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All sourcesNASA NTRSDOE OSTI
NASA NTRS1999

SPARCLE Optical System Design and Operational Characteristics

The SPAce Readiness Coherent Lidar Experiment (SPARCLE) is the first demonstration of a coherent Doppler wind lidar in space. SPARCLE will be flown aboard a space shuttle In the middle part of 2001 as a stepping stone towards the development and deployment of a long-life-time operational instrument in the later part of next decade. SPARCLE is an ambitious project that is intended to evaluate the suitability of coherent lidar for wind measurements, demonstrate the maturity of the technology for space application, and provide a useable data set for model development and validation. This paper describes the SPARCLE's optical system design, fabrication methods, assembly and alignment techniques, and its anticipated operational characteristics. Coherent detection is highly sensitive to aberrations in the signal phase front, and to relative alignment between the signal and the local oscillator beams. Consequently, the performance of coherent lidars is usually limited by the optical quality of the transmitter/receiver optical system. For SPARCLE having a relatively large aperture (25 cm) and a very long operating range (400 km), compared to the previously developed 2-micron coherent lidars, the optical performance requirements are even more stringent. In addition with stringent performance requirements, the physical and environment constraints associated with this instrument further challenge the limit of optical fabrication technologies.

Amzajerdian, Farzin↗
NASA NTRS2003

A Computational Study of the Abrupt Wing Stall (AWS) Characteristics for Various Fighter Jets: F/A-18E and F-16C - Part 1

Steady state Computational Fluid Dynamic (CFD) simulations are used to gain an understanding of the physics behind the Abrupt Wing Stall (AWS) phenomenon and to arrive at static Figures of Merit (FOMs). Navier-Stokes simulations are conducted using the NASA Langley developed TetrUSS simulation suite which is based on tetrahedral, unstructured grids. The physics of the AWS phenomenon is understood by comparing CFD simulation results on two aircraft; a pre-production F/A-18E configuration which exhibits AWS phenomenon under certain geometric and flow conditions, and an F-16C aircraft configuration that does not. The CFD code is first validated against two sets of experimental data to build confidence in its use for the problem of AWS. An attempt is then made to understand the possible causes of AWS by analyzing and comparing the detailed flow fields between the two configurations under a variety of flow conditions. Based on this approach, a number of static Figures of Merit are developed to predict the potential existence of AWS. The FOMs include the break in the lift and wing root bending moment versus angle-of-attack (AoA) curves and the rate of change of sectional lift with respect to AoA. A companion paper, in Part II, describes a similar CFD study on two other aircraft, the AV-8B Harrier and F/A-18C. Results from both these studies, as well as other CFD studies conducted as part of the AWS program are used to recommend a CFD procedure for predicting the existence of AWS in future aerospace designs.

Parikh, Paresh↗
NASA NTRS2005

Microgravity Experiments on Bubble Removal in the Hydrodynamic Focusing Bioreactor - Space (HFB-S)

The Hydrodynamic Focusing Bioreactor-Space (HFB-S) is being developed as a possible replacement for the Rotating Wall Perfused Vessel (RWPV) bioreactor currently planned for use on the International Space Station (ISS). The HFB-S is being developed with the ability to remove gas bubbles that may inadvertently enter the system during long duration experiments (approx. 1-3 months). The RWPV has been used in the past with great success on Shuttle flights and Mir missions, but has occasionally experienced problems with gas bubbles entering the fluid-filled vessel. These bubbles are harmful to the cell science, and bubble removal in the RWPV is problematic. The HFB-S has an access port on the rotation axis that allows for bubble removal under specific operating conditions without detrimentally affecting the cell tissue. Experiments on bubble removal with the HFB-S were conducted in the microgravity environment on NASA's KC-135 Reduced Gravity Aircraft. The first set of flights provided useful data on bubble trajectories that are validating computational predictions. The second set of flights free-floated the apparatus and tested the most recent configuration of the bioreactor while focusing on the bubble removal process itself. These experiments have shown that gas bubbles can successfully be driven to the removal port and purged in microgravity. The last day's experiments had an excellent microgravity environment due to calm air, and the experience gained in previous flights allowed successful bubble removal 18 out of 35 tries, remarkable given the microgravity time constraints and g-jitter on the KC-135.

Nahra, H. K.↗
NASA NTRS2006

Local Heat Flux Measurements with Single and Small Multi-element Coaxial Element-Injectors

To support NASA's Vision for Space Exploration mission, the NASA Marshall Space Flight Center conducted a program in 2005 to improve the capability to predict local thermal compatibility and heat transfer in liquid propellant rocket engine combustion devices. The ultimate objective was to predict and hence reduce the local peak heat flux due to injector design, resulting in a significant improvement in overall engine reliability and durability. Such analyses are applicable to combustion devices in booster, upper stage, and in-space engines with regeneratively cooled chamber walls, as well as in small thrust chambers with few elements in the injector. In this program, single and three-element injectors were hot-fire tested with liquid oxygen and gaseous hydrogen propellants at The Pennsylvania State University Cryogenic Combustor Laboratory from May to August 2005. Local heat fluxes were measured in a 1-inch internal diameter heat sink combustion chamber using Medtherm coaxial thermocouples and Gardon heat flux gauges, Injector configurations were tested with both shear coaxial elements and swirl coaxial elements. Both a straight and a scarfed single element swirl injector were tested. This paper includes general descriptions of the experimental hardware, instrumentation, and results of the hot-fire testing for three coaxial shear and swirl elements. Detailed geometry and test results the for shear coax elements has already been published. Detailed test result for the remaining 6 swirl coax element for the will be published in a future JANNAF presentation to provide well-defined data sets for development and model validation.

Jones, Gregg↗
NASA NTRS2010

Individualized Cognitive Modeling for Close-Loop Task Mitigation

An accurate real-time operator functional state assessment makes it possible to perform task management, minimize risks, and improve mission performance. In this paper, we discuss the development of an individualized operator functional state assessment model that identifies states likely leading to operational errors. To address large individual variations, we use two different approaches to build a model for each individual using its data as well as data from subjects with similar responses. If a subject's response is similar to that of the individual of interest in a specific functional state, all the training data from this subject will be used to build the individual model. The individualization methods have been successfully verified and validated with a driving test data set provided by University of Iowa. With the individualized models, the mean squared error can be significantly decreased (by around 20%).

Zhang, Guangfan↗
NASA NTRS2010

Application of the Tor Vergata Scattering Model to L Band Backscatter During the Corn Growth Cycle

At the USDA's Optimizing Production Inputs for Economic and Environmental Enhancement (OPE3) experimental site in Beltsville, Maryland, USA) a field campaign took place throughout the 2002 corn growth cycle from May 10th (emergence of corn crops) to October 2nd (harvest). One of the microwave instruments deployed was the multi-frequency (X-, C- and L-band) quad-polarized (HH, HV, VV, VH) NASA GSFC/George Washington University (GWU) truck mounted radar. During the field campaign, this radar system provided once a week fully polarized C- and L-band (4.75 and 1.6 GHz) backscatter measurements from incidence angle of 15, 35, and 55 degrees. In support of microwave observations, an extensive ground characterization took place, which included measurements of surface roughness, soil moisture, vegetation biomass and morphology. The field conditions during the campaign are characterized by several dry downs with a period of drought in the month of August. Peak biomass the corn canopies was reached on July 24th with a total biomass of approximately 6.5 kg/sq m. This dynamic range in both soil moisture and vegetation conditions within the data set is ideal for the validation of discrete medium vegetation scattering models. In this study, we compare the L band backscatter measurements with simulations by the Tor Vergata model (ferrazzoli and Guerriero 1996). The measured soil moisture, vegetation biomass and most reliably measured vegetation morphological parameters (e.g. number of leaves, number of stems and stem height) were used as input for the Tor Vergata model. The more uncertain model parameters (e.g. surface roughness, leaf thickness) and the stem diameter were optimized using a parameter estimation routine based on the Levenberg-Marquardt algorithm. As cost function for this optimization, the HH and VV polarized backscatter measured and stimulated by the TOR Vergata model for incidence angle of 15, 35, and 55 degrees were used (6 measurements in total). The calibrated Tor Vergata model simulations are in excellent agreement with the measurements of Root Mean Squared Differences (RMSD's) of 0.8, 0.9 and 1.4 dB for incidences of 15, 35 and 55 degrees, respectively. The results from this study that a physically based scattering model with the appropriate parameterization can accurately simulate backscatter measurements and, as such, have the potential of being used for the retrieval of biophysical variables (e.g. soil moisture and vegetation biomass).

Joseph, A. T.↗
NASA NTRS2011

Detecting Abnormal Machine Characteristics in Cloud Infrastructures

In the cloud computing environment resources are accessed as services rather than as a product. Monitoring this system for performance is crucial because of typical pay-peruse packages bought by the users for their jobs. With the huge number of machines currently in the cloud system, it is often extremely difficult for system administrators to keep track of all machines using distributed monitoring programs such as Ganglia1 which lacks system health assessment and summarization capabilities. To overcome this problem, we propose a technique for automated anomaly detection using machine performance data in the cloud. Our algorithm is entirely distributed and runs locally on each computing machine on the cloud in order to rank the machines in order of their anomalous behavior for given jobs. There is no need to centralize any of the performance data for the analysis and at the end of the analysis, our algorithm generates error reports, thereby allowing the system administrators to take corrective actions. Experiments performed on real data sets collected for different jobs validate the fact that our algorithm has a low overhead for tracking anomalous machines in a cloud infrastructure.

Bhaduri, Kanishka↗
NASA NTRS2004

Lessons Learned Study Final Report for the Exploration Systems Mission Directorate

This report is the final product of a 90-day study performed for the Exploration Systems Mission Directorate. The study was to assemble lessons NASA has learned from previous programs that could help the Exploration Systems Mission Directorate pursue the Exploration vision. It focuses on those lessons that should have the greatest significance to the Directorate during the formulation of program and mission plans. The study team reviewed a large number of lessons learned reports and data bases, including the Columbia Accident Investigation Board and Rogers Commission reports on the Shuttle accidents, accident reports from robotic space flight systems, and a number of management reviews by the Defense Sciences Board, Government Accountability Office, and others. The consistency of the lessons, findings, and recommendations validate the adequacy of the data set. In addition to reviewing existing databases, a series of workshops was held at each of the NASA centers and headquarters that included senior managers from the current workforce as well as retirees. The full text of the workshop reports is included in Appendix A. A lessons learned website was opened up to permit current and retired NASA personnel and on-site contractors to input additional lessons as they arise. These new lessons, when of appropriate quality and relevance, will be brought to the attention of managers. The report consists of four parts: Part 1 provides a small set of lessons, called the Executive Lessons Learned, that represent critical lessons that the Exploration Systems Mission Directorate should act on immediately. This set of Executive Lessons and their supporting rationale have been reviewed at length and fully endorsed by a team of distinguished NASA alumni; Part 2 contains a larger set of lessons, called the Selected Lessons Learned, which have been chosen from the lessons database and center workshop reports on the basis of their specific significance and relevance to the near-term work of the Exploration Directorate. These lessons frequently support the Executive lessons but are more general in nature; Part 3 consists of the reports of the center workshops that were conducted as part of this activity. These reports are included in their entirety (approximately 200 pages) in Appendix G and have significance for specific managers; Part 4 consists of the remainder of the lessons that have been selected by this effort and assembled into a database for the use of the Explorations Directorate. The database is archived and hosted in the Lessons Learned Knowledge Network, which provides a flexible search capability using a wide variety of search terms. Finally, a spreadsheet lists databases searched and a bibliography identifies reports that have been reviewed as sources of lessons for this task. NASA has been presented with many learning opportunities. We have conducted numerous programs, some extremely successful and others total failures. Most have been documented with a formal lessons learned activity, but we have not always incorporated these learning opportunities into our normal modes of business. For example, the Robbins Report of 2001 clearly indicates that many project failures of the past two decades were the result of violating well documented best practices, often in direct violation of management instructions and directives. An overarching lesson emerges: that disciplined execution in accordance with proven best practices is the greatest single contributor to a successful program. The Lessons Learned task team offers a sincere hope that the lessons presented herein will be helpful to the Exploration Systems Directorate in charting and executing their course. The success of the Directorate and of NASA in general depends on our collective ability to move forward without having to relearn the lessons of those who have gone before.

Van Laak, Jim↗
NASA NTRS2019

Assessing Dual-Polarization Radar Estimates of Extreme Rainfall During Hurricane Harvey

Hurricane Harvey hit the Texas Gulf Coast as a major hurricane on August 25, 2017 before exiting the state as a tropical storm on August 29, 2017. Left in its wake was historic flooding, with some locations measuring more than 60 inches of rain over a five-day period. The WSR-88D radar (KHGX) maintained operations for the entirety of the event. Rain gauge data from the Harris County Flood Warning System (HCFWS) was used for validation with the full radar data set to retrieve daily and event-total precipitation estimates for the period August 25-29, 2017. The KHGX precipitation estimates were then compared to the HCFWS gauges. Three different hybrid polarimetric rainfall retrievals were used, along with attenuation-based retrieval that employs the radar-observed differential propagation. An advantage of using a attenuation-based retrieval is its immunity to partial beam blockage and calibration errors in reflectivity and differential reflectivity. All of the retrievals are susceptible to changes in the observed Drop Size Distribution (DSD). No in situ DSD data were available over the study area, so changes in the DSD were interpreted by examining the observed radar data. We examined the parameter space of two key values in the attenuation retrieval to test the sensitivity of the rain retrieval. Selecting a value of α=0.015 and β=0.600 and β=0.625 provided the best overall results, relative to the gauges, but more work needs to be done to develop an automated technique to account for changes in the ambient DSD.

David B Wolff↗
NASA NTRS2019

Measured and Simulated Acoustic Signature of a Full-Scale Aircraft with Airframe Noise Reduction Technology Installed

Microphone phased-array and pole-mounted microphone data gathered during the NASA Acoustics Research Measurements flight tests were used to benchmark results from companion full-scale aeroacoustics simulations. Conducted with the lattice Boltzmann solver PowerFLOW®, the simulations predicted the acoustic behavior of various tested aircraft configurations. Emphasis was placed on those flown during the third flight test - a Fowler flap-equipped Gulfstream G-III with and without noise abatement technology on the main landing gear. Direct comparisons between experimental and synthetic microphone phasedarray data were achieved by applying the same processing and deconvolution technique to both sets of data. To extend the validation of the computations to the metric used for noise certification, the Effective Perceived Noise Level, a high-fidelity digital model of the nose landing gear, which was excluded from earlier computations, was developed and integrated into the G-III aircraft geometry. The acoustic study presented here demonstrates that the simulated beamform maps and corresponding integrated farfield spectra accurately predict the locations and strengths of the prominent airframe noise sources present on the G-III aircraft.

Khorrami, Mehdi R.↗
NASA NTRS2022

Investigation of the Drivers and Atmospheric Impacts of Energetic Electron Precipitation

The drivers and atmospheric impacts of energetic electron precipitation are not yet well understood. Further, electron precipitation is often poorly represented in atmospheric modeling. Additional investigations of the drivers and impacts of electron precipitation are needed to improve models and space weather forecasting requirements. To accurately represent the troposphere through the ionosphere in model simulations, it is vital to account for the chemistry accurately. Electron precipitation is a frequent, yet often ignored middle to high latitude forcing that can have dramatic effects on the middle and upper atmosphere. Over the past decade, several electron precipitation data sets have been developed, however, validation has been difficult due to the lack of independent observations of electron fluxes. Additionally, the limited number of satellites making measurements of global magnetospheric wave activity in concert with the resulting electron precipitation restricts our ability to accurately capture the drivers simultaneously with the precipitation. Accurate characterization of the drivers is needed for physics-based magnetosphere modeling. Likewise, accurate precipitating electron fluxes and relative energies are needed to improve our atmospheric modeling studies. Finally, in order to properly validate and improve our current modeling efforts, observations of atmospheric composition are necessary.

Joshua Pettit↗
NASA NTRSDate not supplied

Snow Depth from AMSR-2 Using Multispectral Satellite Data in an Artificial Neural Network

By using diffusion theory and Monte Carlo lidar radiative transfer simulations, Hu et al. (2022b) has derived snow depth from the first-, second- and third-order moments of the lidar backscattering pathlength distribution. Lu et al. (2022) calculated the snow depth by applying the methods to the satellite ICESat-2 lidar measurements over the Arctic sea ice, as well as land surfaces of Northern Hemisphere. In this paper, an artificial neural network (ANN) algorithm, employing several channels from Advanced Microwave Scanning Radiometer 2 (AMSR-2) and the humidity vertical profiles from Global Modeling and Assimilation Office (GMAO) Goddard Earth Observing System for Instrument Teams (GEOS-IT) product, is trained to determine snow depth identified by time and geolocation matched 2019 ICESat-2 snow-depth data during winter months over the Arctic sea ice. The trained ANN snow-depth was applied to 2018 AMSR-2 clear pixel data, although the algorithms perform reasonably well in thinner clouds. The validation data (different from the training set) of ANN snow depth from AMSR-2 showed a good agreement with time matched and co-located snow-depth values from ICESat-2. The bias was near zero, with mean absolute error (MAE) 0.05 cm and a root-mean-square-error (RMSE) 0.08 cm. Prior applying the trained ANN snow depth to AMSR-2 data, a cloud screening algorithm was developed with a similar approach. A separate ANN cloud mask was trained to determine an AMSR-2 pixel is clear or cloudy with time and geolocation matched 2015 CALIOP Vertical Feature Mask (VFM) over Arctic sea ice. The ANN cloud mask from AMSR-2 under-estimated cloud fraction by 3-6% compared to CALIOP . The additional research is needed to conclusively evaluate the ANN cloud mask accuracy. Finally, this paper will lay the foundation for a sustained long-term snowfall and snow-storm monitoring system. The future Cloud Aerosol LIdar for Global scale Observations of the ocean-Land Atmosphere system (CALIGOLA) mission will provide a means to calculate snow depth from the lidar backscattering pathlength distribution, benefiting from the UV, visible and infrared pulses. With the calculated snow depth as the truth one could develop a machine learning algorithm, as it was done in this paper, using a passive microwave instrument available at that time to generate a wide range of snow depth data, covering extensive spatial areas in the cross-orbit direction.

Neural Network↗
NASA NTRS2017

Testing the Accuracy of Data-Driven MHD Simulations of Active Region Evolution

Models for the evolution of the solar coronal magnetic field are vital for understanding solar activity, yet the best measurements of the magnetic field lie at the photosphere, necessitating the development of coronal models which are "data-driven" at the photosphere. We present an investigation to determine the feasibility and accuracy of such methods. Our validation framework uses a simulation of active region (AR) formation, modeling the emergence of magnetic flux from the convection zone to the corona, as a ground-truth data set, to supply both the photospheric information and to perform the validation of the data-driven method. We focus our investigation on how the accuracy of the data-driven model depends on the temporal frequency of the driving data. The Helioseismic and Magnetic Imager on NASA's Solar Dynamics Observatory produces full-disk vector magnetic field measurements at a 12-minute cadence. Using our framework we show that ARs that emerge over 25 hours can be modeled by the data-driving method with only approximately 1 percent error in the free magnetic energy, assuming the photospheric information is specified every 12 minutes. However, for rapidly evolving features, under-sampling of the dynamics at this cadence leads to a strobe effect, generating large electric currents and incorrect coronal morphology and energies. We derive a sampling condition for the driving cadence based on the evolution of these small-scale features, and show that higher-cadence driving can lead to acceptable errors. Future work will investigate the source of errors associated with deriving plasma variables from the photospheric magnetograms as well as other sources of errors, such as reduced resolution, instrument bias, and noise.

Leake, James E.↗
NASA NTRS2016

Reducing Multisensor Satellite Monthly Mean Aerosol Optical Depth Uncertainty: 1. Objective Assessment of Current AERONET Locations

Various space-based sensors have been designed and corresponding algorithms developed to retrieve aerosol optical depth (AOD), the very basic aerosol optical property, yet considerable disagreement still exists across these different satellite data sets. Surface-based observations aim to provide ground truth for validating satellite data; hence, their deployment locations should preferably contain as much spatial information as possible, i.e., high spatial representativeness. Using a novel Ensemble Kalman Filter (EnKF)- based approach, we objectively evaluate the spatial representativeness of current Aerosol Robotic Network (AERONET) sites. Multisensor monthly mean AOD data sets from Moderate Resolution Imaging Spectroradiometer, Multiangle Imaging Spectroradiometer, Sea-viewing Wide Field-of-view Sensor, Ozone Monitoring Instrument, and Polarization and Anisotropy of Reflectances for Atmospheric Sciences coupled with Observations from a Lidar are combined into a 605-member ensemble, and AERONET data are considered as the observations to be assimilated into this ensemble using the EnKF. The assessment is made by comparing the analysis error variance (that has been constrained by ground-based measurements), with the background error variance (based on satellite data alone). Results show that the total uncertainty is reduced by approximately 27% on average and could reach above 50% over certain places. The uncertainty reduction pattern also has distinct seasonal patterns, corresponding to the spatial distribution of seasonally varying aerosol types, such as dust in the spring for Northern Hemisphere and biomass burning in the fall for Southern Hemisphere. Dust and biomass burning sites have the highest spatial representativeness, rural and oceanic sites can also represent moderate spatial information, whereas the representativeness of urban sites is relatively localized. A spatial score ranging from 1 to 3 is assigned to each AERONET site based on the uncertainty reduction, indicating its representativeness level.

aerosol optical depth↗
NASA NTRS1981

A diagnostic model for studying daytime urban air quality trends

A single cell Eulerian photochemical air quality simulation model was developed and validated for selected days of the 1976 St. Louis Regional Air Pollution Study (RAPS) data sets; parameterizations of variables in the model and validation studies using the model are discussed. Good agreement was obtained between measured and modeled concentrations of NO, CO, and NO2 for all days simulated. The maximum concentration of O3 was also predicted well. Predicted species concentrations were relatively insensitive to small variations in CO and NOx emissions and to the concentrations of species which are entrained as the mixed layer rises.

Brewer, D. A.↗
NASA NTRS2015

Technical Report Series on Global Modeling and Data Assimilation: Soil Moisture Active Passive (SMAP) Project Assessment Report for the Beta-Release L4_SM Data Product - Volume 40

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 beta release scheduled for 30 October 2015. The primary objective of the beta release is to allow users to familiarize themselves with the data product before the validated product becomes available. The beta release also allows users to conduct their own assessment of the data and to provide feedback to the L4_SM science data product team. The assessment of the 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 upscaling errors from the point-scale to the grid cell scale of the data product. Based on the limited set of core validation sites, the assessment presented here meets the criteria established by the Committee on Earth Observing Satellites for Stage 1 validation and supports the beta release of the data. The validation against sparse network measurements and the evaluation of the assimilation diagnostics address Stage 2 validation criteria by expanding the assessment to regional and global scales.

ubRMSE↗
NASA NTRS1989

Ozone reference models for the middle atmosphere (new CIRA)

Models of ozone vertical structure were generated that were based on multiple data sets from satellites. The very good absolute accuracy of the individual data sets allowed the data to be directly combined to generate these models. The data used for generation of these models are from some of the most recent satellite measurements over the period 1978 to 1983. A discussion is provided of validation and error analyses of these data sets. Also, inconsistencies in data sets brought about by temporal variations or other factors are indicated. The models cover the pressure range from from 20 to 0.003 mb (25 to 90 km). The models for pressures less than 0.5 mb represent only the day side and are only provisional since there was limited longitudinal coverage at these levels. The models start near 25 km in accord with previous COSPAR international reference atmosphere (CIRA) models. Models are also provided of ozone mixing ratio as a function of height. The monthly standard deviation and interannual variations relative to zonal means are also provided. In addition to the models of monthly latitudinal variations in vertical structure based on satellite measurements, monthly models of total column ozone and its characteristic variability as a function of latitude based on four years of Nimbus 7 measurements, models of the relationship between vertical structure and total column ozone, and a midlatitude annual mean model are incorporated in this set of ozone reference atmospheres. Various systematic variations are discussed including the annual, semiannual, and quasibiennial oscillations, and diurnal, longitudinal, and response to solar activity variations.

Keating, G. M.↗
NASA NTRS2016

An Efficient Ray-Tracing Method for Determining Terrain Intercepts in EDL Simulations

The calculation of a ray's intercept from an arbitrary point in space to a prescribed surface is a common task in computer simulations. The arbitrary point often represents an object that is moving according to the simulation, while the prescribed surface is fixed in a defined frame. For detailed simulations, this surface becomes complex, taking the form of real-world objects such as mountains, craters or valleys which require more advanced methods to accurately calculate a ray's intercept location. Incorporation of these complex surfaces has commonly been implemented in graphics systems that utilize highly optimized graphics processing units to analyze such features. This paper proposes a simplified method that does not require computationally intensive graphics solutions, but rather an optimized ray-tracing method for an assumed terrain dataset. This approach was developed for the Mars Science Laboratory mission which landed on the complex terrain of Gale Crater. First, this paper begins with a discussion of the simulation used to implement the model and the applicability of finding surface intercepts with respect to atmosphere modeling, altitude determination, radar modeling, and contact forces influencing vehicle dynamics. Next, the derivation and assumptions of the intercept finding method are presented. Key assumptions are noted making the routines specific to only certain types of surface data sets that are equidistantly spaced in longitude and latitude. The derivation of the method relies on ray-tracing, requiring discussion on the formulation of the ray with respect to the terrain datasets. Further discussion includes techniques for ray initialization in order to optimize the intercept search. Then, the model implementation for various new applications in the simulation are demonstrated. Finally, a validation of the accuracy is presented along with the corresponding data sets used in the validation. A performance summary of the method will be shown using the analysis from the Mars Science Laboratory's terminal descent sensing model. Alternate uses will also be shown for determining horizon maps and orbiter set times.

Shidner, Jeremy D.↗