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NASA NTRS2018

Coupling Fractional Thermal Runaway Calorimetry (FTRC) Results with Statistical Analysis Methods

Important factors to consider when optimizing Lithium-ion (Li-ion) battery thermal management systems, from a battery safety perspective, include the total energy released during a single cell thermal runaway (TR) event and the fraction of the total energy that is released through the cell casing versus through the ejecta material. A fractional thermal runaway calorimeter (FTRC) was used to examine the TR behavior of the following 18650-format Li-ion cells: (1) Molicel 18650-J, (2) LG 18650-HG2, (3) Samsung 18650-30Q, (4) 3.35 Ah LG 18650 test cell, (5) Sony 18650-VC7, and (6) LG 18650-MJ1. These cells were intentionally selected to represent a variety of manufacturers, chemistries, capacities, and safety features. Because of the large number of random and non-random variables associated with the FTRC results, a linear regression model, developed using engineering statistical methods, was utilized to provide final interpretation of the TR behavior for each cell type and design variable combination.

Walker, William↗

NASA NTRS2018

Generating Exploration Mission-3 Trajectories to a 9:2 NRHO using Machine Learning

The purpose of this thesis is to design a machine learning algorithm platform that provides expanded knowledge of mission availability through a launch season by improving trajectory resolution and introducing launch mission forecasting. The specific scenario addressed in this paper is one in which data is provided for four deterministic translational maneuvers through a mission to a Near Rectilinear Halo Orbit (NRHO) with a 9:2 synodic frequency. Current launch availability knowledge under NASA's Orion Orbit Performance Team is established by altering optimization variables associated to given reference launch epochs. This current method can bean abstract task and relies on an orbit analyst to structure a mission based on an established mission design methodology associated to the performance of Orion and NASA's Space Launch System. Introducing a machine learning algorithm trained to construct mission scenarios within the feasible range of known trajectories reduces the required interaction of the orbit analyst by removing the needed step of optimizing the orbit to fit an expected translational response required of the spacecraft. In this study, k-Nearest Neighbor and Bayesian Linear Regression successfully predicted classical orbital elements for the launch windows observed. However both algorithms had limitations due to their approaches to model fitting. Training machine learning algorithms off of classical orbital elements introduced a repetitive approach to reconstructing mission segments for different arrival opportunities through the launch window and can prove to be a viable method of launch window scan generation for future missions.

Guzman, Esteban↗

NASA NTRS2018

Net Radiative Effects of Dust in the Tropical North Atlantic Based on Integrated Satellite Observations and in Situ Measurements

In this study, we integrate recent in situ measurements with satellite retrievals of dust physical and radiative properties to quantify dust direct radiative effects on shortwave (SW) and longwave (LW) radiation (denoted as DRESW and DRELW, respectively) in the tropical North Atlantic during the summer months from 2007 to 2010. Through linear regression of the CERES-measured top-of atmosphere (TOA) flux versus satellite aerosol optical depth (AOD) retrievals, we estimate the instantaneous DRESW efficiency at the TOA to be -49+/-7.1 W/sq.m AOD(exp -1) and -36+/-4:8W/sq.m AOD(exp -1) based on AOD from MODIS and CALIOP, respectively. We then perform various sensitivity studies based on recent measurements of dust particle size distribution (PSD), refractive index, and particle shape distribution to determine how the dust microphysical and optical properties affect DRE estimates and its agreement with the above-mentioned satellite-derived DREs. Our analysis shows that a good agreement with the observation-based estimates of instantaneous DRESW and DRELW can be achieved through a combination of recently observed PSD with substantial presence of coarse particles, a less absorptive SW refractive index, and spheroid shapes. Based on this optimal combination of dust physical properties we further estimate the diurnal mean dust DRE(sub SW) in the region of -10W/sq.m at TOA and -26W/sq.m at the surface, respectively, of which ~30% is canceled out by the positive DRE(sub LW). This yields a net DRE of about -6:9 and -18.3 W/sq.m at TOA and the surface, respectively. Our study suggests that the LW flux contains useful information on dust particle size, which could be used together with SW observations to achieve a more holistic understanding of the dust radiative effect.

Song, Qianqian↗

NASA NTRS2019

The Southern Ocean Sea Surface Temperature Response to Ozone Depletion: A Multi-Model Comparison

The effect of the Antarctic ozone hole extends downwards from the stratosphere, with clear signatures in surface weather patterns including a positive trend in the Southern Annular Mode (SAM). Several recent studies have used coupled climate models to investigate the impact of these changes on Southern Ocean sea surface temperature (SST), notably motivated by the observed cooling from the late 1970s. Here we examine the robustness of these model results through comparison of both previously published and new simulations. We focus on the calculation of â€˜climate response functions' (CRFs), transient responses to an instantaneous step-change in ozone concentrations. The CRF for most models consists of a rapid cooling of SST, followed by a slower warming trend. However, inter-model comparison reveals large uncertainties, such that even the sign of the impact of ozone depletion on historical SST, when reconstructed from the CRF, remains unconstrained. Comparison of these CRFs with SST responses to a hypothetical step-change in the SAM, inferred through lagged linear regression, shows broadly similar results. Causes of uncertainty are explored by examining relationships between model climatologies and their CRFs. The inter-model spread in CRFs can be reproduced by varying a single subgrid-scale mixing parameter within a single model. Antarctic sea-ice CRFs are also calculated: these do not generally exhibit the two-time-scale behavior of SST, suggesting a complex relationship between the two. Finally, by constraining model climatology-response relationships with observational values, we conclude that ozone depletion in unlikely to have been the primary driver of the observed SST cooling trend.

Climate response functions (CRF)↗

NASA NTRS2016

The Global Infrared Energy Budget of the Thermosphere from 1947 to 2016 and Implications for Solar Variability

We present an empirical model of the global infrared energy budget of the thermosphere over the past 70 years. The F(sub 10.7), Ap, and Dst indices are used in linear regression fits to the 14.5 year time series of radiative cooling by carbon dioxide and nitric oxide measured by the Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the TIMED satellite. Databases of these indices are used to develop the radiative cooling time series from 1947. No consistent relation between the occurrence of peak sunspot number and peak infrared cooling is found over the past six solar cycles. The total infrared energy radiated by the thermosphere, integrated over a solar cycle, is nearly constant over five complete solar cycles studied. This is a direct consequence of the geoeffective solar energy also being nearly constant over the same intervals. These results provide a new metric for assessing the terrestrial context of the long‐term record of solar‐related indices.

Mlynczak, Martin G.↗

NASA NTRS2019

Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering

NASA has been developing and demonstrating new concepts and technologies for Integrated Arrival, Departure, and Surface (IADS) traffic management capabilities under the Airspace Technology Demonstration 2 (ATD-2) project. One of the IADS capabilities in the ATD-2 project is surface metering enabled by a tactical surface scheduler to provide controllers with the pushback advisories for departures at gates. The tactical surface scheduler uses the estimated flight ready times provided by airlines, called Earliest Off-Block Times (EOBTs), as input to calculate the target off-block times for pushback advisories. However, the EOBTs are often inaccurate and deviate from the actual flight ready times, which may reduce the benefits of surface metering. In this paper, a linear regression model is developed to model the EOBT uncertainty distribution over time based on actual EOBT data collected at Charlotte airport. This EOBT model is integrated with a tactical surface scheduler and a fast-time simulation tool. To evaluate the impact of the EOBT accuracy on airport surface operations, fast-time simulations are implemented for selected traffic scenarios under different levels of modelled EOBT accuracy. The simulation results show that the EOBT uncertainty affects several performance metrics related to the surface metering, such as gate hold, taxi time reduction, and target takeoff time predictability, which in turn influences ATD-2's scheduler performance.

tactical surface scheduler↗

NASA NTRS2019

Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering

tactical surface scheduler↗

NASA NTRS2018

Trusted Communication: Utilizing Speech Communication to Enhance Human-Machine Teaming Success

An area of increasing interest for the next generation of aircraft is autonomy and the integration of increasingly autonomous systems into the national airspace. Such an integration requires humans to work closely with autonomous systems, forming teams. Our hypothesis is that a team composed of both humans and autonomous systems will operate better than either entity alone. We have existing procedures for certifying pilots to operate in the national airspace and are currently working on methods for validating the function of autonomous systems, however we have no method in place for assessing the interaction of these two disparate systems. Communication is one avenue. This paper will examine the use of language as a metric for ascertaining human-machine teaming effectiveness. A proof-of-concept of the application of two communication-based analysis techniques, Linguistic Inquiry and Word Count (LIWC) and Latent Semantic Analysis (LSA), for the prediction of success in human/chatbot teaming was conducted. By running these analyses over data from the 2014 and 2015 Loebner Prize competitions of human/chatbot teaming, numerical scores were obtained that can be associated with scores provided by human judges during the competition. Correlating their LIWC and LSA data with the scores provided by the judges, and using linear regression over this correlation, formulae were obtained that predict the score of human/chatbot interaction. These formulae were tested over the 2013 Loebner Prize transcripts, determining that, though there was strong correlation between predicted and actual scores, the predictive success of this method was not strong. However, with specialized topic spaces and lexica, as well as larger data sets, the predictive power of these metrics will improve. Given the importance of providing metrics for human-machine system team success and given the promise shown by the communication-basedLIWCand LSAmethods, continuing research in this area is necessary. After examining the potential for using communication and spoken language as a metric for the success of human/autonomous system teaming, this paper then examines aspects inherent to communication systems that may contribute to unreliability and reduced trust. Modern natural language processing tools rely on deep learning algorithms to create language rules that produce accurate results, but these rules are uninterpretable. The resulting blackbox system lacks transparency necessary for full validation and complete trust. Additionally, speech-based interfaces pose other difficulties to developing coordinated teamwork between humans and autonomous systems. Human communication is infrequently limited to speech only, instead usually relying on a combination of verbal, gestural, and general body language communication. Reducing an analysis of team effectiveness to a study of spoken language alone is problematic as it leaves these other equally important forms of communication out. This paper will examine these problems and the general deficiencies in speech-based metrics for human-machine teaming.

E L Meszaros↗

NASA NTRS2018

TPSAS-NF1676L-32008-DND

Thermosphere climate is controlled in part by cooling to space driven by infrared radiation, primarily from nitric oxide (NO, 5.3μm) and carbon dioxide (CO2, 15μm). The Sounding of the Atmosphere using Broadband Emission Radiometry (SABER) instrument on the Thermosphere-Ionosphere-Mesosphere Energetics and Dynamics (TIMED) satellite has been measuring the vertical distribution of infrared radiation emitted by these species (and others) for nearly 17 years-–more than 6000 days. This data set spans much of Solar Cycle (SC) 23 and all of SC 24 to date. A thermosphere climate index (TCI) has been derived by noting the correlation between 60-day running averages of NO cooling power and three long-term solar and geomagnetic indexes: F10.7 cm solar radio flux, the Ap index of geoeffective solar activity, and the disturbance storm time (Dst) index. A multiple linear regression (MLR) fit of these three parameters to the NO cooling rate over the SABER mission has a better than 0.98 correlation. Applying the MLR constant and coefficients from that fit provides a good estimate of thermospheric cooling back to 1947, the extent of the data available for all three indexes. We examined the percentile distribution of the TCI in quintiles over the five complete solar cycles, and assigned.

Linda Hunt↗

NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate Resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends in Washington State, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗

NASA NTRS2020

Washington Health & Air Quality: Quantifying Air Quality Parameters and Validating Air Pollution Sources Impacting the Health of Puget Sound Residents Through the Use of NASA and ESA Remote Sensing Data

In the Puget Sound region of Washington, high levels of air pollutants put residents’ health at risk by increasing their likelihood of developing critical respiratory conditions. This project used remotely-sensed data to investigate aerosol optical depth (AOD) from NASA satellite sensors including the Terra and Aqua MODerate resolution Imaging Spectroradiometer (MODIS) and European Space Agency Copernicus Sentinel-5 Precursor TROPOspheric Monitoring Instrument (TROPOMI). The team visualized the most recent data in Google Earth Engine (GEE) API to display air pollution trends from Northern California to British Columbia, which will support the Puget Sound Clean Air Agency’s (PSCAA) decision-making processes. The team performed linear regressions using the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm to form a relationship between ground-level microscopic particles (PM2.5) and AOD in the Puget Sound region, validating the relationship using concentration readings taken from Environmental Protection Agency (EPA) air quality monitors. The team utilized estimated PM2.5 and other satellite data to produce a web-based tool and to evaluate the effectiveness of using such a tool for near real-time air quality monitoring within a particular region. The team found that the tool provides useful supplementary data that fills in the gaps of the PSCAA’s air monitoring network.

Health & Air Quality↗

NASA NTRS2021

First Measurements of Ambient PM2.5 in Kinshasa, Democratic Republic of Congo and Brazzaville, Republic of Congo Using Field-calibrated Low-cost Sensors

Estimates of air pollution mortality in sub-Saharan Africa are limited by a lack of surface observations of fine particulate matter (PM2.5). Despite being large metropolises, Kinshasa, Democratic Republic of the Congo (DRC), population 14.3 million, and Brazzaville, Republic of the Congo (ROC), population 2.4 million, have no reference air pollution monitors at the time of writing. Recently, a few reference monitors have been deployed in other parts of sub-Saharan Africa, including Kampala, Uganda. A low-cost PurpleAir PM2.5 monitor was collocated next to the Kampala US Embassy BAM-1020 (Met One Beta Attenuation Monitor) starting in August 2019. Raw PurpleAir data are strongly correlated with the BAM (r(exp 2) = 0.88), but have a mean absolute error of approximately 14 μg/cu.m. Two calibration models, multiple linear regression and a random forest approach, decrease mean absolute error (MAE) from 14.3 μg/cu.m to 3.4 µg/cu.m or less and improve the the r(exp 2) from 0.88 to 0.96. Given )the similarity in climate and emissions in Kampala, we apply the collocated field correction factors to four PurpleAir sensors in Kinshasa, DRC and one in neighboring Brazzaville, ROC deployed beginning April 2018. Annual average PM2.5 for 2019 in Kinshasa is estimated at 43.5 µg/cu.m, more than 4 times higher than WHO Interim Target 1 of 10 µg/cu.m. Surface PM2.5 and aerosol optical depth were each about 40% lower during the 2020 COVID19 lockdown period compared to the same time period in 2019, which cannot be explained by changes in meteorology or wildfire emissions alone. Our results highlight the need for clean air solutions implementation in the Congo.

Celeste McFarlane↗

NASA NTRSDate not supplied

Application of Artificial Intelligence for Surface PM2.5 Estimations from Geostationary Satellite and Atmospheric Numerical Model Data

PM2.5, particulate matter (PM) with a diameter less than or equal to 2.5 μm, is emitted from anthropogenic fuel combustion and forest fires. Due to their small size, PM2.5 can penetrate into respiratory systems and cause or exacerbate serious illness. The US Environmental Protection Agency (EPA) regulates the levels of surface PM2.5 but surface monitoring has spatial and temporal limitations. The Aerosol Optical Depth (AOD) retrievals from the Geostationary Operational Environmental Satellite (GOES) missions and meteorological factors can be utilized as an alternative technique to estimate surface PM2.5 levels at a higher spatial and temporal resolution compared to surface monitors. Traditional estimation approaches rely on linear regression techniques and have limitations modeling the nonlinear relationship between the meteorological factors, AOD retrievals, and surface PM2.5. We compare different machine learning techniques and identify the best-suited model that can represent the nonlinearity between the factors affecting PM2.5 levels

Manisha Khatri↗

NASA NTRSDate not supplied

Oculometric Analysis of Saccadic Compensation for Visual Motion Processing Impairment due to Alcohol and Sleep Disruption

The Visuomotor Control Laboratory at Ames Research Center has developed a 5-minute ocular tracking test that computes 21 largely independent metrics of visuomotor performance, reflecting neural signal processing along a number of distinct pathways through cortex, brainstem, and cerebellum. Human sensorimotor performance is resilient to the challenges and stressors of many operational environments, in part, because overall performance is achieved through multiple parallel systems. Our multidimensional oculometrics allow us to examine impacts on these sub-components separately. To illustrate this, we contrasted the effects of two mild neural stressors, acute sleep-deprivation and low-dose alcohol. We have previously shown that, in both cases, oculometric analysis is a highly sensitive indicator of impairment. Here we quantified not only the observed impact on the performance of one sub-system, smooth pursuit, which uses high-level cortical processing of visual motion to track a moving object, but also the observed (partial) compensation by an evolutionarily older mid-brain and brainstem subsystem, saccades, which generates jumps in eye position to catch up with the target when smooth pursuit is inadequate. Specifically, we examined the dose-response (effect size vs. dose size) of the ground lost (pursuit deficit) and the ground recouped (saccadic compensation) across three separate studies – acute low-dose alcohol administration (16 subjects), acute sleep loss (12 subjects), and acute sleep loss with caffeine intervention (9 subjects). We computed the dose-response slopes using linear regression. The figure below shows that, in the case of acute sleep deprivation, the resulting slopes for ground lost and ground recouped (mean ± SE across subjects) were significantly different (paired t-test, t(11) = 5.17, p < 0.001), indicating poor saccadic compensation. However, when sleep loss was coupled with caffeine ingestion, ground lost was decreased and ground recouped increased such that the slopes were no longer different (t(8) = -0.05, p = 0.965). With alcohol, the two slopes were large albeit not significantly different (t(15) = 0.96, p = 0.351), indicating significant pursuit impairment but effective saccadic compensation. Our findings show that sleep deprivation and alcohol affect oculomotor performance differently. Low-dose alcohol effects appear predominantly cortical, with effective brainstem compensation. Sleep loss and circadian disruption however appears to affect both cortical and brainstem pathways with caffeine providing an effective countermeasure to both effects. Beyond the mere detection of impairment, our oculometric assessment allows us to characterize the nature of the deficit, to provide insight into the neural substrate, and to assess the effectiveness of countermeasures.

pursuit↗

NASA NTRS2017

Accuracy/Computation Performance of a New Trilateration Scheme for GPS-Style Localization

We recently introduced a new geometric trilateration (GT) method for GPS-style positioning. Preliminary singlepoint analysis using simplistic error assumptions indicates that the new scheme delivers almost indistinguishable localization accuracy as the traditional Newton-Raphson (NR) approach. Also, the same computation procedure can be used to perform high-accuracy relative positioning between a reference vehicle and an arbitrary number of target vehicles. This scheme has the potential to enable a) new mission concepts in collaborative science, b) in-situ navigation services for human Mars missions, and c) lower cost and faster acquisition of GPS signals for consumer-grade GPS products. The new GT scheme differs from the NR scheme as follows: 1. The new scheme is derived from Pythagoras Theorem, whereas the NR method is based on the principle of linear regression. 2. The NR method uses the absolute locations (xi, yi, zi)’s of the GPS satellites as input to each step of the localization computation. The GT method uses the Directional Cosines Ui’s from Earth’s center to the GPS satellite Si. 3. Both the NR method and the GT method iterate to converge to a localized solution. In each iteration step, multiple matrix operations are performed. The NR method constructs a different matrix in each iterative step, thus requires performing a new set of matrix operations in each step. The GT scheme uses the same matrix in each iteration, thus requiring computing the matrix operations only once for all subsequent iterations. In this paper, we perform an in-depth comparison between the GT scheme and the NR method in terms of a) GPS localization accuracy in the GPS operation environment, b) its sensitivity with respect to systematic errors and random errors, and c) computation load required to converge to a localization solution.

Lee, Charles↗

NASA NTRS2019

Improving SMAP freeze-thaw retrievals for pavements using effective soil temperature from GEOS-5: Evaluation against in situ road temperature data over the U.S

Seasonal freeze-thaw (FT) affects over half the northern hemisphere and impacts many key processes of the Earth System such as energy exchange, hydrology and vegetation. Nearly all past studies using spaceborne FT retrievals have focused on characterizing FT specifically for natural environments. FT in the built environment is also routinely studied and a topic of great interest, especially with regards to transportation infrastructure. Whereas natural FT process are frequently investigated using spaceborne observations, FT studies of roads are often limited to local scales, using in situ or nearby weather station data only. Comparisons between FT retrievals obtained from NASA's Soil Moisture Active Passive (SMAP) satellite and roads in Alaska (AK) and the Contiguous United States (CONUS) showed that spaceborne FT retrievals had good agreement with road data. But those results also indicated that NASA FT retrievals in CONUS were relatively too warm compared to road data. If SMAP FT retrievals were to be used for identifying FT transition timing for applications by the transportation community, it is also important for frozen conditions to be identified more accurately. This work is primarily concerned with improving frozen retrievals made in CONUS by calculating new Normalized Polarization Ratio (NPR) thresholds as compared to those currently used in SMAP FT. We found that focusing on a temporal subset of October through May for comparisons greatly improved the correlation between NPR and effective soil temperature (Teff, one of SMAP's ancillary datasets), often from about zero to 0.6. We then applied linear regression between NPR and Teff to obtain new NPR thresholds resulting in the FT-Roads (FT-R) product. NASA FT and FT-R were evaluated against road data at about 1000 locations in CONUS and a battery of different tests indicated that FT-R performed better under nearly all conditions compared to NASA FT. Overall, NASA FT accuracies were 69% and 80% for 6 am and 6 pm SMAP retrievals, while FT-R achieved accuracies of 79% and 82%. We also investigated the potential for using Teff for road FT (6 am, only) and found that those comparisons were even more accurate (84%). We've also quantified inter- and intraregional differences of SMAP FT performance and found that accuracy metrics vary over twice as much between geographic subdivisions (9%) as compared to between the states within a subdivision (4%). Most importantly, the main goal of improving the detection of in situ frozen conditions in CONUS was realized, with FT-R accurately detecting frozen conditions >50% more frequently than NASA FT.

Passive microwave↗

NASA NTRSDate not supplied

Saccade-Pursuit Coordination during Ocular Tracking across Different Impairment Sources

Purpose: Sleep loss and alcohol have been shown to impair smooth pursuit and its underlying visual motion processing in humans. This study examines the saccadic compensation for this poor pursuit. Methods: Using an established behavioral ocular-tracking paradigm (Liston & Stone, 2014, doi:10.1167/14.14.12), we examined the dose-response of ground lost (pursuit deficit integrated across our 300-ms steady-state tracking interval) and ground gained (increased saccadic response harnessed to compensate)across three separate studies – acute low-dose alcohol administration (LDA; n = 16 subjects), acute sleep loss(ASL; n = 12), and chronic sleep restriction (CSR; n = 12). We computed dose-responses as the linear regression slopes of ground lost and gained across treatment dose (% blood alcohol concentration [BAC] or hours awake). For the CSR study, we computed the mean effect for a single dose (5-hours nightly sleep for 1week). Results: For LDA, there was significantly increased ground lost (P < 0.001) and gained (P < 0.001) with increased %BAC. In addition, the dose-responses were not significantly different (P = 0.35), indicating effectively complete saccadic compensation due to significant increases in both saccadic rate (P < 0.05) and amplitude (P < 0.001). For ASL, there was significantly increased ground lost with time awake (P < 0.01),however ground gained was significantly lower (P < 0.001), indicating, at best, incomplete compensation due to a significant increase in saccadic rate (P < 0.001) but not amplitude (P = 0.10). With CSR, pursuit was again significantly impaired (P < 0.05), with saccadic rate significantly increased (P < 0.05) but, surprisingly, amplitude was significantly decreased (P < 0.05), effectively eliminating ground gained (P = 0.90). Conclusions: Our analyses show that LDA, ASL, and CSR affect tracking differently, suggesting the involvement of different brain pathways. With LDA, the effect appears largely due to the cortical impairment of visual motion processing with largely healthy brainstem and mid-brain responses (driving effective saccadic compensation). ASL and CSR however appear to affect both cortical and sub-cortical pathways, with at best partial saccadic compensation. Lastly, CSR is associated with an additional compromise due to a maladaptive decrease in saccade amplitude.

saccade↗

NASA NTRS2022

Comparison of U.S. and Russian Slow Crack Growth Data and Models

Both the US and Russian space programs use similar predictive models for design of fused silica windows on the International space station. The Russian model can be derived from the power expression for slow crack growth (SCG) or “static fatigue.” The US uses both power and exponential models. Despite the similarity of models and data fitting approach (linear regression and right censoring), different SCG parameters have been derived by US and Russian parties for the same material (Russian fused silica) tested in a similar manner. The difference appears to be related to the use of short-term strength data along with the longer-term static fatigue data, with the power law parameter n being very sensitive to the conversion of strength data into equivalent static data. This hybrid approach is feasible if strength data is measured with a constant stress rate and is appropriately converted to a static equivalent. More research into the approach is needed. However, because of the nonlinear behavior of fused silica in log(v) – log(K(I) ) space and the sensitivity of parameter estimation to fit range, the exponential model is a better choice regardless of test method. Functions are given to convert parameters from the Russian model to those in the US model. However, accurate conversion is hampered by the lack of inert strength data. When the same test technique is used, US and Russian materials exhibit very similar parameters.

Silica, strength, impact, crack growth, windows, I↗