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NASA NTRS1993

The importance of properties in modeling

Casting and welding of superalloys, stainless steel and titanium alloys are processes which can be improved through modeling of heat flow, fluid flow, residual stress development, and microstructural evolution. These simulations require inputs of thermophysical data, some of which involves the partially or totally liquid state. In particular, these processes involve melting, flow in the liquid, and solidification. Modeling of such processes can lead to an improved understanding of defects such as shrinkage, inclusions, cracks, incomplete filling (or penetration), macrosegregation, improper grain structure, and deviations from dimensional specifications. Effective modeling can shorten process development time and improve quality. An approach to these problems is to develop efficient models; validate through correlations with thermal, distortion, and microstructural data; run parametric studies; extract knowledge based rules; and apply to adaptive closed loop control systems. With the appropriate pre- and post-processing, such analyses can be made 'user friendly'. This would include graphical user interfaces as well as realistic images and color maps. In such form, these models can be used for sensitivity analyses, which are useful in defining appropriate sensors and in the development of control strategies. Such modeling can be done at several levels, e.g., the MARO level, modeling large scale phenomena such as heat and fluid flow or material deformation; the MICRO level, modeling the development of dendrites, grains or precipitates; or at the NANO level, modeling point defects, dislocations, stacking faults, etc. There are many computational issues associated with these simulations, e.g. computational efficiency and accuracy. In addition, there are many materials issues, not the least of which is the availability of accurate high temperature thermophysical data for complex alloys. This would include latent heat of fusion, temperature dependent heat capacity and thermal conductivity (for liquid and solid), viscosity, surface tension, thermal expansion, mechanical properties, etc. Preliminary data is frequently gathered from the literature; however, this is often not available for modern alloys. If additional data are required, measurements can be used; however, these are costly, time consuming and can be erroneous due to a lack of testing standards or impure materials. Microstructural predictors can be extracted from thermal information, e.g. cooling rate and thermal gradient; the prediction of microstructure is dependent on solidus and liquidus temperature, mushy zone permeability, the solidification curve, volume changes, phase transformations, alloying effects (such as surface tension or viscosity), mold/metal reactions, metal/environment reactions, etc. Defect maps may be needed to predict the onset of shrinkage, hot cracking or 'freckling'. Constants may be needed for stress relaxation, dendrite coarsening, vaporization, etc. Visualization was used as a tool to better comprehend complex data sets associated with the analysis of directional solidification (including crystal growth) and welding. Examples include not only isotherms, but also cooling rate, growth rate and thermal gradient. The latter two are not single valued scalars, but rather time and space dependent vector fields. Efficient models were developed for both casting and welding to predict heat flow and the relationship to dendrite and grain growth. These codes include many of the non-linear effects, e.g. radiation, which dominate these processes. The home-built FDM code(s) were designed to be useful not only to the scientist, but also to the process engineer. Special output can be requested to compare directly to experimental data. Visualization procedures were developed to visualize critical results, e.g. fusion zone width at the surface opposite that where the arc is applied ('penetration'). Both elaborate and simplified distortion analyses were carried out. It is clear that extensive mechanical property data are critical in order to accurately predict residual stress patterns. A scheme is currently being developed to integrate these modeling tools into a set of control algorithms; however, the success of this approach is critically dependent on the availability of accurate high temperature thermophysical data.

Giamei, A. F.↗

NASA NTRS1999

A Study of Direct and Cloud-Mediated Radiative Forcing of Climate Due to Aerosols

The Intergovernmental Panel on Climate Change (IPCC) has reported that in the southeastern US and eastern China, the general greenhouse warming due to anthropogenic gaseous emissions is dominated by the cooling effect of anthropogenic aerosols. To verify this model prediction in eastern China and southeastern US, we analyzed regional patterns of climate changes at 72 stations in eastern China during 1951- 94 (44 years), and at 52 stations in the southeastern US during 1949-94 (46 years) to detect the fingerprint of aerosol radiative forcing. It was found that the mean rates of change of annual mean daily, maximum, minimum temperatures and diurnal temperature range (DTR) in eastern China were 0.8, -0.2, 1.8, and -2.0 C/100 years respectively, while the mean rates of change of annual mean daily, maximum, minimum temperatures and DTR in the southeastern US were -0.2, -0.6, 0.2, and -0.8 C/100 years, respectively. This indicates that the high rate of increase in annual mean minimum temperature in eastern China results in a slightly warming trend of daily temperature, while the high rate of decrease in annual mean maximum temperature and low rate of increase in annual mean minimum temperature lead to the cooling trend of daily temperature in the southeastern US. We found that the warming from the longwave forcing due to both greenhouse gases and aerosols was completely counteracted by the shortwave aerosol forcing in the southeastern US in the past 46 years. A slightly overall warming trend in eastern China is evident; winters have become milder. This finding is explained by hypothesizing that increasing energy usage during the past 44 years has resulted in more coal and biomass burning, thus increasing the emission of absorbing soot and organic aerosols in eastern China. Such emissions, in addition to well-known Asia dust and greenhouse gases, may be responsible for the winter warming trend in eastern China that we have reported here. The sensitivity of aerosol radiative properties to aerosol composition, size distribution, relative humidity (RH) is examined for the following aerosol systems: inorganic and organic ions (Cl-, Br-, NO3 -, SO4 2-, Na+, NH4 +, K+, Ca2+, Mg2+, HCOO-, CH3COO-, CH3CH2COO-, CH3COCOO-, OOCCOO2-, MSA-1); water-insoluble inorganic and organic compounds (elemental carbon, n-alkanes, SiO2, Al2O3, Fe2O3 and other organic compounds). The partial molar refraction method was used to calculate the real part of the refractive index. It was found that the asymmetry factor increased by approximately 48% with the real part varying from 1.40 to 1.65, and the single scattering albedo decreased by 24% with the imaginary part varying from -0.005 to -0.1. The asymmetry factor increased by 5.4 times with the geometric standard deviation varying from 1.2 to 3.0. The radiation transmission is very sensitive to the change in size distribution; other factors are not as significant. To determine the aerosol direct radiative forcing (ADRF), the aerosol optical depth (AOD) values at the three operational wavelengths (415, 500 and 673 nm) were determined at a regionally representative site, namely, Mt. Gibbs (35.78 deg N, 82.29 deg W, elevation 2006 m) in Mt. Mitchell State Park, NC, and a site located in an adjacent valley (Black Mountain, 35.66 deg N, 82.38 deg W, elevation 951 m) in the southeastern US. The two sites are separated horizontally by 10 km and vertically by 1 km. It was found that the representative total AOD values at 500 nm at the valley site for highly polluted (HP), marine (M) and continental (C) air masses were 0.68 +/- 0.33, 0.29 +/- 0.19 and 0.10 +/- 0.04, respectively. A search-graph method was used to retrieve the columnar size distribution (number concentration N, effective radius reff and geometric standard deviation=?g) from the optical depth observations at three operational wavelengths. The ground albedo, single scattering albedo and imaginary part of the refractive index were calculated using a mathematically unique procedure involving a Mie code and a radiative transfer code in conjunction with the retrieved aerosol size distribution, AOD, and diffuse-direct irradiance ratio. It was found that N, r(eff) and sigma(g) were in the ranges of 10 to 1.7 x 10(exp 4)/cubic cm, 0.09 to 0.68 micrometers and 1.12 to 2.95, respectively. The asymmetry factor and single scattering albedo were in the ranges of 0.63 to 0.75 and 0.74 to 0.97 respectively. The ground albedo for the forested terrain and imaginary part of refractive index were found to be in the ranges of 0.06 to 0.29 and 0.005 to 0.051 respectively. On the basis of these aerosol radiative properties obtained at the research sites and computations using the Column Radiation Model (CRM) of National Center of Atmospheric Research (NCAR) Community Climate Model (CCM3), it was found that the average cloud-free 24-hour ADRF values were -13 +/- 8, -8 +/- 3, -33 +/- 16 W/square m for marine, continental, and polluted air masses, respectively. On the assumption that the fractional coverage of clouds is 0.61, it was estimated that the annual mean ADRF was 7 +/- 2 W/square m in the southeastern US. The review with respect to the current knowledge of organic acids shows that aerosol formate and acetate concentrations range from 0.02 to 5.3 nmol/cubic m and from 0.03 to 12.4 nmol/cubic m respectively, and that between 34% to 77% of formate and between 21% to 66% of acetate are present in the fine fraction of aerosols. It was found that although most (98-99%) of these volatile organic acids were present in the gas phase, their concentrations in the aerosol particles were sufficient to make them a good candidate for cloud condensation nuclei (CCN). It is hypothesized that organic acids are at least one of the primary sources of CCN in the atmosphere due to their ubiquitous presence in the troposphere, especially over the continental forested areas. The results of our measurements at Palmer Station, Antarctica show that the daily average CCN concentrations at 0.3% and 1% supersaturations ranged from 0.3 to 160/cubic cm and from 4 to 168/cubic cm, respectively, during the period from 17 January to 26 February, 1994. New evidence for substantial and definitive CCN enhancement near and within cloud has been observed at Mt. Mitchell, North Carolina. The results show that the average monthly CCN concentrations were 460 +/- 217, 386 +/- 286, 429 +/- 228 and 238 +/- 134/cubic cm for in-cloud, overcast, clear and rainy conditions, respectively. The typical CCN spectra show that there were a lot of small CCN produced and the ion concentrations (especially H+ and SO4 2-) were very high during the CCN enhancement period. The significantly positive correlation between black carbon (BC) and CCN at 1% supersaturation indicates that a percentage of the BC measured at the site may be in the form of an internal mixture and participated in the formation of CCN.

Yu, Shao-Cai↗

NASA NTRS1994

Attitude stability of a spinning spacecraft during appendage deployment/retraction

The work presented is motivated by the need for a national satellite rescue policy, not the ad hoc policy now in place. In studying different approaches for a national policy, the issue of capture and stabilization of a tumbling spacecraft must be addressed. For a rescue mission involving a tumbling spacecraft, it may be advantageous to have a rescue vehicle which is compact and 'rigid' during the rendezvous/capture phase. After capture, passive stabilization techniques could be utilized as an efficient means of detumbling the resulting system (i.e., both the rescue vehicle and captures spacecraft). Since the rescue vehicle is initially compact and 'rigid,' significant passive stabilization through energy dissipation can only be achieved through the deployment of flexible appendages. Once stabilization is accomplished, retraction of the appendages before maneuvering the system to its final destination may also prove advantageous. It is therefore of paramount interest that we study the effect of appendage deployment/retraction on the attitude stability of a spacecraft. Particular interest should be paid to appendage retraction, since if this process is destabilizing, passive stabilization as proposed may not be useful. Over the past three decades, it has been an 'on-again-off-again affair' with the problem of spacecraft appendage deployment. In most instances, these studies have been numerical simulations of specific spacecraft configurations for which there were specific concerns. The primary focus of these studies was the behavior of the appendage during deployment; the effects of appendage retraction was considered only in one of these studies. What is missing in the literature is a thorough study of the effects of appendage deployment/retraction on the attitude stability of a spacecraft. This paper presents a rigorous analysis of the stability of a spinning spacecraft during the deployment or the retraction of an appendage. The analysis is simplified such that meaningful insights into the problem can be inferred; it is not overly simplified such that critical dynamical behavior is neglected. The system is analyzed assuming that the spacecraft hub is rigid. The appendage deployment mechanism is modeled as a point mass on a massless rod whose length undergoes prescribed changes. Simplified flexibility effects of the appendage are included. The system is examined for stability by linearizing the equations in terms of small deviations from steady, noninterfering coning motion. Routh's procedure for analyzing small deviations from steady motion in dynamical systems is utilized in the analysis. The system of equations are nondimensionalized to facilitate parametric studies. The results are presented in terms of a reduced number of nondimensional parameters so that some general conclusions may be drawn. Verification of the linear analysis is presented through numerical simulations of the complete nonlinear, nonautonomous, coupled equations.

Fitz-Coy, Norman↗

NASA NTRS2002

Crystal Growth of ZnSe and Related Ternary Compound Semiconductors by Vapor Transport

The objective of the project is to determine the relative contributions of gravity-driven fluid flows to the compositional distribution, incorporation of impurities and defects, and deviation from stoichiometry observed in the crystals grown by vapor transport as results of buoyance-driven convection and growth interface fluctuations caused by irregular fluid-flows. ZnSe and related ternary compounds, such as ZnSeS and ZnSeTe, were grown by vapor transport technique with real time in-situ non-invasive monitoring techniques. The grown crystals were characterized extensively to correlate the grown crystal properties with the growth conditions. The following are the research progress in the past two years. In-situ monitoring of partial pressure by optical absorption technique and visual observation of the growing crystal were performed during vapor growth of ZnSe. Low-temperature photoluminescence (PL) spectra and glow discharge mass spectroscopy (GDMS) were measured on ZnSe starting materials provided by various vendors and on bulk crystals grown from these starting materials by physical vapor transport (PVT) to study the effects of purification and contamination during crystal growth process. Optical characterization was performed on wafers sliced from the grown crystals of ZnSe, ZnTe and ZnSe(1-x),Te(x), (0<x<0.4). Energy band gaps at room temperature were determined from optical transmission measurements and a best fit curve to the band gap vs. composition, x, data gives a bowing parameter of 1.45. Low-temperature photoluminescence (PL) spectra of ZnSe and ZnTe were dominated by near band edge emissions and no deep donor-acceptor pairs were observed. The PL spectrum exhibited a broad emission for the ZnSe(1-x),Te(x), samples, 0.09<x<0.39. The single broad PL emission spectra and the spectra measured as a function of temperature were interpreted as being associated with the exciton bound to Te clusters because of the high Te content in these samples. To validate numerical codes, in-situ monitoring during the PVT of HgI2 was designed. Optical absorption spectra of the vapor phase over HgI2 were measured for wavelengths between 200 and 600nm at sample temperatures between 349 and 610K. The Beer's Law constants for 15 wavelengths between 200 and 440 nm were determined. From these constants the vapor pressure of HgI2 was established as a function of temperature for the liquid and the solid Beta-phases To characterize the growth conditions during the PVT growth of In-doped ZnSe the optical absorbance of the vapor phase over the In-Se system were measured and were used to obtain the partial pressures of Se2(g) and In2Se(g).

Su, Ching-Hua↗

NASA NTRS2015

Designing an Exploration Atmosphere Prebreathe Protocol

Extravehicular activities (EVAs) at remote locations must maximize limited resources such as oxygen (O2) and also minimize the risk of decompression sickness (DCS). A proposed remote denitrogenation (prebreathe) protocol requires astronauts to live in a mildly hypoxic atmosphere at 8.2 psia while periodically performing EVAs at 4.3 psia. Empirical data are required to confirm that the protocol meets the current accept requirements: less than or equal to 15% incidence of Type I DCS, less than or equal to 20% incidence of Grade IV venous gas emboli (VGE), both at 95% statistical confidence, with no Type II DCS symptom during the validation trial. METHODS: A repeated measures statistical design is proposed in which groups of 6 subjects with physical characteristics similar to active-duty astronauts would first become equilibrated to an 8.2 psia atmosphere in a hypobaric chamber containing 34% O2 and 66% N2, over 48 h, and then perform 4 simulated EVAs at 4.3 psia over the next 9 days. In the equilibration phase, subjects undergo a 3-h 100% O2 mask prebreathe prior to and during a 5-min ascent to 8.2 psia to prevent significant tissue N2 supersaturation on reaching 8.2 psia. Masks would be removed once 34% O2 is established at 8.2 psia, and subjects would then equilibrate to this atmosphere for 48 h. The hypoxia is equivalent to breathing air at 1,220 meters (4,000 ft) altitude, just as was experienced in the shuttle 10.2 psia - 26.5% O2 staged denitrogenation protocol and the current ISS campout denitrogenation protocol. For simulated EVAs, each subject dons a mask and breathes 85% O2 and 15% N2 during a 3-min depressurization to 6.0 psia, holds for 15 min, and then completes a 3-min depressurization to 4.3 psia. The simulated EVA period starts when 6.0 psia is reached and continues for a total of 240 min (222 min at 4.3 psia). During this time, subjects will follow a prescribed repetitive activity against loads in the upper and lower body with mean metabolic rate approaching 1500 BTU/hr [378 kcal/hr (O2 consumption about 1.3 l(sub STPD)/min)] in ambulatory subjects. Noninvasive Doppler ultrasound bubble monitoring for VGE in the pulmonary artery will be performed on subjects by 2 Doppler Technicians at about 15 min intervals while at 4.3 psia. At the end of this period, a 15-min repressurization returns all subjects back to 8.2 psia and the cycle is repeated 3 additional times with a day of rest between simulated EVAs. RESULTS: With an assumed 1.5% probability of DCS [P(DCS)] and accounting for within-subject correlation, running the proposed study with 20 subjects has a 95% probability of meeting the accept criterion for DCS. But if the true probability of DCS is 3.0%, then 30 subjects would be needed to achieve about the same probability to meet our accept criterion. These results assume a standard deviation of 1.4 for the between-subjects random component of P(DCS) on a logit scale, which was estimated from a previous study.

Conkin, Johnny↗

NASA NTRSDate not supplied

Decision Aid for Conjunction Risk Mitigation by Differential Drag

In the previous five years, the rate of conjunctions that the NASA Conjunction Assessment Risk Analysis (CARA) team processed and analyzed has more than tripled. (NASA CARA, 2024) New missions in the early development phases are now required to plan for dealing with conjunctions under the present space environment, and also projecting forward into a future likely with even further increased utilization of the space environment. Some missions are investigating the possibility of using differential drag to remediate conjunctions without expending limited fuel or for missions without propulsive capabilities. The NASA CARA team studied the historical record of conjunctions to evaluate the circumstances under which differential drag may be successfully applied and have developed a series of tables to use as a decision aid for missions considering differential drag. Currently, if a CARA-protected mission with maneuvering capabilities is predicted to have a conjunction with probability of collision (Pc) greater than 7E-5 (the default value of the ‘yellow threshold’, which may have some other value agreed by CARA and the mission during the Orbital Collison Avoidance Planning (OCAP) process), CARA will use its Maneuver Trade Space (MTS) tool to evaluate and recommend options for the timing and magnitude of a risk mitigation maneuver (RMM), based on the mission’s capabilities. If the conjunction’s Pc is greater than 1E-4, the ‘red threshold’, then an RMM must be executed per NASA Procedural Requirements (NPR) 8079.1 (NASA, 2023), although missions may execute an RMM even if the Pc is lower. The magnitude of the maneuver is typically a few cm/s, and CARA estimates how many will be required for the mission’s nominal lifetime – typically a few per year – during the OCAP process, to inform the mission’s delta-V requirement. For traditional satellites, this is usually smaller than other requirements for orbit insertion, maintenance, and disposal, but for CubeSats or other small satellite missions, a propulsion system may not provide much more than a few cm/s of delta-V or may not fit at all within the available budget of money, time, size, weight, and/or power (SWaP). Conversely, CubeSats often have deployable solar panels, which offer the capacity to have much higher areas facing some directions than others. Such a mission can instead use ‘differential drag’ to remediate a conjunction -- in other words, change its drag area (usually increasing) to deviate from the predicted collision course. This is how Planet’s Dove spacecraft maintain their formations and remediate conjunction risks without having on-board propulsion (Foster, et al., 2017) (Griffith, et al., 2021). CARA has been developing improvements to MTS to support differential-drag for NASA's missions -- where it is effective. CARA records all conjunctions of their protected payloads, with historical records starting in 2005 (with significant conjunction events starting to occur on or after 2013). From this record, approximately 7,300 had a Pc greater than 1E-4 at 3 days prior to the time of closest approach, the time analyzed for differential drag efficacy. Of those, approximately 4,300 had fully-defined covariance matrices stored for both the primary and secondary objects; this set of conjunctions is the basis for the analysis of this work. CARA’s MTS tool was used to propagate the primary satellite forward from that decision point with varying degrees of increase to ballistic coefficient (BC). Because these conjunctions came from multiple missions, the nondimensional ‘delta-BC’ factor was used to quantify and normalize the increase in ballistic coefficient, defined as follows: Delta-BC = BC_new / BC_old - 1 Positive delta-BC factors represent an increase in drag compared to the nominal attitude, while negative delta-BC factors (to a minimum of -1) represent a decrease in drag. At the conclusion of the differential-drag ‘maneuver’, the Pc was recalculated to evaluate whether or not the conjunction was mitigated (Pc < 3E-6). These results were then binned and sorted along several axes, including altitude, amount of delta-BC, and (pre-maneuver) rate of energy dissipation (EDR), to identify underlying patterns. The altitude plot is shown in Figure 1. To validate this analysis, we consulted the record of a NASA mission which uses differential drag to maintain its orbit and remediate conjunction risk. CARA’s empirical record of the mission’s orbit history suggests it achieves a delta-BC of 2.2. Of the twenty-one RMM plans that were submitted by this mission, eighteen were matched with conjunctions in the historical record; of those, twelve were successfully remediated (final measured Pc < 3E-6), and six were not. This is consistent with the expected efficacy for missions orbiting at that altitude. We are presently simulating this mission’s RMMs with MTS; this work is ongoing, but so far, the MTS results are qualitatively in agreement with the empirical results -- correctly predicting that a maneuver would or would not remediate a conjunction, if not exactly matching the final post-remediation Pc value. We found that differential drag was most successful for satellites with perigees below 560 km, and which could adopt an average delta-BC of 2 or greater (that is, increasing their ballistic coefficient by a factor of 3). However, this is a difficult threshold for a mission to clear; very few spacecraft are capable of adopting a high-drag configuration for 72 hours continuously. Planet’s Dove spacecraft use differential drag to remediate conjunctions (Griffith, et al., 2021), and they have a maximum delta-BC factor of 9, but in practice (with mission and charging constraints) they achieve a time-averaged delta-BC that is closer to 2 (Foster, et al., 2017). A mission’s differential drag utility strongly depends on the operational constraints that has the capacity to limit the time-averaged delta-BC. A mission with a high maximum delta-BC of 5 or more can have an effective delta-BC of less than 1 due to operational constraints such as instrument and solar panel pointing, especially if this constraint results in holding an intermediate drag value for most of its orbit. CARA has developed tables that can be used as decision aids to advise missions-in-development about the best way to utilize their differential drag capabilities. For missions below 560 km with the operational flexibility to devote multiple days to holding a high-drag configuration (or a sufficiently high drag ratio to compensate for limitations on that time), they are -- more likely than not -- able to successfully remediate high-risk conjunctions. Conversely, missions that do not meet these exacting criteria -- most missions -- can instead be advised to use on-board propulsion systems to perform RMMs, or to turn their minimum-area face towards the approach vector, thereby reducing Pc at the moment of conjunction due to the decreased Hard-Body Radius (HBR), that is a strongly correlated variable in the Pc calculations. (NASA, 2023)

conjunction assessment↗

NASA NTRS2016

Soil Moisture Active Passive Mission L4_SM Data Product Assessment (Version 2 Validated Release)

During the post-launch SMAP calibration and validation (Cal/Val) phase there are two objectives for each science data product team: 1) calibrate, verify, and improve the performance of the science algorithm, and 2) validate the accuracy of the science data product as specified in the science requirements and according to the Cal/Val schedule. This report provides an assessment of the SMAP Level 4 Surface and Root Zone Soil Moisture Passive (L4_SM) product specifically for the product's public Version 2 validated release scheduled for 29 April 2016. The assessment of the Version 2 L4_SM data product includes comparisons of SMAP L4_SM soil moisture estimates with in situ soil moisture observations from core validation sites and sparse networks. The assessment further includes a global evaluation of the internal diagnostics from the ensemble-based data assimilation system that is used to generate the L4_SM product. This evaluation focuses on the statistics of the observation-minus-forecast (O-F) residuals and the analysis increments. Together, the core validation site comparisons and the statistics of the assimilation diagnostics are considered primary validation methodologies for the L4_SM product. Comparisons against in situ measurements from regional-scale sparse networks are considered a secondary validation methodology because such in situ measurements are subject to up-scaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the wide geographic range of the sparse network sites, and the global assessment of the assimilation diagnostics, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 2 validation and supports the validated release of the data. An analysis of the time average surface and root zone soil moisture shows that the global pattern of arid and humid regions are captured by the L4_SM estimates. Results from the core validation site comparisons indicate that "Version 2" of the L4_SM data product meets the self-imposed L4_SM accuracy requirement, which is formulated in terms of the ubRMSE: the RMSE (Root Mean Square Error) after removal of the long-term mean difference. The overall ubRMSE of the 3-hourly L4_SM surface soil moisture at the 9 km scale is 0.035 cubic meters per cubic meter requirement. The corresponding ubRMSE for L4_SM root zone soil moisture is 0.024 cubic meters per cubic meter requirement. Both of these metrics are comfortably below the 0.04 cubic meters per cubic meter requirement. The L4_SM estimates are an improvement over estimates from a model-only SMAP Nature Run version 4 (NRv4), which demonstrates the beneficial impact of the SMAP brightness temperature data. L4_SM surface soil moisture estimates are consistently more skillful than NRv4 estimates, although not by a statistically significant margin. The lack of statistical significance is not surprising given the limited data record available to date. Root zone soil moisture estimates from L4_SM and NRv4 have similar skill. Results from comparisons of the L4_SM product to in situ measurements from nearly 400 sparse network sites corroborate the core validation site results. The instantaneous soil moisture and soil temperature analysis increments are within a reasonable range and result in spatially smooth soil moisture analyses. The O-F residuals exhibit only small biases on the order of 1-3 degrees Kelvin between the (re-scaled) SMAP brightness temperature observations and the L4_SM model forecast, which indicates that the assimilation system is largely unbiased. The spatially averaged time series standard deviation of the O-F residuals is 5.9 degrees Kelvin, which reduces to 4.0 degrees Kelvin for the observation-minus-analysis (O-A) residuals, reflecting the impact of the SMAP observations on the L4_SM system. Averaged globally, the time series standard deviation of the normalized O-F residuals is close to unity, which would suggest that the magnitude of the modeled errors approximately reflects that of the actual errors. The assessment report also notes several limitations of the "Version 2" L4_SM data product and science algorithm calibration that will be addressed in future releases. Regionally, the time series standard deviation of the normalized O-F residuals deviates considerably from unity, which indicates that the L4_SM assimilation algorithm either over- or under-estimates the actual errors that are present in the system. Planned improvements include revised land model parameters, revised error parameters for the land model and the assimilated SMAP observations, and revised surface meteorological forcing data for the operational period and underlying climatological data. Moreover, a refined analysis of the impact of SMAP observations will be facilitated by the construction of additional variants of the model-only reference data. Nevertheless, the “Version 2” validated release of the L4_SM product is sufficiently mature and of adequate quality for distribution to and use by the larger science and application communities.

SMAP L4_SM↗

NASA NTRS2021

Understanding the Space Weathering of Mercury Through Laboratory Experiments

Introduction: Airless surfaces across the solar system are continually modified by energetic particles from solar wind and micrometeoroid bombardment [1,2]. This process is known as space weathering, and it alters the chemical, microstructural, and optical properties of surface regoliths on airless bodies, including Mercury. On the Moon and S-type asteroids, the reflectance spectral signatures of space weathering include reddening (increasing reflectance with increasing wavelength), darkening (lowering of reflectance), and the attenuation of characteristic absorption bands [2]. Such spectral changes are driven by the production of Fe-bearing nanoparticles(npFe) through both solar wind irradiation and micrometeoroid bombardment. While our understanding of space weathering for the Moon and near-Earth S-types asteroids is advanced, insight into how these processes operate on other planetary bodies is limited. In particular, Mercury experiences a uniquely intense space weathering environment than planetary counterparts at 1 AU, including a moreintense solar wind flux and higher velocity micrometeoroid impacts [4]. Additionally, Mercury has a surface composition unique in the inner solar system, including regions of the surface with very low albedo known as the low reflectance material (LRM), which is enriched in carbon, likely graphite, up to 4wt.% [5].In addition, the concentration of Fe across Mercury’s surface islow (<2 wt.%) compared to the Moonor S-type asteroids asteroids[6]. Our understanding of the effects of space weathering on C-rich and Fe-poor phases is limited. Since Fe plays a critical role inthe development of space weathering characteristicson other airless surfaces(e.g., npFe), its limited availability may significantly affect the development of space weathering features in Mercury surface materials. We can simulate space weathering processes in the laboratory to explore their effects on the microstructural, chemical, and spectral characteristics of Mercury surface materials[7]. Here we used pulsed laser irradiation to simulate the short duration, high-temperature events associated with micrometeoroid impacts. We performed coordinated analyses including reflectance spectroscopy and electron microscopy to investigate the spectral, chemical, and microstructural changes in these mercurian analog samples. Methods: For these experiments, we usedforsteritic olivine with varying FeOcontents, a mineral phase proposed to be abundant on the surface of Mercury. We mixed each sample with graphite to simulate LRM regions of the surface. Wesynthesized the olivinesamplesat 1-bar at NASA’s Johnson Space Centerand prepared pressed powder pellets for laser irradiation [8].We prepared three samples, each with a base layer of olivineto maintain structural integrity and topped witha surface layer containing the graphite-olivine mixture: 1) Sample SC-001 San Carlos olivine(Fo90.91),2)Sample F-S-002 with0.05 wt.% FeO olivine, and 3) F-T-004 with 0.53 wt.% FeO olivine. Each sample was mixed with 5 wt.% powdered graphite and had grain sizes ranging from 45to 125μm. We irradiated each sample usinga pulsed Nd-YAG laser, (l=1064 nm, ~6 ns pulse duration, energy of 48 mJ/pulse) while undervacuumat Northern Arizona University. The laser was rastered1x and then 5x over the surfaceof each sample to simulate progressive space weathering. We collected in situreflectance spectra from the samples after each laser pulse witha Nicolet IS50 Fourier-Transform Infrared spectrometer (lfrom 0.65-2.5 μm). We used an FEI Nova NanoSEM200scanning electron microscope (SEM) and a Hitachi TM4000 Plus benchtop SEM at Purdue University to image the surface morphology and topography of the samples. We extracted thin sections for analysis in the transmission electron microscope(TEM)using the FEI Helios NanoLab 660 focused ion beam (FIB) SEM at the University of Arizona.We performed analysis of the microstructural and chemical characteristics of the samples using the 200 keV JEOL 2500 scanning TEM at Johnson Space Center. Reflectance Spectroscopy Results:Reflectance spectra for each sample are shown in Fig. 1.SC-001:The spectrum of the unirradiated sample exhibits a weak 1.0 μm absorption feature, associated with Fe2+in the olivine,and low overall reflectance (Fig. 1a). Thereflectance and the depth of the absorption band increases after 1x laser raster but are at their lowest after 5x laser rasters.F-T-004:The unirradiated sample has a blue-sloped spectrum with low reflectance without identifiable absorption features (Fig. 1b). With progressive laser irradiation, the sample reflectance increases and becomes strongly red-sloped.F-S-002:The unirradiated sample exhibits a dark, blue-slopedspectrum. The brightness of the sample increases significantly from <0.2average reflectance over >0.8 reflectance in the most irradiated sampleand thespectral slope also becomesslightly reddened (Fig. 1c). Microstructural and Chemical Analysis: Two primary alteration textures were observed in the samples exposed to simulated space weathering: 1) fluffy C-rich,and 2) vesiculated melt. The fluffy C-rich texture is composed oflow-densitydeposits distributed across the surface of the sample(Fig. 2A). Analysis of a FIB section extracted from a low-density C-rich region in sample SC-001 reveals multiple globule-type deposits, discrete from stacked graphite, likely produced via melting from the laser irradiation [9].The vesiculated melt textureis smooth and uniformly distributed across isolated regions of the sample surface. The vesicles measure up to 100s of nmin diameter. Analysis of a FIB section from this texture was extracted from sample F-T-004 reveals a layer of amorphous melt material, close to 100 nm thick and uniform across the FIB section (Fig. 2B). Isolated regions of this melt layer contain small nanoparticles, <5 nm in diameter. Chemical analysis through energy dispersive X-ray spectroscopy reveals the composition of this layer is enriched in Si and depleted in Mg and O compared to the underlying sample. Implications for Space Weathering on Mercury: Previous experiments simulating space weathering of Mercury have showndarkening and reddening of spectra[7,10].However, our use of low-Fe materials and graphite to create a sample set more analogous to the mercurian surface. Our results indicate that sample composition plays a significant and important role in the space weathering of Mercury. In particular, our spectral data demonstrates a strong correlation between spectral slope, Fe content, and simulated space weathering. While the variation in FeO content between samples F-S-002 and F-T-004 is <0.6 wt.%, the spectra deviate from flat to strongly red-sloped(F-T-004). This reddeningmay be linked to the presence of very small nanoparticles observed in the melt textures extracted from sample F-T-004. For the SC-001 sample, the fluffy C-rich textures may be developed by the amalgamation of small graphite particles into these unique morphologies. Such observations indicate that space weathering on Mercury may result in both familiar and new microstructural and chemical characteristics. References: [1]Hapke B. (2001) J. Geophys. Res.-Planet.,106,10039–10073. [2]Pieters C.M. and Noble S.K. (2016) J. Geophys. Res-Planet., 121, 1865–1884. [3] Lucey P.G., and Riner, M.A. (2011) Icarus,212, 451-462.[4]CintalaM.J.(1992)J. Geophys. Res.-Planet.,97,947–973.[5]Klima R.L.et al.(2018)Geophys.Res.Letters, 45, 2945–2953. [6]Nittler L.R., et al. (2011) Science 333, 1847-1850.[7]Sasaki S. and Kurahashi E. (2004) Space weathering on Mercury, Adv.Space Res., 33, 2152-2155.[8] Vander KaadenK.E., et al. (2018) LPSCXLIX, Abstract 1230. [9] McGlaun M.L. et al. (2019) LPSCL, Abstract 2019. [10] TrangD.et al. (2018)LPSCXLIX,Abstract2083

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Monitoring Airspace Complexity and Determining Contributing Factors

The national airspace has evolved over many years to accommodate increased traffic demand [1] while simultaneously maintaining one of the safest forms of transportation [2], [3]. One of the reasons for this success is the ability of the system and the operators to adapt and accommodate to situations that routinely disrupt optimal operations. These situations may include: adverse weather, delays, early arrivals, equipment outages, and other factors that are outside the operators ability to control. These factors can lead to states where automation is unable to properly handle these issues and therefore air traffic controllers and pilots have to intervene, ultimately increasing communication between operators resulting in higher workload. As controller workload increases to handle sub-optimal operating conditions this can be viewed as an increase in complexity. The reasoning for this is because humans are now required to make tactical decisions in response to external factors, resulting in a departure from the strategic plan where operations would be more efficiently managed. Human operators control airspace complexity under rigid regulations that are constantly changing. The airspace is divided into sectors and the number of aircraft assigned to each controller is limited for safe handling. There has been past work that devised airspace complexity metrics in commercial aviation and related these metrics to controller workload (e.g., [4],[5]). The upper bounds on the system load are pre-determined. Such bounds on complexity make for a safe system, but the system cannot scale and adapt to autonomous, dense, and heterogeneous traffic, including the many types of Unmanned Aerial Vehicles (UAVs) envisioned to be added to the operations. We hypothesize that, as traffic density and heterogeneity grow, and other key metrics change, there will be phase transitions at which the way traffic should be managed changes significantly [6]. We offer a method for in-time detection of contributing factors that lead to phase transitions, characterized by increased complexity. To the best of our knowledge, there is no tool similar to our proposed effort that identifies such contributing factors or precursor patterns. To define the scope we are proposing to measure complexity from the viewpoint of the Terminal Radar Approach Control Facilities (TRACON) controller’s perspective. In particular we are analyzing arrivals into KSFO. With safety as the top concern for airspace operators, it is important to recognize that as density and heterogeneity grow, the focus of the system will change. Times of the day when the airspace has low density and heterogeneity, the flights will follow more efficient paths where the aircraft move on established routes that are more or less directly to the destination. However, when density and heterogeneity increases, the system will begin changing focus to avoiding conflicts and collisions and route the flights in a more flexible way. Higher flexibility requires more communication and coordination between controllers and pilots which the current automation is unable to handle. This paper proposes a novel approach that monitors airspace complexity at multiple scales, uses a Machine Learning-based tool that predicts when operations will transition to a regime of greater complexity, and identifies actions that can reduce the complexity while still maintaining efficient and safe operations. We demonstrate our proposed approach using data from multiple complementary sources. This includes, but is not limited to: historical aircraft surveillance data from NASA’s Sherlock Data Warehouse [7], METAR weather data, and airport configuration data from Aviation System Performance Metrics (ASPM). The surveillance data flight paths are sampled at a variable sample rate — increasing as the aircraft approaches the airport. This is due to how Sherlock manages flight track stitching between different radar facilities which have different sampling rates. The weather and performance data are logged at defined intervals throughout the day at a courser refresh rate. In addition to the logged data and metrics, we leverage pre-defined Standard Terminal Arrival Routes (STARs) procedures to characterize the path of each flight. Each flight files for one of these routes in the flight plan well before entering the terminal airspace, and approximately follows the route until it leaves the STAR, typically on the final fix of a runway transition. However, most flights do not always fly the full STAR procedure to completion [8], but the majority do adhere to the fixes within the common route of the procedure. Our approach leverages fixes in the common route of each of the STARs to build a reference path to the airport. This allows us to characterize the flight paths in what we are defining as the “maneuvering area” (the airspace between the STAR and before the flight is lined up on the runway’s final approach) to determine how off nominal the flights are to calculate its complexity score. Determining airspace complexity is a concept that does not have a concrete answer. In designing this metric, we consider what increases the workload for the air traffic controllers. Consequently more specialized vectoring maneuvers results in higher workload. Accordingly, we start with a theory: each flight has a direct path it takes from the STAR’s common route to the final approach’s outer marker fix for the flight’s landing runway. It is important to note that the direct path is only used as a reference. If the majority of the flights have a large consistent offset as compared to other routes it does not necessarily mean that those flights have higher complexity. We are merely building a distribution based on this direct path for that particular STAR and runway pair to determine the normal mode of operations for that route. Flights that are in the upper tail of these distributions will result in higher complexity scores and flights that fly in the median will represent the normal mode of operations and therefore will have lower complexity scores. Since flights following each STAR route take different paths to the airport, we have a different distribution for each STAR route and therefore can model these distributions to compute a complexity score from their respective normalized distributions. To evaluate the effectiveness of our proposed airspace complexity metric we will compare against an established approach based on trajectory clustering [9]. This unsupervised learning technique consists of the following steps: (1) identify the general maneuvering areas (waypoints) by performing $\kappa$-means or DBSCAN clustering on locations where aircraft frequently turn based on the surveillance radar track data, (2) map flight trajectories onto sequences of waypoints, and (3) cluster the sequences based on their common subsequences. From a high-level perspective, this baseline model learns nominal operations in the airspace through the sequence of waypoints that are representative of where aircraft change direction and defines deviations from the nominal operations as “complex.” Therefore, more deviations from the nominal operations correspond to higher complexity values. For our validation, we re-implemented this technique and tune model hyper-parameters to correctly detect waypoints for the arrival traffic into the San Francisco bay area. We will compute the complexity measure over a one-year period using our proposed technique as well as the baseline. Our validation will be based on each technique’s ability to detect a set of undesirable outcomes (e.g., go-arounds, holding patterns, average time in the airspace, etc.). Since our current complexity metric is derived from the offset from the direct reference path, it’s important to understand what causes these offsets. In many of the flights with high offset distance, flights performing holding patterns and S turns can be observed. These maneuvering tactics are utilized to add distance between the aircraft and the destination runway to prevent multiple flights from having conflicting arrival times. In order to predict a rise in complexity (or the precursor to complexity), it’s necessary to be able to identify these potential conflicts (which in turn, result in higher offsets). To do this, we define a “representative flight” for each STAR route and runway pair. This flight is approximately the path the flight would take if there was a clear path with no other flights in the airspace — including the time remaining to the airport. We first identify the flights for a given STAR runway pair using the offset to the reference path distributions that fall between the 44-55 percentiles. This yields the flights that conform to the most normal mode of operation. Each of these flights is partitioned based on the percent complete from the entry point into the maneuvering areas from 0\% – 100\% complete. Then for each percent “bin”, we take the median value of the flight’s latitude/longitude coordinates, airspeed, and (non causal) time remaining to the airport to construct a lookup table for each percent complete bin on a given route. As a flight enters the maneuvering area, we can find the estimated arrival time of a flight to the airport by finding the closest point to the representative path’s percent complete bin (relative to the flight’s current position at any snapshot in the airspace) and therefore retrieve the corresponding remaining time left on the “representative path”. We assume that the flight will follow the representative path to completion when deriving these estimates. We can then compare these estimated arrival times against other flights for the same snapshot in time to identify potential conflicts. If more flights are estimated to arrive within a tolerance window than there are runways available, then we have a potential conflict. We can use this derived measure along with other factors expected to add disruption to the operation such as weather and runway configuration changes as an input to machine learning tools to detect precursors that increases in our complexity measure. This novel method will assist in uncovering insights into the contributing factors that lead to increased complexity that may allow for in-time responses to avoid reaching a high complexity state in the airspace.

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