 ) estimated from reconstructed daily predictions closely matched estimates from daily observations, whereas Q 10 values derived from monthly or seasonal observations deviated substantially, suggesting that coarse temporal sampling may contribute to uncertainty in reported Q 10 values. Consistent daily reconstructions further enabled trend analyses for well-performing, predominantly temperate sites and showed increasing soil CO 2 efflux at most sites from 2003 to 2020, with more variable summer trends. Despite limitations, these results demonstrate the potential of LSTM models to reconstruct daily soil CO 2 efflux and reduce estimation uncertainties from sparse observations.

Smykalov, Valerie [Pennsylvania State University, ↗
NASA NTRS2001

A TRMM-Calibrated Infrared Rainfall Algorithm Over Brazil

An improved version of the Convective/Stratiform Technique (CST) and its application over Brazil are presented. Keeping the major components of traditional CST, we have modified the scheme for classing convective and stratiform rainfall and tested various schemes to eliminate non-raining cirrus clouds. The parameters of the technique are calibrated using Tropical Rainfall Measuring Mission (TRMM) multi-sensor observations, including TMI, PR and Visible Infrared Scanner (VIRS). This technique takes advantage of the high temporal sampling rate of geosynchronous infrared channel and the better instantaneous rain observation of TRMM Microwave Imager (TMI) and PR. Moreover, sparse TMI rain estimates are used to dynamically calibrate the parameters of the technique to improve its performance. The technique is applied to make rainfall estimates on various time scales and to study rainfall statistics such as the distributions of rain intensity and storm duration over a four-month period beginning January 1999. The study period coincides with the TRMM/Large-Scale Biosphere-Atmosphere experiment in Amazonia (LBA) ground validation experiment, and observations from the LBA radar are used to validate the technique. Results show the mean diurnal cycle of precipitation over the LBA area, and are compared to radar data from the Tropical Oceans and Global Atmosphere (TOGA) radar. When the larger geographic region is considered, the analysis of the mean hourly estimates revealed the pronounced effects of rivers, topography and local circulations on the rainfall.

Negri, Andrew J.↗
NASA NTRS2015

Wide-Field Infrared Survey Explorer Observations of Young Stellar Objects in the Lynds 1509 Dark Cloud in Auriga

The Wide-Field Infrared Survey Explorer (WISE) has uncovered a striking cluster of young stellar object (YSO) candidates associated with the L1509 dark cloud in Auriga. The WISE observations, at 3.4, 4.6, 12, and 22 microns, show a number of objects with colors consistent with YSOs, and their spectral energy distributions suggest the presence of circumstellar dust emission, including numerous Class I, flat spectrum, and Class II objects. In general, the YSOs in L1509 are much more tightly clustered than YSOs in other dark clouds in the Taurus-Auriga star forming region, with Class I and flat spectrum objects confined to the densest aggregates, and Class II objects more sparsely distributed. We estimate a most probable distance of 485-700 pc, and possibly as far as the previously estimated distance of 2 kpc.

Infrared↗