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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 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS1991

Sensor fusion III: 3-D perception and recognition; Proceedings of the Meeting, Boston, MA, Nov. 5-8, 1990

The volume on data fusion from multiple sources discusses fusing multiple views, temporal analysis and 3D motion interpretation, sensor fusion and eye-to-hand coordination, and integration in human shape perception. Attention is given to surface reconstruction, statistical methods in sensor fusion, fusing sensor data with environmental knowledge, computational models for sensor fusion, and evaluation and selection of sensor fusion techniques. Topics addressed include the structure of a scene from two and three projections, optical flow techniques for moving target detection, tactical sensor-based exploration in a robotic environment, and the fusion of human and machine skills for remote robotic operations. Also discussed are K-nearest-neighbor concepts for sensor fusion, surface reconstruction with discontinuities, a sensor-knowledge-command fusion paradigm for man-machine systems, coordinating sensing and local navigation, and terrain map matching using multisensing techniques for applications to autonomous vehicle navigation.

Schenker, Paul S.↗
DOE OSTI2024

3D Reconstruction of a High-Energy Diffraction Microscopy Sample Using Multi-modal Serial Sectioning with High-Precision EBSD and Surface Profilometry

High-energy diffraction microscopy (HEDM) combined with in situ mechanical testing is a powerful nondestructive technique for tracking the evolving microstructure within polycrystalline materials during deformation. This technique relies on a sophisticated analysis of X-ray diffraction patterns to produce a three-dimensional reconstruction of grains and other microstructural features within the interrogated volume. However, it is known that HEDM can fail to identify certain microstructural features, particularly smaller grains or twinned regions. Characterization of the identical sample volume using high-resolution surface-specific techniques, particularly electron backscatter diffraction (EBSD), can not only provide additional microstructure information about the interrogated volume but also highlight opportunities for improvement of the HEDM reconstruction algorithms. In this study, a sample fabricated from undeformed “low solvus, high refractory” nickel-based superalloy was scanned using HEDM. The volume interrogated by HEDM was then carefully characterized using a combination of surface-specific techniques, including epi-illumination optical microscopy, zero-tilt secondary and backscattered electron imaging, scanning white light interferometry, and high-precision EBSD. Custom data fusion protocols were developed to integrate and align the microstructure maps captured by these surface-specific techniques and HEDM. The raw and processed data from HEDM and serial sectioning have been made available via the Materials Data Facility (MDF) at https://doi.org/10.18126/4y0p-v604 for further investigation.

36 MATERIALS SCIENCE↗
NASA NTRS2006

An Overview of Inter-comparison Methodologies for Terra and Aqua MODIS Calibration

With increasing efforts on data fusion and long-term climate data records (CDR) using observations made by multiple sensors, on the same or different platforms, their cross-calibration and validation work has become more and more important. The uncertainties of the climate models and data records depend not only on the calibration quality of individual sensors but also on their calibration consistency. This paper provides an overview of methodologies used by the MODIS Characterization Support Team (MCST) at NASA GSFC for the inter-comparison studies of Terra and Aqua MODIS on-orbit calibration. Each MODIS was built with a set of onboard calibrators (OBC) that include a blackbody (BB), a solar diffuser (SD), and a solar diffuser stability monitor (SDSM). The BB is primarily used for the thermal emissive bands (TEB) calibration and the SD/SDSM system for the reflective solar bands (RSB) calibration. Although the-instrument design and calibration approach are nearly identical for both Terra and Aqua MODIS and they all went through an extensive and similar pre-launch calibration and characterization process, still their on-orbit calibration consistency needs to be carefully examined and validated as many science products have been generated from observations made by both instruments. Methodologies discussed in this paper include inter-comparison studies using the Moon, a third sensor, and ground targets. Our results show that Terra and Aqua reflective solar bands and thermal emissive bands have been calibrated consistently with excellent long-term stabilities. For the 11 and 12 micrometers sea surface temperature (SST) bands, the calibration difference of Terra and Aqua MODIS is less than 0.2K.

Xiong, X.↗
NASA NTRSDate not supplied

Infusing Statistical Thinking into the NASA Quesst Community Test Campaign

Statistical thinking permeates many important decisions as NASA plans its Quesst mission, which will culminate in a series of community overflights using the X-59 aircraft to demonstrate low-noise supersonic flight. Month-long longitudinal surveys will be deployed to assess human perception and annoyance to this new acoustic phenomenon. NASA works with a large contractor team to develop systems and methodologies to estimate noise doses, to test and field socio-acoustic surveys, and to study the relationship between the two quantities, dose and response, through appropriate choices of statistical models. This latter dose-response relationship will serve as an important tool as national and international noise regulators debate whether overland supersonic flights could be permitted once again within permissible noise limits. In this presentation we highlight several areas where statistical thinking has come into play, including issues of sampling, classification and data fusion, and analysis of longitudinal survey data that are subject to rare events and the consequences of measurement error. We note several operational constraints that shape the appeal or feasibility of some decisions on statistical approaches, and we identify several important remaining questions to be addressed.

Bayesian model↗
DOE OSTI2024

Relating Aerial Infrared Thermography Defects to Photovoltaic Performance: Preprint

In this research, we examine the relationship between aerial IR defect analysis and photovoltaic (PV) performance data for twelve utility- and commercial-scale solar sites in the United States. To do this, we fuse the site diagram geoJSON's, aerial infrared thermography (aIRT) defect analyses, and associated inverter time series, allowing for a direct comparison between site defects and time series data. Defect analyses were provided by Zeitview, under its Solar Insights platform. Following the data fusion process, we look at the relationship between system performance and aIRT defects. We investigate the relationship between degradation and hotspot defects, as well as the relationship between AC power data and offline strings and misaligned modules. In general, system degradation was not affected by long-term or balance-of-system (BoS) defects as they occurred infrequently in the data set. However, for one system, a near statistically significant relationship (p-value=0.057) was found when comparing the degradation of inverter blocks with several multi-hotspot defects to all other inverter blocks without this particular defect. There was strong alignment when comparing short-term recoverable module defects such as stuck trackers and offline strings to time series data. In general, we found that when an inverter block has more than 80% of modules flagged for one of these defects, its AC power time data is flat-lined and the inverter block is not producing.

aerial inspection↗
DOE OSTI2025

Combining Observations and Models: A Review of the CARDAMOM Framework for Data‐Constrained Terrestrial Ecosystem Modeling

The rapid increase in the volume and variety of terrestrial biosphere observations (i.e., remote sensing data and in situ measurements) offers a unique opportunity to derive ecological insights, refine process‐based models, and improve forecasting for decision support. However, despite their potential, ecological observations have primarily been used to benchmark process‐based models, as many past and current models lack the capability to directly integrate observations and their associated uncertainties for parameterization. In contrast, data assimilation frameworks such as the CARbon DAta MOdel fraMework (CARDAMOM) and its suite of process‐based models, known as the Data Assimilation Linked Ecosystem Carbon Model (DALEC), are specifically designed for model‐data fusion. This review, motivated by a recent CARDAMOM community workshop, examines the development and applications of CARDAMOM, with an emphasis on its role in advancing ecosystem process understanding. CARDAMOM employs a Bayesian approach, using a Markov Chain Monte Carlo algorithm to enable data‐driven calibration of DALEC parameters and initial states (i.e., carbon pool sizes) through observation operators. CARDAMOM's unique ability to retrieve localized model process parameters from diverse datasets—ranging from in situ measurements to global satellite observations—makes it a highly flexible tool for analyzing spatially variable ecosystem responses to environmental change. However, assimilating these data also presents challenges, including data quality issues that propagate into model skill, as well as trade‐offs between model complexity, parameter equifinality, and predictive performance. We discuss potential solutions to these challenges, such as reducing parameter equifinality by incorporating new observations. This review also offers community recommendations for incorporating emerging datasets, integrating machine learning techniques, strengthening collaboration with remote sensing, field, and modeling communities, and expanding CARDAMOM's relevance for localized ecosystem monitoring and decision‐making. CARDAMOM enables a deep, mechanistic understanding of terrestrial ecosystem dynamics that cannot be achieved through empirical analyses of observational datasets or weakly constrained models alone.

Bayesian inference↗
NASA NTRSDate not supplied

Bond Line Thickness Estimation in Composite Structures Using Multiple Inspection Techniques

Imaging and other nondestructive evaluation techniques are commonly used for material characterization and defect recognition in safety critical aerospace applications, with data fusion providing the framework for uncertainty quantification in these contexts. Most commonly, forward physics-based modeling predicts the response conditioned on material properties and defect assumptions, and probabilistic methods are used to infer the hidden state of subject of the inspection from a combination of prior information, likelihoods, and inspection data. In this paper Bayesian methods are used to estimate bond thickness in lap joints comprised of aluminum adherends using a combination of infrared thermography, digital radiography, and ultrasound. The accuracy of the fused estimates are validated against data generated from synthetic specimens, and by comparison against high resolution X-ray computed tomography inspections of built specimens.

thermal nondestructive evaluation↗
NASA NTRSDate not supplied

Bond Line Thickness Estimation in Composite Structures Using Multiple Inspection Techniques

Imaging and other nondestructive evaluation techniques are commonly used for material characterization and defect recognition in safety critical aerospace applications, with data fusion providing the framework for uncertainty quantification in these contexts. Most commonly, forward physics-based modeling predicts the response conditioned on material properties and defect assumptions, and probabilistic methods are used to infer the hidden state of the subject of the inspection from a combination of prior information, likelihoods, and inspection data. In this paper Bayesian methods are used to estimate bond thickness in lap joints comprised of aluminum adherends using a combination of infrared thermography and ultrasound. The concept of the conflation of probability distributions is applied to combine the posterior distributions derived from thermography and ultrasound and the quality of the fused estimates are compared against the individual estimates against synthetic data that was created to mimic the inspection of a lap joint comprised of aluminum adherends.

thermal nondestructive evaluation↗
NASA NTRS2020

Biomass Estimation from Simulated GEDI, ICESat-2 and NISAR Across Environmental Gradients in Sonoma County, California

Estimates of the magnitude and distribution of aboveground carbon in Earth’s forests remain uncertain, yet knowledge of forest carbon content at a global scale is critical for forest management in support of climate mitigation. In light of this knowledge gap, several upcoming spaceborne missions aim to map forest aboveground biomass, and many new biomass products are expected from these datasets. As these new missions host different technologies, each with relative strengths and weaknesses for biomass retrieval, as well as different spatial resolutions, consistently comparing or combining biomass estimates from these new datasets will be challenging. This paper presents a demonstration of an inter-comparison of biomass estimates from simulations of three NASA missions (GEDI, ICESat-2 and NISAR) over Sonoma county in California, USA. We use a high resolution, locally calibrated airborne lidar map as our reference dataset, and emphasize the importance of considering uncertainties in both reference maps and spaceborne estimates when conducting biomass product validation. GEDI and ICESat-2 were simulated from airborne lidar point clouds, while UAVSAR’s L-band backscatter was used as a proxy for NISAR. To estimate biomass for the lidar missions we used GEDI’s footprint-level biomass algorithms, and also adapted these for application to ICESat-2. For UAVSAR, we developed a locally trained biomass model, calibrated against the ALS reference map. Each mission simulation was evaluated in comparison to the local reference map at its native product resolution (25 m, 100m transect, and 1 ha) yielding RMSEs of 57%, 75%, and 89% for GEDI, NISAR, and ICESat-2 respectively. RMSE values increased for GEDI’s power beam during simulated daytime conditions (64%), coverage beam during nighttime conditions (72%), and coverage beam daytime conditions (87%). We also test the application of GEDI’s biomass modeling framework for estimation of biomass from ICESat-2, and fine that ICESat-2 yields reasonable biomass estimates, particularly in relatively short, open canopies. Results suggest that while all three missions will produce datasets useful for biomass mapping, tall, dense canopies such as those found in Sonoma County present the greatest challenges for all three missions, while steep slopes also prove challenging for single-date SAR based biomass retrieval. Our methods provide guidance for the inter-comparison and validation of spaceborne biomass estimates through the use of airborne lidar reference maps, and could be repeated with on-orbit estimates in any area with high quality field plot and ALS data. These methods allow for regional interpretations and filtering of multi-mission biomass estimates toward improved wall-to-wall biomass maps through data fusion.

GEDI↗
NASA NTRS1990

Image reconstruction of IRAS survey scans

The IRAS survey data can be used successfully to produce images of extended objects. The major difficulties, viz. non-uniform sampling, different response functions for each detector, and varying signal-to-noise levels for each detector for each scan, were resolved. The results of three different image construction techniques are compared: co-addition, constrained least squares, and maximum entropy. The maximum entropy result is superior. An image of the galaxy M51 with an average spatial resolution of 45 arc seconds is presented, using 60 micron survey data. This exceeds the telescope diffraction limit of 1 minute of arc, at this wavelength. Data fusion is a proposed method for combining data from different instruments, with different spacial resolutions, at different wavelengths. Data estimates of the physical parameters, temperature, density and composition, can be made from the data without prior image (re-)construction. An increase in the accuracy of these parameters is expected as the result of this more systematic approach.

Bontekoe, Tj. Romke↗
NASA NTRS1997

Image Registration Workshop Proceedings

Automatic image registration has often been considered as a preliminary step for higher-level processing, such as object recognition or data fusion. But with the unprecedented amounts of data which are being and will continue to be generated by newly developed sensors, the very topic of automatic image registration has become and important research topic. This workshop presents a collection of very high quality work which has been grouped in four main areas: (1) theoretical aspects of image registration; (2) applications to satellite imagery; (3) applications to medical imagery; and (4) image registration for computer vision research.

LeMoigne, Jacqueline↗
NASA NTRS1998

MODIS Science Team Member Semi-annual Report

This paper presents a semi-annual report of the MODerate resolution imaging Spectroradiometer (MODIS) Science Team Members. The most important activities undertaken during this reporting period are the following: 1) Versions 2.1 and 2.2 surface reflectance L2/L3 DAAC/SDST delivery; 2) Version 2.0 1km and 250m VI product delivery (assist Arizona); 3) Version 2.1 surface reflectance L2 testing; 4) Land Synthetic data set generator improvements; 5) QA; 6) Surface reflectance error budget generation (SWAMP request); 7) SCF Hardware; 8) Aerosol transport modeling; 9) Aerosol optical depth retrieval from AVHRR data; 10) Aerosol characteristics retrieval from SeaWIFS/AVHRR fusioned data; 11) Validation activities; 12) Aerosol climatology; and 13) 6S code. The report includes summaries of the topics above.

Vermote, Eric↗
NASA NTRS2010

Future Flight Opportunities and Calibration Protocols for CERES: Continuation of Observations in Support of the Long-Term Earth Radiation Budget Climate Data Record

The goal of the Clouds and the Earth s Radiant Energy System (CERES) project is to provide a long-term record of radiation budget at the top-of-atmosphere (TOA), within the atmosphere, and at the surface with consistent cloud and aerosol properties at climate accuracy. CERES consists of an integrated instrument-algorithm validation science team that provides development of higher-level products (Levels 1-3) and investigations. It involves a high level of data fusion, merging inputs from 25 unique input data sources to produce 18 CERES data products. Over 90% of the CERES data product volume involves two or more instruments. Continuation of the Earth Radiation Budget (ERB) Climate Data Record (CDR) has been identified as critical in the 2007 NRC Decadal Survey, the Global Climate Observing System WCRP report, and in an assessment titled Impacts of NPOESS Nunn-McCurdy Certification on Joint NASA-NOAA Climate Goals . Five CERES instruments have flown on three different spacecraft: TRMM, EOS-Terra and EOS-Aqua. In response, NASA, NOAA and NPOESS have agreed to fly the existing CERES Flight Model (FM-5) on the NPP spacecraft in 2011 and to procure an additional CERES Sensor with modest upgrades for flight on the JPSS C1 spacecraft in 2014, followed by a CERES follow-on sensor for flight in 2018. CERES is a scanning broadband radiometer that measures filtered radiance in the SW (0.3-5 m), total (TOT) (0.3-200 m) and WN (8-12 m) regions. Pre-launch calibration is performed on each Flight Model to meet accuracy requirements of 1% for SW and 0.5% for outgoing LW observations. Ground to flight or in-flight changes are monitored using protocols employing onboard and vicarious calibration sources. Studies of flight data show that SW response can change dramatically due to optical contamination. with greatest impact in blue-to UV radiance, where tungsten lamps are largely devoid of output. While science goals remain unchanged for ERB Climate Data Record, it is now understood that achieving these goals is more difficult for two reasons. The first is an increased understanding of the dynamics of the Earth/atmosphere system which demonstrates that separation of natural variability from anthropogenic change on decadal time scales requires observations with higher accuracy and stabilit

Priestley, Kory J.↗
NASA NTRSDate not supplied

TPSAS-NF1676L-10735-DND

The Clouds and the Earth's Radiant Energy System (CERES) project has now surpassed the 10-year mark, has combined rigorous calibration, improved scene identification, and multi-satellite data fusion to produce a climate-accuracy data set of global 1° gridded radiative surface and TOA fluxes, and cloud properties. CERES provides the climate community with four observed TOA flux products. The ERBE-like product uses the same algorithms as ERBE in order to be compared with the ERBE time period. The SSF product uses the improved scene identification and radiance to flux conversion. The SYN product uses GEO derived clouds and fluxes to provide diurnally complete TOA fluxes. The EBAF product provides net balanced fluxes tied to the ocean heat storage term. This product is suited for climate modelers estimating the global mean energy budget. Each successive CERES flux product applies the improvements over the previous product. This presentation will focus on the diurnal averaging improvements by the inclusion of imager radiances from 5 geostationary (GEO) satellites at 3-hourly intervals to estimate the flux in between CERES observations. The regional monthly mean flux differences based on SYN (CERES/GEO) and SSF (CERES-only) can exceed 20 Wm^-2. CERES shortwave and longwave fluxes have been rigorously validated and are key elements in evaluating the ability of climate models to predict both past and future climate states. The CERES normalization of GEO derived fluxes in order to maintain the CERES instrument calibration and are consistent globally within 0.1 Wm^-2, even if the GEO calibration is artificially altered by ±5%, twice the anticipated calibration error. The predicted GEO TOA fluxes normalized to Terra have been compared at Aqua times to Aqua flux observations, as well as with GERB hourly fluxes over the METEOSAT domain. Similarly ground based radiometers have been used to validate the CERES radiative transfer computed surface fluxes and have confirmed that the CERES/GEO fluxes have improved the computed surface flux product. Although the GEO based fluxes are of inferior quality then the CERES observed fluxes, they capture the diurnal signal more effectively than using constant meteorology between CERES measurements. The validation of the GEO derived fluxes and clouds will be shown.

D. R. Doelling↗
NASA NTRS1992

Sensor fusion IV: Control paradigms and data structures; Proceedings of the Meeting, Boston, MA, Nov. 12-15, 1991

Various papers on control paradigms and data structures in sensor fusion are presented. The general topics addressed include: decision models and computational methods, sensor modeling and data representation, active sensing strategies, geometric planning and visualization, task-driven sensing, motion analysis, models motivated biology and psychology, decentralized detection and distributed decision, data fusion architectures, robust estimation of shapes and features, application and implementation. Some of the individual subjects considered are: the Firefly experiment on neural networks for distributed sensor data fusion, manifold traversing as a model for learning control of autonomous robots, choice of coordinate systems for multiple sensor fusion, continuous motion using task-directed stereo vision, interactive and cooperative sensing and control for advanced teleoperation, knowledge-based imaging for terrain analysis, physical and digital simulations for IVA robotics.

Schenker, Paul S.↗
DOE OSTI2026

Two datasets are better than one: method of double moments for 3D reconstruction in cryo-EM

Cryo-electron microscopy is a powerful imaging technique for reconstructing three-dimensional molecular structures from noisy tomographic projection images of randomly oriented particles. We introduce a new data fusion framework, termed the method of double moments, which reconstructs molecular structures from two instances of the second-order moment of projection images obtained under distinct orientation distributions: one uniform, the other non-uniform and unknown. We prove that these moments generically uniquely determine the underlying structure, up to a global rotation and reflection, and we develop a convex-relaxation-based algorithm that achieves accurate recovery using only second-order statistics. Our results demonstrate the advantage of collecting and modeling multiple datasets under different experimental conditions, illustrating that leveraging dataset diversity can substantially enhance reconstruction quality in computational imaging tasks.

Kam’s method↗
DOE OSTI2024

A New Coupled Biogeochemical Modeling Approach Provides Accurate Predictions of Methane and Carbon Dioxide Fluxes Across Diverse Tidal Wetlands

Abstract Tidal wetlands provide valuable ecosystem services, including storing large amounts of carbon. However, the net exchanges of carbon dioxide (CO 2 ) and methane (CH 4 ) in tidal wetlands are highly uncertain. While several biogeochemical models can operate in tidal wetlands, they have yet to be parameterized and validated against high‐frequency, ecosystem‐scale CO 2 and CH 4 flux measurements across diverse sites. We paired the Cohort Marsh Equilibrium Model (CMEM) with a version of the PEPRMT model called PEPRMT‐Tidal, which considers the effects of water table height, sulfate, and nitrate availability on CO 2 and CH 4 emissions. Using a model‐data fusion approach, we parameterized the model with three sites and validated it with two independent sites, with representation from the three marine coasts of North America. Gross primary productivity (GPP) and ecosystem respiration (R eco ) modules explained, on average, 73% of the variation in CO 2 exchange with low model error (normalized root mean square error (nRMSE) <1). The CH 4 module also explained the majority of variance in CH 4 emissions in validation sites ( R 2 = 0.54; nRMSE = 1.15). The PEPRMT‐Tidal‐CMEM model coupling is a key advance toward constraining estimates of greenhouse gas emissions across diverse North American tidal wetlands. Further analyses of model error and case studies during changing salinity conditions guide future modeling efforts regarding four main processes: (a) the influence of salinity and nitrate on GPP, (b) the influence of laterally transported dissolved inorganic C on R eco , (c) heterogeneous sulfate availability and methylotrophic methanogenesis impacts on surface CH 4 emissions, and (d) CH 4 responses to non‐periodic changes in salinity.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Climate Nowcasting

The climate is changing so rapidly that climatologies based on historical statistics cannot reliably capture the current risk of extreme weather events hazardous to society. Decision relevant projections of weather extreme probability over the next 10–15 years are needed to enable adaptation and resilience in the face of this evolving risk. Current weather forecasts/predictions and long-term climate projections for decades into the future are inadequate for providing this information to stakeholders that need it, targeting forecast horizons either too short or too far into the future. We argue that a new approach is needed: climate nowcasting. Climate nowcasting would focus on user-inspired extreme metrics over the next 10–15 year time frame, targeting specific impacts and locations down to a local scale, by engaging with stakeholders to understand their needs and provide information in a format relevant for decision making. Importantly, climate nowcasting will not consist of a single approach or data set, involving rather data fusion from different sources of information: simulations, observations and data driven methods, likely through different weights depending on the metric of interest. Predictions must be accompanied by serious engagement with stakeholders facing climate risks, and clearly present uncertainties and limitations of any prediction. Such a vision is very different from how typical climate or weather forecasts are applied today.

Gettelman, Andrew↗