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NASA NTRS1986

Thermodynamic evaluation of transonic compressor rotors using the finite volume approach

A method was developed which calculates two-dimensional, transonic, viscous flow in ducts. The finite volume, time marching formulation is used to obtain steady flow solutions of the Reynolds-averaged form of the Navier Stokes equations. The entire calculation is performed in the physical domain. The method is currently limited to the calculation of attached flows. The features of the current method can be summarized as follows. Control volumes are chosen so that smoothing of flow properties, typically required for stability, is now needed. Different time steps are used in the different governing equations to improve the convergence speed of the viscous calculations. A new pressure interpolation scheme is introduced which improves the shock capturing ability of the method. A multi-volume method for pressure changes in the boundary layer allows calculations which use very long and thin control volumes. A special discretization technique is also used to stabilize these calculations. A special formulation of the energy equation is used to provide improved transient behavior of solutions which use the full energy equation. The method is then compared with a wide variety of test cases. The freestream Mach numbers range from 0.075 to 2.8 in the calculations. Transonic viscous flow in a converging diverging nozzle is calculated with the method; the Mach number upstream of the shock is approximately 1.25. The agreement between the calculated and measured shock strength and total pressure losses is good. Essentially incompressible turbulent boundary layer flow in a adverse pressure gradient is calculated and the computed distribution of mean velocity and shear stress are in good agreement with the measurements. At the other end of the Mach number range, a flat plate turbulent boundary layer with a freestream Mach number of 2.8 is calculated using the full energy equation; the computed total temperature distribution and recovery factor agree well with the measurements when a variable Prandtl number is used through the boundary layer.

Nicholson, S.↗
NASA NTRS1999

Physical Control of Biological Productivity Off the Coast of Peru During the 1997-1998 El Nino

Satellite observations and an ecosystem model are used to understand the variability in the planktonic ecosystem off Peru for the period January 1996 to May 1998. The objective of this study is to quantify the changes in the ecosystem components, carbon pathways, and available food for small pelagic fish that occur associated with the change in physical forcing due to El Nino. Two periods are distinguished based on the observed sea level anomaly: a La Nina (LaN) period (1996) in which sea level was below normal and El Nino (EN), the average conditions for December 1997, in which the sea level was anomalously high. There are three phytoplankton size classes (pico-, nano-, and net-phytoplankton) which compete for nutrients and are eaten by three zooplankton size classes. The ecosystem model is forced by alongshore wind speed measured by the NASA Scatterometer (NSCAT) and the European Remote-sensing Satellites (ERS-1 and ERS-2). Larger, slower growing organisms are more sensitive to physical disturbance than smaller organisms (Carr, 1998]. In the present simulation as well, the primary effect of the El Nino (reduced nutrient supply, and increased temperature) is to reduce the biomass of large cells (netphytoplankton) and consequently of the zooplankton that rely on large cells as food source. EN conditions are accompanied by a rearrangement of carbon pathways: comparable uptake goes into reduced biomass accumulation, increased losses to respiration, reduced carbon export, and much reduced carbon available to fish. The star indicates the remotely sensed biomass (assuming a constant carbon to chlorophyll ratio of 60) as measured by the Ocean Color and Temperature Sensor (Nov.-Dec. 1996) and the Sea-viewing Wide Field-of-view Sensor (Dec. 1997). The model, which assumes no light limitation, overestimates total phytoplankton biomass. Additional Information is contained in the original.

Carr, Mary-Elena↗
NASA NTRS2013

RUSHMAPS: Real-Time Uploadable Spherical Harmonic Moment Analysis for Particle Spectrometers

RUSHMAPS is a new onboard data reduction scheme that gives real-time access to key science parameters (e.g. moments) of a class of heliophysics science and/or solar system exploration investigation that includes plasma particle spectrometers (PPS), but requires moments reporting (density, bulk-velocity, temperature, pressure, etc.) of higher-level quality, and tolerates a lowpass (variable quality) spectral representation of the corresponding particle velocity distributions, such that telemetry use is minimized. The proposed methodology trades access to the full-resolution velocity distribution data, saving on telemetry, for real-time access to both the moments and an adjustable-quality (increasing quality increases volume) spectral representation of distribution functions. Traditional onboard data storage and downlink bandwidth constraints severely limit PPS system functionality and drive cost, which, as a consequence, drives a limited data collection and lower angular energy and time resolution. This prototypical system exploit, using high-performance processing technology at GSFC (Goddard Space Flight Center), uses a SpaceCube and/or Maestro-type platform for processing. These processing platforms are currently being used on the International Space Station as a technology demonstration, and work is currently ongoing in a new onboard computation system for the Earth Science missions, but they have never been implemented in heliospheric science or solar system exploration missions. Preliminary analysis confirms that the targeted processor platforms possess the processing resources required for realtime application of these algorithms to the spectrometer data. SpaceCube platforms demonstrate that the target architecture possesses the sort of compact, low-mass/power, radiation-tolerant characteristics needed for flight. These high-performing hybrid systems embed unprecedented amounts of onboard processing power in the CPU (central processing unit), FPGAs (field programmable gate arrays), and DSP (digital signal processing) elements. The fundamental computational algorithm de constructs 3D velocity distributions in terms of spherical harmonic spectral coefficients (which are analogous to a Fourier sine-cosine decomposition), but uses instead spherical harmonics Legendre polynomial orthogonal functions as a basis for the expansion, portraying each 2D angular distribution at every energy or, geometrically, spherical speed-shell swept by the particle spectrometer. Optionally, these spherical harmonic spectral coefficients may be telemetered to the ground. These will provide a smoothed description of the velocity distribution function whose quality will depend on the number of coefficients determined. Successfully implemented on the GSFC-developed processor, the capability to integrate the proposed methodology with both heritage and anticipated future plasma particle spectrometer designs is demonstrated (with sufficiently detailed design analysis to advance TRL) to show specific science relevancy with future HSD (Heliophysics Science Division) solar-interplanetary, planetary missions, sounding rockets and/or CubeSat missions.

Figueroa-Vinas, Adolfo↗
NASA NTRSDate not supplied

Improving Computational Efficiency of Prognostics Algorithms in Resource-Constrained Settings

In engineering and aerospace applications, it is vital to operational success to have insight into the expected performance and health of physical systems. The field of prognostics and health management provides quantitative methods for monitoring, predicting, and managing system health. Prognostics algorithms can be employed to assess the current state of a system, propagate the system throughout time, and predict potential anomalies or failures that may occur. While they can provide accurate prediction results, effective prognostics algorithms can be challenging to use in resource-constrained settings due to computational limitations and high computational latency, leading to obsolete predictions. Thus, computationally efficient and accurate algorithms are necessary for future remaining useful life predictions. In this work, we implement new algorithmic approaches for prediction, quantitatively compare them via a battery degradation use-case, and provide recommendations of potential improvements to a prognostics framework. One approach to prediction is through sampling, whereby the current state of a physical system is sampled many times and each sample is propagated forward until failure is reached, resulting in a distribution of failure values. To improve the efficiency of this process, we implemented five new algorithmic approaches to prediction, including three distinct sampling methods (standard Monte Carlo, Quasi-Monte Carlo, and Latin Hypercube Sampling), a variable time step algorithm, and a variable sample size algorithm. To compare the algorithms, we employ a variety of metrics designed specifically to analyze both computational efficiency and model accuracy. Our metrics include accuracy to compare the average predicted value to ground truth, mean absolute deviation to illustrate dispersion, specific percentile error to describe accuracy within a user-defined risk tolerance, and code run-time. To quantitatively analyze our results, we employ a use-case of degradation of a Lithium-ion battery. We use an electrochemistry-based model to describe the current health state of the battery, and implement our prediction algorithms to propagate forward in time until end-of-discharge (EOD) is reached. Notably, through this work it was found that none of our sampling approaches had a significant impact on computational efficiency or model accuracy in predicting EOD of the battery. We find that while the sampling methods are unique, the distributions they generate are similar, ultimately producing final predictions that are nearly identical. In exploring the effect of the time step within the prediction algorithm, we found that prediction accuracy was highly dependent on the time step used, and that implementing a variable time step within a particular prediction may provide an increase in computational efficiency while also maintaining prediction accuracy. Finally, implementing a variable sample size also affected prediction, and our results show that tuning both the magnitude and timing of the sample size adjustment can result in improved computation speed and maintained prediction accuracy. Taken together, our findings highlight the challenge of performing prognostics in resource-constrained settings, and illustrate the potential of developing new prediction algorithms to improve computational efficiency.

prognostics↗
NASA NTRS1994

Technology Benefit Estimator (T/BEST): User's Manual

The Technology Benefit Estimator (T/BEST) system is a formal method to assess advanced technologies and quantify the benefit contributions for prioritization. T/BEST may be used to provide guidelines to identify and prioritize high payoff research areas, help manage research and limited resources, show the link between advanced concepts and the bottom line, i.e., accrued benefit and value, and to communicate credibly the benefits of research. The T/BEST software computer program is specifically designed to estimating benefits, and benefit sensitivities, of introducing new technologies into existing propulsion systems. Key engine cycle, structural, fluid, mission and cost analysis modules are used to provide a framework for interfacing with advanced technologies. An open-ended, modular approach is used to allow for modification and addition of both key and advanced technology modules. T/BEST has a hierarchical framework that yields varying levels of benefit estimation accuracy that are dependent on the degree of input detail available. This hierarchical feature permits rapid estimation of technology benefits even when the technology is at the conceptual stage. As knowledge of the technology details increases the accuracy of the benefit analysis increases. Included in T/BEST's framework are correlations developed from a statistical data base that is relied upon if there is insufficient information given in a particular area, e.g., fuel capacity or aircraft landing weight. Statistical predictions are not required if these data are specified in the mission requirements. The engine cycle, structural fluid, cost, noise, and emissions analyses interact with the default or user material and component libraries to yield estimates of specific global benefits: range, speed, thrust, capacity, component life, noise, emissions, specific fuel consumption, component and engine weights, pre-certification test, mission performance engine cost, direct operating cost, life cycle cost, manufacturing cost, development cost, risk, and development time. Currently, T/BEST operates on stand-alone or networked workstations, and uses a UNIX shell or script to control the operation of interfaced FORTRAN based analyses. T/BEST's interface structure works equally well with non-FORTRAN or mixed software analysis. This interface structure is designed to maintain the integrity of the expert's analyses by interfacing with expert's existing input and output files. Parameter input and output data (e.g., number of blades, hub diameters, etc.) are passed via T/BEST's neutral file, while copious data (e.g., finite element models, profiles, etc.) are passed via file pointers that point to the expert's analyses output files. In order to make the communications between the T/BEST's neutral file and attached analyses codes simple, only two software commands, PUT and GET, are required. This simplicity permits easy access to all input and output variables contained within the neutral file. Both public domain and proprietary analyses codes may be attached with a minimal amount of effort, while maintaining full data and analysis integrity, and security. T/BESt's sotware framework, status, beginner-to-expert operation, interface architecture, analysis module addition, and key analysis modules are discussed. Representative examples of T/BEST benefit analyses are shown.

Generazio, Edward R.↗
NASA NTRS2012

The Pursuit of K: Reflections on the Current State-of-the-Art in Stress Intensity Factor Solutions for Practical Aerospace Applications

The elastic stress intensity factor (SIF, commonly denoted as K) is the foundation of practical fracture mechanics (FM) analysis for aircraft structures. This single parameter describes the first-order effects of stress magnitude and distribution as well as the geometry of both structure/component and crack. Hence, the calculation of K is often the most significant step in fatigue analysis based on FM. This presentation will provide several reflections on the current state-of-the-art in SIF solution methods used for practical aerospace applications, including a brief historical perspective, descriptions of some recent and ongoing advances, and comments on some remaining challenges. Newman and Raju made significant early contributions to practical structural analysis by developing closed-form SIF equations for surface and corner cracks in simplified geometries, often based on empirical fits of finite element (FE) solutions. Those solutions (and others like them) were sometimes revised as new analyses were conducted or limitations discovered. The foundational solutions have exhibited striking longevity, despite the relatively "coarse" FE models employed many decades ago. However, in recent years, the accumulation of different generations of solutions for the same nominal geometry has led to some confusion (which solution is correct?), and steady increases in computational capabilities have facilitated the discovery of inaccuracies in some (not all!) of the legacy solutions. Some examples of problems and solutions are presented and discussed, including the challenge of maintaining consistency with legacy design applications. As computational power has increased, the prospect of calculating large numbers of SIF solutions for specific complex geometries with advanced numerical methods has grown more attractive. Fawaz and Andersson, for example, have been generating literally millions of new SIF solutions for different combinations of multiple cracks under simplified loading schemes using p-version FE methods. These data are invaluable, but questions remain about their practical use, because the tabular databases of key results needed to support practical life analysis can occupy gigabytes of storage for only a few classes of geometries. The prospect of using such advanced numerical methods to calculate in real time only those K solutions actually needed to support a specific crack growth analysis is also tempting, but the stark reality is that the computational cost is still so high that the approach is not practical except for specific, critical application problems. Some thoughts are offered about alternative paradigms. Compounding approaches are some of the earliest building blocks of SIF development for more complex geometries. These approaches are especially attractive because of their very low computational cost and their conceptual robustness; they are, in some ways, an intriguing contrast and complement to the brute-force numerical methods. In recent years, researchers at NRC-Canada have published remarkable results showing how compounding approaches can be used to generate accurate solutions for very difficult problems. Examples are provided of some successes--and some limitations--using this approach. These closed-form, tabulated numerical, and compounding approaches have typically been used for simple remote loading with simple load paths to the crack. However, many significant cracks occur in complex stress gradient fields. This is a job for weight function (WF) methods, where the arbitrary stress distribution on the crack plane in the corresponding uncracked body (typically determined using FE methods) is used to determine K. Several significant recent advances in WF methods and solutions are highlighted here. Fueled by advanced 3D numerical methods, many new solutions have been generated for classic geometries such as surface and corner cracks with wide ranges of geometrical validity. A new WF formulation has also be developed for part-through cracks considering the arbitrary stress gradients in all directions in the crack plane (so-called bivariant solutions). Basic WF methods have recently been combined with analytical expressions for crack plane stresses to develop a large family of accurate SIF solutions for corner, surface, and through cracks at internal or external notches with very wide ranges of shapes, sizes, acuities, and offsets. Finally, WF solutions are much faster than FE or boundary element solutions, but can still be much slower than simple closed-form solutions, especially for bivariant solutions that can require 2D numerical integration. Novel pre-integration and dynamic tabular methods have been developed that substantially increase the speed of these advanced WF solutions. The practical utility of advanced SIF methods, including both WF and direct numerical methods, is greatly enhanced if the FM life analysis can be directly and efficiently linked with digital models of the actual structure or component (e.g., FE models for stress analysis). Two recent advances of this type will be described. One approach directly interfaces the FM life analysis with the FE model of the uncracked component (including stress results). Through a powerful graphical user interface, simplified FM life models can be constructed (and visualized) directly on the component model, with the computer collecting the geometry and stress gradient information needed for the life calculation. An even more powerful paradigm uses expert logic to automatically build an optimum simple fracture model at any and every desired location in the component model, perform the life calculation, and even generate fatigue crack growth life contour maps, all with minimal user intervention. This paradigm has also been extended to the automatic calculation of fracture risk, considering uncertainty or variability in key input parameters such as initial crack size or location. Another new integrated approach links the engineering life analysis, the component model, and a 3D numerical fracture analysis built with the same component model to generate a table of SIF values at a specific location that can then be employed efficiently to perform the life calculation. Some attention must be given to verification and validation (V&V) issues and challenges: how good are these SIF solutions, how good is good enough, and does anyone believe the life answer? It is important to think critically about the different sources of error or uncertainty and to perform V&V in a hierarchal, building-block manner. Some accuracy issues for SIF solutions, for example, may actually involve independent material behavior issues, such as constraint loss effects for crack fronts near component surfaces, and can be a source of confusion. Recommendations are proposed for improved V&V approaches. This presentation will briefly but critically survey the range of issues and advances mentioned above, with a particular view towards assembling an integrated approach that combines different methods to create practical tools for real-world design and analysis problems. Examples will be selectively drawn from the recent literature, from recent enhancements in the NASGRO and DARWIN computer codes, and from previously unpublished research

CraigMcClung, R.↗
NASA NTRS2019

Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project: ACAS Xu HITL Overview

This presentation provides an overview of a recently completed human-in-the-loop simulation, conducted as part of the Unmanned Aircraft Systems (UAS) Integration in the National Airspace System (NAS) Project. This study examined how to present resolution advisories (RAs) issued by Airborne Collision Avoidance System (ACAS) Xu, which can be in the vertical dimension, the horizontal dimension, or both (i.e., "blended"). The study varied the location of the ACAS Xu traffic information - it was either presented within an 'integrated' display or a 'standalone' display, where the traffic information was separated from the vehicle control interfaces and navigational information. The presentation covers the test set-up, scenario design, and facilities utilized in this experiment. The primary dependent variables are listed, but no detailed results are included in this outbrief since data analysis is still underway. Goal: assess ACAS Xu Run 5 in a HITL setting to measure pilot & system performance – RWC alerting and guidance – RA alerting and guidance • Horizontal-only, vertical-only, and blended RAs • Strengthening RAs, reversals and added/removed RAs during blended maneuvers – Incorporate realistic sensor noise into the simulation environment – Compare pilot and DAA system performance back to NASA’s Phase 1 DAA MOPS V&V HITL • We leveraged the findings of an engineering analysis conducted in March to help inform how to implement the Xu logic – Part 1 focused on how to display horizontal-only, vertical-only and blended RAs – Part 2 focused on how to display automated RA responses Xu Engineering Analysis Results Part 1 objectives: – Characterize pilot responses to (canned) ACAS Xu RAs in a variety of display configurations • With vs. Without text accompaniment • Simple vs. ‘Advanced’ aural alerting • Results – Pilots struggled to meet 5 seconds initial response requirement • Particularly against horizontal and blended RAs – Pilots failed to respond more quickly to secondary RAs • Expected response time to subsequent RAs = 2.5 seconds No clear effect of the different alerting conditions on response times – Response times primarily driven by RA type • Pilots demonstrated high level of compliance with RAs and self-reported Xu alerting and guidance as being acceptable – 1/5 reported text as being necessary – 4/5 wanted the aural alert to retain the original RA sequence (i.e., issue a follow-on RA second rather than first) – 4/5 wanted to retain the “Maintain Heading/Vertical Speed” aural alert in the event that the pilot reached their target response at the time of secondary RA Based on these results, we decided on the following for the HITL experimental design: – Incorporated an ‘auto-fill’ feature in Vigilant Spirit that removes the need for pilots to manually enter a heading or altitude for RAs • Pilots only have to click ‘Send’ to upload the RA target heading/altitude – Did not include an RA ‘text box’ • The auto-fill feature largely replaces the purpose of the text box – Using a combination of the ‘Basic’ and ‘Advanced’ aural alerting • In case of blended maneuvers will issue “Maintain Heading/Vertical Rate” if pilot has reached 1st RA target at the time the 2nd RA is issued • Integration and testing with Xu Run 5 also resulted in us adding display logic to modify how horizontal RAs are presented – Target heading was shown to update at approx. 1 Hz making it difficult for pilots to implement – Display logic limited horizontal RA strengthening to once every 5 seconds • Did not impact timing of reversals, new RAs (i.e., blended), or CoC Engineering Analysis Results

human systems integration↗
NASA NTRSDate not supplied

Urban Air Mobility Noise: Current Practice, Gaps, and Recommendations

AN Air Mobility (UAM) is an opportunity for aviation to improve transportation systems across the world. Representative UAM vehicle attributes include electrical vertical takeoff and landing (eVTOL) vehicles that can accommodate up to 6 passengers (or equivalent cargo), are possibly autonomous, perform missions of up to 100 nautical miles at altitudes up to 3000 ft. above ground level, have flight speeds up to 200 knots, and weigh between 800 and 8000 pounds. Along with the many anticipated benefits, there will be noise issues that need to be addressed. In 2018, NASA formed an Urban Air Mobility Noise Working Group (UNWG) to assemble noise experts from industry, universities and government agencies to identify, discuss, and address UAM noise issues. This oral presentation summarizes technology gaps and goals associated with four areas of interest: Tools & Technologies, Ground & Flight Testing, Human Response & Metrics, and Regulation & Policy, and is drawn from a draft white paper [1] by the same title. Tools & Technologies include noise prediction tools and noise reduction technologies that have been developed for conventional rotorcraft and fixed-wing vehicles that may be applicable or need to be modified for UAM. Prediction tools need to be able to account for variable speed rotors and other temporal variation effects that impact community noise. A reprioritization of noise sources needs to be done since UAM vehicles include multiple rotors/propellers, often in proximity to one another and/or the airframe, with dynamic transition, and new noise sources such as electric motors or hybrid-electric propulsion. Scattering and propagation methods need to be developed that include the vehicle components and surfaces near a receiver such as buildings and vertiports. Validation databases are needed to quantify prediction uncertainties. Prediction tools used to evaluate community noise will need source models appropriate for a wide range of UAM vehicles. Existing noise reduction technologies need to be evaluated and new noise reduction technologies should be developed in anticipation of future noise requirements. Although the prediction and treatment of interior cabin noise is a secondary goal, it is recognized that new tools and methods may be needed due to the uniqueness of the vehicle design and the presence of both acoustic and structure-borne loads. Ground & Flight Testing has been a critical part of validating noise reduction technologies and verifying that an air vehicle is ready for certification. UAM vehicles introduce new challenges for test procedures such as different source noise directivity, unsteady sources due to maneuvers, and a variety of takeoff and approach trajectories. The operating environment will be more complex than current aircraft with the introduction of vertiports in populated areas with “urban canyons” making reflections an important part of noise prediction and annoyance. It is expected that new test procedures and measurement methods will be necessary. Consideration will need to be given for both piloted and autonomous operations. Human Response & Metrics may be very different for UAM noise compared to current experience with airport noise. Current metrics used to certify rotorcraft and fixed-wing aircraft may not be as useful for evaluating UAM noise. Operations at lower altitudes may influence annoyance. Psychoacoustic and community testing will be needed to quantify annoyance and assess appropriate metrics. In addition to conventional noise level metrics, considerations such as audibility and temporal variation of the sound may be required. Differences between indoor or outdoor exposure will have an impact with dependence on urban and residential flight paths. Aircraft noise is currently regulated at a national level and typically involves partnerships with the industry to establish regulations. Regulators realize that current policies and procedures may not be appropriate for some of the emerging air vehicles and new procedures may be needed to address UAM noise. Development of new policies and procedures are needed so that local communities do not hastily attempt to establish their own restrictions that will both limit growth of the market and create an inconsistent and confusing regulatory environment. To expedite this development, it is crucial early measurement data are shared through partnership arrangements to support both noise certification and noise modeling/noise assessment. At the same time, an effective engagement strategy should be developed to address local community noise issues associated with UAM vehicles and flight operations as they arise.

urban air mobility↗
NASA NTRS1996

Response of the neuromuscular unit to spaceflight: what has been learned from the rat model

Despite the inherent limitations placed on spaceflight investigations, much has been learned about the adaptations of the neuromuscular system to weightlessness from studies of rats flown for relatively short periods (approximately 4-22 days). Below is a summary of the major effects of spaceflight observed in muscles of rats that are not in their rapid growth stage: 1. Skeletal muscles atrophy rapidly during spaceflight; significant atrophy is observed as early as after 4 days of flight. 2. The atrophic response appears to be related to the primary function of the muscle. In the hindlimb, the relative amount of atrophy can be characterized as slow extensors > fast extensors > fast flexors. This pattern of relative atrophy does not appear to be occurring in the forelimb; however, not enough data are available to draw any definitive conclusions at this time. 3. Both slow and fast fibers atrophy during spaceflight, with the largest fibers within an individual muscle generally showing the greatest atrophic response. Interestingly, the amount of fiber atrophy appears to reach a plateau after about 14 days of flight. 4. Adaptations have been observed in the concentration and content of all muscle proteins pools, with the protein pools in slow muscles the most affected. 5. Some slow and fast fibers in predominantly slow and fast muscles show shifts in their histochemical and biochemical properties, toward those observed in a "faster" phenotype. 6. Some fibers, presumably expressing slow MHC isoforms before flight, begin to express fast MHC isoforms during flight. 7. The oxidative capacity of the muscles or fibers is relatively unaffected by spaceflight, particularly in the slow muscles. Any change in whole-body fatigability associated with spaceflight most likely reflects the loss in muscle and fiber mass. 8. The glycolytic capacity of the muscles and muscle fibers is enhanced after spaceflight. This metabolic adaptation seems to be related to the shift in the contractile proteins towards "faster" isoforms. 9. The vascularity of muscles appears to be maintained after flight, based, at least, on histological observations of capillarity. 10. The force capabilities of the muscles and fibers appear to decrease in parallel with the decreases in size, i.e., the specific tension is not significantly affected after flight. 11. Changes in the speed-related properties of the slow muscles are consistent with the adaptations in the myosin molecule, i.e., the slow muscles and some fibers in the slow muscles become "faster." 12. Some muscle fiber and neuromuscular junction damage has been observed after flight, particularly in the slow muscles. The extent of damage may be related to the amount of time that the muscles are allowed to reload before removal, i.e., in general, shorter intervals result in less fiber damage. 13. Adaptations in the motor (ventral horn) and sensory (spinal ganglia) neurons have been quite variable, but this may be related to the amount of time that the muscles are allowed to reload before removal. Morphological adaptations after relatively short periods of reloading may reflect a decrease in the activation of the neural elements during flight.

manned↗
NASA NTRSDate not supplied

Using XR for Improving Scientific Discovery With Numerical Weather Models

Earth science (ES) digital twins will help us understand the complex interactions and interrelationships that make up our Earth system and the impacts of earth science phenomena on it. Our work addresses two underdeveloped areas in current ES digital twin work: improving the understanding and interaction with ES model outputs by using Virtual and Mixed Reality (XR) tools and improving the non-intuitive mapping of continuous ES natural phenomena to gridded reference frames in current numerical models. Traditionally, scientists working on ES view and analyze the results of calculated or measured observables with static 1-dimensional (1D), 2D or 3D plots displayed on flat computer screens or paper. Using such limited mediums, it can be very difficult to identify, track and understand the evolution of key features due to poor viewing angles and the nature of flat computer screens. In addition, numerical models, such as the NASA Goddard Earth Observing System (GEOS) ES model, are almost exclusively formulated, visualized and analyzed in an Eulerian reference frame with fixed grid points in space and time. However, ES phenomena such as convective clouds, hurricanes and wildfire smoke plumes are visualized and analyzed in a Lagrangian reference frame: therefore it is often difficult and unnatural to understand these phenomena in relation to each other, visualized either in an Eulerian or Lagrangian context. In 3D visualizations, data generally takes one of three forms: gridded (e.g., voxelized) data, where space is divided into regions; point clouds, where data is represented as a set of points; and meshes, where objects are rendered as surfaces composed of small polygons (usually triangles). A gridded, Eulerian reference frame has been the default representation for the 2D visual analysis of atmospheric data in part because the numerical methods used to generate atmospheric model data in the first place use a gridded approach, with equations defining the relationships between the physical variables in each of a grid's cells across successive timesteps. In our work, we are particularly interested in data from GEOS. Another reason why gridded representations tend to be used for visualizing data from such models is because trajectories are difficult to interpret from representations on 2D surfaces, due to line-of-sight ambiguity. Instead of a fixed grid from GEOS, we embed a trajectory model to simulate particles' movement throughout a GEOS run. We then ingest these particle trajectories as animated point clouds with a NASA open source XR toolkit, the Mixed Reality Exploration Toolkit (MRET), and merge GEOS data with ES phenomena data onto one combined visualization that the user can intuitively interact with. Efficient rendering of arbitrarily large point clouds is an ongoing challenge being addressed by the computer science community, with the GPU-based optimizations and efficient GPU memory utilization a common theme of recent advances, especially for XR, where sustained high frame rate is mandatory to save the user from suffering due to simulation sickness. In this work, we describe and evaluate our progress in choosing and implementing appropriate methods for rendering arbitrarily large point clouds within MRET for XR. While tracking the XR headset enables the immersion of a user within a 3D scene of a data visualization, tracking of XR handheld controllers or user’s hands enables us to implement intuitive user interactions with the visualized datasets. Conventional tools require a user working with an ES visualization to conduct many interactions to commit their intended selections or manipulations with a visualized dataset; for example to specify a set of points in 3D space. Doing so in a 2D flat screen interface has traditionally required specifying a set of points in three distinct 2D coordinate systems (XY, XZ, and YZ), which is cumbersome. In other scientific domains, it has been shown that specifying or selecting a location or volume in XR using handheld controllers or tracked hands allows for greater speed and accuracy. We anticipate the same will hold true for atmospheric data, and we will share initial results of measuring the utility of such an interface. Notably, as the data being visualized is generated by GEOS as a prediction based on initial conditions, an intended application of our tool is to serve as part of an iterative feedback loop. Through XR, a scientist will review and manipulate a GEOS model run, modifying the conditions as needed to do subsequent runs of GEOS. Thereby, XR-based improvements to speed and accuracy of 3D tagging of points minimizes the effort required by both the scientist and the computer cluster conducting the necessary calculations.

Thomas Grubb↗
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 NTRS2011

Solar Dynamics Observatory Guidance, Navigation, and Control System Overview

The Solar Dynamics Observatory (SDO) was designed and built at the Goddard Space Flight Center, launched from Cape Canaveral on February 11, 2010, and reached its final geosynchronous science orbit on March 16, 2010. The purpose of SDO is to observe the Sun and continuously relay data to a dedicated ground station. SDO remains Sun-pointing throughout most of its mission for the instruments to take measurements of the Sun. The SDO attitude control system (ACS) is a single-fault tolerant design. Its fully redundant attitude sensor complement includes sixteen coarse Sun sensors (CSSs), a digital Sun sensor (DSS), three two-axis inertial reference units (IRUs), and two star trackers (STs). The ACS also makes use of the four guide telescopes included as a part of one of the science instruments. Attitude actuation is performed using four reaction wheels assemblies (RWAs) and eight thrusters, with a single main engine used to provide velocity-change thrust for orbit raising. The attitude control software has five nominal control modes, three wheel-based modes and two thruster-based modes. A wheel-based Safehold running in the attitude control electronics box improves the robustness of the system as a whole. All six modes are designed on the same basic proportional-integral-derivative attitude error structure, with more robust modes setting their integral gains to zero. This paper details the final overall design of the SDO guidance, navigation, and control (GN&C) system and how it was used in practice during SDO launch, commissioning, and nominal operations. This overview will include the ACS control modes, attitude determination and sensor calibration, the high gain antenna (HGA) calibration, and jitter mitigation operation. The Solar Dynamics Observatory mission is part of the NASA Living With a Star program, which seeks to understand the changing Sun and its effects on the Solar System, life, and society. To this end, the SDO spacecraft carries three Sun-observing instruments: Helioseismic and Magnetic Imager (HMI), led by Stanford University; Atmospheric Imaging Assembly (AIA), led by Lockheed Martin Space and Astrophysics Laboratory; and Extreme Ultraviolet Variability Experiment (EVE), led by the University of Colorado. The basic mission is to observe the Sun for a very high percentage of the 5-year mission (10-year goal) with long stretches of uninterrupted observations and with constant, high-data-rate transmission to a dedicated ground station to be located in White Sands, New Mexico. These goals guided the design of the spacecraft bus that will carry and service the three-instrument payload. Overarching design goals for the bus are geosynchronous orbit, near-constant Sun observations with the ability to fly through eclipses, and constant HGA contact with the dedicated ground station. A three-axis stabilized ACS is needed both to point at the Sun accurately and to keep the roll about the Sun vector correctly positioned with respect to the solar north pole. This roll control is especially important for the magnetic field imaging of HM I. The mission requirements have several general impacts on the ACS design. Both the AIA and HMI instruments are very sensitive to the blurring caused by jitter. Each has an image stabilization system (ISS) with some ability to filter out high frequency motion, but below the bandwidth of the ISS the control system must compensate for disturbances within the ACS bandwidth or avoid exciting jitter at higher frequencies. Within the ACS bandwidth, the control requirement imposed by AIA is to place the center of the solar disk no more than 2 arc sec, 3 , from a body-defined target based on one of the GTs that accompany the instrument. This body-defined target, called the science reference boresight (SRB), was determined from the postlaunch orientation of the GTs by averaging the bounding telescope boresights for pitch to get a pitch SRB coordinate, and by averaging the bounding boresights for yaw toet the yaw SRB coordinate. The location of this SRB in the 0.5-deg field-of-view for each GT then becomes the central target for each telescope; one GT is selected for use as the ACS controlling guide telescope (CGT) at any given time. Fine Sun-pointing is effected based on this SRB for all three instruments when the Sun is within the linear range of the CGT. In addition to limiting jitter, HMI science requires averaging several observations, making the instrument sensitive to low frequency motion that induces differential motion between each observation. This requires the spacecraft attitude to be stable about the roll axis to approximately 10 arcsec over a ten-minute period. Instrument calibrations require that the spacecraft point the SRB up to 2.5 degrees in pitch and yaw away from the center of the Sun, placing the Sun outside the field-of-view of the guide telescopes. In such instances, when the GTs cannot provide the definitive target for the ACS, on-board attitude determination combined with ephemeris prediction of the Sun direction must provide the definitive target. EVE is capable of observing the Sun with less dependence on attitude control. However, the ground data processing needs for calibrations result in the most strict attitude knowledge requirements for the mission: [35,70,70] arcsec, 3 , of knowledge with respect to the center of the solar disk. In addition to driving the ACS sensor selection, the knowledge requirements, which have their effect primarily during Inertial mode calibrations, drive the accuracy requirements for the solar ephemeris. The need to achieve and maintain geosynchronous orbit (GEO) drove the need for high-efficiency propulsive systems and appropriate attitude control. The main engine provided high specific impulse for the maneuvers to attain GEO, while the smaller ACS thrusters managed the disturbance torques of the larger engine and provided the capability for much smaller adjustment burns on orbit. SDO s large solar profile means that solar radiation pressure is a large torque disturbance, and the momentum buildup from this disturbance and the GEO altitude drives the ACS to use thrusters to manage vehicle momentum. The demanding data capture budget for the mission, however, requires SDO to avoid frequent thruster maneuvers, while concerns about on-orbit jitter restrict the maximum desired wheel speeds desired from the RWAs. The plan for on-orbit wheel speed and momentum management will be discussed as well as what is now being done in operation after the jitter environment was characterized. The SDO ACS hardware complement is single-fault tolerant. Two main processors carry virtually identical copies of the command and data handling and ACS software, and two identical attitude control electronics (ACE) boxes carry Coldfire processors with contingency ACS software and other hardware interface cards; the ACE structure allows reaction wheels to be commanded by the Sun-pointing Safehold independent of the Mil Std 1553 data bus. The sixteen Adcole CSSs are grouped into primary and backup sets of eight sensors, each set providing the ability to calculate a sun vector. Each set of eight eyes provides full 4 -steradian coverage. The Adcole DSS comprises an optics head and a separate electronics box providing a 1553 data interface. The electronics box is mounted inside the Faraday cage created by the spacecraft bus module. The DSS head with its 32- deg square FOV is mounted on the instrument module with its boresight along the spacecraft X axis, nearly aligned with the Sun during observations. Adcole has designed the DSS calibration parameters so that the accuracy is 0.24 arcminutes within 10 deg of the boresight, and diminishes to 3 arcminutes as the Sun moves towards the edges of its FOV . This DSS calibration scheme provides higher accuracy attitude determination over the range of the instrument calibration maneuvers.

Morgenstern, Wendy M.↗
NASA NTRSDate not supplied

VALENTInE: A Concept for a New Frontiers Class Long Duration In-Situ Balloon Mission to Venus

Venus and Earth are similar in bulk composition, size, density, and approximate distance from the Sun, yet Venus’s modern-day climate and surface geology is distinctly different [1]. Previous missions to Venus revealed unusual volcanic features, possible continental crust, widespread volcanic plains, a weak magnetic field [2], and insights into the Venusian atmosphere [3,4]. Unfortunately, data from these missions were limited in spatial and temporal resolution and global extent. A future mission to Venus is critical to address fundamental questions surrounding the chemical composition and dynamics of the Venusian atmosphere [5], its geologic history [6,7], its internal structure [8,9], and its habitability throughout time [10,11]. We present the Venus Air and Land Expedition: a Novel Trailblazer for In situ Exploration (VALENTInE) mission to meet this need. VALENTInE is a variable altitude balloon that will passively float in Venus’s atmosphere between 45 and 55 km altitude. VALENTInE will acquire atmospheric data at varying latitudes and longitudes in addition to mapping the surface geomorphology and mineralogy across multiple terrains. Mission Objectives: The VALENTInE mission concept is driven by four main science objectives: 1. Determine whether the driving force of the superrotation of Venus’s atmosphere is caused by horizontal or vertical momentum transport. 2. Determine whether the atmospheric composition and noble gas inventory of the Venusian atmosphere is a product of outgassing from the initial protoplanetary source or if there are significant contributions from exogenic sources. 3. Determine whether the tesserae regions (particularly Aphrodite Terra) are felsic and relatively older than surrounding regions. 4. Determine if there is any evidence of a recent dynamo preserved in the rock record of Venus. Mission Overview: A balloon architecture provides a robust system that can survive long-term in the Venusian environment while taking accurate measurements of the lower cloud deck and surface. Two prior balloon missions to Venus, VEGA 1 and VEGA 2 in 1984, have demonstrated the potential for such planetary exploration; however, these missions were short-lived (46 hr), with limited range (54 km). Our spacecraft concept consists of a battery powered balloon with a gondola and flyby carrier stage (Fig. 1). Our novel design allows for at least 15 days of atmospheric exploration, including multiple ascents and descents in the Venus atmosphere. VALENTInE is baselined to launch in 2032, cruise on solar power for 128 days, enter, descend, and inflate (EDI) into Venus’s atmosphere above Aphrodite Terra, and float in Venus’s atmosphere for a total nominal mission duration of 15 days. The balloon, able to control its altitude by changing its buoyancy, will make one full circumnavigation every 4–8 days and move poleward ~1 o latitude per day; it will be passively directed by the horizontal air currents on Venus. The balloon can only be controlled in the z direction, and the expected latitude range is ±10° from EDI. The balloon itself will be a tracer for atmospheric structures such as zonal winds [5, 12]. During the 15-day operational period, there will be five dips to 45 km for a compositional study of the lower atmosphere and geological and magnetic mapping of the surface. Dips are used to take images and measurements closer to the surface and to obtain in situ vertical atmospheric profiles between 45 and 55 km. Instruments: The VALENTInE instrument payload will allow for extensive study of the geology, atmosphere, and interior of Venus. The instrument suite consists of six instruments. The mission profile for each instrument is shown in Fig. 2. Lower Atmosphere Meteorology Analyzer (LAMA) is an atmospheric structure instrument consisting of a thermometer, barometer, and accelerometer to continuously measure temperature and pressure as a function of altitude, longitude, and latitude. TracE and Noble Gas Investigator (TENGI) is aneutral mass spectrometer used to sample the dense atmosphere. TENGI will operate continuously at 45 km and 55 km and will measure D/H ratios, as well as Ne, Ar, and O isotopic ratios. Kilometer Scale Spectral Imager (KSSI) is amultispectral imager (850-1150 nm) used to resolve surface features on the order of km. Near InfraRed Multispectral Photometer (NIRMP) is an Infrared (IR) photometer used to image the surface through the clouds to characterize mineral assemblages at five different areas of the surface. NIRMP will be operated during dwell. ELevation REConnaissance (ELREC) is a light detection and ranging (LIDAR) instrument used for measuring topography at five different areas of the surface. The ELREC data will be combined with those of NIRMP to determine mineral assemblages and how they correlate to topography. Magnetic Exploration and Interior Detective (MEID) is a magnetometer used to detect any near-surface magnetic anomalies. The magnetometer will continuously operate at all altitudes. Mission Design: The spacecraft’s propulsion system will have a wet mass of 930 kg and will be launched on an Atlas V rocket. The spacecraft will jettison the payload upon arrival in Venus’s atmosphere before decelerating to orbital velocity. Figure 2. Notional power-dip profile while the balloon is on the dayside. Risks and Challenges: Raising and lowering the spacecraft to dip beneath the haze layer require a large amount of energy. This issue was partly compensated by limiting the number of dips over the 15 day prime mission. Power needs for pumping helium are reduced by slowing the descent speed, which consequently increases spatial resolution of in situ measurements between 45 and 55 km. The main limitation for the duration of the mission is the need to carry 15 days’ worth of batteries. The bus experiences external Venusian temperatures ranging from ~27°C at 55 km altitude to ~110°C at 45 km altitude. These high atmospheric temperatures require thermal protection for the bus (instruments, electronics, and flight systems) during its descent, dwell at 45 km, and ascent. The bus is maintained at a mechanically safe temperature range of -10°C to 50°C using white external paint, multi-layer insulation (MLI), mechanical/thermal isolation (e.g. Ti, composites), and ~57 kg of Phase Change Materials (PCM). Images and spectra taken below the haze dominate the available data transmission regardless of the time spent at 45 km. Therefore, less time at the lower altitude mainly reduces the coverage of in situ measurements there. Further, we assumed an unrealistically low-density material for the helium storage tanks on the gondola. However, this issue is partially resolved if we were to jettison 75% of the spent helium storage tanks. After the entry process, the balloon volume remains mostly inflated and storage tanks are required only for reducing balloon volume in dipping to 45 km. This mission was planned against an expected New Frontiers 5 (NF5) cost cap of $1B, as the NF5 call had not yet been released. The mission we describe fits within the predicted cost cap if the flyby carrier stage, responsible for powering the spacecraft during cruise and for relaying in situ measurements back to Earth, can be contributed by another space agency. Acknowledgments: We thank the JPL Planetary Science Summer School, especially A. Nash, J. Scully, K. Mitchell, L. Lowes, and J. Armijo, and our mentors from JPL Team X. Thank you to our review panel for their insightful review. References: [1] Kane, S. et al. (2019) JGR:Ps, 124 , 2015–2028. [2] Phillips, J. L., & Russell, C. T. (1987). JGR: Space Physics , 92 (A3), 2253-2263. [3] Nakamura, M. et al. (2018) Earth Planets Space, 70 (1), 144. [4] Svedhem, H. et al. (2009) JGR:P 114 (E5). [5] Limaye et al. (2009) Decadal Survey White Paper . [6] Ivanov, M. and Head, J. (2011) PSS, 59 (13) , 1559-1600. [7] Smrekar, S. et al. (2018) Space Sci Rev, 214 (5), 88. [8] O’Rourke, J. et al. (2018) EPSL, 502 , 46-56. [9] O’Rourke J. et al. (2019) GRL , (46), 5768–5777 [10] Way, M. et al. (2016) GRL, 43 (16), 8376-8383. [11] Way, M. et al. (2020) JGR:P, 125 (5), e2019JE006276. [12] Preston, R. A. et al. (1986) Science, 231 (4744), 1414-1416.

Mission Concept↗
NASA NTRS2011

Verification of the Solar Dynamics Observatory High Gain Antenna Pointing Algorithm Using Flight Data

The Solar Dynamics Observatory (SDO) is a NASA spacecraft designed to study the Sun. It was launched on February 11, 2010 into a geosynchronous orbit, and uses a suite of attitude sensors and actuators to finely point the spacecraft at the Sun. SDO has three science instruments: the Atmospheric Imaging Assembly (AIA), the Helioseismic and Magnetic Imager (HMI), and the Extreme Ultraviolet Variability Experiment (EVE). SDO uses two High Gain Antennas (HGAs) to send science data to a dedicated ground station in White Sands, New Mexico. In order to meet the science data capture budget, the HGAs must be able to transmit data to the ground for a very large percentage of the time. Each HGA is a dual-axis antenna driven by stepper motors. Both antennas transmit data at all times, but only a single antenna is required in order to meet the transmission rate requirement. For portions of the year, one antenna or the other has an unobstructed view of the White Sands ground station. During other periods, however, the view from both antennas to the Earth is blocked for different portions of the day. During these times of blockage, the two HGAs take turns pointing to White Sands, with the other antenna pointing out to space. The HGAs handover White Sands transmission responsibilities to the unblocked antenna. There are two handover seasons per year, each lasting about 72 days, where the antennas hand off control every twelve hours. The non-tracking antenna slews back to the ground station by following a ground commanded trajectory and arrives approximately 5 minutes before the formerly tracking antenna slews away to point out into space. The SDO Attitude Control System (ACS) runs at 5 Hz, and the HGA Gimbal Control Electronics (GCE) run at 200 Hz. There are 40 opportunities for the gimbals to step each ACS cycle, with a hardware limitation of no more than one step every three GCE cycles. The ACS calculates the desired gimbal motion for tracking the ground station or for slewing, and sends the command to the GCE at 5 Hz. This command contains the number of gimbals steps for that ACS cycle, the direction of motion, the spacing of the steps, and the delay before taking the first step. The AIA and HMI instruments are sensitive to spacecraft jitter. Pre-flight analysis showed that jitter from the motion of the HGAs was a cause of concern. Three jitter mitigation techniques were developed to overcome the effects of jitter from different sources. The first method is the random step delay, which avoids gimbal steps hitting a cadence on a jitter-critical mode by pseudo-randomly delaying the first gimbal step in an ACS cycle. The second method of jitter mitigation is stagger stepping, which forbids the two antennas from taking steps during the same ACS cycle in order to avoid constructively adding jitter from two antennas. The third method is the inclusion of an instrument No Step Request (NSR), which allows the instruments to request a stoppage in gimbal stepping during the times when they are taking images. During the commissioning phase of the mission, a jitter test was performed onboard the spacecraft. Various sources of jitter, such as the reaction wheels, the High Gain Antenna motors, and the motion of the instrument filter wheels, were examined to determine the level of their effect on the instruments. During the HGA portion of the test, the jitter amplitudes from the single step of a gimbal were examined, as well as the amplitudes due to the execution of various gimbal rates. These jitter levels are compared with the gimbal jitter allocations for each instrument. Additionally, the jitter test provided insight into a readback delay that exists with the GCE. Pre-flight analysis suggested that gimbal steps scheduled to occur during the later portion of an ACS cycle would not be read during that cycle, resulting in a delay in the telemetered current gimbal position. Flight data from the jitter test confirmed this expectation. Analysis is presentehat shows the readback delay does not have a negative impact on gimbal control. The decision was made to consider implementing two of the jitter mitigation techniques on board the spacecraft: stagger stepping and the NSR. Flight data from two sets of handovers, one set without jitter mitigation and the other with mitigation enabled, were examined. The trajectory of the predicted handover was compared with the measured trajectory for the two cases, showing that tracking was not negatively impacted with the addition of the jitter mitigation techniques. Additionally, the individual gimbal steps were examined, and it was confirmed that the stagger stepping and NSRs worked as designed. An Image Quality Test was performed to determine the amount of cumulative jitter from the reaction wheels, HGAs, and instruments during various combinations of typical operations. In this paper, the flight results are examined from a test where the HGAs are following the path of a nominal handover with stagger stepping on and HMI NSRs enabled. In this case, the reaction wheels are moving at low speed and the instruments are taking pictures in their standard sequence. The flight data shows the level of jitter that the instruments see when their shutters are open. The HGA-induced jitter is well within the jitter requirement when the stagger step and NSR mitigation options are enabled. The SDO HGA pointing algorithm was designed to achieve nominal antenna pointing at the ground station, perform slews during handover season, and provide three HGA-induced jitter mitigation options without compromising pointing objectives. During the commissioning phase, flight data sets were collected to verify the HGA pointing algorithm and demonstrate its jitter mitigation capabilities.

Bourkland, Kristin L.↗