Onboard Atmospheric Modeling and Prediction for Autonomous Aerobraking Missions

Aerobraking has proven to be an effective means of increasing the science payload for planetary orbiting missions and/or for enabling the use of less expensive launch vehicles. Though aerobraking has numerous benefits, large operations cost have been required to maintain the aerobraking time line without violating aerodynamic heating or other constraints. Two operations functions have been performed on an orbit by orbit basis to estimate atmospheric properties relevant to aerobraking. The Navigation team typically solves for an atmospheric density scale factor using DSN tracking data and the atmospheric modeling team uses telemetric accelerometer data to recover atmospheric density profiles. After some effort, decisions are made about the need for orbit trim maneuvers to adjust periapsis altitude to stay within the aerobraking corridor. Autonomous aerobraking would reduce the need for many ground based tasks. To be successful, atmospheric modeling must be performed on the vehicle in near real time. This paper discusses the issues associated with estimating the planetary atmosphere onboard and evaluates a number of the options for Mars, Venus and Titan aerobraking missions

Autonomous Aerobraking: A Design, Development, and Feasibility Study

Aerobraking has been used four times to decrease the apoapsis of a spacecraft in a captured orbit around a planetary body with a significant atmosphere utilizing atmospheric drag to decelerate the spacecraft. While aerobraking requires minimum fuel, the long time required for aerobraking requires both a large operations staff, and large Deep Space Network resources. A study to automate aerobraking has been sponsored by the NASA Engineering and Safety Center to determine initial feasibility of equipping a spacecraft with the onboard capability for autonomous aerobraking, thus saving millions of dollars incurred by a large aerobraking operations workforce and continuous DSN coverage. This paper describes the need for autonomous aerobraking, the development of the Autonomous Aerobraking Development Software that includes an ephemeris estimator, an atmospheric density estimator, and maneuver calculation, and the plan forward for continuation of this study

Autonomous Aerobraking Using Thermal Response Surface Analysis

Aerobraking is a proven method of significantly increasing the science payload that can be placed into low Mars orbits when compared to an all propulsive capture. However, the aerobraking phase is long and has mission cost and risk implications. The main cost benefit is that aerobraking permits the use of a smaller and cheaper launch vehicle, but additional operational costs are incurred during the long aerobraking phase. Risk is increased due to the repeated thermal loading of spacecraft components and the multiple attitude and propulsive maneuvers required for successful aerobraking. Both the cost and risk burdens can be significantly reduced by automating the aerobraking operations phase. All of the previous Mars orbiter missions that have utilized aerobraking have increasingly relied on onboard calculations during aerobraking. Even though the temperature of spacecraft components has been the limiting factor, operational methods have relied on using a surrogate variable for mission control. This paper describes several methods, based directly on spacecraft component maximum temperature, for autonomously predicting the subsequent aerobraking orbits and prescribing apoapsis propulsive maneuvers to maintain the spacecraft within specified temperature limits. Specifically, this paper describes the use of thermal response surface analysis in predicting the temperature of the spacecraft components and the corresponding uncertainty in this temperature prediction

An Atmospheric Variability Model for Venus Aerobraking Missions

Aerobraking has proven to be an enabling technology for planetary missions to Mars and has been proposed to enable low cost missions to Venus. Aerobraking saves a significant amount of propulsion fuel mass by exploiting atmospheric drag to reduce the eccentricity of the initial orbit. The solar arrays have been used as the primary drag surface and only minor modifications have been made in the vehicle design to accommodate the relatively modest aerothermal loads. However, if atmospheric density is highly variable from orbit to orbit, the mission must either accept higher aerothermal risk, a slower pace for aerobraking, or a tighter corridor likely with increased propulsive cost. Hence, knowledge of atmospheric variability is of great interest for the design of aerobraking missions. The first planetary aerobraking was at Venus during the Magellan mission. After the primary Magellan science mission was completed, aerobraking was used to provide a more circular orbit to enhance gravity field recovery. Magellan aerobraking took place between local solar times of 1100 and 1800 hrs, and it was found that the Venusian atmospheric density during the aerobraking phase had less than 10% 1 sigma orbit to orbit variability. On the other hand, at some latitudes and seasons, Martian variability can be as high as 40% 1 sigmaFrom both the MGN and PVO mission it was known that the atmosphere, above aerobraking altitudes, showed greater variability at night, but this variability was never quantified in a systematic manner. This paper proposes a model for atmospheric variability that can be used for aerobraking mission design until more complete data sets become available

Implementation and Simulation Results using Autonomous Aerobraking Development Software

An Autonomous Aerobraking software system is currently under development with support from the NASA Engineering and Safety Center (NESC) that would move typically ground-based operations functions to onboard an aerobraking spacecraft, reducing mission risk and mission cost. The suite of software that will enable autonomous aerobraking is the Autonomous Aerobraking Development Software (AADS) and consists of an ephemeris model, onboard atmosphere estimator, temperature and loads prediction, and a maneuver calculation. The software calculates the maneuver time, magnitude and direction commands to maintain the spacecraft periapsis parameters within design structural load and/or thermal constraints. The AADS is currently tested in simulations at Mars, with plans to also evaluate feasibility and performance at Venus and Titan

Autonomous Aerobraking Development Software: Phase One Performance Analysis at Mars, Venus, and Titan

When entering orbit about a planet or moon with an appreciable atmosphere, instead of using only the propulsion system to insert the spacecraft into its desired orbit, aerodynamic drag can be used after the initial orbit insertion to further decelerate the spacecraft. Several past NASA missions have used this aerobraking technique to reduce the fuel required to deliver a spacecraft into a desired orbit. Aerobraking was first demonstrated at Venus with Magellan in 1993 and then was used to achieve the science orbit of three Mars orbiters: Mars Global Surveyor in 1997, Mars Odyssey in 2001, and Mars Reconnaissance Orbiter in 2006. Although aerobraking itself reduces the propellant required to reach a final low period orbit, it does so at the expense of additional mission time to accommodate the aerobraking operations phase (typically 3-6 months), a large mission operations staff, and significant Deep Space Network (DSN) coverage. By automating ground based tasks and analyses associated with aerobraking and moving these onboard the spacecraft, a flight project could save millions of dollars in operations staffing and DSN costs (Ref. 1)

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment