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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 1,225 records · Page 68
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Convective Enhancement of Icing Roughness Elements in Stagnation Region Flows

To improve existing ice accretion simulation codes, more data regarding ice roughness and its effects on convective heat transfer are required. To build on existing research on this topic, this study used the Vertical Icing Studies Tunnel (VIST) at NASA Glenn Research to model realistic ice roughness in the stagnation region of a NACA 0012 airfoil. Using the VIST, a test plate representing the leading 2% chord of the airfoil was subjected to flows of 7.62 m/s (25 ft/s), 12.19 m/s (40 ft/s), and 16.76 m/s (55 ft/s). The test plate was fitted with 3 surfaces, each with a different representation of ice roughness: 1) a control surface with no ice roughness, 2) a surface with ice roughness with element height scaled by 10x and streamwise rough zone width from the stagnation point scaled by 10x, and 3) a surface with ice roughness with element height scaled by 10x and streamwise rough zone width from the stagnation point scaled by 25x. Temperature data from the tests were recorded using an infrared camera and thermocouples imbedded in the test plate. From the temperature data, a convective heat transfer coefficient map was created for each case. Additional testing was also performed to validate the VIST's flow quality. These tests included five-hole probe and hot-wire probe velocity traces to provide flow visualization and to study boundary layer formation on the various test surfaces. The knowledge gained during the experiments will help improve ice accretion codes by providing heat transfer coefficient validation data and by providing flow visualization data helping understand current and future experiments performed in the VIST.

Icing↗
NASA NTRS2016

The Asteroid Redirect Mission (ARM): Exploration of a Former Binary NEA?

The National Aeronautics and Space Administration (NASA) is developing the Asteroid Redirect Mission (ARM) as a capability demonstration for future human exploration, including use of high-power solar electric propulsion, which allows for the efficient movement of large masses through deep space. The ARM will also demonstrate the capability to conduct proximity operations with natural space objects and crewed operations beyond the security of quick Earth return. The Asteroid Redirect Robotic Mission (ARRM), currently in formulation, will visit a large near-Earth asteroid (NEA), collect a multi-ton boulder from its surface, conduct a demonstration of a slow push planetary defense technique, and redirect the multi-ton boulder into a stable orbit around the Moon. Once returned to cislunar space in the mid-2020s, astronauts aboard an Orion spacecraft will dock with the robotic vehicle to explore the boulder and return samples to Earth. The ARM is part of NASA's plan to advance technologies, capabilities, and spaceflight experience needed for a human mission to the Martian system in the 2030s. The ARM and subsequent availability of the asteroidal material in cis-lunar space, provide significant opportunities to advance our knowledge of small bodies in the synergistic areas of science, planetary defense, and in-situ resource utilization (ISRU). The current reference target for the ARM is NEA (341843) 2008 EV5, which may have been the primary body of a former binary system (Busch et al., 2011; Tardivel et al., 2016). The ARRM will perform several close proximity operations to investigate the NEA and map its surface. A detailed investigation of this object may allow a better understanding of binary NEA physical characteristics and the possible outcomes for their evolution. An overview of the ARM robotic and crewed segments, including mission operations, and a discussion of potential opportunities for participation with the ARM will be provided in this presentation.

Abell, P. A.↗
NASA NTRS2016

Acoustic Measurements of a Large Civil Transport Main Landing Gear Model

Microphone phased array acoustic measurements of a 26 percent-scale, Boeing 777-200 main landing gear model with and without noise reduction fairings installed were obtained in the anechoic configuration of the Virginia Tech Stability Tunnel. Data were acquired at Mach numbers of 0.12, 0.15, and 0.17 with the latter speed used as the nominal test condition. The fully and partially dressed gear with the truck angle set at 13 degrees toe-up landing configuration were the two most extensively tested configurations, serving as the baselines for comparison purposes. Acoustic measurements were also acquired for the same two baseline configurations with the truck angle set at 0 degrees. In addition, a previously tested noise reducing, toboggan-shaped fairing was re-evaluated extensively to address some of the lingering questions regarding the extent of acoustic benefit achievable with this device. The integrated spectra generated from the acoustic source maps reconfirm, in general terms, the previously reported noise reduction performance of the toboggan fairing as installed on an isolated gear. With the recent improvements to the Virginia Tech tunnel acoustic quality and microphone array capabilities, the present measurements provide an additional, higher quality database to the acoustic information available for this gear model.

Ravetta, Patricio A.↗
NASA NTRS2014

Guaranteed Road Network Search with Small Unmanned Aircraft

The use of teams of small unmanned aircraft in real-world rapid-response missions is fast becoming a reality. One such application is search and detection of an evader in urban areas. This paper draws on results in graph-based pursuit-evasion, developing mappings from these abstractions to primitive motions that may be performed by aircraft, to produce search strategies providing guaranteed capture of road-bound targets. The first such strategy is applicable to evaders of arbitrary speed and agility, offering a conservative solution that is insensitive to motion constraints pursuers may possess. This is built upon to generate two strategies for capture of targets having a known speed bound that require searcher teams of much smaller size. The efficacy of these algorithms is demonstrated by evaluation in extensive simulation using realistic vehicle models across a spectrum of environment classes.

unmanned aircraft systems↗
NASA NTRS2019

Flood Mapping of Recent Major Hurricane Events with Synthetic Aperture Radar, Commercial Imaging, and Aerial Observations

Floodwater mapping is an important remote sensing process that is used for disaster response, recovery, and damage assessment practices. Developing a system to read in Synthetic Aperture Radar (SAR) data and perform land cover classification will allow for the production of near real-time inundation mapping, enabling government and emergency response entities to get a preliminary idea of the situation. SAR is a unique remote sensing tool. Data in this project was obtained by NASA Jet Propulsion Laboratory’s Uninhabited Aerial Vehicle SAR (UAVSAR), an L-band radar mounted to a Gulfstream III jet. Data collected by UAVSAR is similar to what will be available from the NASA-Indian Space Research Organization (NISAR) mission starting in early 2022. Using Python and ArcGIS applications, a model was developed using training samples taken from NOAA post-event aerial photography and UAVSAR data gathered in the aftermath of Hurricane Florence in September 2018.

Melancon, Alexander M.↗
NASA NTRS2021

High-speed wide-angle interleaved scanning technique for a 3D imaging lidar

In this paper, we describe a novel interleaved scanner for an eye-safe 3D scanning lidar system to measure aerodynamic phenomena in a wind tunnel using elastic backscatter from seeding particles. The scanner assembly consists of a rotating polygon scanner for line scanning along the fast axis, a galvanometer (galvo) scanner for scanning along the slow axis, angular position sensors and motor controllers. The polygon scanner sweeps the lidar beam at up to 10,000 lines/s across a 27 deg angular field-of-regard in the fast axis, while the galvo scanner covers an angular range of 20 deg in the slow axis. Using this scanner, the lidar can perform non-intrusive flow visualization, velocimetry, and hard target mapping at mmscale spatial resolution to a standoff range of 5 m at an update rate of 50 Hz for the full field-of-regard. An interleaved scanning methodology for the acquisition of two snapshots of 3D lidar intensity data with a 100 μs time offset is discussed. The design of the control and data acquisition electronics is described. The effects of rapid scanning on the response of a narrow field-of-view lidar are addressed. The design of the scanner is scalable and can be tailored to meet the requirements of other applications by increasing or decreasing the standoff range, angular field-of-regard and scan rate.

3D Imaging Lidar↗
NASA NTRSDate not supplied

SERENE: Saturn Enceladus Return Explorer with Nuclear Electric Propulsion

A ‘quick’ Enceladus sample return mission concept was developed based on the scientist recommendations at the recent ‘Accelerating Space Science with Nuclear Technology Workshop’. The Nuclear Electric Propulsion spacecraft assumed a follow-on 40 kWe nuclear reactor using the demonstrated 40 kWe Fission Surface Power system, expected in the early 2030s. The NEP vehicle also utilized a set of NEXT-C ion thrusters as well as planned Artemis commercial launchers. By launching the 40 kW NEP vehicle on a Starship and adding the propellants of 15 tankers, the 27t probe could be sent on a direct trajectory to Saturn (no Earth or Jupiter flybys) where NEP was used for Saturn capture, spiral down, spiral up and return to the Earth. A small lander obtained the surface Enceladus sample. Using the 40 kW NEP provided a round-trip time of only 16.5 years. A second option was more attractive from a science perspective whereby the NEP vehicle would deliver a large, 6t chemical lander to low Enceladus orbit where it would grab and return a sample to Earth (similar to the recent Orbilander design but in reverse). After deploying the lander, the NEP vehicle would stay in Saturn space performing a moon tour by orbiting four more moons and mapping the large moon of Titan. This option took slightly longer (18.5 yrs) due to the chemical return leg limitations. Both options demonstrated the agility, payload capability, and sample return goals the workshop recommended. An all-chemical option with two stages was roughly analyzed but took 21.5 yrs and required a Jupiter gravity assist.

Nuclear Electric Propulsion↗
DOE OSTI2020

Beam-beam effects in the Super Proton-Proton Collider

The Supper Proton-Proton Collider is a next-generation hadron collider that is now being designed. A baseline design aims for a peak luminosity of about 1*10^35 cm^-2 s^-1. The focus of this article is the effect of beam-beam interactions which are expected to strongly influence stability in the beams. We start with a discussion of a scheme to generate the crossing angles at the interaction points while also correcting the dispersion thus created. The optics constraints on the achievable \b{eta}* were studied. Weak-strong simulations were performed to study single particle dynamics via tune footprints, frequency map analysis and dynamic aperture calculations. The long-range interactions with the smallest separations are shown to determine the dynamic aperture. Empirical scaling laws for the dependence of the dynamic aperture on the transverse separations and on the number of long-range interactions are found. A tune scan shows several alternative working points with slightly better dynamic aperture than the baseline choice. Finally an option to significantly increase the dynamic aperture by increasing the crossing angle for different choices of \b{eta}* was studied.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Converging towards atomic and nuclear pressures (Final Report)

his report details the development of transformational tools and techniques, allowing the accurate characterization of matter at the most extreme conditions yet studied in the high energy density (HED) domain. Before this work, most experiments quantitatively mapping the nature of matter in the HED realm were performed using planar geometry, which allows for the isolation and measurement of variables in a single thermodynamic state. Such measurements include equation of state variables, optical conductivity, heat transport and more. However, due to a variety of plasma processes, such experiments are limited to below ~10 TPa (100 Mbar) pressures. To achieve higher pressures, convergent experiments are needed. Historically, convergent experiments have not been used for benchmark data because they are integral measurements. That is each part of a convergent target undergoes a time dependent wide range of states, making it difficult to accurately isolate any particular quantity for a given thermodynamic state. This effort developed innovative convergent HED platforms and techniques enabling the exploration of matter from atomic to nuclear scale pressures. This effort also performed pioneering experiments that yielded the first rigorous benchmark data at these extreme conditions. This funding award began with the overarching goal to understand the behavior of matter at extreme (atomic-to-nuclear scale) pressures. The motivation for these goals lie in the fact that every time scientists explore matter beyond the threshold of an atomic unit, there is a fundamental shift in science. The atomic unit for energy, mass, charge, length, and time have each been explored, and each time such a threshold was crossed, a new sequence of discoveries was made resulting in significant awards such as the Nobel Prize. The only unexplored atomic unit is pressure, and this effort set the course for exploring matter at and beyond such pressures. That most of the recently discovered extrasolar planets and stars, as well as matter in the late implosion stages of inertial fusion targets, have deep internal pressures at and beyond atomic pressures amplifies the importance of this effort. Much of the initial work in developing techniques to create these atomic-to-nuclear pressures already existed within the HED community, predominantly at large scale laser facilities such as the National Ignition Facility (NIF) and the Omega60 laser the University of Rochester and although these experiments were routinely performed the ability to extract information about the underlying physical states and processes remained elusive due to the complexity of the experiments, extreme scales in both time and space, and the integrated nature of all the measurements. This work built a rigorous framework so such measurements can routinely be made. This was done in part through the introduction of Bayesian inference techniques into the field of HED science. These techniques allow for the self-consistent extraction of the relevant variables and their explicit and implicit correlations, so as to make use of integrated experimental data to constrain physical models and provide accurate uncertainty bounds for the data. The techniques developed here are fully transparent and proved immediately useful providing quantitative rigorous benchmarks for physical states and processes at some of the most extreme conditions yet explored on Earth. This funding is directly or partly responsible for 7 publications in major peer-reviewed scientific journals, a doctoral thesis, 3 invited talks at major conferences, and the training of a post-doc, 2 graduate students, and 1 undergraduate student. Beyond the effort supported by this award launched the introduction of Bayesian inference into the HED physics community and helped push the usage if modern data-science techniques within the physics community at large.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2022

Optimizing Magnetic Force Microscopy Resolution and Sensitivity to Visualize Nanoscale Magnetic Domains

Magnetic force microscopy (MFM) enables mapping local magnetic fields across a sample surface with nanoscale resolution. To perform MFM, an atomic force microscopy (AFM) probe whose tip has been magnetized vertically (i.e., perpendicular to the probe cantilever) is oscillated at a fixed height above the sample surface. The resultant shifts in the oscillation phase or frequency, which are proportional to the magnitude and sign of the vertical magnetic force gradient at each pixel location, are then tracked and mapped. Although the spatial resolution and sensitivity of the technique increases with decreasing lift height above the surface, this seemingly straightforward path to improved MFM images is complicated by considerations such as minimizing topographical artifacts due to shorter range van der Waals forces, increasing the oscillation amplitude to further improve sensitivity, and the presence of surface contaminants (in particular water due to humidity under ambient conditions). In addition, due to the orientation of the probe's magnetic dipole moment, MFM is intrinsically more sensitive to samples with an out-of-plane magnetization vector. Here, high-resolution topographical and magnetic phase images of single and bicomponent nanomagnet artificial spin-ice (ASI) arrays obtained in an inert (argon) atmosphere glovebox with <0.1 ppm O 2 and H 2 O are reported. Further, optimization of lift height and drive amplitude for high resolution and sensitivity while simultaneously avoiding the introduction of topographical artifacts is discussed, and detection of the stray magnetic fields emanating from either end of the nanoscale bar magnets (~250 nm long and <100 nm wide) aligned in the plane of the ASI sample surface is shown. Likewise, using the example of a Ni-Mn-Ga magnetic shape memory alloy (MSMA), MFM is demonstrated in an inert atmosphere with magnetic phase sensitivity capable of resolving a series of adjacent magnetic domains each ~200 nm wide.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Efficient mapping between void shapes and stress fields using Deep Convolutional Neural Networks with sparse data

Establishing fast and accurate structure-to-property relationships is an important component in the design and discovery of advanced materials. Physics-based simulation models like the finite element method (FEM) are often used to predict deformation, stress, and strain fields as a function of material microstructure in material and structural systems. Such models may be computationally expensive and time intensive if the underlying physics of the system is complex. This limits their application to solve inverse design problems and identify structures that maximize performance. In such scenarios, surrogate models are employed to make the forward mapping computationally efficient to evaluate. However, the high dimensionality of the input microstructure and the output field of interest often renders such surrogate models inefficient, especially when dealing with sparse data. Deep convolutional neural network (CNN) based surrogate models have shown great promise in handling such high-dimensional problems. In this paper, a single ellipsoidal void structure under a uniaxial tensile load represented by a linear elastic, high-dimensional and expensive-to-query, FEM model. We consider two deep CNN architectures, a modified convolutional autoencoder framework with a fully connected bottleneck and a UNet CNN, and compare their accuracy in predicting the von Mises stress field for any given input void shape in the FEM model. Additionally, a sensitivity analysis study is performed using the two approaches, where the variation in the prediction accuracy on unseen test data is studied through numerical experiments by varying the number of training samples from 20 to 100.

surrogate modeling; convolutional neural networks;↗
DOE OSTI2025

Flux Channeling Induced Nanoconfinement and Enhancement of Microwaves Imaged by Rabi Oscillation Mapping

With rapid advances in qubit technologies, techniques for localizing, modulating, and measuring RF fields and their impact on qubit performance are of the utmost importance. Here, we demonstrate that flux-channeling from a permalloy nanowire can be used to achieve localized spatial modulation of an RF field and that the modulated field can be mapped with high resolution by using the Rabi oscillations of an NV center. Rabi maps reveal ∼100 mm wavelength microwaves concentrated in sub-300 nm regions with up to ∼16× power enhancement. This modulation is robust over a 20 dBm power range and has no adverse impact on NV T 2 coherence time. Micromagnetic simulations confirm that the modulated field results from the nanowire’s stray field through its constructive/destructive interference with the incident RF field. Our findings provide a new pathway for controlling qubits, amplifying RF signals, and mapping local fields in various on-chip RF technologies.

Diamond↗
NASA NTRS1991

Electronic map interpretation in a dual-task context

The present experiment was designed to assess the extent to which spatial and verbal-analytic (VA) information processing resources are used in performing a simulated aircraft navigation task. Subjects were required to decide whether a 'match' or a mismatch' existed between a schematic 3D perspective forward field of view and a 2D top-down map. On dual-task trials, this navigation task was concurrently performed with either a VA side-task or with one of two tracking tasks. The data suggest that a VA strategy was most likely to be used when stimuli were simple or were mismatches, whereas a spatial mental rotation strategy was apparently used to confirm complex match stimuli. These results indicate that it may be possible to specify conditions wherein navigation is likely to compete for resources critical to other cockpit activities, such as aircraft control and communication.

Williams, Henry P.↗
NASA NTRSDate not supplied

GeoAI advances in specific landform mapping

Landform mapping (also referred to as geomorphology or geomorphometry) can be divided into two domains: general and specific (Evans 2012). Whereas general landform mapping categorizes all elements of the study area into landform classes, such as ridges, valleys, peaks, and depressions, the mapping of specific landforms requires the delineation (even if fuzzy) of individual landforms. The former is mainly driven by physical properties such as elevation, slope, and curvature. The latter, however, must consider the cognitive (human) reasoning that discriminates individual landforms in addition to these physical properties (Arundel and Sinha 2018). Both mapping forms are important. General geomorphometry is needed to understand geological and ecological processes and as boundary layer input to climate and environmental models. Specific geomorphometry supports such activities as disaster management and recovery, emergency response, transportation, and navigation. In the United States, individual landforms of interest are named in the U.S. Geological Survey (USGS) Geographic Names Information System, a point dataset captured specifically to digitize geographic names from the USGS Historical Topographic Map Collection (HTMC). Named landform extent is represented only by the name placement in the HTMC. Recent work has investigated CNN-based deep learning methods to capture these extents in machine-readable form. These studies first relied on physical properties (Arundel et al. 2020) and then included the HTMC as a band in RGB images in limited testing (Arundel et al. 2023). Results from the HTMC dataset surpassed those using just physical properties and using the HTMC alone performed best due to the hillshading and elevation (contour) data incorporated into the topographic maps. However, results fell short of an operational capacity to map all named landforms in the United States. Thus, our current work expands upon past research by focusing on the HTMC and physical information as inputs and the named landform label extents. Specifically, we propose to leverage pre-trained foundation models for segmentation and optical character recognition (OCR) models to jointly map landforms in the United States. Our approach aims to bridge the disparities among the independent information sources to facilitate informed decision-making. The modeling pipeline performs (1) segmentation using the physical information and (2) information extraction using OCR, in parallel. Then a computer vision approach merges the two branches into a labeled segmentation. References: Arundel, Samantha T., Wenwen Li, and Sizhe Wang. 2020. “GeoNat v1.0: A Dataset for Natural Feature Mapping with Artificial Intelligence and Supervised Learning.” Transactions in GIS 24 (3): 556–72. https://doi.org/10.1111/tgis.12633. Arundel, Samantha T, and Gaurav Sinha. 2018. “Validating GEOBIA Based Terrain Segmentation and Classification for Automated Delineation of Cognitively Salient Landforms BT - Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017).” In Proceedings of Workshops and Posters at the 13th International Conference on Spatial Information Theory (COSIT 2017), Lecture Notes in Geoinformation and Cartography, edited by Paolo Fogliaroni, Andrea Ballatore, and Eliseo Clementini, 9–14. Cham: Springer International Publishing. Arundel, Samantha T., Gaurav Sinha, Wenwen Li, David P. Martin, Kevin G. McKeehan, and Philip T. Thiem. 2023. “Historical Maps Inform Landform Cognition in Machine Learning.” Abstracts of the ICA 6 (August): 1–2. https://doi.org/10.5194/ica-abs-6-10-2023. Evans, Ian S. 2012. “Geomorphometry and Landform Mapping: What Is a Landform?” Geomorphology 137 (1): 94–106. https://doi.org/10.1016/j.geomorph.2010.09.029.

machine learning↗
NASA NTRS1986

The Venus radar mapper spacecraft design

This paper discusses the driving mission requirements on the VRM spacecraft, the resulting design, and the spacecraft performance capabilities. The spacecraft design is driven by fine pointing requirements for radar mapping resolution, by extremes of thermal environment in the Venus orbit, and by high instrument power requirements. The spacecraft design emphasized use of as much existing hardware and software as possible to meet the cost constraints. Existing design was used where hardware was not already available. The VRM spacecraft utilizes the spare Voyager High Gain Antenna (HGA) and equipment bus, the Galileo computers and power conditioning equipment, and Mariner Medium Gain Antenna (MGA). Further cost savings were possible by using the HGA for both radar mapping at S-band frequency and high rate data playback at X-band frequency. The use of the Star 48 B solid rocket motor for VOI reduced propulsion system cost and complexity. The design tradeoffs that led to the VRM spacecraft configuration and the performance capabilities of the spacecraft are summarized for each subsystem.

Gamber, T.↗
DOE OSTI2026

Spatially Mapping the CO 2 Alkaline Sorbent Diffuse Microenvironment Using Operando Raman Spectroscopy

When designing a chemical process, the local balance of transport and kinetics, collectively referred to as the diffuse microenvironment, plays a critical role in performance but is difficult to directly observe. This work demonstrates a method of two-dimensional spatial chemical mapping of the diffuse microenvironment in the context of alkali metal hydroxide direct air capture of carbon dioxide using a custom operando gas-absorption flow cell along with confocal Raman spectroscopy. Notably, we observe the concentration boundary layer near the gas–liquid interface and elucidate the interplay of carbonate and bicarbonate ions within it while inferring local hydroxide depletion through continuum modeling. These first of their kind observations provide a technique to compare the performance of direct air capture solvents based on diffuse microenvironment dynamics while also providing metrics important for air contactor design such as boundary layer thickness. Overall, this work showcases a new experimental platform to study interfacial diffuse microenvironments in and outside of the field of direct air capture of carbon dioxide.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2007

GISMO, a 2 mm Bolometer Camera Optimized for the Study of High Redshift Galaxies

The 2mm spectral range provides a unique terrestrial window enabling ground based observations of the earliest active dusty galaxies in the universe and thereby allowing a better constraint on the star formation rate in these objects. We present a progress report for our bolometer camera GISMO (the Goddard-IRAM Superconducting 2-Millimeter Observer), which will obtain large and sensitive sky maps at this wavelength. The instrument will be used at the IRAM 30 m telescope and we expect to install it at the telescope in 2007. The camera uses an 8 x 16 planar array of multiplexed TES bolometers, which incorporates our recently designed Backshort Under Grid (BUG) architecture. GISMO will be very efficient at detecting sources serendipitously in large sky surveys. With the background limited performance of the detectors, the camera provides significantly greater imaging sensitivity and mapping speed at this wavelength than has previously been possible. The major scientific driver for the instrument is to provide the IRAM 30 m telescope with the capability to rapidly observe galactic and extragalactic dust emission, in particular from high-zeta ULI RGs and quasar s, even in the summer season. The instrument will fill in the SEDs of high redshift galaxies at the Rayleigh-Jeans part of the dust emission spectrum, even at the highest redshifts. Our source count models predict that GISMO will serendipitously detect one galaxy every four hours on the blank sky, and that one quarter of these galaxies will be at a redshift of zeta 6.5.

Staguhn, J.↗
NASA NTRS2021

Assessment of M2020 Terrain Relative Landing Accuracy: Flight Performance vs Predicts

Terrain Relative Navigation (TRN) was a critical enabling Entry, Descent, and Landing (EDL) technology that enabled Mars 2020 mission Perseverance rover to land at Jezero crater. TRN pro-vides real-time, autonomous, map-relative position determination and generates a landing target based on a priori knowledge of hazards. The required performance for TRN was to land within 60m of the selected target. The required 60m was sub-allocated to various error sources in three major categories: targeting error, knowledge error, and control error. The targeting error is the error in selecting an appropriate landing target and the knowledge of the target on the surface. It includes the Lander Vision System (LVS) position localization with respect the ground, the synchronization between the Lander Vision System measurement and the main Navigation filter, and errors associated with the LVS Reference Map and Safe Target Selec-tion (STS). The knowledge error is the contribution of knowledge growth from the synchronization with LVS to touchdown. The control error encompasses how accurately the system could stay on the desired reference trajectory. The TRN error budget uses a combination of analysis, simulation, and hardware test-ing results to bound the various error contributions obtained during the verification and validation process. This paper first presents a description the TRN system, focusing on the architecture of LVS and STS. The paper then gives detailed overview of the TRN error budget, with a description of the major error contribu-tions in each of the three categories. Next, the paper gives the results for three versions of the error budget, pre-launch, in-flight pre-landing, and post-landing. The paper compares the pre-flight analysis, the pre-landing analysis using in-flight data during cruise, to the post-landing analysis of the TRN performance. Pre-landing analysis best estimate of the landing performance was 33m, compared to the 60m require-ment. Post-landing analysis estimated a landing accuracy of 8.53m or better, much better than the 33m pre-landing estimate. The actual post-landing imagery calculated the distance of the rover to the targeted location to be 5m. The post-landing analysis closely bounds the image-based assessment of landing accu-racy, indicating the success of the error budget architecture in bounding the landing accuracy, as well as the fidelity of the simulations used to model and predict performance.

Chen, Allen↗