Search NASA⌕ Search

SEARCH · Search NASA

Results for “spacecraft simulation”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 541 records · Page 30
All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

Discrete event simulation and the resultant data storage system response in the operational mission environment of Jupiter-Saturn /Voyager/ spacecraft

The Data Storage Subsystem Simulator (DSSSIM) simulating (by ground software) occurrence of discrete events in the Voyager mission is described. Functional requirements for Data Storage Subsystems (DSS) simulation are discussed, and discrete event simulation/DSSSIM processing is covered. Four types of outputs associated with a typical DSSSIM run are presented, and DSSSIM limitations and constraints are outlined.

Mukhopadhyay, A. K.↗
NASA NTRS1988

Estimating Geophysical Parameters From Gravity Data

ORBSIM program developed for accurate extraction of parameters of geophysical models from Doppler-radio-tracking data acquired from orbiting planetary spacecraft. Model of proposed planetary structure used in numerical integration along simulated trajectories of spacecraft around primary body. Written in FORTRAN 77.

Sjogren, William L.↗
NASA NTRS1971

A study of the flexible body dynamics of the RAE-A satellite

Spacecraft flight data was compared with simulated data computed by the RAE and IMP dynamics simulator computer programs. There was not sufficient data available to provide definitive data on antenna boom position to the Radio Astronomy experimenter. There was, however, enough data to indicate the trend of antenna boom stiffness and temperature gradient. It was possible to obtain a good fit of simulated data to measured spacecraft attitude data when the antenna booms were extended to 450 feet. The value of the RAE-A antenna boom stiffness appears to be significantly less than the preflight ground measured value. It was confirmed that an improved simulation can be obtained by using values of the root angles of the antenna booms and by using an angle of the damper plane of motion, relative to the plane of the antennas, which differed from preflight ground measurements.

Source record↗
NASA NTRSDate not supplied

A Science-Focused Artificial Intelligence (AI) Responding in Real-Time to New Information: Capability Demonstration for Ocean World Missions

Introduction: Artificial intelligence (AI) has long been considered a potential mechanism to explore increasingly challenging environments, including those with extreme temperatures and pressures, limited communication capabilities, or those with demanding terrain. We posit that missions in extreme environments could deploy an onboard AI focused on science observations and goals in order to augment a traditional concept(s) of operations (ConOps). An onboard AI capability could perform functions such as data analysis in order to make high-level decisions, including prioritized data transmission for analysis by ground-based teams or autonomously-guided follow-on analyses that maximize science return. Such a capability would empower missions to respond to scientific data of interest in real-time; a mission could make observations and perform a preliminary analysis to alert ground-based scientists to an observation of interest, enabling an informed, rapid response from Earth-based teams. Enceladus Case Study for Onboard AI: We are developing an onboard AI capability for real-time telemetry response that formulates and carries-out informed decisions in service to established mission goals, enabling increased science return of a mission. We focus our AI development for use on a constellation of SmallSats orbiting Enceladus. Our Enceladus case study tests autonomous decision-making capabilities in scenarios with complex orbital dynamics, plume ejecta, extreme cold environments, power restrictions, and a requirement to maximize science return for a potential positive detection of life, while critically evaluating the potential for false positives. Telemetry includes simulated scientific data, spacecraft onboard operational data (e.g., position, velocity, and rotation), and engineering hardware performance data. Enceladus SmallSat Constellation. Our constellation includes eight SmallSat spacecraft in an 8:35 resonant orbit-based formation, leveraging Saturn’s gravitational forces to maintain stable orbits with global coverage around Enceladus. To our knowledge, we simulate the first stable configuration of multiple spacecraft in closed orbits around Enceladus, using a full ephemeris force model (Russell and Lara, 2009). Each spacecraft’s orbit will precess, causing an eastward ground track shift (from an orbiter’s perspective) of each spacecraft for each orbit. However, all spacecraft return to their original positions relative to Enceladus after eight Enceladus revolutions around Saturn. We model communication pathways between SmallSats to understand how information would need to be transmitted across the constellation to enable AI-driven decision-making and resource allocation across the fleet. Capability Demonstration. Our simulated capability demonstration inputs position, velocity, and rotation telemetry from our Enceladus-focused constellation simulations, and mass spectrometry data collected from abiotic and biotic laboratory-analog ocean world experiments (Theiling et al., 2018; Theiling, 2021; Da Poian et al., 2023). Data from these experiments are used to simulate MS measurements and different scenarios of science observations for onboard analysis performed on each of the eight spacecraft. For these demonstrations, we integrate 24 machine learning (ML) algorithms into an onboard intelligence as a ‘knowledge base’, including algorithms evaluating data quality and those predicting (with % confidence) gas composition, ocean aqueous chemistry, and whether the sample was influenced by microbial life. The onboard AI capability is designed to use the knowledge base to come to a consensus-based decision in the interpretation of the observed data in order to request additional action outside of a pre-defined ConOps. Requested actions could include e.g., prioritized downlink to Earth (for analysis by ground-based teams) or follow-on analyses performed across the constellation. The spacecraft’s intelligent onboard planner must then determine whether sufficient resources (e.g., time, power, etc.) are available and weigh the request with mission priorities. In our simulation, the constellation is able to identify potential biosignatures using onboard ML algorithms, evaluate the confidence of that prediction, and perform follow-on analyses across the fleet to confirm the detection, in order to best prepare a transmission of these data to Earth-based teams.

astrobiology↗
NASA NTRS1972

A shuttle and space station manipulator system for assembly, docking, maintenance, cargo handling and spacecraft retrieval (preliminary design). Volume 4: Simulation studies

Laboratory simulations of three concepts, based on maximum use of available off-the-shelf hardware elements, are described. The concepts are a stereo-foveal-peripheral TV system with symmetric steroscopic split-image registration and 90 deg counter rotation; a computer assisted model control system termed the trajectory following control system; and active manipulator damping. It is concluded that the feasibility of these concepts is established.

Source record↗
NASA NTRS1973

Modal analysis for Liapunov stability of rotating elastic bodies

This study consisted of four parallel efforts: (1) modal analyses of elastic continua for Liapunov stability analysis of flexible spacecraft; (2) development of general purpose simulation equations for arbitrary spacecraft; (3) evaluation of alternative mathematical models for elastic components of spacecraft; and (4) examination of the influence of vehicle flexibility on spacecraft attitude control system performance. A complete record is given of achievements under tasks (1) and (3), in the form of technical appendices, and a summary description of progress under tasks two and four.

Colin, A. D.↗
NASA NTRS1973

Dynamics and control of advanced space vehicles, volume 1

The following studies are reported: (1) Modal analyses of elastic continua for Liapunov stability analysis of flexible spacecraft; (2) development of general purpose simulation equations for arbitrary spacecraft; (3) evaluation of alternative mathematical models for elastic components of spacecraft; and (4) examination of the influence of vehicle flexibility on spacecraft attitude control system performance.

Likins, P. W.↗
NASA NTRS1974

Description and simulation of an integrated power and attitude control system concept for space-vehicle application

An Integrated Power and Attitude Control System (IPACS) concept with potential application to a broad class of space missions is discussed. A description is given of the basic concept of combining the onboard energy storage and attitude control functions by storing energy in spinning flywheels which are used to provide control torques. A shuttle-launched Research and Applications Module (RAM) A303B solar-observatory mission having stringent pointing requirements (1.0 arc second) is selected to investigate possible interactions between energy storage and attitude control. A simulation of this spacecraft involving actual laboratory-model control-system hardware is presented. Simulation results are discussed which indicate that the IPACS concept, even in a failure-mode configuration, can readily meet the RAM A303B pointing requirements.

Will, R. W.↗