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NASA NTRS2019

Bumper: A Tool for Analyzing Spacecraft Micrometeoroid and Orbital Debris Risk

“Bumper” is NASA’s computer program for analyzing spacecraft micrometeoroid and orbital debris (MMOD) risk. Bumper was developed in the late-1980s and has been continuously used and maintained since. The user base has grown from a few government entities to now include numerous commercial entities as well. The NASA Johnson Space Center (JSC) Hypervelocity Impact Technology (HVIT) Team is responsible for all aspects of the Bumper software. Bumper has been used to characterize MMOD risk on hundreds of spacecraft. All of the International Space Station (ISS) modules, visiting vehicles and numerous external components and systems have been analyzed. Bumper was used to analyze each of the Space Shuttle missions since STS-50. The Orion Multi-Purpose Crew Vehicle (MPCV) MMOD shielding is being developed using Bumper as well. Bumper has also been used on numerous telescopes (Hubble, James Webb, and Fermi Gamma-ray Space Telescopes), scientific probes (Stardust, New Horizons, Parker Solar Probe), and Earth observation satellites (Landsat, Joint Polar Satellite System). Bumper is also being used to analyze the micrometeoroid risk and support design of the Deep Space Gateway (DSG) and Mars Sample Return (MSR) missions. The HVIT Bumper Configuration Control Board (CCB) ensures that all changes to the code are approved, reviewed, and documented. Most of the changes are made to add new MMOD damage “ballistic limit equations” (BLEs). BLEs are typically added in response to completion of a hypervelocity impact (HVI) test series and development of an associated BLE. Other less frequent changes include updates of the debris or meteoroid environment models, feature enhancements, and feature retirement. Some BLEs are commercially sensitive and/or proprietary, so the CCB also manages code user-version control and software distribution. The current version – “Bumper 3” – is a FORTRAN executable that utilizes a 64-bit architecture. Bumper 3 has numerous features that make it a powerful tool for analyzing spacecraft MMOD risk. Bumper uses the latest orbital debris and micrometeoroid environment models. Bumper also easily processes large spacecraft geometry models, recognizes hidden surfaces, permits BLE assignment by name or number, and conducts quality checks of the spacecraft geometry model. Bumper 3 can also be used to estimate the effects of particle penetration through thin, high-standoff distance hardware components such as solar arrays and radiators. This is done using a special HVIT-developed technique know as the “3-Part Analysis.” The paper introduces the Bumper 3 MMOD risk analysis code and provides an example MMOD risk assessment showing Bumper’s role in the overall MMOD protection design process.

Lear, Dana M.↗
NASA NTRS2021

NASA SpaceCube Edge TPU SmallSat Card for Autonomous Operations and Onboard Science-Data Analysis

Using state-of-the-art artificial intelligence (AI)frameworks onboard spacecraft is challenging because common spacecraft processors cannot provide comparable performance to datacenters with server-grade CPUs and GPUs available for terrestrial applications and advanced deep-learning networks. This limitation makes small, lo w-p o we r AI microchip architectures, such as the Google Coral Edge Tensor Processing Unit (TPU), attractive for space missions where the application-specific design enables both high-performance and power-efficient computing for AI applications. To address these challenging considerations for space deployment, this research introduces the design and capabilities of a CubeSat-sized Edge TPU-based co-processor card, known as the SpaceCube Low-power Ed g e Artificial Intelligence Resilient Node (SC-LEARN). This design conforms to NASA’s CubeSat Card Specification (CS2) for integration into next-generation SmallSat and CubeSat systems. This paper describes the overarching architecture and design of the SC-LEARN, as well as, the supporting test card designed for rapid prototyping and evaluation. The SC-LEARN was developed with three operational modes: (1) a high-performance parallel-processing mode,(2)a fault-tolerant mode for onboard resilience, and (3) a power-saving mode with cold spares. Importantly, this research also elaborates on both training and quantization of Tensor Flow models for the SC-LEARN for use onboard with representative, open-source datasets. Lastly, we describe future research plans, including radiation-beam testing and flight demonstration.

Advanced avionics↗
NASA NTRSDate not supplied

VIPER – Volatiles Investigating Polar Exploration Rover: Mission Overview

VIPER is a low cost, lunar volatiles detection and measurement mission that will be delivered to the lunar south pole by one of NASA’s Commercial Lunar Payload Services partners and will characterize the nature of the volatiles in the area and extrapolate this data to create global lunar water resource maps. It will be the first mining expedition on another world while simultaneously addressing fundamental planetary science questions. Prospecting for lunar water at the poles is the next step in understanding the resource potential and addressing key theories about water emplacement and retention. It now appears that potentially economically significant amounts of water ice exists at the poles of the Moon, however, the distribution of this water is still not understood at a level sufficient to fully evaluate economic models. The water ice (and other potential volatiles), the “ore body”, needs to be understood at the scales of 10s to 100s of meters to evaluate localization, extraction and processing techniques. To accomplish this, VIPER will survey permanently-shadowed regions, semi-permanent shadowed regions, and even semi and full sunlit areas in order to have a comprehensive survey of polar region volatiles, to best inform future mission architectures. In order to characterize the volatiles, a payload suite consisting of a neutron spectrometer, mass spectrometer, near infrared spectrometer and a 1-meter drill will be hosted on the VIPER mobile lunar rover platform. Since VIPER is a relatively low cost, schedule-constrained, risk-tolerant mission, there are architectural limitations that require unique mission planning constraints to enable exploration of the lunar south pole region. These regions offer unique challenges such as uncertain terrain conditions, rock and crater hazards, lunar dust, multipath communications effects, extreme thermal environments and multiple overlapping planning constraints. Mission design/traverse planning, system design capabilities and mission operations are all highly linked through initial development phases. We will describe the current mission overview and the unique approaches taken by the VIPER project based on our programmatic framework and unique mission environment.

VIPER↗
NASA NTRSDate not supplied

NASA’s Advance Information Systems Technology (AIST) Program

NASA’s Earth Science Technology Office (ESTO) develops early-stage technologies to enhance scientific understanding. ESTO’s technologies can be reliably and confidently applied to a broad range of science measurements and missions, as well as facilitate practical applications to benefit society at large. As the lead technology office within the Earth Science Division of the NASA Science Mission Directorate, ESTO is focused on the technological challenges inherent to space-based investigations of our planet's dynamic, interrelated systems. Through flexible, science-driven technology strategies and a competitive selection process, ESTO-funded technologies have supported numerous Earth science missions as well as commercial applications. From next generation sensors and instruments to communication and information systems, ESTO technologies enable many NASA missions and data services. The Advanced Information Systems Technology (AIST) Program is one focal area of ESTO. AIST innovates information system technologies that enable the development of new observing systems as well as agile science investigations through data analytics and artificial intelligence tools and algorithms. AIST utilizes an end-to-end development approach with the goal of infusing mature technologies into future missions, measurements, and analysis. One thrust of AIST is data exploitation and analysis using an analytic center framework to make observational data and model output more accessible and usable to scientists conducting specific investigations as well as to extract higher-level science content and information from the data. The framework accelerates scientific discovery by harmonizing the data, tools, and computational resources. NASA’s upcoming Surface, Biology and Geology (SBG) mission is one such mission whose data architecture will leverage many of the technologies developed through the AIST program. This talk will highlight current and future program development goals.

Laura Rogers↗
NASA NTRS2015

SLS-SPEC-159 Cross-Program Design Specification for Natural Environments (DSNE) Revision D

This document is derived from the former National Aeronautics and Space Administration (NASA) Constellation Program (CxP) document CxP 70023, titled "The Design Specification for Natural Environments (DSNE), Revision C." The original document has been modified to represent updated Design Reference Missions (DRMs) for the NASA Exploration Systems Development (ESD) Programs. The DSNE completes environment-related specifications for architecture, system-level, and lower-tier documents by specifying the ranges of environmental conditions that must be accounted for by NASA ESD Programs. To assure clarity and consistency, and to prevent requirements documents from becoming cluttered with extensive amounts of technical material, natural environment specifications have been compiled into this document. The intent is to keep a unified specification for natural environments that each Program calls out for appropriate application. This document defines the natural environments parameter limits (maximum and minimum values, energy spectra, or precise model inputs, assumptions, model options, etc.), for all ESD Programs. These environments are developed by the NASA Marshall Space Flight Center (MSFC) Natural Environments Branch (MSFC organization code: EV44). Many of the parameter limits are based on experience with previous programs, such as the Space Shuttle Program. The parameter limits contain no margin and are meant to be evaluated individually to ensure they are reasonable (i.e., do not apply unrealistic extreme-on-extreme conditions). The natural environments specifications in this document should be accounted for by robust design of the flight vehicle and support systems. However, it is understood that in some cases the Programs will find it more effective to account for portions of the environment ranges by operational mitigation or acceptance of risk in accordance with an appropriate program risk management plan and/or hazard analysis process. The DSNE is not intended as a definition of operational models or operational constraints, nor is it adequate, alone, for ground facilities which may have additional requirements (for example, building codes and local environmental constraints). "Natural environments," as the term is used here, refers to the environments that are not the result of intended human activity or intervention. It consists of a variety of external environmental factors (most of natural origin and a few of human origin) which impose restrictions or otherwise impact the development or operation of flight vehicles and destination surface systems. These natural environments include the following types of environments: Terrestrial environments at launch, abort, and normal landing sites (winds, temperatures, pressures, surface roughness, sea conditions, etc.); Space environments (ionizing radiation, orbital debris, meteoroids, thermosphere density, plasma, solar, Earth, and lunar-emitted thermal radiation, etc.); Destination environments (Lunar surface and orbital, Mars atmosphere and surface, near Earth asteroids, etc.). Many of the environmental specifications in this document are based on models, data, and environment descriptions contained in the CxP 70044, Constellation Program Natural Environment Definition for Design (NEDD). The NEDD provides additional detailed environment data and model descriptions to support analytical studies for ESD Programs. For background information on specific environments and their effects on spacecraft design and operations, the environment models, and the data used to generate the specifications contained in the DSNE, the reader is referred to the NEDD paragraphs listed in each section of the DSNE. Also, most of the environmental specifications in this document are tied specifically to the ESD DRMs in ESD-10012, Revision B, Exploration Systems Development Concept of Operations (ConOps). Coordination between these environment specifications and the DRMs must be maintained. This document should be compatible with the current ESD DRMs, but updates to the mission definitions and variations in interpretation may require adjustments to the environment specifications.

Roberts, Barry C.↗
NASA NTRS2015

In-Space Manufacturing: Pioneering a Sustainable Path to Mars

In order to provide meaningful impacts to exploration technology needs, the In-Space Manufacturing (ISM) Initiative must influence exploration systems design now. In-space manufacturing offers: dramatic paradigm shift in the development and creation of space architectures; efficiency gain and risk reduction for low Earth orbit and deep space exploration; and "pioneering" approach to maintenance, repair, and logistics leading to sustainable, affordable supply chain model. In order to develop application-based capabilities in time to support NASA budget and schedule, ISM must be able to leverage the significant commercial developments, which requires innovative, agile collaborative mechanisms (contracts, challenges, SBIR's, etc.); and NASA-unique investments to focus primarily on adapting the technologies and processes to the microgravity environment. We must do the foundational work - it is the critical path for taking these technologies from lab curiosities to institutionalized capabilities: characterize, certify, institutionalize, design for Additive Manufacturing (AM). Ideally, International Space Station (ISS) U.S. lab rack or partial rack space should be identified for in-space manufacturing utilization in order to continue technology development of a suite of capabilities required for exploration missions, as well as commercialization on ISS.

Werkheiser, Niki↗
NASA NTRS2012

Optimization of the Multi-Spectral Euclidean Distance Calculation for FPGA-based Spaceborne Systems

Due to the high quantity of operations that spaceborne processing systems must carry out in space, new methodologies and techniques are being presented as good alternatives in order to free the main processor from work and improve the overall performance. These include the development of ancillary dedicated hardware circuits that carry out the more redundant and computationally expensive operations in a faster way, leaving the main processor free to carry out other tasks while waiting for the result. One of these devices is SpaceCube, a FPGA-based system designed by NASA. The opportunity to use FPGA reconfigurable architectures in space allows not only the optimization of the mission operations with hardware-level solutions, but also the ability to create new and improved versions of the circuits, including error corrections, once the satellite is already in orbit. In this work, we propose the optimization of a common operation in remote sensing: the Multi-Spectral Euclidean Distance calculation. For that, two different hardware architectures have been designed and implemented in a Xilinx Virtex-5 FPGA, the same model of FPGAs used by SpaceCube. Previous results have shown that the communications between the embedded processor and the circuit create a bottleneck that affects the overall performance in a negative way. In order to avoid this, advanced methods including memory sharing, Native Port Interface (NPI) connections and Data Burst Transfers have been used.

Cristo, Alejandro↗
NASA NTRS2009

NASA Tech Briefs, March 2009

Topics covered include: Improved Instrument for Detecting Water and Ice in Soil; Real-Time Detection of Dust Devils from Pressure Readings; Determining Surface Roughness in Urban Areas Using Lidar Data; DSN Data Visualization Suite; Hamming and Accumulator Codes Concatenated with MPSK or QAM; Wide-Angle-Scanning Reflectarray Antennas Actuated by MEMS; Biasable Subharmonic Membrane Mixer for 520 to 600 GHz; Hardware Implementation of Serially Concatenated PPM Decoder; Symbolic Processing Combined with Model-Based Reasoning; Presentation Extensions of the SOAP; Spreadsheets for Analyzing and Optimizing Space Missions; Processing Ocean Images to Detect Large Drift Nets; Alternative Packaging for Back-Illuminated Imagers; Diamond Machining of an Off-Axis Biconic Aspherical Mirror; Laser Ablation Increases PEM/Catalyst Interfacial Area; Damage Detection and Self-Repair in Inflatable/Deployable Structures; Polyimide/Glass Composite High-Temperature Insulation; Nanocomposite Strain Gauges Having Small TCRs; Quick-Connect Windowed Non-Stick Penetrator Tips for Rapid Sampling; Modeling Unsteady Cavitation and Dynamic Loads in Turbopumps; Continuous-Flow System Produces Medical-Grade Water; Discrimination of Spore-Forming Bacilli Using spoIVA; nBn Infrared Detector Containing Graded Absorption Layer; Atomic References for Measuring Small Accelerations; Ultra-Broad-Band Optical Parametric Amplifier or Oscillator; Particle-Image Velocimeter Having Large Depth of Field; Enhancing SERS by Means of Supramolecular Charge Transfer; Improving 3D Wavelet-Based Compression of Hyperspectral Images; Improved Signal Chains for Readout of CMOS Imagers; SOI CMOS Imager with Suppression of Cross-Talk; Error-Rate Bounds for Coded PPM on a Poisson Channel; Biomorphic Multi-Agent Architecture for Persistent Computing; and Using Covariance Analysis to Assess Pointing Performance.

Source record↗
NASA NTRS2024

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗
NASA NTRS2014

Novel Catalysts and Processing Technologies for Production of Aerospace Fuels from Non-Petroleum Raw Materials

Transportation fuels production (including aerospace propellants) from non-traditional sources (gases, waste materials, and biomass) has been an active area of research and development for decades. Reducing terrestrial waste streams simultaneous with energy conversion, plentiful biomass, new low-cost methane sources, and/or extra-terrestrial resource harvesting and utilization present significant technological and business opportunities being realized by a new generation of visionary entrepreneurs. We examine several new approaches to catalyst fabrication and new processing technologies to enable utilization of these nontraditional raw materials. Two basic processing architectures are considered: a single-stage pyrolysis approach that seeks to basically re-cycle hydrocarbons with minimal net chemistry or a two-step paradigm that involves production of supply or synthesis gas (mainly carbon oxides and H2) followed by production of fuel(s) via Sabatier or methanation reactions and/or Fischer-Tröpsch synthesis. Optimizing the fraction of product stream relevant to targeted aerospace (and other transportation) fuels via modeling, catalyst fabrication and novel reactor design are described. Energy utilization is a concern for production of fuels for either terrestrial or space operations; renewable sources based on solar energy and/or energy efficient processes may be mission enabling. Another important issue is minimizing impurities in the product stream(s), especially those potentially posing risks to personnel or operations through (catalyst) poisoning or (equipment) damage. Technologies being developed to remove (and/or recycle) heteroatom impurities are briefly discussed as well as the development of chemically robust catalysts whose activities are not diminished during operation. The potential impacts on future missions by such new approaches as well as balance of system issues are addressed.

space processing↗
NASA NTRS2014

Novel Catalysts and Processing Technologies for Production of Aerospace Fuels from Non-Petroleum Raw Materials

Transportation fuels production (including aerospace propellants) from non-traditional sources (gases, waste materials, and biomass) has been an active area of research and development for decades. Reducing terrestrial waste streams simultaneous with energy conversion, plentiful biomass, new low-cost methane sources, and/or extra-terrestrial resource harvesting and utilization present significant technological and business opportunities being realized by a new generation of visionary entrepreneurs. We examine several new approaches to catalyst fabrication and new processing technologies to enable utilization of these non-traditional raw materials. Two basic processing architectures are considered: a single-stage pyrolysis approach that seeks to basically re-cycle hydrocarbons with minimal net chemistry or a two-step paradigm that involves production of supply or synthesis gas (mainly carbon oxides and hydrogen) followed by production of fuel(s) via Sabatier or methanation reactions and/or Fischer-Tropsch synthesis. Optimizing the fraction of product stream relevant to targeted aerospace (and other transportation) fuels via modeling, catalyst fabrication and novel reactor design are described. Energy utilization is a concern for production of fuels for either terrestrial or space operations; renewable sources based on solar energy and/or energy efficient processes may be mission enabling. Another important issue is minimizing impurities in the product stream(s), especially those potentially posing risks to personnel or operations through (catalyst) poisoning or (equipment) damage. Technologies being developed to remove (and/or recycle) heteroatom impurities are briefly discussed as well as the development of chemically robust catalysts whose activity are not diminished during operation. The potential impacts on future missions by such new approaches as well as balance of system issues are addressed.

propellants↗
NASA NTRS2014

SpaceCube v2.0 Space Flight Hybrid Reconfigurable Data Processing System

This paper details the design architecture, design methodology, and the advantages of the SpaceCube v2.0 high performance data processing system for space applications. The purpose in building the SpaceCube v2.0 system is to create a superior high performance, reconfigurable, hybrid data processing system that can be used in a multitude of applications including those that require a radiation hardened and reliable solution. The SpaceCube v2.0 system leverages seven years of board design, avionics systems design, and space flight application experiences. This paper shows how SpaceCube v2.0 solves the increasing computing demands of space data processing applications that cannot be attained with a standalone processor approach.The main objective during the design stage is to find a good system balance between power, size, reliability, cost, and data processing capability. These design variables directly impact each other, and it is important to understand how to achieve a suitable balance. This paper will detail how these critical design factors were managed including the construction of an Engineering Model for an experiment on the International Space Station to test out design concepts. We will describe the designs for the processor card, power card, backplane, and a mission unique interface card. The mechanical design for the box will also be detailed since it is critical in meeting the stringent thermal and structural requirements imposed by the processing system. In addition, the mechanical design uses advanced thermal conduction techniques to solve the internal thermal challenges.The SpaceCube v2.0 processing system is based on an extended version of the 3U cPCI standard form factor where each card is 190mm x 100mm in size The typical power draw of the processor card is 8 to 10W and scales with application complexity. The SpaceCube v2.0 data processing card features two Xilinx Virtex-5 QV Field Programmable Gate Arrays (FPGA), eight memory modules, a monitor FPGA with analog monitoring, Ethernet, configurable interconnect to the Xilinx FPGAs including gigabit transceivers, and the necessary voltage regulation. The processor board uses a back-to-back design methodology for common parts that maximizes the board real estate available. This paper will show how to meet the IPC 6012B Class 3A standard with a 22-layer board that has two column grid array devices with 1.0mm pitch. All layout trades such as stack-up options, via selection, and FPGA signal breakout will be discussed with feature size results. The overall board design process will be discussed including parts selection, circuit design, proper signal termination, layout placement and route planning, signal integrity design and verification, and power integrity results. The radiation mitigation techniques will also be detailed including configuration scrubbing options, Xilinx circuit mitigation and FPGA functional monitoring, and memory protection.Finally, this paper will describe how this system is being used to solve the extreme challenges of a robotic satellite servicing mission where typical space-rated processors are not sufficient enough to meet the intensive data processing requirements. The SpaceCube v2.0 is the main payload control computer and is required to control critical subsystems such as autonomous rendezvous and docking using a suite of vision sensors and object avoidance when controlling two robotic arms.

Xilinx FPGA↗
NASA NTRS2005

A Fundamental Mathematical Model of a Microbial Predenitrification System

Space flight beyond Low Earth Orbit requires sophisticated systems to support all aspects of the mission (life support, real-time communications, etc.). A common concern that cuts across all these systems is the selection of information technology (IT) methodology, software and hardware architectures to provide robust monitoring, diagnosis, and control support. Another dimension of the problem space is that different systems must be integrated seamlessly so that communication speed and data handling appear as a continuum (un-interrupted). One such team investigating this problem is the Advanced Integration Matrix (AIM) team whose role is to define the critical requirements expected of software and hardware to support an integrated approach to the command and control of Advanced Life Support (ALS) for future long-duration human space missions, including permanent human presence on the Moon and Mars. A goal of the AIM team is to set the foundation for testing criteria that will assist in specifying tasks, control schemes and test scenarios to validate and verify systems capabilities. This project is to contribute to the goals of the AIM team by assisting with controls planning for ALS. Control for ALS is an enormous problem it involves air revitalization, water recovery, food production, solids processing and crew. In more general terms, these systems can be characterized as involving both continuous and discrete processes, dynamic interactions among the sub-systems, nonlinear behavior due to the complex operations, and a large number of multivariable interactions due to the dimension of the state space. It is imperative that a baseline approach from which to measure performance is established especially when the expectation for the control system is complete autonomous control.

Hoo, Karlene A.↗
NASA NTRS2016

Streamlining the Design Tradespace for Earth Imaging Constellations

Distributed Spacecraft Missions (DSMs) are gaining momentum in their application to Earth Observation (EO) missions owing to their unique ability to increase observation sampling in spatial, spectral, angular and temporal dimensions simultaneously. DSM design includes a much larger number of variables than its monolithic counterpart, therefore, Model-Based Systems Engineering (MBSE) has been often used for preliminary mission concept designs, to understand the trade-offs and interdependencies among the variables. MBSE models are complex because the various objectives a DSM is expected to achieve are almost always conflicting, non-linear and rarely analytical. NASA Goddard Space Flight Center is developing a pre-Phase A tool called "Trade-space Analysis Tool for Constellations" (TAT-C) to initiate constellation mission design. The tool will allow users to explore the tradespace between various performance, cost and risk metrics (as a function of their science mission) and select Pareto optimal architectures that meet their requirements. This paper focuses on the tradespace search and how it can be streamlined by combining physical rules, as well as well-designed orbit and coverage computations, thus yielding significant speed-ups. Two use cases are shown as representative examples of the utility of TAT-C generated trades, and results are preliminarily validated against AGI's Systems Tool Kit.

Science Data Processing↗
NASA NTRS1996

Improving Conceptual Design for Launch Vehicles

This report summarizes activities performed during the second year of a three year cooperative agreement between NASA - Langley Research Center and Georgia Tech. Year 1 of the project resulted in the creation of a new Cost and Business Assessment Model (CABAM) for estimating the economic performance of advanced reusable launch vehicles including non-recurring costs, recurring costs, and revenue. The current year (second year) activities were focused on the evaluation of automated, collaborative design frameworks (computation architectures or computational frameworks) for automating the design process in advanced space vehicle design. Consistent with NASA's new thrust area in developing and understanding Intelligent Synthesis Environments (ISE), the goals of this year's research efforts were to develop and apply computer integration techniques and near-term computational frameworks for conducting advanced space vehicle design. NASA - Langley (VAB) has taken a lead role in developing a web-based computing architectures within which the designer can interact with disciplinary analysis tools through a flexible web interface. The advantages of this approach are, 1) flexible access to the designer interface through a simple web browser (e.g. Netscape Navigator), 2) ability to include existing 'legacy' codes, and 3) ability to include distributed analysis tools running on remote computers. To date, VAB's internal emphasis has been on developing this test system for the planetary entry mission under the joint Integrated Design System (IDS) program with NASA - Ames and JPL. Georgia Tech's complementary goals this year were to: 1) Examine an alternate 'custom' computational architecture for the three-discipline IDS planetary entry problem to assess the advantages and disadvantages relative to the web-based approach.and 2) Develop and examine a web-based interface and framework for a typical launch vehicle design problem.

John R. Olds↗
NASA NTRS2014

Virtualization - A Key Cost Saver in NASA Multi-Mission Ground System Architecture

With science team budgets being slashed, and a lack of adequate facilities for science payload teams to operate their instruments, there is a strong need for innovative new ground systems that are able to provide necessary levels of capability processing power, system availability and redundancy while maintaining a small footprint in terms of physical space, power utilization and cooling.The ground system architecture being presented is based off of heritage from several other projects currently in development or operations at Goddard, but was designed and built specifically to meet the needs of the Science and Planetary Operations Control Center (SPOCC) as a low-cost payload command, control, planning and analysis operations center. However, this SPOCC architecture was designed to be generic enough to be re-used partially or in whole by other labs and missions (since its inception that has already happened in several cases!)The SPOCC architecture leverages a highly available VMware-based virtualization cluster with shared SAS Direct-Attached Storage (DAS) to provide an extremely high-performing, low-power-utilization and small-footprint compute environment that provides Virtual Machine resources shared among the various tenant missions in the SPOCC. The storage is also expandable, allowing future missions to chain up to 7 additional 2U chassis of storage at an extremely competitive cost if they require additional archive or virtual machine storage space.The software architecture provides a fully-redundant GMSEC-based message bus architecture based on the ActiveMQ middleware to track all health and safety status within the SPOCC ground system. All virtual machines utilize the GMSEC system agents to report system host health over the GMSEC bus, and spacecraft payload health is monitored using the Hammers Integrated Test and Operations System (ITOS) Galaxy Telemetry and Command (TC) system, which performs near-real-time limit checking and data processing on the downlinked data stream and injects messages into the GMSEC bus that are monitored to automatically page the on-call operator or Systems Administrator (SA) when an off-nominal condition is detected. This architecture, like the LTSP thin clients, are shared across all tenant missions.Other required IT security controls are implemented at the ground system level, including physical access controls, logical system-level authentication authorization management, auditing and reporting, network management and a NIST 800-53 FISMA-Moderate IT Security plan Risk Assessment Contingency Plan, helping multiple missions share the cost of compliance with agency-mandated directives.The SPOCC architecture provides science payload control centers and backup mission operations centers with a cost-effective, standardized approach to virtualizing and monitoring resources that were traditionally multiple racks full of physical machines. The increased agility in deploying new virtual systems and thin client workstations can provide significant savings in personnel costs for maintaining the ground system. The cost savings in procurement, power, rack footprint and cooling as well as the shared multi-mission design greatly reduces upfront cost for missions moving into the facility. Overall, the authors hope that this architecture will become a model for how future NASA operations centers are constructed!

Ground System Architecture↗
NASA NTRS2015

A Principled Approach to the Specification of System Architectures for Space Missions

Modern space systems are increasing in complexity and scale at an unprecedented pace. Consequently, innovative methods, processes, and tools are needed to cope with the increasing complexity of architecting these systems. A key systems challenge in practice is the ability to scale processes, methods, and tools used to architect complex space systems. Traditionally, the process for specifying space system architectures has largely relied on capturing the system architecture in informal descriptions that are often embedded within loosely coupled design documents and domain expertise. Such informal descriptions often lead to misunderstandings between design teams, ambiguous specifications, difficulty in maintaining consistency as the architecture evolves throughout the system development life cycle, and costly design iterations. Therefore, traditional methods are becoming increasingly inefficient to cope with ever-increasing system complexity. We apply the principles of component-based design and platform-based design to the development of the system architecture for a practical space system to demonstrate feasibility of our approach using SysML. Our results show that we are able to apply a systematic design method to manage system complexity, thus enabling effective data management, semantic coherence and traceability across different levels of abstraction in the design chain. Just as important, our approach enables interoperability among heterogeneous tools in a concurrent engineering model based design environment.

platform-based design↗
NASA NTRS2000

Space Resources Roundtable 2

Contents include following: Developing Technologies for Space Resource Utilization - Concept for a Planetary Engineering Research Institute. Results of a Conceptual Systems Analysis of Systems for 200 m Deep Sampling of the Martian Subsurface. The Role of Near-Earth Asteroids in Long-Term Platinum Supply. Core Drilling for Extra-Terrestrial Mining. Recommendations by the "LSP and Manufacturing" Group to the NSF-NASA Workshop on Autonomous Construction and Manufacturing for Space Electrical Power Systems. Plasma Processing of Lunar and Planetary Materials. Percussive Force Magnitude in Permafrost. Summary of the Issues Regarding the Martian Subsurface Explorer. A Costing Strategy for Manufacturing in Orbit Using Extraterrestrial Resources. Mine Planning for Asteroid Orebodies. Organic-based Dissolution of Silicates: A New Approach to Element Extraction from LunarRegohth. Historic Frontier Processes Active in Future Space-based Mineral Extraction. The Near-Earth Space Surveillance (NIESS) Mission: Discovery, Tracking, and Characterization of Asteroids, Comets, and Artificial Satellites with a microsatellite. Privatized Space Resource Property Ownership. The Fabrication of Silicon Solar Cells on the Moon Using In-Situ Resources. A New Strategy for Exploration Technology Development: The Human Exploration and Development of Space (HEDS) Exploratiori/Commercialization Technology Initiative. Space Resources for Space Tourism. Recovery of Volatiles from the Moon and Associated Issues. Preliminary Analysis of a Small Robot for Martian Regolith Excavation. The Registration of Space-based Property. Continuous Processing with Mars Gases. Drilling and Logging in Space; An Oil-Well Perspective. LORPEX for Power Surges: Drilling, Rock Crushing. An End-To-End Near-Earth Asteroid Resource Exploitation Plan. An Engineering and Cost Model for Human Space Settlement Architectures: Focus on Space Hotels and Moon/Mars Exploration. The Development and Realization of a Silicon-60-based Economy in CisLunar Space. Our Lunar Destiny: Creating a Lunar Economy. Cost-Effective Approaches to Lunar Passenger Transportation. Lunar Mineral Resources: Extraction and Application. Space Resources Development - The Link Between Human Exploration and the Long-term Commercialization of Space. Toward a More Comprehensive Evaluation of Space Information. Development of Metal Casting Molds by Sol-Gel Technology Using Planetary Resources. A New Concept in Planetary Exploration: ISRU with Power Bursts. Bold Space Ventures Require Fervent Public Support. Hot-pressed Iron from Lunar Soil. The Lunar Dust Problem: A Possible Remedy. Considerations on Use of Lunar Regolith in Lunar Constructions. Experimental Study on Water Production by Hydrogen Reduction of Lunar Soil Simulant in a Fixed Bed Reactor.

Ignatiev, A.↗