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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Weather from 250 Miles Up: Visualizing Precipitation Satellite Data (and Other Weather Applications) Using CesiumJS

Geospatial weather visualization remains predominately a two-dimensional endeavor. Even popular advanced tools like the Nullschool Earth display 2-dimensional fields on a 3-dimensional globe. Yet much of the observational data and model output contains detailed three-dimensional fields. In 2014, NASA and JAXA (Japanese Space Agency) launched the Global Precipitation Measurement (GPM) satellite. Its two instruments, the Dual-frequency Precipitation Radar (DPR) and GPM Microwave Imager (GMI) observe much of the Earth's atmosphere between 65 degrees North Latitude and 65 degrees South Latitude. As part of the analysis and visualization tools developed by the Precipitation Processing System (PPS) Group at NASA Goddard, a series of CesiumJS [Using Cesium Markup Language (CZML), JavaScript (JS) and JavaScript Object Notation (JSON)] -based globe viewers have been developed to improve data acquisition decision making and to enhance scientific investigation of the satellite data. Other demos have also been built to illustrate the capabilities of CesiumJS in presenting atmospheric data, including model forecasts of hurricanes, observed surface radar data, and gridded analyses of global precipitation. This talk will present these websites and the various workflows used to convert binary satellite and model data into a form easily integrated with CesiumJS.

web applications↗
NASA NTRS2018

Toward Cooling Uniformity: Investigation of Spiral, Sweeping Holes, and Unconventional Cooling Paradigms

Surface infrared thermography, hotwire anemometry, and thermocouple surveys were performed on two new film cooling hole geometries: spiral/rifled holes and fluidic sweeping holes. Ways to quantify the efficacy of novel cooling holes that are asymmetric, not uniformly spaced or that show variation from hole to hole are presented. The spiral holes attempt to induce large-scale vorticity to the film cooling jet as it exits the hole to prevent the formation of the kidney shaped vortices commonly associated with film cooling jets. The fluidic sweeping hole uses a passive in-hole geometry to induce jet sweeping at frequencies that scale with blowing ratios. The spiral hole performance is compared to that of round holes with and without compound angles. The fluidic hole is of the diffusion class of holes and is therefore compared to a 777 hole and square holes. A patent-pending spiral hole design showed the highest potential of the nondiffusion type hole configurations. Velocity contours and flow temperature were acquired at discreet cross-sections of the downstream flow field. The passive fluidic sweeping hole shows the most uniform cooling distribution but suffers from low span-averaged effectiveness levels due to enhanced mixing. The data was taken at a Reynolds number of 11,000 based on hole diameter and freestream velocity. Infrared thermography was taken for blowing ratios of 1.0, 1.5, 2.0, and 2.5 at a density ratio of 1.05. The flow inside the fluidic sweeping hole was studied using 3D unsteady RANS. A section on ideas for future work is included that addresses issues of quantifying cooling uniformity and provides some ideas for changing the way we think about cooling such as changing the direction of cooling or coupling acoustic devices to cooling holes to regulate frequency.

Shyam, Vikram↗
NASA NTRS2018

Science Diplomacy Through Cities: Applying NASA Earth Observations at the Urban Scale

NASA's scientific expertise and data products are enhancing cities' environmental monitoring activities by pioneering applications of remote sensing and model-based Earth Observations at the urban scale. The above activities have greatly benefitted from engaging stakeholders and city practitioners from the start. Further, NASA's collaborations with cities have: Advanced NASA science, in testing new products and validating of satellite datasets, while meeting the needs of city governments. Broadened Rio de Janeiro's regional viewpoint and strengthened its relationships with neighboring cities. Scientific collaborations with cities benefit from: Selecting city partners with a high level of technical capacity and willing to make strong investments in joint projects. Sustained communication and face-to-face interactions. Well-defined deliverables, with dedicated resources and personnel. Pairing global datasets and projections with in situ measurements and local knowledgeSensitivity to local working culture and politics.

urban environment↗
NASA NTRS2014

NASA World Wind: Visualization Technology for Spatial Data

Spatial information intelligence is a global issue that will increasingly affect our ability to survive as a species. Collectively we must better appreciate the complex relationships that make life on Earth possible. Providing spatial information in its native context can accelerate our ability to process that information. To maximize this ability to process information, three basic elements are required: data delivery (server technology), data access (client technology), and data processing (information intelligence). NASA World Wind provides the open source client technology based on open standards. The possibilities for data processing and data sharing are enhanced by this inclusive infrastructure for geographic information. It is interesting that this open source and open standards approach, unfettered by proprietary constraints, provides for unlimited innovation with this technology.

Geospatial↗
NASA NTRS2019

Improved Earth Oblateness Rate Reveals Increased Ice Sheet Losses and Mass-Driven Sea Level Rise

Satellite laser ranging (SLR) observations are routinely applied toward the estimation of dynamic oblateness, C(sub 20), which is the largest globally integrated component of Earth's time-variable gravity field. Since 2002, GRACE and GRACE Follow-On have revolutionized the recovery of higher spatial resolution features of global time-variable gravity, with SLR continuing to provide the most reliable estimates of C (sub 20).We quantify the effect of various SLR processing strategies on estimating C(sub 20) and demonstrate better signal recovery with the inclusion of GRACE-derived low-degree gravity information in the forward model. This improved SLR product modifies the Antarctic and Greenland Ice Sheet mass trends by -15.4 and -3.5 Gt/year, respectively, as compared to CSR TN11, and improves global mean sea level budget closure by modifying sea level rise by +0.08 mm/year. We recommend that this new C(sub 20) product be applied to RL06 GRACE data products for enhanced accuracy and scientific interpretation.

Loomis, B. D.↗
NASA NTRSDate not supplied

Monitoring the Calibration of the DSCOVR EPIC Instrument’s Visible Cchannels Using MODIS and VIIRS as a Reference

The Earth Polychromatic Imaging Camera (EPIC) instrument aboard the Deep Space Climate Observatory (DSCOVR) satellite has a constant unique view of the sunlit disk of the Earth from the Lagrange-1 (L1) point nearly a million miles away from the Earth. Due to EPIC not having any on-board calibration systems, the ten spectral channels of EPIC must be inter-calibrated using vicarious on-orbit methods, such as ray-matching with well-calibrated low Earth orbiting satellites like the Aqua-MODIS and SNPP-VIIRS radiometers. The recently released EPIC version 3 L1B data has greatly enhanced the residual navigation errors found in prior versions, thereby decreasing the calibration uncertainty in the aforementioned ray-matching calibration techniques. The automated navigation correction used by these ray-matching methods for further improving the EPIC geolocation will be compared with a new navigation correction method utilizing optical flow between the EPIC and MODIS/VIIRS images. The DSCOVR satellite went into safe mode on June 27, 2019 due to an anomaly with the satellite’s attitude control system, but operations recommenced on March 2, 2020. The calibration of the EPIC instrument before it went into safe mode and after it resumed will be analyzed in order to investigate any potential calibration shifts or discontinuities caused by the spacecraft anomaly.

Conor Haney↗
NASA NTRSDate not supplied

Radiation Data Portal: Connection of Radiation Measurements on Airplane Flights with Observations of Solar-Terrestrial Environment

The impact of solar radiation dramatically increases at high altitudes in the Earth’s atmosphere and in space. Therefore, continuous monitoring of the radiation environment is critical for the safety of aircraft and spacecraft crews and passengers. Addressing the problem requires a complex approach of integration of different data sources and enhancement of the visualization and search capabilities. The Radiation Portal Database represents an interactive web-based application for convenient search and visualization of in-flight radiation measurements and exploration of various properties related to the radiation environment. The primary element of the Radiation Portal back-end is a MySQL relational database that currently contains the radiation measurements obtained from the Automated Radiation Measurements for Aerospace Safety (ARMAS)device, and soft X-ray and proton fluxes from Geostationary Orbiting Environmental Satellite (GOES). The developed Application Programming Interface (API) and related Python routines allow a user to retrieve the database records directly and efficiently, without interaction with the web interface. As a use case of the Radiation Portal, we examine the properties of the ARMAS flights taken during the enhanced Solar Proton (SP) fluxes and compare them to the flights of similar time and location taken during SP-quiet periods.

SMD↗
NASA NTRSDate not supplied

Low-Temperature Production of Cosmic Grains Analogs From Gas-Phase Molecular Precursors with the NASA Ames' COSmIC Facility

Complex carbon molecules and ions are ubiquitous in space and form the building blocks of carbonaceous cosmic dust grains, ultimately contributing to the formation of planets. Here, we report the first experimental investigations of the low temperature chemical pathways leading to the production of cosmic grain analogs from gas phase molecular precursors in order to better understand the evolution of cosmic carbon. The experiments were performed using the COsmic SImulation Chamber (COSmIC) to generate and characterize grains formed from gas phase precursors under controlled conditions representative of astrophysical environments [1]. Using COSmIC, it is possible to investigate the evolution of cosmic carbon from the formation of neutral and ionized gas phase hydrocarbons and PAHs [2], to carbon grains [3, 4]. This is achieved by using a discharge nozzle to (1) produce an adiabatic jet expansion and cool down Ar-hydrocarbon/PAH gas mixtures to astrophysically relevant temperature (150 K) before inducing chemistry by generating a plasma discharge in the stream of the expansion. This plasma-induced chemistry results in the formation of complex molecules and solid particles, analogs of cosmic grains. Solid grains are produced in-situ in the plasma expansion, carried by the accelerated gas in the expansion, and collected on substrates placed a few centimeters downstream of the electrodes. The results of a preliminary solid phase ex-situ analysis of cosmic grain analogs produced at low temperature in COSmIC from gas mixtures including molecular precursors of cosmic carbon grains, i.e., such as CH4 and C2H2 found in circumstellar ejecta of late C stars [3] as well as PAHs ubiquitously detected in IR emission bands seen in galactic and extragalactic environments [4] will be discussed. Scanning Electron Microscopy imaging was used to provide insight on the morphology and growth structure of the grains produced in COSmIC, and to investigate how the precursors used to produce the grains affect these parameters [3]. Laser desorption mass spectrometry was used to identify the molecules making up the main structures within the condensed grains [4]. NIR to FIR optical properties are characterized to provide critical information (functional groups, optical constants) to the scientific community, for use in radiative transfer models in particular, and to help decipher and enhance the return data from space observations.

F. Salama↗
NASA NTRS2021

Modeling of Guided Waves for Aerospace Applications

Advancements in computer hardware has led to new possibilities for rapid modeling and simulation capabilities across many scientific fields. Nondestructive evaluation (NDE) can benefit from increased use of simulation tools to guide optimization of inspection and health monitoring methods, enhance understanding of data, aid in development of defect characterization methods, and generate data sets for use with machine learning and model-assisted probability of detection. Recent work at NASA has entailed development and benchmarking of both custom simulation codes and commercial simulation tools for ultrasonic wave propagation. This paper describes recent work at NASA in modeling of guided waves in composites and other aerospace materials. Results and computational speeds for a composite benchmark case are reported for a custom finite difference Rotated Staggered Grid code and for the commercial finite element software package, Pogo. Recent progress in linking NDE models to parametric analysis tools is also discussed.

Nondestructive evaluation↗
NASA NTRS2020

Imagery and UV Spectroscopy of the LMC Supernova Remnant N103B Using HST

We present Hubble Space Telescope(HST)/Wide Field Camera 3 multiband imagery of N103B, which is the remnant of a SN Ia in the Large Magellanic Cloud, as well as HST/Cosmic Origins Spectrograph(COS)ultraviolet spectroscopy of the brightest radiatively shocked region. The images show a wide range of morphology and relative emission-line intensities, from smooth Balmer-line dominated collisionless shocks that are due to the primary blast wave to clumpy radiative shock filaments that are due to secondary shocks in density enhancements. The COS data show strong FUV line emissions, despite a moderately high extinction along this line of sight. We use the COS data with previous optical spectra to constrain the shock conditions, we refine the abundance analysis, and we find abundances that are typical of the local interstellar medium within the uncertainties. Under an assumption that the material being shocked was shed from the pre-supernova system, this finding places constraints on any significant enrichment in that material, and thus on the non-degenerate star in what was presumably a single-degenerate SN Ia.

William P. Blair↗
NASA NTRS2020

A Composite Analysis of Snowfall Modes from Four Winter Seasons in Marquette, Michigan

Presented are four winter seasons of data from an enhanced precipitation instrument suite based at the National Weather Service (NWS) Office in Marquette (MQT), Michigan (250–500 cm of annual snow accumulation). In 2014 the site was augmented with a Micro Rain Radar (MRR) and a Precipitation Imaging Package (PIP). MRR observations are utilized to partition large-scale synoptically driven (deep)and surface-forced (shallow) snow events. Coincident PIP and NWS MQT meteorological surface observations illustrate different characteristics with respect to snow event category. Shallow snow events are often extremely shallow, with MRR-indicated precipitation heights of less than 1500 m above ground level. Large vertical reflectivity gradients indicate efficient particle growth, and increased boundary layer turbulence inferred from observations of spectral width implies increased aggregation in shallow snow events. Shallow snow events occur 2 times as often as deep events; however, both categories contribute approximately equally to estimated annual accumulation. PIP measurements reveal distinct regime-dependent snow microphysical differences, with shallow snow events having broader particle size distributions and comparatively fewer small particles and deep snow events having narrower particle size distributions and comparatively more small particles. In addition, coincident surface meteorological measurements indicate that most shallow snow events are associated with surface winds originating from the northwest (over Lake Superior), cold temperatures, and relatively high surface pressures, which are characteristics that are consistent with cold-air outbreaks. Deep snow events have meteorologically distinct conditions that are accordant with midlatitude cyclones and frontal structures, with mostly southwest surface winds, warmer temperatures approaching freezing, and lower surface pressures.

Claire Pettersen↗
NASA NTRSDate not supplied

Machine learning based noise reduction for satellite products: application to solar-induced fluorescence retrievals using simulated and real data

In the past two decades, global satellite measurements of terrestrial chlorophyll solar-induced fluorescence (SIF) have been used widely for a number of different applications related to physiology, phenology, and productivity of plants. However, SIF retrievals are inherently noisy due to the relatively small SIF spectral signature in comparison with observational noise. In this work, we examine how a spectral-based approach that employs principal component analysis along with a relatively shallow artificial neural network can be used to reduce noise and other artifacts in satellite level 2 (L2) products. We first apply the approach in a controlled environment in which radiance spectra are simulated with a full atmospheric and surface radiative transfer model for different scenarios including various SIF values that are known. Various levels of noise can be added to the simulated spectra. Resulting noisy and noise-reduced SIF retrievals are compared with the true values to assess performance. We then apply the noise reduction approach to real SIF derived from instruments flying on meteorological satellites. The results are evaluated by comparing SIF retrievals from different platforms with each other and with other independent data sets, showing enhanced capability to capture seasonal and interannual variability in SIF.

Chlorophyll fluorescence↗
NASA NTRSDate not supplied

Low-Earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) Aeroshell Performance

On November 10, 2022, NASA launched the Low-earth Orbit Flight Test of an Inflatable Decelerator (LOFTID) reentry vehicle (RV)as a secondary payload mounted inside the launch vehicle (LV) adaptor on an Atlas V 401 out of the Vandenburg Space Force Base (VSFB). The primary payload, Joint Polar Satellite System-2 (JPSS-2), was delivered successfully to a sun-synchronous trajectory shortly after launch, at which point the Centaur upper stage performed a burn to de-orbit the system. Once on the desired trajectory to enter the atmosphere, the top of the payload adaptor was ejected to expose the LOFTID RV. Next, the LOFTID aeroshell, a Hypersonic Inflatable Aerodynamic Decelerator (HIAD), was deployed and inflated without issue. The Centaur pointed the LOFTID RV to the desired attitude to enter the atmosphere, spun the assembly to three rpm, separated the LOFTID RV, and finally performed a divert maneuver to prevent the Centaur from re-contacting the LOFTID RV after atmospheric entry. The LOFTID RV maintained the design attitude and spin rate from separation to atmospheric entry, a duration of roughly 30 minutes, demonstrating the aeroshell did not experience any energy damping from inflatable structure flexibility. The LOFTID RV entered the atmosphere over Alaska at >8km/sec and decelerated as designed, demonstrating aerodynamically stable flight from hypersonic entry through subsonic parachute deployment. On-board visible-spectrum cameras captured the physical response of the heatshield through all phases of flight. Co-located infrared cameras captured the temperature distribution of the aftside of the heat shield anchored to a distribution of thermocouples on the inflatable structure (IS) in the field of view. Thermocouples were also embedded in the forward side of the aeroshell both in the Flexible Thermal Protection System (FTPS) as well as the IS. This paper will document aeroshell’s response to the atmospheric entry. The visible-spectrum cameras captured mechanical response of the deployable aeroshell to the loads encountered in the different phases of entry. Events seen on the visible-spectrum cameras correlate with the measurements of the load cell pins on webbing elements that attach the aeroshell to the centerbody structure. Thermocouple data captured verifies the aerothermal response of the aeroshell was in-kind with pre-flight analysis predictions although somewhat lower in most locations. This data will enable enhancement of HIAD predictive capability. Post-flight inspection of the aeroshell after splashdown and ocean recovery shows that all the aeroshell materials and manufacturing techniques developed over the past two decades of HIAD technology development performed as expected at large scale, a scale which cannot be replicated in ground test facilities. The LOFTID RV remained high in the water after touching down under parachute, and maintained buoyancy and integrity for the hour it took to position the recovery vessel near the vehicle after splashdown. There was no indication the HIAD would have experienced an issue maintaining buoyancy for many more hours after water impact as the component maintained adequate inflation pressure more than 24 hours after water extraction with no additional inflation gas in tanks, as the inflation gas remaining in the tank was purposely vented prior to splashdown.

HIAD↗
NASA NTRSDate not supplied

Reconfigurable Metasurface Optics at NASA LaRC Towards Space Image Sensing

Reconfigurable optical metasurfaces are rapidly emerging as a major frontier in photonics research, development, and applications. They promise compact, lightweight, and energy-efficient reconfigurable optical systems with unprecedented performance and functions that can be dynamically defined on-demand. Space applications represent an emerging area in which these characteristics are highly prized. The ability to dynamically tune optical functions through selective modulation of electromagnetic waves is crucial to the advancement of a variety of sensing applications, from imaging spectrometers to light detection and ranging (LiDAR). This presentation introduces a reconfigurable metasurface optic project led by a research team at NASA Langley Research Center (NASA LaRC) since 2019. It covers advances in phase change material-based reconfigurable optics, performance data on reliability enhancement of photonic devices, image sensing system architectures, and mission concepts enabled through these advances.

metasurface↗
NASA NTRSDate not supplied

A Novel Framework for Multi-Path Data Fusion in Earth Observation and New Observing Strategies: Applications to Predicting Forest Canopy Height

Exponential growth of data from Earth Observation (EO) assets has necessitated the development of sophisticated methods for data interpretation and management. NASA’s New Observing Strategy (NOS) approach aims to coordinate operations among complex heterogenous systems of constellations, requiring advanced Artificial Intelligence and Machine Learning (AI/ML) techniques. Despite significant advancements in AI/ML across various domains, the EO and machine learning for satellite (SatML) fields remain fragmented, often relying on adapted techniques rather than domain-specific solutions. We present a novel end-to-end data fusion framework tailored specifically for EO and SatML, addressing this gap by facilitating rapid development of AI/ML applications. This framework, called, Multimodal Earth Observation Workflow for Machine Learning (MEOW-ML), sup- ports the entire AI/ML lifecycle, from dataset manipulation, to model training, evaluation, and logging, and is designed to expedite the development of next-generation NOS deployments and SOTA in EO. We apply our framework to predict canopy height model (CHM) derived from lidar data. We integrate multiple data modalities through a hierarchical, multi-path model architecture, effectively identifying and leveraging the unique strengths of each data source to enhance predictive accuracy. Our experiments demonstrate that the multi-path architecture outperforms traditional single-path models and provides significant advantages in both accuracy and computational efficiency.

Mark Moussa↗
NASA NTRS2024

Evaluation of Machine Learning and Deep Learning Algorithms for Fire Prediction in Southeast Asia

Vegetation fires are prevalent in South/Southeast Asian countries, making fire prediction crucial due to their potential environmental, economic, and social impacts. Accurate predictions of fires facilitate timely interventions, helping to mitigate uncontrolled fires that can lead to biodiversity loss and air quality issues. In this study, we utilize VIIRS satellite-derived fire data alongside six machine learning and deep learning models—Simple Persistence, Multi-Layer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), CNN-LSTM, and ConvLSTM—to determine the most effective fire prediction model, using Root Mean Square Error (RMSE) as the metric. Our results indicate that the CNN model is the most reliable in regions with spatial dependencies, such as Brunei, Indonesia, Malaysia, the Philippines, Timor-Leste, and Thailand. Conversely, the ConvLSTM model excels in countries with complex spatiotemporal dynamics like Laos, Myanmar, and Vietnam. The CNN-LSTM hybrid model also performed well in Cambodia, suggesting a need for a balanced approach in areas requiring both spatial and temporal feature extraction. Furthermore, simpler models like Persistence and MLP showed limitations in capturing dynamic patterns and temporal dependencies. Our findings highlight the importance of evaluating models before implementing any decision support systems (DSS) in fire management. By tailoring models to specific regional fire data, we can enhance prediction accuracy and responsiveness, ultimately improving fire risk management in Southeast Asia and beyond.

Deep learning↗
NASA NTRS2025

On the Characterization and Mitigation of Noise in Space-borne Microwave Sounding Instruments

Space-borne microwave sounding instruments have become vital data sources for weather prediction and climate change studies. Among the various radiometer configurations, the total power microwave radiometer is particularly appealing for current and future operational satellites due to its superior sensitivity and simple design. However, its performance is vulnerable to degradation caused by receiver gain fluctuations, electronic 1/f noise, and other time varying receiver characteristics. For Numerical Weather Prediction (NWP) users, 1/f noise introduces inter-channel correlations, complicating the assimilation of affected observations and reducing their accuracy. Addressing this noise issue in ground data processing system is essential to enhance the utility of microwave sounding data. This paper focuses on the characterization and mitigation of noise in current and future microwave sounding instruments, with particular emphasis on the impact of 1/f noise. Various methods are applied to quantitatively characterize noise features in both frequency and time domains. Additionally, the influence of calibration parameters on 1/f noise are analyzed. Based on these findings, we propose a mitigation algorithm for reducing noise during the on-orbit calibration of microwave sounding instruments, aiming to improve the quality of retrieved data for operational use.

calibration↗
DOE OSTI2023

Sensor Anomaly Detection for Nuclear Reactor Systems Utilizing Linear Regression and K-Means Unsupervised Machine Learning

Nuclear reactors and related systems are becoming increasingly complex due to advancing technologies in next-generation power reactors. This increased complexity necessitates enhanced automation and data management capabilities. To successfully realize autonomous systems, methods must be developed to handle vast volumes of data and effectively distinguish anomalous data from noise and expected data. While impressive models utilizing digital twins and similar approaches are under development, here we propose a simplified model for analyzing fundamental methods and techniques. Initially, we created a general dataset by using initial data from PCTRAN in order to represent ideal steady-state conditions. We then inserted anomalies based on prevalent sensor anomaly types (e.g., point anomalies, linear drift, and downward deviations), along with unusual anomalies such as exponential drift and upward deviations. To detect anomalies, we developed a program that employs data partitioning and linear regression to preprocess and filter the anomalous data. A K-Means machine learning (ML) method was then applied to separate and count the data within the anomalous partition. The results from all datasets—apart from exponential growth—demonstrated positive outcomes, with each returning multiple instances of greaterthan-95% accuracy. We conducted further investigations using Idaho National Laboratory’s RAVEN software to perform a sensitivity analysis on the input variables (R 2 Tolerance, Slope Tolerance, and Window Size) and found that the output variables (Accuracy and Time) were most sensitive to the Window Size. Despite the promising results published, further development is required to effectively apply these methods to nuclear systems. Nevertheless, the strengths of this approach are evident and hold promise for future applications in the field.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗