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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 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

The Effect of Solution Chemistries and Freezing Temperatures on the Morphology of Cryogenic Opal-A (COA): Implications for Past Climates on Mars

Cryogenic opal-A (COA) is a type of hydrated amorphous silica that forms when silica-rich geothermal fluids erupt sub-aerially and are exposed to air temperatures below 0 °C. As the fluid cools and water-ice forms, the solution increases in ionic strength and reaches saturation of opal-A. This results in the precipitation of opal-A between water ice crystals. Natural COA is found embedded within frozen fluids near hydrothermal sources, and has been shown to preserve signs of biological activity. In this study, two solution chemistries (hydrochloric acid (HCl) and sulfuric acid (H2SO4)) and three freezing temperatures (-20, -80, -196 ºC) were used to synthesize COA in the lab. SEM analyses showed that COA frozen at -20 ºC in HCl solutions produced smooth silica branches similar to what was observed in previous studies. COA precipitated at lower freezing temperatures demonstrated progressively smaller particle sizes and unique morphologies such as halite dendrites within silica, desiccation cracks, and pores. COA made in H2SO4 solutions was more angular and fragmented than COA made in HCl solutions. Particle size also decreased with decreasing freezing temperature. COA textures have implications for terrestrial samples as well as future samples returned from Mars because they can provide information about past climate and fluid chemistry.

J V Hogancamp↗
NASA NTRS2015

Formation of Cirrus Clouds near the Tropical Tropopause and Their Implications for Stratospheric Humidity, Radiation Budgets, and Climate

Optically-thin cirrus clouds are a nearly ubiquitous feature of the transition layer between the tropical troposphere and stratosphere (known as the Tropical Tropopause Layer). Given their high occurrence frequency, these clouds have significant impacts on the Earth's radiation budget, with a particularly strong influence on the outgoing longwave radiation. Further, ice crystal growth and sedimentation in these clouds dehydrates air in the cold TTL to very low water vapor mixing ratios. Since transport from the troposphere into the stratosphere occurs primarily through the tropical tropopause and the Brewer-Dobson circulation transports air from the TTL throughout the stratosphere, cloud processes in the TTL cold trap essentially regulate the humidity of the entire stratosphere. It has been shown that even small changes in stratospheric humidity can have significant impacts on the Earth's radiation budget and climate, with magnitudes comparable to anthropogenic changes in greenhouse gases. The humidity of air crossing the TTL cold trap depends to first order on tropical tropopause temperatures, but also on deep convection reaching the TTL, large-scale transport (in balance with TTL radiative heating), atmospheric waves, and cloud microphysical processes. Gaps in our understanding of TTL transport and cloud processes motivated the NASA Airborne Tropical TRopopause EXperiment (ATTREX). ATTREX used the long-range (16,000 km), high-altitude (20 km) Global Hawk unmanned aircraft system equipped with twelve instruments measuring clouds, water vapor, meteorological conditions, chemical tracers, chemical radicals, and radiation. I will discuss the physics of TTL cirrus formation and the impact of these clouds on water vapor and radiation budgets, including new results emerging from the ATTREX measurements.

Jensen, Eric↗
NASA NTRSDate not supplied

Seeding Mechanism for High-Pressure Nozzles

NASA tests new launch and entry vehicle configurations in wind tunnels, where flow visualizations and quantitative flow field measurements are often desired. Some of these vehicles have rocket motors for propulsion, retro-propulsion, or reaction control. Unseeded,high-pressure air is often used to simulate these rocket motor plumes. However, it is difficult to make off-body measurements in these regions, for several reasons. First, the plumes themselves are usually unseeded and at such low pressures that the flow velocity cannot currently be measured. Second, the plumes displace seeded tunnel air, preventing measurements such as laser sheet visualizations. Third, they force shock waves ahead of the vehicle, which melts the ice crystal fog commonly used for many visualization and measurement techniques. The Virginia Tech Advanced Propulsion and Power Lab has designed and tested a novel method for seeding the flow in these small-scale, high-pressure nozzles to allow quantitative measurement with a particle-based measurement such as particle image velocimetry (PIV). The method involves a Venturi contraction to draw the seed liquid out of a reservoir and into the nozzle channel, wherein shearing forces atomize the seed into particles. The concept was tested with a laser sheet visualization, which showed the nozzle spray pattern and the controllability of the seeding flow rate. This relatively inexpensive and simple technique may prove useful in wind tunnel experiments involving particle-based laser diagnostics and small, high-pressure nozzles.

particle image velocimetry↗
NASA NTRSDate not supplied

Particle Seeding Method for Small-Scale, High-Pressure Nozzles

NASA tests new launch and reentry vehicle configurations in wind tunnels, where flow visualizations and quantitative flowfield measurements are often desired. Some of these vehicles have rocket motors for propulsion, retro-propulsion, or reaction control. High-pressure air is used to supply these rocket motor plumes. However, it is difficult to make off-body measurements in these regions, for several reasons. First, the plumes themselves lack seeding particles for flow diagnostics, and at the low pressures after expansion, Rayleigh scattering or other molecular techniques yield insufficient signal for flow velocity measurements. Second, the plumes displace particle-seeded tunnel air, preventing measurements in the vicinity of the plume. Third, the plumes force shock waves ahead of the vehicle, which melts the ice crystal fog commonly used for visualization and measurement techniques in certain facilities. In the current work, a novel method for seeding the flow in these small-scale, high-pressure nozzles has been devised and initially demonstrated, potentially enabling quantitative and qualitative measurements with particle-based instruments such as Doppler global velocimetry or particle image velocimetry. The method involves a Venturi contraction to draw the seed liquid out of a reservoir and into the nozzle channel, wherein shearing forces atomize the seed into particles. The concept was tested with a laser sheet visualization, which demonstrated that the flow rate of liquid spray was controllable; a valve could be adjusted to drop the flow rate by up to 65%. This relatively inexpensive and simple technique may prove useful in wind tunnel experiments involving particle-based laser diagnostics and small, high-pressure nozzles.

Aditya S. Acharya↗
NASA NTRSDate not supplied

Particle Seeding Method for Small-Scale, High-Pressure Nozzles

NASA tests new launch and reentry vehicle configurations in wind tunnels, where flow visualizations and quantitative flowfield measurements are often desired. Some of these vehicles have rocket motors for propulsion, retro-propulsion, or reaction control. High-pressure air is used to supply these rocket motor plumes. However, it is difficult to make off-body measurements in these regions, for several reasons. First, the plumes themselves lack seeding particles for flow diagnostics, and at the low pressures after expansion, Rayleigh scattering or other molecular techniques yield insufficient signal for flow velocity measurements. Second, the plumes displace particle-seeded tunnel air, preventing measurements in the vicinity of the plume. Third, the plumes force shock waves ahead of the vehicle, which melts the ice crystal fog commonly used for visualization and measurement techniques in certain facilities. In the current work, a novel method for seeding the flow in these small-scale, high-pressure nozzles has been devised and initially demonstrated, potentially enabling quantitative and qualitative measurements with particle-based instruments such as Doppler global velocimetry or particle image velocimetry. The method involves a Venturi contraction to draw the seed liquid out of a reservoir and into the nozzle channel, wherein shearing forces atomize the seed into particles. The concept was tested with a laser sheet visualization, which demonstrated that the flow rate of liquid spray was controllable; a valve could be adjusted to drop the flow rate by up to 65%. This relatively inexpensive and simple technique may prove useful in wind tunnel experiments involving particle-based laser diagnostics and small, high-pressure nozzles.

lasers↗
NASA NTRSDate not supplied

An Automated Refinement Process for Particle Trajectory Methods in GlennICE

Computational methods for ice accretion can simulate the impact of water drops and ice crystals on an aircraft surface in a Lagrangian reference frame or in the Eulerian reference frame. In the Eulerian reference frame, particles are considered a continuous fluid while in the Lagrangian frame individual particle trajectories are calculated. Methods that use the Eulerian reference frame are typically easier to develop as established modules used for continuum mechanics can be leveraged. The Eulerian systems can also be faster since the user does not have to simulate millions of particles in order to achieve good results. It is imperative therefore that a Lagrangian method optimize the release points of trajectories such that accurate solutions can be obtained while minimizing as much as possible the number of trajectories computed. This paper will present a methodology for this refinement process and demonstrate its effectiveness on sample three dimensional test cases.

William B Wright↗
NASA NTRSDate not supplied

An Automated Refinement Process for Particle Trajectory Methods in GlennICE

Computational methods for ice accretion can simulate the impact of water drops and ice crystals on an aircraft surface in a Lagrangian reference frame or in the Eulerian reference frame. In the Eulerian reference frame, particles are considered a continuous fluid while in the Lagrangian frame individual particle trajectories are calculated. Methods that use the Eulerian reference frame are typically easier to develop as established modules used for continuum mechanics can be leveraged. The Eulerian systems can also be faster since the user does not have to simulate millions of particles in order to achieve good results. It is imperative therefore that a Lagrangian method optimize the release points of trajectories such that accurate solutions can be obtained while minimizing as much as possible the number of trajectories computed. This paper will present a methodology for this refinement process and demonstrate its effectiveness on sample three dimensional test cases.

William Wright↗
NASA NTRS2022

A Comprehensive Machine Learning Study to Classify Precipitation Type over Land from Global Precipitation Measurement Microwave Imager (GPM-GMI) Measurements

Precipitation type is a key parameter used for better retrieval of precipitation characteristics as well as to understand the cloud–convection–precipitation coupling processes. Ice crystals and water droplets inherently exhibit different characteristics in different precipitation regimes (e.g., convection, stratiform), which reflect on satellite remote sensing measurements that help us distinguish them. The Global Precipitation Measurement (GPM) Core Observatory’s microwave imager (GMI) and dual-frequency precipitation radar (DPR) together provide ample information on global precipitation characteristics. As an active sensor, the DPR provides an accurate precipitation type assignment, while passive sensors such as the GMI are traditionally only used for empirical understanding of precipitation regimes. Using collocated precipitation type flags from the DPR as the “truth”, this paper employs machine learning (ML) models to train and test the predictability and accuracy of using passive GMI-only observations together with ancillary information from a reanalysis and GMI surface emissivity retrieval products. Out of six ML models, four simple ones (support vector machine, neural network, random forest, and gradient boosting) and the 1-D convolutional neural network (CNN) model are identified to produce 90–94% prediction accuracy globally for five types of precipitation (convective, stratiform, mixture, no precipitation, and other precipitation), which is much more robust than previous similar effort. One novelty of this work is to introduce data augmentation (subsampling and bootstrapping) to handle extremely unbalanced samples in each category. A careful evaluation of the impact matrices demonstrates that the polarization difference (PD), brightness temperature (Tc) and surface emissivity at high-frequency channels dominate the decision process, which is consistent with the physical understanding of polarized microwave radiative transfer over different surface types, as well as in snow and liquid clouds with different microphysical properties. Furthermore, the view-angle dependency artifact that the DPR’s precipitation flag bears with does not propagate into the conical-viewing GMI retrievals. This work provides a new and promising way for future physics-based ML retrieval algorithm development.

machine learning/artificial intelligence↗
NASA NTRS2022

Direct Comparisons Between GPM-DPR and CloudSat Snowfall Retrievals

Two spaceborne radars currently in orbit enable the sampling of snowfall near the surface and throughout the atmospheric column, namely, CloudSat’s Cloud Profiling Radar (CPR) and the Global Precipitation Measurement mission’s Dual-Frequency Precipitation Radar (GPM-DPR). In this paper, a direct comparison of the CPR’s 2C-SNOW-PROFILE (2CSP), the operational GPM-DPR algorithm (2ADPR) and a neural network (NN) retrieval applied to the GPM-DPR data is performed using coincident observations between both radars. Examination of over 3500 profiles within moderate to strong precipitation (Ka band ≥ 18 dBZ) show that the NN retrieval provides the closest retrieval of liquid equivalent precipitation rate R immediately above the melting level to the R retrieved just below the melting layer, agreeing within 5%. Meanwhile, 2CSP retrieves a maximum value of R at −15°C, decreases by 35% just above the melting layer, and is about 50% smaller than the GPM-DPR retrieved R below the melting layer. CPR-measured reflectivity shows median reduction of 2–3 dB from −15° to −2.5°C, likely the reason for the 2CSP retrieval reduction of R . Two case studies from NASA field campaigns [i.e., Olympic Mountains Experiment (OLYMPEX) and Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS)] provide analogs to the type of precipitating systems found in the comparison between retrieval products. For the snowfall events that GPM-DPR can observe, this work suggests that the 2CSP retrieval is likely underestimating the unattenuated reflectivity, resulting in a potential negative, or low, bias in R . Future work should investigate how frequently the underestimated reflectivity profiles occur within the CPR record and quantify its potential effects on global snowfall accumulation estimation.

Atmosphere↗
NASA NTRSDate not supplied

New Methods to Determine the Electromagnetic Effects from Precipitation Static

The Electrostatics and Surface Physic Laboratory has devised a new method to determine the electromagnetic effects from precipitation static or p-static as its commonly called. P-static occurs as a result of triboelectric charging of granular materials or ice crystals bombarded onto the surfaces of air and/or space vehicles at high speeds. The electrostatic charge that builds up on the outer surface can have deleterious effects that arise during discharge events. These discharges can cause loss of communication to and from the vehicle from ground stations, EMI interference, radio frequency noise, etc… There are even cases of total loss of vehicles caused by p-static charging. We have developed a method to simulate the extensive tribocharging of insulating materials that occurs during p-static through the use of corona charging which allows testing to occur within a lab environment. These tests simulate the discharges that occur on rocket vehicles during ascent as a result of gas breakdown by performing the corona charging tests inside a vacuum chamber and subsequently removing the air. The simultaneous gas discharges emit electromagnetic interference (EMI) which can be captured and monitored.

Charles R Buhler↗
NASA NTRSDate not supplied

ARM Data as A Resource for Validation of NASA PACE Cloud Retrievals

NASA’s Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) mission will launch in January 2024 and continue and improve upon satellite data records in its eponymous domains. PACE will carry a broad-swath hyperspectral imager, OCI, which will provide MODIS/VIIRS-type cloud data products (i.e. a cloud mask, top height, visible optical thickness, droplet effective radius, phase, and derived water path). It will also carry two multi-angle polarimeters (HARP2 and SPEXone) which will not only provide the above but also enable retrievals of additional cloud properties (e.g. droplet effective variance, ice crystal asymmetry parameter). Validating satellite-based cloud retrievals is challenging. We plan to use several ARM data streams to evaluate PACE cloud data products and are prototyping our analyses using retrievals from MODIS on the Aqua satellite and OLCI on the Sentinel-3A satellite. This poster shows how we plan to use ARM data to evaluate liquid water path (via MWRRET) and cloud top height retrievals (via KARZASRCL), with example results from these proxy sensors and ARM data from the SGP, ENA, and NSA sites. We seek comments from and collaborations with the ARM community to get the most out of our respective data streams.

ARM↗
NASA NTRS2026

Satellite Optical Remote Sensing of Clouds and Aerosols: From Particle Single-Scattering and Gaseous Absorption Through Radiative Transfer to Retrieval Products

Clouds and aerosols are fundamental regulators of Earth’s radiation budget and climate system, influencing both solar and terrestrial radiation through scattering, absorption, and emission processes. Accurate characterization of their physical and radiative properties from space requires a rigorous understanding of particle single-scattering, gaseous absorption, and radiative transfer in the atmosphere, as well as reliable inversion methods. This review synthesizes the physical foundations and algorithmic implementations of satellite-based passive optical remote sensing of clouds and aerosols, spanning the ultraviolet to thermal infrared spectral range. Beginning with electromagnetic scattering theory and state-of-the-art methods for computing single-scattering by nonspherical particles and computationally efficient methods for accounting for atmospheric absorption, we discuss the radiative transfer framework underpinning cloud and aerosol retrievals. The connection between single-scattering and multiple-scattering is rigorously formulated. We then summarize operational and research-grade retrieval techniques, including cloud masking and thermodynamic phase determination, CO₂ slicing for cloud-top pressure, the Nakajima-King shortwave bi-spectral, and infrared split-window approaches for cloud optical thickness and effective particle size, inversion algorithms for determining aerosol properties from multi-spectral and/or multi-angle radiometric and polarimetric measurements, and active-passive sensing synergy. Examples of the global cloud and aerosol climatologies are illustrated using observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) and the Multi-angle Imaging SpectroRadiometer (MISR). Furthermore, the unique strengths of active remote sensing techniques based on spaceborne lidar observations are briefly elaborated in the context of studying ice clouds composed of randomly and horizontally oriented ice crystals, which is a significant challenge for conventional passive remote sensing techniques. By connecting physical theory to practical retrievals, this review highlights both the maturity of current methodologies and the remaining challenges in reducing uncertainties in particle morphology, vertical structure, absorption, and aerosol-cloud interactions. Furthermore, the impact of artificial intelligence (AI) on atmospheric remote sensing is briefly addressed.

Aerosols↗
DOE OSTI2026

Atmospheric Modeling and Denoising for Millimeter-Wave Line Intensity Mapping

Line-intensity mapping (LIM) offers a promising approach to mapping large-scale cosmic structure, and the greatest obstacle for ground-based observations at millimeter wavelengths is foreground contamination from atmospheric emission. In this work, we present a simulation and denoising framework designed to isolate and subtract atmospheric fluctuations from LIM data, modeled after the instrument parameters of the South Pole Telescope Summertime Line Intensity Mapper (SPT-SLIM). We generate mock observations spanning 125-175 GHz containing cosmic signals, precipitable water vapor screens, ice crystal fluctuations, and photon noise. We then implement a spatial-spectral atmospheric removal pipeline combining per-pixel linear template regression with a two-dimensional Fourier-domain filter. The framework is evaluated under simulated conditions in the South Pole and the Atacama Desert across three key metrics: cosmic signal preservation, foreground subtraction efficiency, and instrument noise injection. Our pipeline achieves atmospheric suppression at large spatial scales, and these results establish a physically grounded foundation for atmosphere removal in ground-based LIM data collection.

Saye, L. K. [UC, Berkeley (main)] (ORCID:000900078↗
NASA NTRS1992

Crystallization of amorphous ice as the cause of Comet P/Halley's outburst at 14 AU

An explanation is provided for the postperihelion eruption of Comet P/Halley, detected in February 1991 and believed to have started three months earlier, namely, the crystallization of amorphous ice taking place in the interior of the porous nucleus, at depths of a few tens of meters, accompanied by the release of trapped gases. Numerical calculations show that for a bulk density of 0.5 g/cu cm and a pore size of 1 micron crystallization occurs on the outbound leg of Comet P/Halley's orbit, at heliocentric distances between 5 AU and 17 AU. The trapped gas is released and flows to the surface through the porous medium. It may also open wider channels, as the internal pressures obtained surpass the tensile strength of cometary ice. The outflowing gas carries with it grains of ice and dust, and thus can explain the large amounts of dust observed in the coma at 14.3 AU and beyond.

Prialnik, D.↗
DOE OSTI2025

Dynamic compression effects of H 2 ⁡O in a dynamic diamond anvil cell: Origin of metastable ice VII and its crystal growth kinetics

We report on the structural verification of metastable ice VII solidifying in the phase space of ice VI at 1.80 GPa at room temperature. Using time-resolved (TR) x-ray diffraction and TR ruby luminescence paired with high-speed microphotography utilizing a dynamic diamond anvil cell, an initial compression rate range from 0.12 to 95.84 GPa/s was explored. The solidification pressure of metastable ice VII has a potential sigmoidal dependence upon compression rate with a turnover compression rate of ∼80 GPa/s. The preferred crystallization of ice VII in the stability field of ice VI is due to the increased nucleation rate of ice VII over ice VI at 1.77 GPa that is driven by the surface energy difference between the liquid and solid phases along with the change in Gibbs free energy of solidification. The dynamic pressure-volume–compression behaviors of ice phases (VI and VII) show a lattice stiffening in both phases, especially during the compression loading. It is also found that the compression rate greatly affects the solid-solid phase transition between ice VI and VII but does not affect the liquid-solid transition between water and ice VI as much. Lastly, a third phase transition was found to occur after metastable ice VII transforms into high-density amorphous (HDA) ice, which could be a disordered hydrogen-bonded network configuration of ice VII forming out of HDA ice facilitated by the decoupling of the oxygen movement and reorientation of the H 2⁡ O molecule. These results demonstrate the complexity of a seemingly simple molecule H 2⁡ O, how it can readily change its static properties with the modification of (de)compression rate, and highlight the need to use multiple TR structural and spectroscopic probes at higher time resolutions to realize the most comprehensive understanding.

Chemical bonding↗