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NASA NTRS2017

Open Source and Design Thinking at NASA: A Vision for Future Software

NASA Mission Control Software for the Visualization of data has historically been closed, accessible only to small groups of flight controllers, often bound to a specific mission discipline such as flight dynamics, health and status or mission planning. Open Mission Control Technologies (MCT) provides new capability for NASA mission controllers and, by being fully open source, opens up NASA software for the visualization of mission data to broader communities inside and outside of NASA. Open MCT is the product of a design thinking process within NASA, using participatory design and design sprints to build a product that serves users.

Trimble, Jay↗
NASA NTRS2017

Orbit Determination Strategy and Simulation Performance for OSIRIS-REx Proximity Operations

The Origins Spectral Interpretation Resource Identification Security Regolith Explorer (OSIRISREx)is a NASA New Frontiers mission to the near-earth asteroid Bennu that will rendez vousin 2018, create a comprehensive and detailed set of observations over several years, collect a regolith sample, and return the sample to Earth in 2023. The Orbit Determination (OD) team isa sub-section of the Flight Dynamics System responsible for generating precise reconstructions and predictions of the spacecraft trajectory. The OD team processes radiometric data, LIDAR, as well as center-finding and landmark-based Optical Navigation images throughout the proximity operations phase to estimate and predict the spacecraft location within several meters. Stringent knowledge requirements stress the OD teams concept of operations and procedures to produce verified and consistent high quality solutions for observation planning, maneuver planning, and onboard sequencing. This paper will provide insight into the OD concept of operations and summarize the OD performance expected during the approach and early proximity operation phases,based on our pre-encounter knowledge of Bennu. Strategies and methods used to compare and evaluate predicted and reconstructed solutions are detailed. The use of high fidelity operational tests during early 2017 will stress the teams concept of operations and ability to produce precise OD solutions with minimal turn-around delay.

Leonard, Jason M.↗
NASA NTRS2017

Genetic Algorithm-Guided, Adaptive Model Order Reduction of Flexible Aircrafts

This paper presents a methodology for automated model order reduction (MOR) of flexible aircrafts to construct linear parameter-varying (LPV) reduced order models (ROM) for aeroservoelasticity (ASE) analysis and control synthesis in broad flight parameter space. The novelty includes utilization of genetic algorithms (GAs) to automatically determine the states for reduction while minimizing the trial-and-error process and heuristics requirement to perform MOR; balanced truncation for unstable systems to achieve locally optimal realization of the full model; congruence transformation for "weak" fulfillment of state consistency across the entire flight parameter space; and ROM interpolation based on adaptive grid refinement to generate a globally functional LPV ASE ROM. The methodology is applied to the X-56A MUTT model currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that X-56A ROM with less than one-seventh the number of states relative to the original model is able to accurately predict system response among all input-output channels for pitch, roll, and ASE control at various flight conditions. The GA-guided approach exceeds manual and empirical state selection in terms of efficiency and accuracy. The adaptive refinement allows selective addition of the grid points in the parameter space where flight dynamics varies dramatically to enhance interpolation accuracy without over-burdening controller synthesis and onboard memory efforts downstream. The present MOR framework can be used by control engineers for robust ASE controller synthesis and novel vehicle design.

Numerical Analysi↗
NASA NTRS2016

Handling Qualities Optimization for Rotorcraft Conceptual Design

Over the past decade, NASA, under a succession of rotary-wing programs has been moving towards coupling multiple discipline analyses to evaluate rotorcraft conceptual designs. Handling qualities is one of the component analyses to be included in such a future Multidisciplinary Analysis and Optimization framework for conceptual design of VTOL aircraft. Similarly, the future vision for the capability of the Concept Design and Assessment Technology Area of the U.S Army Aviation Development Directorate also includes a handling qualities component. SIMPLI-FLYD is a tool jointly developed by NASA and the U.S. Army to perform modeling and analysis for the assessment of the handling qualities of rotorcraft conceptual designs. Illustrative scenarios of a tiltrotor in forward flight and singlemain rotor helicopter at hover are analyzed using SIMPLI-FLYD and the conceptual design sizing tool, NDARC, integrated in a single process. The effects of variations of design parameters such as horizontal tail and tail rotor size were evaluated in the form of margins to fixed- and rotary-wing handling qualities metrics and the computed vehicle empty weight. The handling qualities design margins are shown to vary across the flight envelope due to both changing flight dynamic and control characteristics and changing handling qualities specification requirements. The current SIMPLI-FLYD capability and future developments are discussed in the context of an overall rotorcraft conceptual design process.

Rotorcraft Conceptual Design↗
NASA NTRS2016

Feasibility of Varying Geo-Fence Around an Unmanned Aircraft Operation Based on Vehicle Performance and Wind

Managing trajectory separation is critical to ensuring accessibility, efficiency, and safety in the unmanned airspace. The notion of geo-fences is an emerging concept, where distance buffers enclose individual trajectories and areas of operation in order to manage the airspace. Currently, the Air Traffic Management system for commercial travel defines static distance buffers around the aircraft; however, commercial UASs are envisioned to operate in significantly closer proximity to other UAS requiring a geo-fence for spacing operations. The geo-fence size can be determined based on vehicle performance characteristics, state of the airspace, weather, and other unforeseen events such as emergency or disaster response. Calculation of the geo-fence size could be determined as part of pre-flight planning and during real-time operations. A largely non-homogeneous fleet of UASs will be operating in low altitude and will likely be commercially developed. Due to intellectual property concerns, the operators may not provide detailed specifications of the control system to UTM. In addition, the huge variety of UAS makes modeling each control system prohibitive and flight data for these vehicles may not exist. Therefore, a generalized, simple geo-fence sizing algorithm must be developed such that it does not rely on detailed knowledge of the vehicle control system, accounts for the presence of urban winds, and is sufficiently accurate. In this work, two simple models are investigated to determine its feasibility as an adequate means for calculating the geo-fence size. The vehicle data used in this work are provided by UAS manufactures who have partnered with NASA's UTM project and some publicly available websites. The first model utilizes wind data processed from the NOAA HRRR (Hourly Rapid Refresh) product and Sonar Annemometer data provided by San Jose State. The second model utilizes OpenFOAM which is a CFD code used to generate a wind field for flow around a single building. The key vehicle performance parameters can include UAS response time to disturbances, command to actuation latency, control system rate limits, time to recovery to desired path, and aerodynamics. It was found that the first model provides an initial understanding of geo-fence sizing, but does not provide enough accuracy to provide UTM with an efficient means of scheduling vehicles. The results of the second model reveal that modeling UAS controls systems with a linearized plant and gain scheduled PID controller does not allow capture the UAS flight dynamics within a significant envelope of the wind disturbances.

uncertainty↗
NASA NTRS2018

A Small Spacecraft Swarm Deployment and Stationkeeping Strategy for Sun-Earth L1 Halo Orbits

Spacecraft orbits about the Sun-Earth librarian point L1 have been of interest since the 1950s. An L1 halo orbit was first achieved with the International Sun-Earth Explorer-3 (ISEE-3) mission, and similar orbits around Sun-Earth L1 were achieved in the Solar and Heliospheric Observatory (SOHO), Advanced Composition Explorer (ACE), Genesis, and Deep Space Climate Observatory (DSCOVR) missions. With recent advancements in CubeSat technology, we envision that it will soon be feasible to deploy CubeSats at L1. As opposed to these prior missions where one large satellite orbited alone, a swarm of CubeSats at L1 would enable novel science data return, providing a topology for intersatellite measurements of heliophysics phenomena both spatially and temporally, at varying spatial scales.The purpose of this iPoster is to present a flight dynamics strategy for a swarm of numerous CubeSats orbiting Sun-Earth L1. The presented method is a coupled, two-part solution. First, we present a deployment strategy for theCubeSats that is optimized to produce prescribed, time-varying intersatellite baselines for the purposes of collectingmagnetometer data as well as radiometric measurements from cross-links. Second, we employ a loose controlstrategy that was successfully applied to SOHO and ACE for minimized stationkeeping propellant expenditure. Weemphasize that the presented solution is practical within the current state-of-the-art and heritage CubeSat technology,citing capabilities of CubeSat designs that will launch on the upcoming Exploration Mission 1 (EM-1) to lunar orbitsand beyond. Within this iPoster, we present animations of the simulated deployment strategy and resulting spacecrafttrajectories. Mission design parameters such as total v required for long-term station keeping andminimum/maximum/mean spacecraft separation distances are also presented.

Optimal control↗
NASA NTRS2016

V&V on a NASA Technology Demonstration Project: Low Density Supersonic Decelerator

Given their smaller budgets, but higher allowed risk posture, technology demonstration missions face different Verification and Validation (V&V) challenges than typical NASA missions. Despite these challenges, the Low Density Supersonic Decelerator (LDSD) project, managed by NASA’s Jet Propulsion Laboratory (JPL), has been extremely successful in testing new supersonic atmospheric decelerator technologies. A contribution to the project’s success is the unique V&V program that emphasized efficiency and flexibility. This paper will provide an overview of LDSD test objectives, Supersonic Flight Dynamics Tests (SFDT) performed so far, unique requirements structure and V&V processes implemented. The paper will focus on the V&V of the SFDT test architecture. Furthermore, lessons learned will also be presented at the end of the paper to aid future technology demonstration projects.

Murry, Michael L.↗
NASA NTRS2019

Mid-Course Correction Contingency Analysis for the James Webb Space Telescope

This investigation details two analyses performed as part of an early orbit contingency operations study related to the James Webb Space Telescope’s limited ability to maneuver in a sunward direction. First, contingency planning developed by the Flight Dynamics Team and shared with the Science and Operations Center to quickly assess the available timeline in the event of a delayed mid-course correction maneuver is presented. Second, the methods for recovering from a maneuver over-burn using observatory geometry to exploit the solar radiation pressure perturbation contributions from the large sunshield as well as adjusting the maneuver campaign to recover the observatory are examined.

JWST↗
NASA NTRSDate not supplied

System Identification of Flexible Aircraft: Lessons Learned from the X-56A Phase 1 Flight Tests

The X-56A Multi-Utility Technology Testbed (MUTT) is a subscale airplane that was de-signed as an experimental flight research platform for improving aeroelastic modeling and control technologies. The Phase 1 flight tests, conducted from 2017 to 2019 at the NASA Arm-strong Flight Research Center (AFRC), included 39 flights and approximately 1000 research maneuvers, some of which demonstrated stable closed-loop flight beyond the open-loop flutter speed. This paper summarizes the system identification effort to extract nondimensional stability and control derivatives from the flight test data for constructing aeroelastic models of the flight dynamics. Topics discussed include instrumentation, experiment design, model postulation and reduction, parameter estimation, and others. Throughout the paper, unique challenges for the identification of flexible aircraft, practical aspects of the analysis, and lessons learned are presented.

Jared A Grauer↗
NASA NTRSDate not supplied

TPSAS-NF1676L-12076-DND

AirSTAR is the collection of Ground Support Systems, Subscale Flight Vehicles and Operations Expertise required for conducting remotely piloted flight-dynamics and controls research.

David Cox↗
NASA NTRS2015

TPSAS-NF1676L-22679-DND

An overview of Langley Research Center (LaRC), the Research Directorate, and the Flight Dynamics and Control (FDC) discipline is included in the appendix (back-up slides).

Carey Buttrill↗
NASA NTRS2020

Aircraft System Identification from Multisine Inputs and Frequency Responses

The identification of aircraft flight dynamics is often performed using frequency responses, which are nonparametric models that quantify the steady-state magnitude and phase of a dynamic system response to sinusoidal inputs, as a function of frequency. Frequency responses are computed from measured input and output data, and then model parameters, such as stability and control derivatives, are estimated to best fit a parametric model to the empirical frequency response data. The utility of this approach is due to the familiarity of engineers with frequency responses, a number of theoretical and practical advantages under specific conditions, the availability of software packages, and many other reasons.

System identification↗
NASA NTRSDate not supplied

VTXO: the Virtual Telescope for X-ray Observations

The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Frensel Lenses (PFLs) in a virtual X-ray telescope with 1 km focal length and with nearly 50 milli-arcsecond angular resolution. Laboratory characterization of PFLs have demonstrated near diffraction-limited angular resolution in the X-ray band, but they require long focal lengths to achieve this quality of imaging. VTXO is formed by using precision formation flying of two SmallSats: a smaller, 6U OpticsSat that houses the PFLs and navigation beacons while a larger, ESPA-class DetectorSat contains an X-ray camera, a charged-particle radiation monitor, a precision star tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly-elliptical supersynchronous geostationary transfer orbit to allow the inertial formation to form and hold around the 90,000 km apogee for 10 hours of the 32.5-hour orbit with nearly a year mission lifetime. The guidance, navigation, and control (GN&C) for the formation flying uses standard CubeSat avionics packages, a precision star tracker, imaging beacons on the Optics Sat, and a radio ranging system that also serves as an inter-satellite communication link. VTXO’s fine angular resolution enables measuring the environments nearly an order of magnitude closer to the central engines of bright compact X-ray sources compared to the current state of the art. This X-ray imaging capability allows for the study of the effects of dust scattering nearer to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure nearer to the compact object in X-ray novae such as Cyg X-1and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The In this paper, the VTXO science performance, SmallSat and instrument designs,and mission description is be described. The VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS3) missions.

John Krizmanic↗
NASA NTRSDate not supplied

VTXO: The Virtual Telescope for X-ray Observations

The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Frensel Lenses (PFLs) in a virtual X-ray telescope with⇠1 km focal length and with⇠50 milli-arc second angular resolution. VTXO is formed by using precision formation flying of two SmallSats: a smaller OpticsSat that houses the PFLs and navigation beacons while a larger DetectorSat contains an X-ray camera, a precision start tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly elliptical super synchronous orbit allow the formation to hold in an inertial frame around the 90,000 km apogee for 10 hours of the 32.5 hour orbit with nearly a year mission lifetime. VTXO’s fine angular resolution enables measuring the environments close to the central engines of bright compact X-ray sources. This X-ray imaging capability allows for the study of the effects of dust scattering near to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure near to the compact object in X-ray novae such as Cyg X-1 and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The VTXO SmallSat and instrument designs, mission parameters, and science performance are described. VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS3) missions

John F Krizmanic↗
NASA NTRSDate not supplied

Analysis of Improved Navigation Data for NASA Near Space Network (NSN) Direct-to-Earth (DTE) Ground Stations

Spacecraft navigation is a complex concept that requires a collaboration between network entities and constant corrective action to be successful in the highly variable environment. NASA’s Near Space Network (NSN), Direct-to-Earth (DTE) ground station team is constantly exploring methods to provide better service to NASA spacecraft, including providing radiometric tracking data, subject to cost constraints. Navigation teams, such as the Goddard Space Flight Center (GSFC) Flight Dynamics Facility (FDF), use the tracking data to perform orbit determination to assess where a spacecraft has been, identify where a spacecraft is now, and predict where a spacecraft will be in the future. As NASA plans for more missions beyond low Earth orbit (LEO), there is a need for improved navigation performance which can be directly achieved through improvements in the radiometric tracking data. NASA’s new DTE Ka-band antennas have finer autotrack angle resolution, and the availability of low-cost improved off-the-shelf ground station components, which are involved with producing navigation data. NASA formed a team to provide an assessment of the current NSN, DTE spacecraft navigation service (radiometric tracking) performance baseline with the end goal of defining needed future enhancements to meet the needs for spacecraft above LEO. The communications and navigation community is deriving new techniques in an effort to better service the navigation needs of spacecraft using ground station networks. This paper provides an overview Earth-orbit, direct-to-ground navigation techniques and performance, the development of specifications for new antennas, accuracy of data, spacecraft requirements, accuracy of orbit determination, history of challenges, and considerations for improvements. The discussions in this paper may assist ground station, spacecraft, and mission design teams advance navigation capabilities and more effectively meet requirements.

networks↗
NASA NTRSDate not supplied

Global Reference Atmospheric Model (GRAM) Advancements and Additions

Introduction:The Global Reference Atmospheric Model (GRAM) is one of the most widely used engineering models of the atmosphere. GRAM development and maintenance has been led by NASA Marshall Space Flight Center (MSFC). The NASA Science Mission Directorate (SMD) has provided funding support to upgrade the GRAMs since Fiscal Year 2018. NASA Langley Research Center has been working with MSFC on the upgrades.This presentation will provide details regarding the upgrades that have been made to the existing GRAMs, the development of new GRAMs, as well as the ongoing objectives, tasks, and milestones re-lated to the GRAM upgrades funded by NASA SMD. GRAM: The GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of the atmospheric properties for numerous planetary destinations.They provide mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path.Theyhave been widely used by the engineering community because of their ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. MSFC has been developing and updating GRAMs since 1974; GRAMs are currently available for Earth, Mars, Venus, Neptune, and Titan. GRAM Upgrade Status: Code Moderization.The planetary GRAMs are being rearchitected from Fortran to a common object-ori-entedC++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ releases of the existing planetary GRAMs (Mars, Venus, Neptune, and Titan-GRAM) in the GRAM Suite are straight conversions from the latest Fortran version. Model Upgrades.The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemerishas been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. The GRAM team has received updated Mars General Circulation Model (MGCM) datasets from NASA Ames Research Center. Mars Global Ionosphere-Thermosphere Model (M-GITM) data is being obtained toreplace the Mars Thermospheric General Circulation Model (MTGCM) data in legacy Mars-GRAM. M-GITM and updated MGCM data will be incorporated into a future GRAM Suite release.Twoprojects that will improve the atmospheric model data in the GRAMs have been funded by the GRAM team since Fiscal Year 2020. Sanjay Limaye and Patrick Fry at the University of Wisconsin are reanalyzing the Venus Express radio occultation observations and analyzing the Akatsukiradio occultation observations.This will lead tothe calculation of number density, temperature, and pressure profiles for the 40-90 km altitude range. Kunio Sayanagi, Justin Garland, and Ryan McCabeat Hampton University are developing empirical global models for Venus,Jupiter, Saturn, Uranus, Neptune, and Titan that incorporates the latest data available for each of these planetary destinations. Upgraded GRAM Releases. GRAM Suite Version 1.0 was released in May 2020 and contains the rearchitected Neptune-GRAM, including the common GRAM framework and planet–specific code. GRAM Suite Version 1.1 was released in September 2020 and addedthe rearchitected Titan-GRAM to the GRAM Suite. A User Guide and Programmer’s Manualarereleased with all GRAMs. The rearchitected Mars and Venus-GRAMs will be released in upcomingversions of the GRAM Suite. New GRAM Releases. New GRAMs have been de-veloped for Uranus and Jupiter. Uranus-GRAM is based on an individual profile generated by Gary Allen (ARC) from Voyager 2 occultation data and will be released in GRAM Suite Version 1.2. Jupiter-GRAM is based on individual profile produced from Al Seiff’s Ju-piter model[1].Jupiter-GRAM will be released in GRAM Suite Version 1.3. Saturn-GRAM is currently under development and will be released in a future version of the GRAM Suite. Conclusions: GRAMs are vital and frequently used toolsets. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. NASA SMD funding has been essential to addressing current limitations and accomplishing GRAM developmental goals. Continua-tion ofSMD fundingwill ensure the development, up-grades, and maintenance of the GRAMs. References: [1] Seiff, A., et al. (1998) JGR, 103, 22,857-22,889. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD

atmospheric models↗
NASA NTRSDate not supplied

Outer Planet Global Reference Atmospheric Model (GRAM) Upgrades

Introduction: The Global Reference Atmospheric Model (GRAM) is one of the most widely used engineering models of planetary atmospheres. The GRAM upgrades are being developed by NASA Marshall Space Flight Center and NASA Langley Research Center. This presentation will provide details regarding the upgrades to the existing GRAMs, the development of new GRAMs, and the ongoing objectives, tasks, and milestones related to the GRAM upgrades funded by the NASA Science Mission Directorate (SMD). GRAM: The GRAMs are engineering-oriented atmospheric models that estimate mean values and statistical variations of the atmospheric properties for numerous planetary destinations. They provide mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path. They are extensively used by the engineering community because of their ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. GRAMs are currently available for Earth, Mars, Venus, Neptune, Titan, and Uranus. Outer Planet GRAM Upgrade Status: Code Modernization. The outer planet GRAMs have been rearchitected from Fortran to a common object-oriented C++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ releases of the rearchitected legacy outer planet GRAMs (Neptune and Titan-GRAM) are straight conversions from the latest Fortran version. Model Upgrades. The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemeris has been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. In FY20, the GRAM project established a contract with Hampton University to develop empirical global models for Jupiter, Saturn, Uranus, Neptune, and Titan. Upgraded Outer Planet GRAM Releases. GRAM Suite Version 1.0 was released in May 2020 and contains the rearchitected Neptune-GRAM, including the common GRAM framework and planet–specific code. GRAM Suite Version 1.1 was released in September 2020 and added the rearchitected Titan-GRAM to the GRAM Suite. A User Guide and Programmer’s Manual are released with all GRAMs. New Outer Planet GRAM Releases. New GRAMs have been developed for Uranus and Jupiter. Uranus-GRAM is based on the NASA Ames Research Center (ARC) Uranus Atmospheric Model [1,2] and was released in GRAM Suite Version 1.2 in July 2021. Jupiter-GRAM is based on Galileo probe Atmospheric Structure Instrument (ASI) data from Seiff et al. [3] Saturn-GRAM is also under development. Both Jupiter and Saturn-GRAM will be released in future versions of the GRAM Suite. Conclusions: GRAMs are vital and frequently used toolsets. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. Titan-GRAM atmosphere model upgrades will be included in the next phase of GRAM tasks. References: [1] Allen Jr., G.A. et al. (2014) 11th International Planetary Probe Workshop, Abstract #8023. [2] Allen Jr., G.A. et al. (2014) Workshop on the Study of the Ice Giant Planets, Abstract #2001. [3] Seiff, A. et al. (1998) JGR, 103, 22,857 -22,889. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD.

atmospheric models↗
NASA NTRSDate not supplied

Venus Global Reference Atmospheric Model (Venus-GRAM) Upgrades

Introduction: The Venus Global Reference Atmospheric Model (Venus-GRAM) is one of the most widely used engineering models of Venus’ atmosphere. The Venus-GRAM upgrades are being developed by NASA Marshall Space Flight Center (MSFC) and NASA Langley Research Center (LaRC). This presentation will provide details regarding the upgrades that have been made to Venus-GRAM and the ongoing objectives, tasks, and milestones related to the GRAM upgrades funded by the NASA Science Mission Directorate (SMD). Venus-GRAM: Venus-GRAM is an engineering-oriented atmospheric model that estimates mean values and statistical variations of the atmospheric properties of Venus. Venus-GRAM provides mean values and variability for any point in the atmosphere as well as seasonal, geographic, and altitude variations. Venus-GRAM outputs include atmospheric density, temperature, pressure, winds, and chemical composition along a user-defined path. It is extensively used by the engineering community because of its ability to create realistic dispersions. GRAMs have been integrated into high fidelity flight dynamic simulations of launch, entry, descent and landing (EDL), aerobraking and aerocapture. GRAMs are currently available for Earth, Mars, Venus, Neptune, Titan, and Uranus. The lower atmosphere model in Venus-GRAM (up to 250 km) is based on the Venus International Reference Atmosphere (VIRA) [1]. The Venus-GRAM thermosphere (250 to 1000 km) is based on a MSFC-developed model [2] which assumes an isothermal temperature profile initialized using VIRA conditions at 250 km [3]. The VIRA version included in Venus-GRAM includes Pioneer Venus Orbiter and Probe data as well as Venera probe data, but it does not include a solid planet model or a high-resolution gravity model [4]. Venus-GRAM Upgrade Status: Code Modernization. Venus-GRAM has been rearchitected from Fortran to a common object-oriented C++ framework called the GRAM Suite. This new architecture creates a common GRAM library of data models and utilities. The first C++ release of the rearchitected Venus-GRAM is a straight conversion from the latest Fortran version. Model Upgrades. The focus of the model upgrade task is to improve the atmosphere models in the existing GRAMs and to establish a foundation for developing GRAMs for additional destinations. The GRAM ephemeris has been upgraded to the NASA Navigation and Ancillary Information Facility (NAIF) SPICE toolkit (version N0066). The calculation of the speed of sound has also been improved in the GRAMs. In FY20, the GRAM project established contracts to improve the model data within Venus-GRAM. Hampton University is developing an empirical global model for Venus. The University of Wisconsin is reanalyzing the Venus Express radio occultation observations and analyzing the Akatsuki thermal imaging data. Upgraded Venus-GRAM Release. GRAM Suite Version 1.3 will be released in September 2021 and will contain the rearchitected Venus-GRAM, including the common GRAM framework and planet–specific code. A User Guide and Programmer’s Manual are released with all GRAMs. Conclusions: GRAMs are frequently used toolsets and vital in assessing effects of atmospheres on interplanetary spacecraft during the program life cycle process. Releases of the GRAM Suite, upgrades of the existing planetary GRAMs, and development of new planetary GRAMs are ongoing. Venus-GRAM atmosphere model upgrades will be included in the next phase of GRAM tasks. References: [1] Kliore, A. J. et al. (1985) ASR, 5, 11, 1-304. [2] Justh, H. L. et al. (2006) AIAA/AAS Astrodynamics Specialist Conference & Exhibit, Abstract AIAA-2006-6394. [3] Guide to Reference and Standard Atmosphere Models, BSR/AIAA G-003-2010. [4] Limaye, S. S. (2012), LPSC VEXAG Townhall Meeting. Acknowledgments: The authors gratefully acknowledge support from the NASA SMD.

atmospheric models↗