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NASA NTRSDate not supplied

Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere and surface flux data for climate studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models. The FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate their broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.

Sun, Moguo↗
NASA NTRS2015

Ultimately Reliable Pyrotechnic Systems

This paper presents the methods by which NASA has designed, built, tested, and certified pyrotechnic devices for high reliability operation in extreme environments and illustrates the potential applications in the oil and gas industry. NASA's extremely successful application of pyrotechnics is built upon documented procedures and test methods that have been maintained and developed since the Apollo Program. Standards are managed and rigorously enforced for performance margins, redundancy, lot sampling, and personnel safety. The pyrotechnics utilized in spacecraft include such devices as small initiators and detonators with the power of a shotgun shell, detonating cord systems for explosive energy transfer across many feet, precision linear shaped charges for breaking structural membranes, and booster charges to actuate valves and pistons. NASA's pyrotechnics program is one of the more successful in the history of Human Spaceflight. No pyrotechnic device developed in accordance with NASA's Human Spaceflight standards has ever failed in flight use. NASA's pyrotechnic initiators work reliably in temperatures as low as -420 F. Each of the 135 Space Shuttle flights fired 102 of these initiators, some setting off multiple pyrotechnic devices, with never a failure. The recent landing on Mars of the Opportunity rover fired 174 of NASA's pyrotechnic initiators to complete the famous '7 minutes of terror.' Even after traveling through extreme radiation and thermal environments on the way to Mars, every one of them worked. These initiators have fired on the surface of Titan. NASA's design controls, procedures, and processes produce the most reliable pyrotechnics in the world. Application of pyrotechnics designed and procured in this manner could enable the energy industry's emergency equipment, such as shutoff valves and deep-sea blowout preventers, to be left in place for years in extreme environments and still be relied upon to function when needed, thus greatly enhancing safety and operational availability.

Scott, John H.↗
NASA NTRSDate not supplied

Understanding Relationships Between Satellite, Model, and Ground-Based Surface Temperature Characterizations From Overcast to Clear Conditions in Support of Satellite Remote Sensing of Clouds and Radiation

Accurate and consistent global estimates of cloud coverage and their properties are fundamental to long-term Earth radiation budget (ERB) monitoring efforts like the Clouds and the Earth’s Radiant Energy System (CERES) project. Cloud detection algorithms often apply thresholding approaches to identify where clouds occur by comparing satellite-measured radiances with those that are expected under cloud-free conditions. In addition, once a cloud is detected, the derivation of cloud optical and microphysical properties also requires knowledge of the background radiances below the cloud. In the infrared, knowledge of the surface emissivity and the expected skin temperature under both cloudy and cloud-free conditions is needed. These traits are generally well known over the oceans. Over land, however, comparisons between satellite-derived land surface temperature (LST) with that characterized in numerical weather analyses reveal large differences in many parts of the world, often exceeding 5 K, which can lead to significant satellite cloud detection and cloud property retrieval errors. Furthermore, clouds have a dramatic influence on the LST, and therefore characterization of that model parameter also depends on the capability of the model to accurately resolve clouds. Thus, the LST characterized in models is, at times, a poor approximation for what would otherwise be observed, thereby impeding accurate satellite cloud retrievals. As a result, we seek to develop a more robust method for estimating the LST required for satellite cloud characterizations. This effort is accomplished through a combination of surface emission/air temperature relationship studies in all-sky conditions using ground measurement stations, along with deep neural network (DNN) estimates of expected LST under overcast and cloud-free conditions. We demonstrate that substituting DNN-predicted LST for that generated by numerical models can mitigate model-inherent diurnal dependencies and reduce overall bias and uncertainty relative to satellite/ground observations by 0.5–4 K and 0.5–2 K, respectively. It is expected that this work will lead to improved satellite cloud retrievals that enhance ERB monitoring efforts.

B Scarino↗
NASA NTRS2022

Atmospheric Energy Spectra in Global Kilometre-Scale Models

Eleven 40-day long integrations of five different global models with horizontal resolutions of less than 9 km are compared in terms of their global energy spectra. The method of normal-mode function decomposition is used to distinguish between balanced (Rossby wave; RW) and unbalanced (inertia-gravity wave; IGW) circulation. The simulations produce the expected canonical shape of the spectra, but their spectral slopes at mesoscales, and the zonal scale at which RW and IGW spectra intersect differ significantly. The partitioning of total wave energies into RWs an IGWs is most sensitive to the turbulence closure scheme and this partitioning is what determines the spectral crossing scale in the simulations, which differs by a factor of up to two. It implies that care must be taken when using simple spatial filtering to compare gravity wave phenomena in storm-resolving simulations, even when the model horizontal resolutions are similar. In contrast to the energy partitioning between the RWs and IGWs, changes in turbulence closure schemes do not seem to strongly affect spectral slopes, which only exhibit major differences at mesoscales. Despite their minor contribution to the global (horizontal kinetic plus potential available) energy, small scales are important for driving the global mean circulation. Our results support the conclusions of previous studies that the strength of convection is a relevant factor for explaining discrepancies in the energies at small scales. The models studied here produce the major large-scale features of tropical precipitation patterns. However, particularly at large horizontal wavenumbers, the spectra of upper tropospheric vertical velocity, which is a good indicator for the strength of deep convection, differ by factors of three or more in energy. High vertical kinetic energies at small scales are mostly found in those models that do not use any convective parameterisation.

DYAMOND↗
NASA NTRSDate not supplied

Early Information Parameter-Set Analysis for Satellite Close Approaches using Machine Learning

Understanding orbital mechanics is essential in space flight and navigation applications, and leveraging modern force models for flight path projection remains an important aspect in space mission design and operation. However, force models do not capture all the dynamics or perturbations in the space environment and thus are subject to errors in predicting the state vectors. The further out the predicted miss distance between spacecraft is from the time of closest approach (TCA), the larger the propagated errors in the predicted miss distance at TCA is. The dependency on these force models for spacecraft flight state prediction calls for a more reliable method that can quantify, or even reduce, these propagated errors. With recent advances in the field artificial intelligence, specifically in machine and deep learning algorithms, a model that implements these approaches can improve on the modern force model approach. The goal for this work is to provide an early-information decision-making threshold, in order to prioritize risk assessment implementation, given the ongoing increase of space objects. In analyzing the relationship of several parameters from conjunction data messages(CDMs) and solar information, early information becomes viable in miss distance prediction with unsupervised learning techniques, which learn the parameters that are linked together with miss distance and probability of collision (Pc) variables. Another approach implemented for identifying relationships within CDMs is supervised learning, in which a shallow neural network binary classifier learns to distinguish events with Pc values¡108. These parameters detected in the unsupervised process are then applied to a regression neural network, which predicts the miss distance at TCA for a specific event within a given uncertainty bound. For the regression neural network, a Long Short Term Memory (LSTM) neural network is implemented, which yields memory about each time step in an event. Using an LSTM network, the model learns to predict miss distance within 0.2km of the value measured at TCA. Although there is a limited amount of "close miss" data to train a network, the network learns to associate parameters, like large energy dissipation rates with the secondary object, with an elevated Pc

Brianna I. Robertson↗
NASA NTRSDate not supplied

CERES FluxByCldTyp NB2BB Fluxes Improvement based on Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

Flux Improvement based on Machine Learning for the CERES FluxByCldTyp Data Product

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

Improvement of Radiative Fluxes for the CERES FluxByCldTyp Data Product Based on Machine Learning Technique

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Prelimary results show significant LW improvement.

Moguo Sun↗
NASA NTRSDate not supplied

Improving Radiative Fluxes for the CERES FluxByCldTyp Data Product Using Deep Neural Network

The NASA Clouds and the Earth's Radiant Energy System (CERES) product provides over 20 years of accurately observed top-of-the-atmosphere (TOA) and surface flux data record for climate monitoring and diagnostic studies. The interaction between clouds and radiation interaction is a key factor that dominate climate feedbacks but is not well understood. To further advance our understanding of the cloud-radiation interaction, a new CERES FluxByCldTyp (FBCT) product has been developed that contains radiative fluxes by cloud-type, which can provide more stringent constraints when validating models and reveal more insight into the interactions between clouds and climate. For CERES partly cloudy and multiple cloud-type footprints, the FBCT product utilizes Moderate Resolution Imaging Spectroradiometer (MODIS) narrow-band (NB) imager channel radiances partitioned by cloud-type within a CERES footprint to estimate the cloud-type broadband fluxes. The MODIS multi-channel derived broadband fluxes were compared with the CERES observed footprint fluxes and were found to be within 1% and 2.5% for LW and SW, respectively, as well as being mostly free of cloud property dependencies. The FBCT all-sky and clear-sky monthly averaged fluxes were found to be consistent with the CERES SSF1deg product. This study takes advantage of recent progress in machine learning (ML) field by applying deep neural network algorithm to improve fluxes based on MODIS NB radiances. The preliminary study shows ML produce are an improvement over the current FBCT Edition 4 NB2BB algorithm. Furthermore, unlike Ed4 NB2BB, the new ML method convert NB radiances directly to broadband fluxes. For future Ed5, new NB radiances are proposed and used by ML to improve fluxes calculation. Preliminary results show significant LW improvement.

Sun, Moguo↗
NASA NTRS2001

Radiation transport modeling and assessment to better predict radiation exposure, dose, and toxicological effects to human organs on long duration space flights

NASA is very interested in improving its ability to monitor and forecast the radiation levels that pose a health risk to space-walking astronauts as they construct the International Space Station and astronauts that will participate in long-term and deep-space missions. Human exploratory missions to the moon and Mars within the next quarter century, will expose crews to transient radiation from solar particle events which include high-energy galactic cosmic rays and high-energy protons. Because the radiation levels in space are high and solar activity is presently unpredictable, adequate shielding is needed to minimize the deleterious health effects of exposure to radiation. Today, numerous models have been developed and used to predict radiation exposure. Such a model is the Space Environment Information Systems (SPENVIS) modeling program, developed by the Belgian Institute for Space Aeronautics. SPENVIS, which has been assessed to be an excellent tool in characterizing the radiation environment for microelectronics and investigating orbital debris, is being evaluated for its usefulness with determining the dose and dose-equivalent for human exposure. Thus far. the calculations for dose-depth relations under varying shielding conditions have been in agreement with calculations done using HZETRN and PDOSE, which are well-known and widely used models for characterizing the environments for human exploratory missions. There is disagreement when assessing the impact of secondary radiation particles since SPENVIS does a crude estimation of the secondary radiation particles when calculating LET versus Flux. SPENVIS was used to model dose-depth relations for the blood-forming organs. Radiation sickness and cancer are life-threatening consequences resulting from radiation exposure. In space. exposure to radiation generally includes all of the critical organs. Biological and toxicological impacts have been included for discussion along with alternative risk mitigation methods--shielding and anti-carcinogens. c 2001. Elsevier Science Ltd. All rights reserved.

STS-91 Shuttle Project↗
NASA NTRS2000

Optical Analysis of Transparent Polymeric Material Exposed to Simulated Space Environment

Many innovations in spacecraft power and propulsion have been recently tested at NASA, particularly in non-chemical propulsion. One improvement in solar array technology is solar concentration using thin polymer film Fresnel lenses. Weight and cost savings were proven with the Solar Concentrator Arrays with Refractive Linear Element Technology (SCARLET)-II array on NASA's Deep Space I spacecraft. The Fresnel lens concentrates solar energy onto high-efficiency solar cells, decreasing the area of solar cells needed for power. Continued efficiency of this power system relies on the thin film's durability in the space environment and maintaining transmission in the 300 - 1000 nm bandwidth. Various polymeric materials have been tested for use in solar concentrators, including Lexan(TM), polyethylene terephthalate (PET), several formulations of Tefzel(Tm) and Teflon(TM), and DC 93-500, the material selected for SCARLET-II. Also tested were several innovative materials including Langley Research Center's CPI and CP2 polymers and atomic oxygen- resistant polymers developed by Triton Systems, Inc. The Environmental Effects Group of the Marshall Space Flight Center's Materials, Processes, and Manufacturing Department exposed these materials to simulated space environment and evaluated them for any change in optical transmission. Samples were exposed to a minimum of 1000 equivalent Sun hours of near-UV radiation (250 - 400 nm wavelength). Materials that appeared robust after near-UV exposure were then exposed to charged particle radiation equivalent to a five-year dose in geosynchronous orbit. These exposures were performed in MSFC's Combined Environmental Effects Test Chamber, a unique facility with the capability to expose materials simultaneously or sequentially to protons, low-energy electrons, high-energy electrons, near UV radiation and vacuum UV radiation. Reflectance measurements can be made on the samples in vacuum. Prolonged exposure to the space environment will decrease the polymer film's transmission and thus reduce the conversion efficiency. A method was developed to normalize the transmission loss and thus rank the materials according to their tolerance to space environmental exposure. Spectral results and the material ranking according to transmission loss are presented.

Edwards, David L.↗
NASA NTRS2005

X-Ray Fluorescence Solvent Detection at the Substrate-Adhesive Interface

With environmental regulations limiting the use of volatile organic compounds, low-vapor pressure solvents have replaced traditional degreasing solvents for bond substrate preparation. When used to clean and prepare porous bond substrates such as phenolic composites, low vapor pressure solvents can penetrate deep into substrate pore networks and remain there for extended periods. Trapped solvents can interact with applied adhesives either prior to or during cure, potentially compromising bond properties. Currently, methods for characterizing solvent time-depth profiles in bond substrates are limited to bulk gravimetric or sectioning techniques. While sectioning techniques such as microtome allow construction of solvent depth profiles, their depth resolution and reliability are limited by substrate type. Sectioning techniques are particularly limited near the adhesive-substrate interface where depth resolution is further limited by adhesive-substrate hardness and, in the case of a partially cured adhesive, mechanical properties differences. Additionally, sectioning techniques cannot provide information about lateral solvent diffusion. Cross-section component mapping is an alternative method for measuring solvent migration in porous substrates that eliminates the issues associated with sectioning techniques. With cross-section mapping, the solvent-wiped substrate is sectioned perpendicular rather than parallel to the wiped surface, and the sectioned surface is analyzed for the solvent or solvent components of interest using a two-dimensional mapping or imaging technique. Solvent mapping can be performed using either direct or indirect methods. With a direct method, one or more solvent components are mapped using red or Raman spectroscopy together with a moveable sample stage and/or focal plane array detector. With an indirect method, an elemental "tag" not present in the substrate is added to the solvent before the substrate is wiped. Following cross sectioning, the tag element can then be mapped by its characteristic x-ray emission using either x-ray fluorescence, or electron-beam energy-and wavelength-dispersive x-ray spectrometry. The direct mapping techniques avoid issues of different diffusion or migration rates of solvents and elemental tags, while the indirect techniques avoid spectral resolution issues in cases where solvents and substrates have adjacent or overlapping peaks. In this study, cross-section component indirect mapping is being evaluated as a method for measuring migration of d-limonene based solvents in glass-cloth phenolic composite (GCP) prior to and during subsequent bonding and epoxy adhesive cure.

Wurth, Laura↗
NASA NTRSDate not supplied

TPSAS-NF1676L-10737-DND

The Clouds and the Earth's Radiant Energy System (CERES) project provides the community a quality climate TOA and surface fluxes and cloud properties and are key elements in evaluating the ability of climate models to predict both past and future climate states. One important aspect of successful climate monitoring is satellite calibration. The CERES instantaneous broadband calibration radiance stability is 0.1% per year, whereas the accuracy of the longwave (LW) and shortwave (SW) is better than 0.5% and 1% respectively. CERES uses the 3-hourly GEO fluxes and clouds to provide diurnally averaged daily and monthly TOA flux means from the CERES measurements onboard the Terra (10:30 AM local equator crossing time) and Aqua (13:30 PM) sun-synchronous satellites. The GEO radiances are calibrated against MODIS as a reference to provide consistent GEO retrieved cloud properties and derived broadband fluxes. These fluxes are then carefully normalized against the well-calibrated CERES fluxes to ensure climate quality fluxes, free of GEO artifacts. With the upcoming release of Edition3 products, CERES has just recalibrated all the CERES instruments to ensure that both Terra and Aqua fluxes are on the same radiometric scale and the removal of all known instrument trends. CERES will also recalibrate all GEO satellites using the entire GEO time record. CERES employs collocated, coincident ray-matched GEO/MODIS radiances as its primary technique, calibrating GEO radiances beginning in the year 2000. The advantage of the ray-matching technique is that it provides a full dynamic range of radiances to check the linearity of the GEO visible instrument. CERES uses deep convective clouds and invariant desert sites to validate the primary technique. All three methods can assess the stability of the GEO visible instrument. Examples of the CERES GEO calibration techniques and validation efforts will be shown at the conference. Special emphasis will be given to the newly incorporated desert site methodology.

R. Bhatt↗
NASA NTRSDate not supplied

Evaluation of Spectral Band Adjustment Factors for Cross-Calibration of Visible Imagers

The NASA CERES EBAF dataset provides TOA SW and LW fluxes for long-term monitoring of the Earth’s energy balance and to validate climate models. The EBAF products, based on the Terra, Aqua, and NOAA20 CERES instrument observed radiances, rely on coincident measurements from the onboard MODIS or VIIRS imagers to determine cloud properties used for angular distribution model scene selection, which is required to convert the CERES observed radiances into flux values. Furthermore, CERES utilizes geostationary imager (GEO) hourly fluxes and clouds to infer the regional averaged daily flux in the SYN1deg product. A seamless transition of fluxes and clouds can only occur if the analogous MODIS, VIIRS, and GEO channels are properly inter-calibrated. The analogous band SRFs differ noticeably and require scene dependent spectral band adjustment factors (SBAF) for proper radiometric scaling between them. Given their disparate but overlapping SRFs, the coincident VIIRS I1 and M5 band TOA reflectance measurements provide the optimal opportunity to validate SBAFs over many surface and cloud conditions. The CERES project maintains SCIAMACHY, GOME-2, and Hyperion scene-stratified hyper-spectral reflectance measurements that can be convolved with sensor pair SRFs to compute the corresponding SBAF. This study highlights the SCIAMACHY, GOME-2, and Hyperion based SBAFs for different MODIS, VIIRS, and GEO inter-calibration targets, including all-sky tropical ocean (ATO), Libya-4, Dome-C, and deep convective clouds. To mitigate the observed sensor radiance fluctuations due to atmospheric absorption and aerosol variations, the Earth target observed radiances are correlated with multiple atmospheric parameters, such as precipitable water and ozone. The remaining M5 and I1 mean spectral band radiance difference should be resolved by the SBAF correction. The formulation of hyper-spectral sensor based SBAFs and validation methods can be verified in future utilizing high-accuracy, SI-traceable hyper-spectral measurements from CLARREO Pathfinder.

David R Doelling↗
NASA NTRS2018

Instability and Target Design in a Z-Pinch Driven Pulsed Fission / Fusion Engine

Advanced forms of propulsion are required to drastically improve upon the limits of existing technology and enable deep space exploration. Nuclear reactors, both fission and fusion, have great potential as systems with high energy density for spacecraft propulsion. There are a variety of nuclear propulsion concepts that have been proposed over the last several decades. Some of these rely on the confinement of plasmas by both magnetic and inertial means. When discussing the plasma confinement one must consider the stability of the system. The conditions of the plasma dictate the burn rate of the nuclear fuel; therefore, the plasma must maintain a minimum set of conditions in order to achieve an adequate burn. Instabilities limit plasma confinement through growing fluctuations in density and pressure. This results in turbulent mixing and loss of confinement. Thus it is desirable to find ways of managing the impact of instabilities that arise in order for a sufficient burn of the fuel such that surplus energy can be converted into a propulsive force. Note that the same instabilities plague the confinement of plasmas for terrestrial power generation. Over the last several years there has been research relating to the development of a Pulsed Fission-Fusion Engine (PuFF) conducted by the authors and others at their respective institutions. The propulsion system concept centers on the use of a z-pinch to compress a plasma in order to induce nuclear reactions. The plasma along with the additional energy released by the fission and fusion processes is expanded with a magnetic nozzle to produce thrust. The concept, if developed, is expected to have an improvement in specific impulse of several orders of magnitude in comparison with chemical propulsion systems. The z-pinch has been explored in the past as a method to confine a plasma via magnetic confinement fusion (MCF) for power generation. It suffers; however, from instabilities that so far have prevented break even conditions.

Taylor, Brian↗
NASA NTRS2006

Comet Mineralogy as Inferred from Infrared Spectra of Comets

For most comets, infrared (IR) spectroscopy (remote sensing) is the method through which we diagnose the mineralogy and size distribution of dust in their comae. The shape and contrast of the IR spectral features depend on the particle size: optically active minerals (absorbing of visible and near-IR solar photons) and submicron solid grains or highly porous (> 90% vacuum) grains primarily contribute to the shapes of the observed resonances. Comet mineralogies typically are determined by fitting thermal emission models of ensembles of discrete mineral grains to observed IR spectral energy distributions. The absorptivities (Q-abs) and scattering efficiencies (Q-scat) of the discrete mineral grains are computed using Mie scattering, Maxwell-Garnet mixing, Discrete Dipole Approximation, and Multi-Layered Sphere codes. These techniques when applied to crystalline minerals, specifically olivine (Mg_x, Fe_1-x)2 Si04, x>0.9, require the use of ellipsoidal shaped particles with elongated axial ratios or hollow spheres to produce the shapes of the resonances observed both from comet comae and laboratory samples. The wavelength positions of the distinct resonances from submicron-radii crystalline silicates, as well as their thermal equilibrium temperatures, constrain the crystalline olivine to have a relatively high Mg-content (x>0.9, or Fo>90). Only resonances computed for submicron Mg-rich crystalline olivine and crystalline orthopyroxene match the observed IR spectral features. However, this has led to the interpretation that micron-radii and larger crystals are absent from comet comae. Furthermore, the mass fraction of silicate crystals is dependent upon whether just the submicron portion of the size distribution is being compared or the submicron crystals compare to the aggregates of porous amorphous silicates that are computationally tractable as porous spheres. We will discuss the Deep Impact results as examples of these challenges to interpreting mid-IR spectra of short-period comets.

Wooden, Diane H.↗
NASA NTRS2020

Evaluation of global terrestrial evapotranspiration using state-of-the-art approaches in remote sensing, machine learning and land surface modeling

Evapotranspiration (ET) is critical in linking global water, carbon and energy cycles. However, direct measurement of global terrestrial ET is not feasible. Here, we first reviewed the basic theory and state-of-the-art approaches for estimating global terrestrial ET, including remote-sensing-based physical models, machine-learning algorithms and land surface models (LSMs). We then utilized 4 remote-sensing-based physical models, 2 machine-learning algorithms and 14 LSMs to analyze the spatial and temporal variations in global terrestrial ET. The results showed that the ensemble means of annual global terrestrial ET estimated by these three categories of approaches agreed well, with values ranging from 589.6 mm/yr (6.56×10^4 cu.km/yr) to 617.1 mm/yr (6.87×10^4 cu.km/yr). For the period from 1982 to 2011, both the ensembles of remote-sensing-based physical models and machine-learning algorithms suggested increasing trends in global terrestrial ET (0.62 mm/sq.yr with a significance level of p<0.05 and 0.38 mm yr−2 with a significance level of p<0.05, respectively). In contrast, the ensemble mean of the LSMs showed no statistically significant change (0.23 mm/sq.yr, p>0.05), although many of the individual LSMs reproduced an increasing trend. Nevertheless, all 20 models used in this study showed that anthropogenic Earth greening had a positive role in increasing terrestrial ET. The concurrent small interannual variability, i.e., relative stability, found in all estimates of global terrestrial ET, suggests that a potential planetary boundary exists in regulating global terrestrial ET, with the value of this boundary being around 600 mm/yr. Uncertainties among approaches were identified in specific regions, particularly in the Amazon Basin and arid/semiarid regions. Improvements in parameterizing water stress and canopy dynamics, the utilization of new available satellite retrievals and deep-learning methods, and model–data fusion will advance our predictive understanding of global terrestrial ET.

surface modeling↗
NASA NTRSDate not supplied

Evaluating the Feasibility of Data Simulation for Radiation Research: Estimating RBE Values for HZE Radiation

As the drive for deep space exploration intensifies, a comprehensive understanding of the health effects of radiation exposure becomes paramount to the future of human space flight. However, epidemiological data for radiation exposure, particularly to high-energy (HZE) ions, is limited, partially due to the financial and logistical costs of radiation studies. As an alternative, this study aims to assess the viability of data simulation strategies to accurately model potential study parameters prior to utilizing laboratory conditions. This study estimates a relative biological effectiveness (RBE) factor based on the solid tumor data for outbred mice provided by Edmundson et al. 2020. Excess relative risk (ERR) models for HZE-Fe ions and gamma radiation were estimated using Poisson regression with Weibull models to represent the background solid tumor hazard without radiation. RBE values were calculated from the ratio of the heavy ion linear slope to the gamma linear slope. The parameters from these models were then applied to simulate iterations of 300 datasets across HZE-Fe doses of 0.05, 0.2, 0.4, and 0.75 Gy; gamma radiation doses of 0.75, 2, and 3 Gy, as well as an unirradiated control group. Sample size per dose varied from 100-500 mice across simulations (800-4000 mice total per dataset). 1500 datasets were generated total (300 each for sample sizes 800, 1600, 3200, and 4000). For each dataset, ERR per radiation type and RBE for HZE-Fe were calculated. The RBE from the Edmondson data was calculated to be 5.55. The RBEs from the simulated data converged around this value across the increasing sample sizes. For n = 4000, the mean RBE across the 300 datasets was 5.71 (95% CI: 5.47-5.95). These results suggest that RBEs calculated from simulated data are credible. Based on this exploration, data simulation is a viable method of testing radiation studies. It provides a method of testing study assumptions and refining research questions prior to embarking on costly laboratory experiments.

Sneha Mehta↗