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At least 847 records · Page 47

An Optimization-Based Toolchain for Parametric Mechanism Design

Design-Build-Test approaches for developing spaceflight hardware are prohibitively time and cost intensive and often lead to suboptimal mechanism designs. Approaches that couple machine learning and high-fidelity physics simulation could eliminate the need for hardware prototyping and dramatically accelerate the engineering design cycle, ultimately reducing cost. This work presents a modular NASA-developed toolchain to optimize hardware mechanisms in a virtual environment using numerical optimization and multi-body physics simulation. The toolchain enables multi-objective optimization, generates parametric CAD files that can be further post-processed by an end user, and can be expanded to optimize full systems and non-mechanical parameters such as feedback control variables. We demonstrate the toolchain through an independently verifiable design problem that optimizes wheel radius to achieve a desired linear velocity in a rigid-body physics environment when the wheel rotates at a constant angular speed, and then post-process the parametric CAD file of the optimal design generated by the tool before ultimately manufacturing it via 3D printing. We end with a discussion of how the toolchain can incorporate other analysis tools, including finite element analysis, computational fluid dynamics, and granular media simulations.

Optimization↗

Definition of the Flexible Image Transport System (FITS), Version 3.0

The Flexible Image Transport System (FITS) has been used by astronomers for over 30 years as a data interchange and archiving format; FITS files are now handled by a wide range of astronomical software packages. Since the FITS format definition document (the "standard") was last printed in this journal in 2001, several new features have been developed and standardized, notably support for 64-bit integers in images and tables, variable-length arrays in tables, and new world coordinate system conventions which provide a mapping from an element in a data array to a physical coordinate on the sky or within a spectrum. The FITS Working Group of the International Astronomical Union has therefore produced this new Version 3.0 of the FITS standard, which is provided here in its entirety. In addition to describing the new features in FITS, numerous editorial changes were made to the previous version to clarify and reorganize many of the sections. Also included are some appendices which are not formally part of the standard. The FITS standard is likely to undergo further evolution, in which case the latest version may be found on the FITS Support Office Web site at http://fits.gsfc.nasa.gov/, which also provides many links to FITS-related resources.

Pence, W. D.↗

Color Image Processing and Object Tracking System

This report describes a personal computer based system for automatic and semiautomatic tracking of objects on film or video tape, developed to meet the needs of the Microgravity Combustion and Fluids Science Research Programs at the NASA Lewis Research Center. The system consists of individual hardware components working under computer control to achieve a high degree of automation. The most important hardware components include 16-mm and 35-mm film transports, a high resolution digital camera mounted on a x-y-z micro-positioning stage, an S-VHS tapedeck, an Hi8 tapedeck, video laserdisk, and a framegrabber. All of the image input devices are remotely controlled by a computer. Software was developed to integrate the overall operation of the system including device frame incrementation, grabbing of image frames, image processing of the object's neighborhood, locating the position of the object being tracked, and storing the coordinates in a file. This process is performed repeatedly until the last frame is reached. Several different tracking methods are supported. To illustrate the process, two representative applications of the system are described. These applications represent typical uses of the system and include tracking the propagation of a flame front and tracking the movement of a liquid-gas interface with extremely poor visibility.

Klimek, Robert B.↗

File Specification for GEOS-5 FP-IT (Forward Processing for Instrument Teams)

The GEOS-5 FP-IT Atmospheric Data Assimilation System (GEOS-5 ADAS) uses an analysis developed jointly with NOAA's National Centers for Environmental Prediction (NCEP), which allows the Global Modeling and Assimilation Office (GMAO) to take advantage of the developments at NCEP and the Joint Center for Satellite Data Assimilation (JCSDA). The GEOS-5 AGCM uses the finite-volume dynamics (Lin, 2004) integrated with various physics packages (e.g, Bacmeister et al., 2006), under the Earth System Modeling Framework (ESMF) including the Catchment Land Surface Model (CLSM) (e.g., Koster et al., 2000). The GSI analysis is a three-dimensional variational (3DVar) analysis applied in grid-point space to facilitate the implementation of anisotropic, inhomogeneous covariances (e.g., Wu et al., 2002; Derber et al., 2003). The GSI implementation for GEOS-5 FP-IT incorporates a set of recursive filters that produce approximately Gaussian smoothing kernels and isotropic correlation functions. The GEOS-5 ADAS is documented in Rienecker et al. (2008). More recent updates to the model are presented in Molod et al. (2011). The GEOS-5 system actively assimilates roughly 2 × 10(exp 6) observations for each analysis, including about 7.5 × 10(exp 5) AIRS radiance data. The input stream is roughly twice this volume, but because of the large volume, the data are thinned commensurate with the analysis grid to reduce the computational burden. Data are also rejected from the analysis through quality control procedures designed to detect, for example, the presence of cloud. To minimize the spurious periodic perturbations of the analysis, GEOS-5 FP-IT uses the Incremental Analysis Update (IAU) technique developed by Bloom et al. (1996). More details of this procedure are given in Appendix A. The analysis is performed at a horizontal resolution of 0.625-degree longitude by 0.5-degree latitude and at 72 levels, extending to 0.01 hPa. All products are generated at the native resolution of the horizontal grid. The majority of data products are time-averaged, but four instantaneous products are also available. Hourly data intervals are used for two-dimensional products, while 3-hourly intervals are used for three-dimensional products. These may be on the model's native 72-layer vertical grid or at 42 pressure surfaces extending to 0.1 hPa. This document describes the gridded output files produced by the GMAO near real-time operational GEOS-5 FP-IT processing in support of the EOS instrument teams. Additional details about variables listed in this file specification can be found in a separate document, the GEOS-5 File Specification Variable Definition Glossary.

3D Var↗

Software Aids In Graphical Depiction Of Flow Data

Interactive Data Display System (IDDS) computer program is graphical-display program designed to assist in visualization of three-dimensional flow in turbomachinery. Grid and simulation data files in PLOT3D format required for input. Able to unwrap volumetric data cone associated with centrifugal compressor and display results in easy-to-understand two- or three-dimensional plots. IDDS provides majority of visualization and analysis capability for Integrated Computational Fluid Dynamics and Experiment (ICE) system. IDDS invoked from any subsystem, or used as stand-alone package of display software. Generates contour, vector, shaded, x-y, and carpet plots. Written in C language. Input file format used by IDDS is that of PLOT3D (COSMIC item ARC-12782).

Stegeman, J. D.↗

Managing the Digital Thread for Structural Applications With Fit for Purpose Materials

With the increased emphasis on reducing the cost and time to market of new materials, the need for analytical tools that enable the virtual design and optimization of materials throughout their processing - internal structure - property - performance envelope, along with the capturing and storing of the associated material and model information across its lifecycle, has become critical. This need is also fueled by the demands for higher efficiency in material testing; consistency, quality and traceability of data; product design; engineering analysis; as well as control of access to proprietary or sensitive information. Consequently, at NASA Glenn Research Center a robust information management system that manages the digital thread across the full material life (i.e., capture, analysis, maintenance, and dissemination of data) cycle directed at the design of ‘fit-for-purpose materials’ is under development. To this end the Application Table has been incorporated within NASA Glenn Research Center’s ICME Information Management framework within the ANSYS Granta MI tool. The Application Table provides a place where material and structural application information/requirements can be linked to marry the “design-the-material” (structural engineering) and the “design-with-material” (material science) paradigms and thereby enable application-driven design and optimization of materials and structures. In additional several associated toolsets, specifically: AIMAOS (Automated Information Management Across Organizations and Scales), Py MILab, and JARIMIS (Just A Rather Intelligent Material Interrogation System) are also under development to assist in the judicious automation of this process. AIMOAS offers users an interactive graphical user interface for connecting material information management systems with both commercial and in-house simulation tools at various length scales to enable such automation in the handoff across scales and maintenance of material digital twins and the digital thread. Py MILab, is an automatic framework for the capture, analysis, maintenance, and storage of material test data. Py MILab uses a modular approach for capturing raw data, analyzing the data, and storing the data in a database, interfaced by neutral file structures, to promote plug-and-play capabilities for various analysis types. Finally, JARIMIS is an expert system that integrates various materials informatics tools (e.g., MicroNet, Surrogate ML models, ANSYS Granta MI, etc.) to enable inverse design of materials and facilitate the application of machine learning (ML) and data science with human in the loop decision making to rapidly discover and optimize new materials.

Digital Transformation↗

The Land Analysis System (LAS)

The Land Analysis System (LAS) is an interactive software system, available in the public domain, for the analysis, display, and management of multispectral and other digital image data. The system was developed to support earth sciences research and development activities. LAS provides over 240 applications functions and utilities, a flexible user interface, complete on-line and hardcopy documentation, extensive image data file management, reformatting, and conversion utilities, and high level device independent access to image display hardware. The capabilities are summarized of the latest release of the system (version 5). Emphasis is given to the system portability and the isolation of hardware and software dependencies in this release.

Lu, Yun-Chi↗

Automation for "Direct-to" Clearances in Air-Traffic Control

A method of automation, and a system of computer hardware and software to implement the method, have been invented to assist en-route air-traffic controllers in the issuance of clearances to fly directly to specified waypoints or navigation fixes along straight paths that deviate from previously filed flight plans. Such clearances, called "direct-to" clearances, have been in use since before the invention of this method and system.

Erzberger, Heinz↗

SAVS: A Space and Atmospheric Visualization Science system

The research environment faced by space and atmospheric scientists in the 1990s is characterized by unprecedented volumes of new data, by ever-increasing repositories of unexploited mission files, and by the widespread use of empirical and large-scale computational models needed for the synthesis of understanding across data sets and discipline boundaries. The effective analysis and interpretation of such massive amounts of information have become the subjects of legitimate concern. With SAVS (a Space and Atmospheric Visualization Science System), we address these issues by creating a 'push-button' software environment that mimics the logical scientific processes in data acquisition, reduction, and analysis without requiring a detailed understanding of the methods, networks, and modules that link the tools and effectively execute the functions. SAVS provides (1) a customizable framework for accessing a powerful set of visualization tools based on the popular AVS visualization software with hooks to PV-Wave and access to Khoros modules, (2) a set of mathematical and statistical tools, (3) an extensible library of discipline-specific functions and models (e.g., MSIS, IRI, Feldstein Oval, IGRF, satellite tracking with CADRE-3, etc.), and (4) capabilities for local and remote data base access. The system treats scalar, vector, and image data, and runs on most common Unix workstations. We present a description of SAVS and its components, followed by several applications based on generic research interests in interplanetary and magnetospheric physics (IMP/ISTP), active experiments in space (CRRES), and mission planning focused on the Earth's thermospheric, ionospheric, and mesospheric domains (TIMED).

Szuszczewicz, E. P.↗

Application of ML/AI for Identifying Earth Science Datasets in Research Publications

NASA Data Active Archive Centers, or DAACs, ingest, store and distribute data acquired from satellites, ground systems as well as modelling data. These data are organized by the datasets, each presenting collection of files usually associated with the certain mission, instrument, processing level, parameter(s), algorithm and/or model. The number of datasets offered by a single DAAC to the public varies. GES DISC, for example, currently offers for public use approximately ~1,300 datasets. While each publicly offered dataset comes with supporting documentation, it is challenging for novice and even experienced scientists to navigate among the datasets that offer similar parameters to find the datasets for their particular research application. Supplying dataset documentation with the scientific paper citations that refer to that dataset provides means for the dataset users to educate themselves with the application research that dataset is being used in. Collecting citations of the papers that use the datasets for their research yield valuable insights into application areas of those datasets, information about usage of the dataset groups for specific applications and those application topics. It also gives insights into the “deep metrics” of the dataset usage, as opposed to the common metrics of the dataset usage such as number of users who downloaded the dataset files and volumes of downloaded data. Association of a certain scientific paper with the dataset(s) presents a challenge because most of the paper authors do not properly cite the datasets, datasets usually have cryptic names and Digital Object Identifiers (DOIs) that are used for dataset identification were assigned to the datasets only few years ago. Simple Google or online library search do not provide even meaningful fraction of the results when performed by the dataset name or DOI, however they provide too many results when the search is done by more broader terms such as mission and instrument names. Attempts to create an AI system capable to identify dataset in the scientific papers have already been made using neural networks classifiers on the basis of the dataset mission, instrument and variable name. This method was applied to NASA SEDAC, which has 41 datasets in total. In GES DISC there can be as many as ~100 datasets per mission/instrument with some of the datasets consisting of multiple variables so there is a need for more differentiating parameters for dataset identification in the paper. The approach we are currently investigating is creating AI classifiers that are based on multiple dataset features, or keywords, extracted from the NASA Earthdata Common Dataset Repository (CMR). The features are weighted based on how precisely they can identify a dataset. The classifier uses preprocessed paper text as input and searches for the CMR datasets whose feature sets are the closest to the feature sets contained in the paper. The challenges of dataset identification include variety of ways the paper authors describe the datasets in their papers and incomplete tagging of the CMR dataset description (DIFs).

Irina Gerasimov↗

State-of-the-Art: Small Spacecraft Technology

When the first edition of NASA’s Small Spacecraft Technology State-of-the-art report was published in 2013, 247 CubeSats and 105 other non-CubeSat small spacecraft under 50 kilograms (kg) had been launched worldwide, representing less than 2% of launched mass into orbit over multiple years. In 2013 alone, around 60% of the total spacecraft launched had a mass under 600 kg, and of those under 600 kg, 83% were under 200 kg and 37% were nanosatellites (1). Of the total 1,849 spacecraft launched in 2021, 94% were small spacecraft with an overall mass under 600 kg, and of those under 600 kg, 40% were under 200 kg, and 11% were nanosatellites (1). Since 2013, the fight heritage for small spacecraft has increased by over 30% and has become the primary source to space access for commercial, government, private, and academic institutions. The total number of spacecraft launched in the past 10 years is 5,681 and 45% of those had a mass. As with all previous editions of this report, the 2022 edition captures and distills a wealth of new information available on small spacecraft systems from NASA and other publicly available sources. This report is limited to publicly available information and cannot reflect major advances in development that are not publicly disclosed. We encourage any opportunity to publish mission outcomes and technology development milestones (e.g., via conference papers, press releases, company website) so they can be reflected in this report. Overall, this report is a survey of small spacecraft technologies sourced from open literature; it does not endeavor to be an original source, and only considers literature in the public domain to identify and classify devices. Commonly used sources for data include manufacturer datasheets, press releases, conference papers, journal papers, public filings with government agencies, news articles, presentations, the compendium of databases accessed via NASA’s Small Spacecraft Systems Virtual Institute (S3VI) Information Search, and engagement with companies. Data not appropriate for public dissemination, such as proprietary, export controlled, or otherwise restricted data, are not considered. As a result, this report includes many dedicated hours of desk research performed by subject matter experts reviewing resources noted above. Content in this 2022 edition is based on data available by October 2022. This report should not be considered as a comprehensive overview of all the technologies but a great reference for the current state-of-the-art SmallSat technologies. The organizational approach for each chapter is relatively consistent with previous editions and includes an introduction of the technology, current development status of the technology’s procurable systems, and summary tables of technologies surveyed. The content in each chapter is uniquely organized to present a mini-stand-alone report on spacecraft subsystems. As in previous years, chapters include information from previous editions but are updated with new and maturating technologies and reference missions. Tables in each section provide a convenient summary of the technologies discussed, with explanations and references in the body text. The authors have attempted to isolate trends in the small spacecraft industry to point out which technologies have been adopted after successful demonstration missions. Lastly, the authors tried to use the terms “SmallSat,” “microsatellite,” “nanosatellite,” and “CubeSat” in a consistent manner, even as these terms are often used interchangeably in the space industry. Every subsystem chapter contains updated information to reflect the growth in the small spacecraft market. Significant changes are included in several chapters. The “Complete Spacecraft Platforms” chapter now includes information on the two main market options, hosted payload services and dedicated buses. The “Power” chapter provides information on the development of solid-state batteries with significantly higher energy than the current state-of-theart lithium-ion batteries. A large effort was made to update the “Communications” chapter to appropriately capture the recent technology maturation of optical communications for SmallSats. The “Ground Data Systems and Mission Operations” chapter was updated to reflect the recent establishment of the Near Space Network and influx of SmallSat Optical Ground Stations. The “Guidance, Navigation and Control” chapter was updated to include Lidar sensor technology. The “Deorbit Systems” chapter includes a discussion of recently proposed changes by the Federal Communications Commission (FCC) to limit a spacecraft’s lifetime to no longer than 5 years after end-of-mission. The “Identification and Tracking” Chapter includes updated information on the progress of SmallSat tracking. Finally, this report now encompasses technology funded by NASA’s Small Spacecraft Technology (SST) program’s SmallSat Technology Partnerships (STP) initiative which is described further in this Introduction. The reader can find the included SST technology in the “On the Horizon” section of the “Thermal Systems”, “Communications”, and “Guidance, Navigation, and Control” chapters. A central element of this report is to list state-of-the-art technologies by NASA standard Technology Readiness Level (TRL) as defined by the 2020 NASA Engineering Handbook, found in NASA NPR 7123.1C NASA Systems Engineering Processes and Requirements. The authors have endeavored to independently verify the TRL value of each technology by reviewing and citing published test results or publicly available data to the best of their ability. Where test results and data disagree with vendors’ own advertised TRL, the authors have attempted to engage the vendors to discuss the discrepancy. Readers are strongly encouraged to follow the references cited in the literature describing the full performance range and capabilities of each technology. Readers of this report should reach out to individual companies to further clarify information. It is important to note that this report takes a broad system-level view. To attain a high TRL, the subsystem must be in a flight-ready configuration with all supporting infrastructure—such as mounting points, power conversion, and control algorithms—in an integrated unit. An accurate TRL assessment requires a high degree of technical knowledge on a subject device, and an in-depth understanding of the mission (including interfaces and environment) on which the device was flown. There is variability in TRL values depending on design factors for a specific technology. For example, differences in TRL assessment based on the operating environment may result from the thermal environment, mechanical loads, mission duration, or radiation exposure. If a technology has flown on a mission without success, or without providing valid confirmation to the operator, such claimed “flight heritage” was discounted. The authors believe TRLs are most accurately determined when assessed within the context of a program’s unique requirements. While the overall capability of small spacecraft has matured since the 2021 edition of this report, technologies are still being developed to make deep space SmallSat missions more routine and more cost effective. Future editions of this report may include content dedicated to the rapidly growing fields of assembly, integration, and testing services, and mission modeling and simulation–all of which are now extensively represented at small spacecraft conferences. Many of these subsystems and services are still in their infancy, but as they evolve and reliable conventions and standards emerge, the next iteration of this report may also evolve to include additional chapters.

Bruce Yost↗

AIRCL: A programmed system for generating NC tapes for airplane models. User's manual

A computer program is presented which calculates the cutter location file needed to machine models of airplane wings or wing-fuselage combinations on numerically controlled machine tools. Input to the program is a data file consisting of coordinates on the fuselage and wing. From this data file, the program calculates tool offsets, determines the intersection between wing and fuselage tool paths, and generates additional information needed to machine the fuselage and/or wing. Output from the program can be post processed for use on a variety of milling machines. Information on program structure and methodology is given as well as the user's manual for implementation of the program.

Akgerman, N.↗

Broadband Fan Noise Prediction System for Turbofan Engines: Setup_BFaNS User's Manual and Developer's Guide - Volume 1

Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the first volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User's Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by step-by-step instructions for installing and running Setup_BFaNS. It concludes with technical documentation of the Setup_BFaNS computer program.

Morin, Bruce L.↗

Broadband Fan Noise Prediction System for Turbofan Engines: BFaNS User's Manual and Developer's Guide - Volume 2

Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the second volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User s Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by step-by-step instructions for installing and running BFaNS. It concludes with technical documentation of the BFaNS computer program.

Morin, Bruce L.↗

Broadband Fan Noise Prediction System for Turbofan Engines: Validation and Test Cases - Volume 3

Pratt & Whitney has developed a Broadband Fan Noise Prediction System (BFaNS) for turbofan engines. This system computes the noise generated by turbulence impinging on the leading edges of the fan and fan exit guide vane, and noise generated by boundary-layer turbulence passing over the fan trailing edge. BFaNS has been validated on three fan rigs that were tested during the NASA Advanced Subsonic Technology Program (AST). The predicted noise spectra agreed well with measured data. The predicted effects of fan speed, vane count, and vane sweep also agreed well with measurements. The noise prediction system consists of two computer programs: Setup_BFaNS and BFaNS. Setup_BFaNS converts user-specified geometry and flow-field information into a BFaNS input file. From this input file, BFaNS computes the inlet and aft broadband sound power spectra generated by the fan and FEGV. The output file from BFaNS contains the inlet, aft and total sound power spectra from each noise source. This report is the third volume of a three-volume set documenting the Broadband Fan Noise Prediction System: Volume 1: Setup_BFaNS User s Manual and Developer s Guide; Volume 2: BFaNS User s Manual and Developer s Guide; and Volume 3: Validation and Test Cases. The present volume begins with an overview of the Broadband Fan Noise Prediction System, followed by validation studies that were done on three fan rigs. It concludes with recommended improvements and additional studies for BFaNS.

Morin, Bruce L.↗

Using a small/low cost computer in an information center

Small/low cost computers are available with I/O capacities that make them suitable for SDI and retrospective searching on any of the many commercially available data bases. A small two-tape computer system is assumed, and an analysis of its run-time equations leads to a three-step search procedure. Run times and costs are shown as a function of file size, number of search terms, and input transmission rates. Actual examples verify that it is economically feasible for an information center to consider its own small, dedicated computer system.

Wilde, D. U.↗

Test Telemetry And Command System (TTACS)

The Jet Propulsion Laboratory has developed a multimission Test Telemetry and Command System (TTACS) which provides a multimission telemetry and command data system in a spacecraft test environment. TTACS reuses, in the spacecraft test environment, components of the same data system used for flight operations; no new software is developed for the spacecraft test environment. Additionally, the TTACS is transportable to any spacecraft test site, including the launch site. The TTACS is currently operational in the Galileo spacecraft testbed; it is also being provided to support the Cassini and Mars Surveyor Program projects. Minimal personnel data system training is required in the transition from pre-launch spacecraft test to post-launch flight operations since test personnel are already familiar with the data system's operation. Additionally, data system components, e.g. data display, can be reused to support spacecraft software development; and the same data system components will again be reused during the spacecraft integration and system test phases. TTACS usage also results in early availability of spacecraft data to data system development and, as a result, early data system development feedback to spacecraft system developers. The TTACS consists of a multimission spacecraft support equipment interface and components of the multimission telemetry and command software adapted for a specific project. The TTACS interfaces to the spacecraft, e.g., Command Data System (CDS), support equipment. The TTACS telemetry interface to the CDS support equipment performs serial (RS-422)-to-ethernet conversion at rates between 1 bps and 1 mbps, telemetry data blocking and header generation, guaranteed data transmission to the telemetry data system, and graphical downlink routing summary and control. The TTACS command interface to the CDS support equipment is nominally a command file transferred in non-real-time via ethernet. The CDS support equipment is responsible for metering the commands to the CDS; additionally for Galileo, TTACS includes a real-time-interface to the CDS support equipment. The TTACS provides the basic functionality of the multimission telemetry and command data system used during flight operations. TTACS telemetry capabilities include frame synchronization, Reed-Solomon decoding, packet extraction and channelization, and data storage/query. Multimission data display capabilities are also available. TTACS command capabilities include command generation verification, and storage.

Fogel, Alvin J.↗

State-of-the-Art Small Spacecraft Technology

The objective of this report is to assess and provide an overview of the state of the art in small spacecraft technologies for use by mission designers, project managers, technologists, and students, connecting current small spacecraft missions to available technologies. This report focuses on the spacecraft system in its entirety, provides current best practices for integration and key considerations for the reader where possible, and presents devices from publicly available sources for each specific spacecraft subsystem. This report is a survey of small spacecraft technologies sourced from open literature; it is not an original source. In addition, this report only considers literature in the public domain. Information presented in this report is limited to SmallSat technology that is publicly available as of September 30, 2024. It does not include information on instrumentation, science payloads, or advances or developments that have not been publicly disclosed. Commonly used sources for data include manufacturer data sheets, press releases, conference papers, journal papers, public filings with government agencies, news articles, presentations, the compendium of databases accessed via NASA’s Small Spacecraft Systems Virtual Institute (S3VI) Information Search, and engagement with companies. Data not appropriate for public dissemination, such as proprietary, export controlled, or otherwise restricted data, are not considered. We encourage the SmallSat community to publish mission outcomes and technology development milestones in publicly available conference papers, press releases, or company websites so they can be reflected in future editions of this report. This report is funded by NASA’s Space Technology Mission Directorate (STMD). It was first commissioned by the Small Spacecraft Technology (SST) program within NASA’s STMD in mid2012 in response to the rapid growth in interest in using small spacecraft for low-Earth orbit, lowcost missions. The report was subsequently updated in 2015, 2018, 2020, 2021, 2022, and 2023 to capture SmallSat technology growth and maturation. In addition to reporting currently available state-of-the-art technologies that have achieved Technology Readiness Level (TRL) 5 or above, a prognosis is provided describing technologies as "on the horizon" if they are being considered for future application.

Sasha V Weston↗