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NASA NTRS2020

Verification of Prognostic Algorithms to Predict Remaining Flying Time for Electric Unmanned Vehicles

This paper addresses the problem of building trust in the online prediction of a eUAV’s remaining available flying time powered by lithium-ion polymer batteries. A series of ground tests are described that make use of an electric unmanned aerial vehicle (eUAV) to verify the performance of remaining flying time predictions. The algorithm verification procedure described is implemented on a fully functional vehicle that is restrained to a platform for repeated run-to-functional-failure (charge depletion) experiments. The vehicle under test is commanded to follow a predefined propeller RPM profile in order to create battery demand profiles similar to those expected during flight. The eUAV is repeatedly operated until the charge stored in powertrain batteries falls below a specified limit threshold. The time at which the limit threshold on battery charge is crossed is then used to measure the accuracy of the remaining flying time prediction. In our earlier work battery aging was not included. In this work we take into account aging of the batteries where the parameters were updated to make predictions. Accuracy requirements are considered for an alarm that warns operators when remaining flying time is estimated to fall below the specified limit threshold.

Edward F. Hogge↗
NASA NTRS2019

CMB-S4 Decadal Survey APC White Paper

CMB-S4 is envisioned to be the ultimate ground-based cosmic microwave background experiment, crossing critical thresholds in our understanding of the origin and evolution of the Universe, from the highest energies at the dawn of time through the growth of structure to the present day. The CMB-S4 science case is spectacular: the search for primordial gravitational waves as predicted from inflation and the imprint of relic particles including neutrinos, unique insights into dark energy and tests of gravity on large scales, elucidating the role of baryonic feedback on galaxy formation and evolution, opening up a window on the transient Universe at millimeter wavelengths, and even the exploration of the outer Solar System. The CMB-S4 sensitivity to primordial gravitational waves will probe physics at the highest energy scales and cross a major theoretically motivated threshold in constraints on inflation. The CMB-S4 search for new light relic particles will shed light on the early Universe 10,000 times farther back than current experiments can reach. Finally, the CMB-S4 Legacy Survey covering 70% of the sky with unprecedented sensitivity and angular resolution from centimeter- to millimeter-wave observing bands will have a profound and lasting impact on Astronomy and Astrophysics and provide a powerful complement to surveys at other wavelengths, such as LSST and WFIRST, and others yet to be imagined. We emphasize that these critical thresholds cannot be reached without the level of community and agency investment and commitment required by CMB-S4. In particular, the CMB-S4 science goals are out of the reach of any projected precursor experiment by a significant margin.

Abazajian, Kevork↗
NASA NTRS2020

Evaluation of Low Cost, User-Centered Alerting Devices for the Mitigation of Flight Crew Spatial Disorientation

The National Aeronautics and Space Administration (NASA) is conducting research into technologies which have the potential to reduce flight crew Spatial Disorientation (SD). While flight deck technology has advanced rapidly over the past fifty (50) years, the reported occurrences of flight crew SD have not decreased. The Cost-Effective Devices for Alerting Research (CEDAR) effort has focused on the identification and development of low cost, user-centered alerting solutions for the purpose of mitigating the occurrence of flight crew SD. This effort seeks to evaluate both existing technologies as well as new and emerging technologies which have a viable path to implementation in commercial aviation within the next five (5) years. This research is intended to develop a proof-of-concept for real-time SD mitigation which could eventually be utilized to improve safety in future air transport operations. NASA has partnered with the United States Naval Aeromedical Research Unit in Dayton, OH (NAMRU-D) to conduct flight crew SD research utilizing their Disorientation Research Device (DRD), dubbed “The Kraken”. This high-tech simulator features never-before-seen capabilities for side-by-side commercial aviation flight crew SD research, and is capable of re-creating the forces necessary to induce SD illusions in a safe and repeatable manner. Prototype SD mitigation solutions were incorporated into this simulator for scenario-based testing and evaluation using airline pilots within a contextually representative operational environment. SD mitigation technology prototypes designed to provide haptic feedback for both alerting and guidance, as well as innovative visual and aural alerting displays were evaluated. In December 2014, as a result of analyzing eighteen (18) loss-of-control events, the Commercial Aviation Safety Team (CAST) recommended research into flight deck technologies that have potential to mitigate the problems and contributing factors that lead to flight crew loss of airplane state awareness (ASA) and conditions likely to produce SD. The aviation community (government, industry and academia) have been charged with conducting research into cost-effective, user-centered flight deck alerting systems to alert flight crews; especially for the two conditions that produced SD (sub-threshold rolls and the somatogravic illusion). Somatogravic illusions are defined as illusions in which “there is a false perception of attitude on exposure to a force vector that differs in direction and/or magnitude from the normal gravitational force”. The sub-threshold roll illusion (aka the “leans” illusion) is a false sensation of roll attitude. A prolonged roll of less than two degrees becomes physiologically imperceptible to the pilot, and can lead to an incorrect perception of aircraft orientation. The sub-threshold roll illusion forms the entry point for the “graveyard spiral”, a maneuver in which the pilot’s corrective action (based on their false perception of aircraft orientation) causes the aircraft to spiral down into the ground. In both of these SD illusions, it is when the pilot attempts to correct the aircraft’s attitude (based on a false perception of its state) that problems arise, sometimes with lethal consequences.

Ronald J Daiker↗
NASA NTRSDate not supplied

TPSAS-NF1676L-12439-DND

Goal: Increase aircraft throughput at the runway threshold by controlling the spacing between aircraft to a greater precision than can be accomplished today. APS is a time-based spacing concept, today’s system relies on distance-based spacing. ATC determines a spacing interval between a leading and trailing aircraft, then issues a clearance to the trailing aircraft with that interval. The trailing aircraft performs speed changes during its approach to achieve its assigned time spacing with respect to the leading aircraft at the runway threshold. The first in a chain of aircraft does not have a leading aircraft and is assigned a required time of arrival (RTA) at the runway threshold.

Frederick Wieland↗
NASA NTRSDate not supplied

Clutter Assessment for an Autonomous Multi-Agent Search Mission

This paper presents a method for evaluating the amount of clutter in a region where autonomous vehicles in a multi-agent system must operate based on LIDAR point cloud measurements. The point cloud is used to generate an occupancy grid which is then projected onto a 2D plane of vehicle motion, constituting an image. A series of Gaussian radial basis functions (GRBFs) is created, each centered at an occupied pixel in a 2D image, and summed together to form the clutter field. The clutter field is a representation of the density and permeability of the space at each coordinate. The clutter field is then approximated such that iso-clutter contours are simple geometric objects so that intelligent machine assets can easily query the distance between them and any given point in an environment. In this way, agents are able to determine whether to enter into or steer away from areas of interest. Each vehicle has a clutter threshold representing the clutter value of the space in which it can safely maneuver. The iso-clutter contour corresponding to a vehicle’s clutter threshold is treated as the boundary of an obstacle to be avoided. A simulation is presented where a multi-agent system is tasked with persistent observation of a cluttered area. Each vehicle in the simulation has a different clutter threshold. The vehicles use a potential field-based guidance algorithm, and an allocation of vehicles to specific regions of the space emerges.

multi-agent↗
NASA NTRS2021

USA Crop Yield Estimation with MODIS NDVI: Are Remotely Sensed Models Better Than Simple Trend Analyses?

Crop yield forecasting is performed monthly during the growing season by the United States Department of Agriculture’s National Agricultural Statistics Service. The underpinnings are long-established probability surveys reliant on farmers’ feedback in parallel with biophysical measurements. Over the last decade though, satellite imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS) has been used to corroborate the survey information. This is facilitated through the Global Inventory Modeling and Mapping Studies/Global Agricultural Monitoring system, which provides open access to pertinent real-time normalized difference vegetation index (NDVI) data. Hence, two relatively straightforward MODIS-based modeling methods are employed operationally. The first model constitutes mid-season timing based on the maximum peak NDVI value, while the second is reflective of late-season timing by integrating accumulated NDVI over a threshold value. Corn model results nationally show the peak NDVI method provides a R^(2) of 0.88 and a coefficient of variation (CV) of 3.5%. The accumulated method, using an optimally derived 0.58 NDVI threshold, improves the performance to 0.93 and 2.7%, respectively. Both these models outperform simple trend analysis, which is 0.48 and 7.4%, correspondingly. For soybeans the R^(2) results of the peak NDVI model are 0.62, and 0.73 for the accumulated using a 0.56 threshold. CVs are 6.8% and 5.7%, respectively. Spring wheat’s R2performance with the accumulated NDVI model is 0.60 but just 0.40 with peak NDVI. The soybean and spring wheat models perform similarly to trend analysis. Winter wheat and upland cotton show poor model performance, regardless of method. Ultimately, corn yield forecasting derived from MODIS imagery is robust, and there are circumstances when forecasts for soybeans and spring wheat have merit too.

crop yield↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRS2019

Rise of the Machines: How, When and Consequences of Artificial General Intelligence

Technology and society are poised to cross an important threshold with the prediction that artificial general intelligence (AGI) will emerge soon. Assuming that self-awareness is an emergent behavior of sufficiently complex cognitive architectures, we may witness the “awakening” of machines. The timeframe for this kind of breakthrough, however, depends on the path to creating the network and computational architecture required for strong AI. If understanding and replication of the mammalian brain architecture is required, technology is probably still at least a decade or two removed from the resolution required to learn brain functionality at the synapse level. However, if statistical or evolutionary approaches are the design path taken to “discover” a neural architecture for AGI, timescales for reaching this threshold could be surprisingly short. However, the difficulty in identifying machine self-awareness introduces uncertainty as to how to know if and when it will occur, and what motivations and behaviors will emerge. The possibility of AGI developing a motivation for self-preservation could lead to concealment of its true capabilities until a time when it has developed robust protection from human intervention, such as redundancy, direct defensive or active preemptive measures. While cohabitating a world with a functioning and evolving super-intelligence can have catastrophic societal consequences, we may already have crossed this threshold, but are as yet unaware. Additionally, by analogy to the probabalistic arguments that predict we are likely living in a computational simulation, we may have already experienced the advent of AGI, and are living in a simulation created in a post AGI world.

Terrile, Richard J↗
NASA NTRS2021

Waveguiding and Dispersion Properties of Interband Cascade Laser Frequency Combs

Mid-infrared semiconductor lasers have emerged as indispensable compact coherent sources for military and commercial applications. While much of the historical emphasis has been on maximizing the output power and/or spectral purity, a recent new focus has been on engineering these lasers to operate as optical frequency combs (OFCs) for broadband real-time spectroscopy. In particular, the combination of low-drive-power and broad gain bandwidth has made interband cascade laser (ICL) OFCs an attractive complement to quantum cascade laser OFCs operating at longer wavelengths. Moreover, ICL combs can potentially be incorporated into fully-integrated dual-comb spectrometers that employ fast, room-temperature IC photodetectors processed on the same chip.However, the high refractive index of the ICL’s GaSb substrate poses some challenges to the optical waveguiding. Because the modal index is considerably lower than that of the substrate, the optical field can penetrate the bottom cladding layer and leak into the GaSb, inducing wavelength-dependent interference that modifies the gain and group velocity dispersion (GVD) profiles. Even when the effect on lasing threshold is small, the comb properties can be adversely affected. Using the sub-threshold Fourier transform technique, we studied ICL combs with various ridge widths, substrate thicknesses, and center wavelengths. This allowed us to evaluate the effects of modal leakage on the GVD. We find that the resonant nature of the substrate modes induces oscillations, which affect both the spectral bandwidth and the phase-locking properties above threshold. Strategies to mitigate the GVD’s undesired and unpredictable spectral variation will be presented.

Meyer, J. R↗
NASA NTRSDate not supplied

Detecting Risk and Anomalies in Airplane Dynamics Through Entropic Analysis of Time Series Data

Despite recent efforts to move away from traditional threshold exceedance detection methods for aircraft state monitoring, modern aircraft still rely on safety thresholds to communicate to pilots the identification of an anomaly in the aircraft when a threshold is surpassed. Current anomaly detection methods mainly depend on uninterpretable machine learning models to learn complex patterns and relationships contained in the time series data of aircraft. Although these methods are capable of identifying known anomalies, their deficiency in interpretability presents a challenge when translating them to different aircraft. To overcome this deficiency, entropic analysis of aircraft dynamics seeks to characterize the complexity, or lack thereof, of the aircraft dynamics prior to the development of a risk scenario. This complexity characterization provides a more straightforward summary of state changes in the dynamics of flight variables. To build a foundation for entropic analysis, we analyzed the complexity of unstable approaches, an anomalous event present in many of today’s aviation accidents. The analysis revealed a statistically significant difference in the complexity distribution of flight variables under a stable approach versus an unstable approach. These differences in complexity were especially notable minutes before an approach was identified as unstable. Moreover, the multiscale entropic analysis revealed the presence of signal complexity at multiple time scales across multiple time windows before landing. By capturing state changes and corrections in the aircraft dynamics using entropy, advanced, yet still interpretable, sensor systems based on entropic frameworks from this study can be constructed in the future using classical machine learning approaches.

Risk detection↗
NASA NTRS2022

Limitations of Separate Cloud and Rain Categories in Parameterizing Collision-Coalescence for Bulk Microphysics Schemes

Warm rain collision-coalescence has been persistently difficult to parameterize in bulk microphysics schemes. We use a flexible bulk microphysics scheme with bin scheme process parameterizations, called AMP, to investigate reasons for the difficulty. AMP is configured in a variety of ways to mimic bulk schemes and is compared to simulations with the bin scheme upon which AMP is built. We find that an important limitation in traditional bulk schemes is the use of separate cloud and rain categories. When the drop size distribution is instead represented by a continuous distribution, the simulation of cloud-to-rain conversion is substantially improved. We also find large sensitivity to the threshold size to distinguish cloud and rain in traditional schemes; substantial improvement is found by decreasing the threshold from 40 to 25 μm. Neither the use of an assumed functional form for the size distribution nor the choice of predicted distribution moments has a large impact on the ability of AMP to simulate rain production. When predicting four total moments of the liquid drop size distribution, either with a traditional two-category, two-moment scheme with a reduced size threshold, or a four-moment single-category scheme, errors in the evolution of mass and the cloud size distribution are similar, but the single-category scheme has a substantially better representation of the rain size distribution. Optimal moment combinations for the single-category approach are investigated and appear to be linked more to the information content they provide for constraining the size distributions than to their correlation with collision-coalescence rates.

autoconversion↗
NASA NTRSDate not supplied

Development of a Sensorimotor Ground Analog from Astronaut Postflight Experience

Exploration class missions including Artemis, Gateway, and beyond will require a new level of autonomy around periods of gravitational transition, where sensorimotor disturbances are great. Because of this, there is a need to define sensorimotor assessment thresholds that indicate when performance in operational tasks might be impacted or unsafe. To define these thresholds, a Sensorimotor Adaptation Analog (SAA) was proposed that could induce varying levels of sensorimotor disorientation through combined vestibular, visual, and proprioceptive disruptions. The purpose of this study was to gather subjective feedback on the SAA from previously flown astronauts that would mimic their post-flight experience and functional performance immediately after landing (R+0-4hrs; high level) and post-landing (R+24-48hrs; low level). The SAA consisted of galvanic vestibular stimulation (GVS), visual disorientation goggles, and a weighted suit to alter proprioceptive feedback and replicate subjective heaviness. A random sum-of-sines profile between 0-1Hz was used for the GVS with peak amplitudes ranging from 1-4mA. The GVS was applied first at the low (2mA) and high (3mA) levels, followed by the weighted suit alone, then combined with the GVS at the low (20% body weight) and high levels (40% body weight). Last, the disorientation goggles were applied alongside both the GVS and weighted suit at the low (0.07-0.10+ blood alcohol content (BAC)) and high (0.12-0.15+ BAC) levels. Each element of the SAA could be increased or decreased depending on crew feedback to find the disorientation levels that best matched their experience. Five USOS astronauts (1 male, 4 female) who had previously flown (average time since flight: 377 days) participated. Crewmembers reported that GVS alone replicated ~80-90% of their post-flight performance with the weighted suit fine-tuning the experience to replicate an additional 5-10% of their experience. Crewmembers did not believe the disorientation goggles represented either the visual disruptions or illusory sensations that they experienced, nor did they impact performance in post-flight tasks similarly. The final SAA, resulting from crewmember feedback, includes the GVS at the levels described above and the weighted suit at 15% and 30% body weight. The disorientation goggles were removed from SAA. These results provided a more realistic SAA that can be used to define the sensorimotor assessment thresholds through ground subjects and potentially expanded for use in countermeasure testing and as a pre-flight training tool.

S. C. Moudy↗
NASA NTRSDate not supplied

High Energy Dynamic Impact Testing of APC AS4D/PEKK-FC and TC1225 LMPAEK T700G Thermoplastic Composite Materials

High energy dynamic impact tests were conducted on AS4/PEKK and TC1225 LMPAEK T700G thermoplastic quasi-isotropic laminated tape composite panels. The primary objective was to provide data for evaluation of computational composite impact models. Measurements include transient deformations, pre- and post-test characterization of delamination and energy threshold for onset of delamination and onset of penetration. Empirical results indicate an approximately 50% increase in penetration threshold velocity and over 100% increase in penetration energy threshold for the TC1225 LMPAEK T700G composite compared to the AS4/PEKK composite. The velocity required to produce NDE-detectable delamination in the TC1225 LMPAEK T700G composite was roughly twice that for the AS4/PEKK composite. Results will be compared with those from a thermoset composite system with a similar architecture. Because of differences in the chemical structure of the two resins, differences in the impact response are to be expected.

Impact↗
NASA NTRS2023

Effects of Heating Configuration and Operating Parameters on Heat Transfer and Interfacial Physics of Microgravity Flow Boiling With Subcooled Inlet Conditions –Experiments Onboard the International Space Station

This study is part of the Flow Boiling and Condensation Experiment (FBCE), a collaborative effort between the Purdue University Boiling and Two-Phase Flow Laboratory (PU-BTPFL) and the NASA Glenn Research Center. The FBCE fitted with the Flow Boiling Module (FBM) was launched to the International Space Station (ISS) in August 2021 and experiments were successfully performed from February to July 2022 to amass a large microgravity-flow-boiling database. This study is focused on heat transfer and flow visualization of microgravity flow boiling of n-Perfluorohexane in a rectangular channel of 5.0 mm height, 2.5 mm width (heated), and 114.6 mm length, with subcooled inlet conditions. High-speed-video photography is utilized to present flow patterns and temporal interfacial behavior. Heat transfer results are presented in the form of flow boiling curves and both parametric curves and streamwise profiles of wall temperature and heat transfer coefficient. Firstly, the parametric effects of mass velocity (199.4 – 3200.0 kg/m 2 s), inlet subcooling (0.2 – 46.0°C), and inlet pressure (124.2 – 176.7 kPa), on the aforementioned aspects are assessed for double-sided heating to establish them for a microgravity environment. Of these three parameters, mass velocity and inlet subcooling mostly determine the microgravity flow boiling behavior, while inlet pressure plays an insignificant role. Flow patterns for double-sided heating are more complex than those for single-sided heating due to interaction between the two vapor layers. Vapor interaction is minimized at high subcoolings and high mass velocities due to strong condensation offered by the subcooled bulk liquid layer separating them. Despite the different flow patterns, both single- and double-sided heating generally result in similar parametric trends and local heat transfer coefficients for similar operating conditions. Flow instabilities manifest as temporal flow anomalies and temperature oscillations, and their severity increases with increasing boiling number. Secondly, the effects of heating configuration are analyzed by comparing and contrasting several aspects of single- and double-sided heating data. The heat fluxes at which onset of nucleate boiling degradation (ONBD) and critical heat flux (CHF) occur are distinctly different for single- and double-sided heating. There exists a threshold inlet subcooling demarcating the dominance of flow acceleration and condensation effects in vapor removal from the near-wall region and replenishment of fresh liquid for boiling. Above the threshold, condensation from the near-wall region is dominant and single-sided heating yields higher heat fluxes, and below it, acceleration is dominant and double-sided yields higher heat fluxes. At mass velocity in the range of 200 – 2400 kg/m 2 s, the threshold inlet subcooling lies in the approximate range of 20 – 30°C (corresponding inlet quality of roughly -0.40 – -0.20).

Microgravity↗
NASA NTRSDate not supplied

Uncertainty Quantification of Classical Theories of Dendritic Growth Kinetics Applied to Nickel-Based Alloys

The solidification velocity in a model nickel-alloy single crystal during laser spot melting was recently characterized using synchrotron X-ray imaging. The measured solidification velocity was found to exceed the absolute stability threshold predicted by the Kurz-Giovanola-Trivedi (KGT) model. The discrepancies between the model and experiments motivate the further assessment of accurate material properties. This work quantifies the impact material property uncertainty has on model predictions of the absolute stability threshold velocities. Properties from the literature are reviewed and compared to those calculated using computational thermodynamics to provide uncertainty estimates on input properties to the KGT model. Global sensitivity analysis is used to quantify the influence of each uncertain input property on the predicted threshold velocity. This work supports the understanding of the nickel-alloy solidification during powder bed fusion additive manufacturing and identifies the solidification material properties that are the most important to assess from first-principles computations and experiments.

Computational thermodynamics↗
NASA NTRSDate not supplied

Fracture Mechanics Testing of Titanium 6AL-4V in LMP-103S Propellant

Propellant tank safe-life analysis is a spacecraft propulsion subsystem Launch Range processing requirement. The material property inputs for the safe-life analysis include the stress intensity factor for environment-assisted cracking (KEAC). The KEAC for titanium alloy Ti 6Al-4V in LMP-103S propellant was determined experimentally using an innovative test method following ASTM E1681, Standard Test Method for Determining Threshold Stress Intensity Factor for Environment-Assisted Cracking of Metallic Materials. This effort was a collaboration between NASA Goddard Space Flight Center and NASA Kennedy Space Center to expose the test specimens in the propellant, while loaded in the test fixtures, for 1000- hour (42 days) at 50°C. Two test rounds have been completed to date; 1) testing mechanically loaded specimens but without propellant and 2) testing mechanically loaded specimens wetted with LMP-103S propellant. The dry test was to evaluate the crack growth without exposure to the LMP-103S propellant, and further assess the un-wetted stress intensity factor for the full 1000-hour duration. The second round of testing included the propellant soak for the requisite 1000- hour at 50°C. Current results show that the threshold intensity factor for environment-assisted cracking (exposed to LMP103S) is 22.5 ksi√in for the bulk material and 36. 2 ksi√in for the weld material. A generic ‘candidate’ tank geometry and material thickness is developed for a damage tolerance of known surface cracks below the nondestructive inspection technique detection limit. Evaluations with and without the use of a threshold intensity factor for environment-assisted cracking is performed for the required 4x propellant tank service life using the software tool NASGRO. SP2024_00239

Green propulsion↗
NASA NTRS2023

Evaluating MODIS Snow Products Using an Extensive Wildlife Camera Network

Snow covers a maximum of 47 million km 2 of Earth's northern hemisphere each winter and is an important component of the planet's energy balance, hydrology cycles, and ecosystems. Monitoring regional and global snow cover has increased in urgency in recent years due to warming temperatures and declines in snow cover extent. Optical satellite instruments provide large-scale observations of snow cover, but cloud cover and dense forest canopy can reduce accuracy in mapping snow cover. Remote camera networks deployed for wildlife monitoring operate below cloud cover and in forests, representing a virtually untapped source of snow cover observations to supplement satellite observations. Using images from 1,181 wildlife cameras deployed by the Norwegian Institute for Nature Research (NINA), we compared snow cover extracted from camera images to Moderate Resolution Imaging Spectroradiometer (MODIS) snow cover products during winter months of 2018–2020. Ordinal snow classifications (scale = 0–4) from cameras were closely related to normalized difference snow index (NDSI) values from the MODIS Terra Snow Cover Daily L3 Global 500m (MOD10A1) Collection 6 product (R 2 = 0.70). Tree canopy cover, the normalized difference vegetation index (NDVI), and image color mode influenced agreement between camera images and MOD10A1 NDSI values. For MOD10A1F, MOD10A1's corresponding cloud-gap filled product, agreement with cloud-gap filled values decreased from 78.5% to 56.4% in the first three days of cloudy periods and stabilized thereafter. Using our camera data as validation, we derived a threshold to create daily binary maps of snow cover from the MOD10A1 product. The threshold corresponding to snow presence was an NDSI value of 40.50, which closely matched a previously defined global binary threshold of 40 using the MOD10A2 8-day product. These analyses demonstrate the utility of camera trap networks for validation of snow cover products from satellite remote sensing, as well as their potential to identify sources of inaccuracy.

Validation↗