Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Mission Operations”

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 1,135 records · Page 63
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

Resource Prospector: A Lunar Volatiles Prospecting and ISRU Demonstration Mission

A variety of recent observations have indicated several possible reservoirs of water and other volatiles. These volatiles, and in particular water, have the potential to be a valuable or enabling resource for future exploration. NASA's Human Exploration and Operations Mission Directorate (HEOMD) Advanced Exploration Systems (AES) is supporting the development of Resource Prospector (RP) to explore the distribution and concentration of lunar volatiles prospecting and to demonstrate In-Situ Resource Utilization (ISRU). The mission includes a NASA developed rover and payload, and a lander will most likely be a contributed element by an international partner or the Lunar Cargo Transportation and Landing by Soft Touchdown (CATALYST) initiative. The RP payload is designed to: (1) locate near-subsurface volatiles, (2) excavate and analyze samples of the volatile-bearing regolith, and (3) demonstrate the form. extractability and usefulness of the materials. RP is being designed with thought given to its extensibility to resource prospecting and ISRU on other airless bodies and Mars. This presentation will describe the Resource Prospector mission, the payload and measurements, and concept of operations

In-Situ Resource Utilization (ISRU)↗
NASA NTRS1982

NASA Deep Space Network Operations Scheduling

The functioning of the Deep Space Network Operations Scheduling, Jet Propulsion Laboratory, CA is reviewed. The primary objectives of the Operations Scheduling are: to schedule the worldwide global allocation of ground communications, tracking facilities, and equipment; and to provide deep space telecommunications for command, tracking, telemetry, and control in support of flight mission operations and tests. Elements of the earth set are Deep Space Stations (DSS) which provide the telecommunications link between the earth and spacecraft; NASA Communications Network; Network Data Processing Area; Network Operations Control Area which provides operational direction to the DSS; Mission Control and Computing systems; and Mission Support areas which provide flight control of the spacecraft. Elements of the space set include mission priorities and requirements which determine the spacecraft queue for allocating network resources. Scheduling is discussed in terms of long-range (3 years), mid-range (8 weeks), and short-range (2 weeks).

Enari, D. M.↗
NASA NTRS2023

Risk Trade-Space Analysis for Safe Human Expeditions to Mars

We assessed the integrated safety, health, and performance risk to crews on long-duration missions, specifically to Mars. Using a systems approach rather than one focused on individual countermeasures, we examined the trade space around several such risks to identify high-potential risk mitigation strategies and characterize aspects of Mars mission architectures that could lower aggregated risk. Current Mars Design Reference missions would require durations well over two years and would increase crew exposure to radiation and microgravity well beyond ISS levels, likely resulting in significantly reduced performance beyond our current capability to mitigate that could jeopardize mission success. A “fast Mars transit” round-trip mission concept was studied using an innovative flight dynamics approach to quantify the minimum total mission energy required for a Mars transit with total mission duration less than 400 days. This approach holds promise for sending humans to Mars and returning them safely with acceptable, potentially mitigatable, exposure to microgravity and radiation using current or near-term technologies. The fast transit concept would also result in fewer time-driven vehicle failures and enable sustainable deployment of humans and infrastructure to Mars on a regular cadence, allowing steady exploration and colonization of Mars. Finally, we conclude that reliance on the Low Earth Orbit (LEO) mission operations paradigm – i.e., one of near-complete real-time dependence on experts at Mission Control to manage the combined state of the mission, vehicle, and crew – is high risk given the communication delays and limited resupply of any Mars mission, and this risk is not eliminated by the shorter missions durations of fast transit scenarios. Based on historical trends, it is highly likely that the crew will face a high-consequence problem of uncertain origin during Mars transit when ground support will be greatly reduced. While it may be possible to reduce anomaly rates through improved reliability analysis and testing, and to reduce anomaly impacts through added robustness, such mitigations address only known failure modes and known uncertainties. Therefore, a radical shift in the Human-Systems Integration Architecture (HSIA) that defines the operational paradigm, systems design, and human-systems interactions is required to improve the risk posture to an acceptable level regardless of mission duration.

Mars↗
NASA NTRS2021

NASA Deep Space Network Commitments for Human Missions to the Moon and Beyond

NASA’s Deep Space Network (DSN) serves as a critical element in the exploration of deep space, typically supporting 30-40 operational missions at any given time, each with unique characteristics and telecommunications requirements. In order to meet the needs of its diverse customer base, DSN relies on the Mission Support Definition and Commitments Office to interface with its customers and to develop, negotiate, and document the appropriate service commitments to meet each mission’s needs. Members of this office provide the needed support throughout each mission’s lifetime. While DSN’s primary focus in recent years has been supporting scientific missions by robotic spacecraft deployed across the solar system, the network is ramping-up to provide support for human spaceflight (HSF) endeavours to the moon and beyond, starting with the Artemis missions which aim to land humans on the lunar surface in the 2020’s. These HSF missions pose unique challenges for the DSN, including technical, operational, and programmatic concerns. Examples of challenges in each of these areas are provided, along with descriptions of how they are being addressed and open issues remaining.

Turcios, Ricky↗
NASA NTRSDate not supplied

Space-Based Solar Power: An Enabler for Expanded Lunar Surface Exploration and Mobility Operations

The achievement of industrial and scientific goals set by the National Aeronautics and Space Administration (NASA) Artemis missions within the Moon to Mars (M2M) architecture greatly relies on the breadth and efficacy of lunar surface exploration. The lunar surface environment presents various challenges and complexities to sustainable mission operations and activities. Power generation and distribution capabilities are vital for exploration on the lunar surface, which can enable activities such as subsurface sample collection, permanently shadowed region (PSR) prospecting, and lunar terrain mapping. Innovative power technologies that demonstrate flexibility, reliability, and high capacity are desirable to achieve sustainable operations and infrastructure on the lunar surface. Studies have proposed solar-powered spacecraft in cislunar orbit capable of beaming power to lunar surface assets as a solution to this problem. This study analyzes the benefit that space-based solar power (SBSP) assets could deliver to the M2M architecture by measuring its potential to augment lunar surface exploration opportunity. Qualitative measures of SBSP’s potential impact will include, but not be limited to, power available across surface elements, surface mobility range, and delivered landed mass. These measures are assessed for a notional surface architecture measured against its baseline configuration (no external power augmentation). Additionally, this presentation will provide an overview of the SBSP system design configuration trade space, subsystem parameter trade space, and results from preliminary spacecraft concept of operations (ConOps) and sizing.

Space-based solar power↗
NASA NTRS1998

VR for Mars Pathfinder

Virtual reality (VR) technology has played an integral role for Mars Pathfinder mission, operations Using an automated machine vision algorithm, the 3d topography of the Martian surface was rapidly recovered fro -a the stereo images captured. by the Tender camera to produce photo-realistic 3d models, An advanced, interface was developed for visualization and interaction with. the virtual environment of the Pathfinder landing site for mission scientists at the Space Flight Operations Facility of the Jet Propulsion Laboratory. The VR aspect of the display allowed mission scientists to navigate on Mars in Bud while remaining here on Earth, thus improving their spatial awareness of the rock field that surrounds the lenders Measurements of positions, distances and angles could be easily extracted from the topographic models, providing valuable information for science analysis and mission. planning. Moreover, the VR map of Mars has also been used to assist with the archiving and planning of activities for the Sojourner rover.

Blackmon, Theodore↗
NASA NTRS2018

International Space Station Operational Experience and Its Impacts on Future Mission Supportability

Operational experience gained on the International Space Station (ISS) has enabled significant improvements in failure rate estimates for various Orbital Replacement Units (ORUs). These improved estimates, in turn, allow more efficient and accurate spare parts allocations for future missions, enabling significant reductions in both logistics mass and risk. This paper examines the value of ISS experience to date in terms of its impact on supportability for future missions. A supportability model is presented that assesses the spares required as a function of mission endurance and risk, taking into account uncertainty in failure rate estimates. Changes in ISS Environmental Control and Life Support (ECLSS) ORU failure rate estimates are described and discussed, both in terms of the overall population of ORUs and the evolution of failure rate estimates over time for a particular item. The value of those updated failure rate estimates is assessed by calculating the estimated spares mass requirements for two cases, using the initial, pre-ISS estimates and using the estimates informed by on-orbit experience. Hidden risk resulting from underestimated failure rates is also assessed. These results indicate that, for a 1,200-day Mars mission, ISS experience has enabled a 3.9 t to 6.0 t reduction in ECLSS spares mass required and uncovered failure rate underestimates that would have resulted in an order of magnitude increase in risk had they not been discovered and corrected. The implications of these results for system development and mission planning are discussed, including approaches to accelerate the rate of failure rate refinement and the risks associated with making changes or introducing new systems. Overall, test time is a critical factor that must be carefully considered in system development, and new systems must budget appropriate time for testing in a relevant environment or accept higher risk and logistics requirements on future missions.

Owens, Andrew C.↗
NASA NTRS2017

Dust Storm Impacts on Human Mars Mission Equipment and Operations

Although it is tempting to use dust impacts on Apollo lunar exploration mission equipment and operations as an analog for human Mars exploration, there are a number of important differences to consider. Apollo missions were about a week long; a human Mars mission will start at least two years before crew depart from Earth, when cargo is pre-deployed, and crewed mission duration may be over 800 days. Each Apollo mission landed at a different site; although no decisions have been made, NASA is investigating multiple human missions to a single Mars landing site, building up capability over time and lowering costs by re-using surface infrastructure. Apollo missions used two, single-use spacecraft; a human Mars mission may require as many as six craft for different phases of the mission, most of which would be re-used by subsequent crews. Apollo crews never ventured more than a few kilometers from their lander; Mars crews may take "camping trips" a hundred kilo-meters or more from their landing site, utilizing pressurized rovers to explore far from their base. Apollo mission designers weren't constrained by human for-ward contamination of the Moon; if we plan to search for evidence of life on Mars we'll have to be more careful. These differences all impact how we will mitigate and manage dust on our human Mars mission equipment and operations.

Rucker, M. A.↗
NASA NTRS1991

Control center operations at the Goddard Space Flight Center

Information is given in viewgraph form on control center operations at the Goddard Space Flight Center. Information is given on multi-mission, dedicated and attached payload support; the mission operations environment; technical challenges; Hubble Space Telescope (HST) science planning interface; operations engineering life cycle; Special Analysis of Maneuverable Satellites (SAMS) operations; and user interface applications.

Connerton, Robert M.↗
NASA NTRS2003

The Propulsive Small Expendable Deployer System (ProSEDS)

The summary of activity during this reporting period, most of which was covered by a no-cost extension of the grant, is as follows: 1) Participation in remote and in-situ (at MSFC EDAC facility) mission operation simulations; 2) Analysis of the decay rate of ProSEDS when starting the mission at a lower altitude; 3) Analysis of the deployment control law performance when deploying at a lower altitude.

Lorenzini, Enrico C.↗
NASA NTRS2004

Terrain Modelling for Immersive Visualization for the Mars Exploration Rovers

Immersive environments are being used to support mission operations at the Jet Propulsion Laboratory. This technology contributed to the Mars Pathfinder Mission in planning sorties for the Sojourner rover and is being used for the Mars Exploration Rover (MER) missions. The stereo imagery captured by the rovers is used to create 3D terrain models, which can be viewed from any angle, to provide a powerful and information rich immersive visualization experience. These technologies contributed heavily to both the mission success and the phenomenal level of public outreach achieved by Mars Pathfinder and MER. This paper will review the utilization of terrain modelling for immersive environments in support of MER.

immersive environments↗
NASA NTRS2014

APGEN Scheduling: 15 Years of Experience in Planning Automation

In this paper, we discuss the scheduling capability of APGEN (Activity Plan Generator), a multi-mission planning application that is part of the NASA AMMOS (Advanced Multi- Mission Operations System), and how APGEN scheduling evolved over its applications to specific Space Missions. Our analysis identifies two major reasons for the successful application of APGEN scheduling to real problems: an expressive DSL (Domain-Specific Language) for formulating scheduling algorithms, and a well-defined process for enlisting the help of auxiliary modeling tools in providing high-fidelity, system-level simulations of the combined spacecraft and ground support system.

sequencing↗
NASA NTRS2016

Mission Status at Aura Science Team MOWG Meeting: EOS Aura

Presentation at the 24797-16 Earth Observing System (EOS) Aura Science Team Meeting (Mission Operations Work Group (MOWG)) at Rotterdam, Netherlands August 29, 2016. Presentation topics include mission summary, spacecraft subsystems summary, recent and planned activities, spacecraft anomalies, data capture, propellant usage and lifetime estimates, spacecraft maneuvers and ground track history, mission highlights and past spacecraft anomalies and reliability estimates.

MOWG↗
NASA NTRS2017

Aura Extended Mission Plans

This will be presented at the Earth Science Constellation Mission Operations Working Group meeting June 13-15, 2017 to discuss the Aura extended mission plans.

summary of efforts↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗
NASA NTRSDate not supplied

Biological Research and Space Health Enabled by Machine Learning to Support Deep Space Missions

A key science goal of the NASA “Moon to Mars” campaign is to understand how biology responds to the Lunar, Martian, and deep space environments in order to advance fundamental knowledge, reduce risk, and support safe, productive human space missions. Through the powerful emerging computer science approaches of artificial intelligence (AI) and machine learning (ML), a paradigm shift has begun in biomedical science and engineered astronaut health systems, to enable Earth-independence and autonomy of mission operations. We present a decadal view of AI/ML architecture to support deep space mission goals, developed in concert with leaders in the field. We describe current AI/ML methods to support 1) fundamental biology, 2) in situ analytics, 3) high performance computing hardware, 4) automated science, 5) self-driving labs, 6) remote data management, 7) integrated real-time mission biomonitoring, and 8) a Precision Space Health system. Cutting-edge AI/ML approaches that can be integrated to support these domains include active learning, explainable AI, adaptive learning, causal inference, knowledge graphs, federated learning, transfer learning, and large language models. Finally, we present results from several current ML projects that are underway in the field to address key challenges of small sample n, high feature count, heterogeneity, and sparse data. These include 1) connecting omics data to phenotypic data using an ensemble model to infer causality of spaceflight rodent liver health disruption, 2) usage of explainable ML to interrogate the muscular underpinnings of spaceflight muscle atrophy, 3) ML models analyzing and determining directed acyclic graphs of human space health risk leveraging rodent bone datasets, 4) usage of large pre-trained models connecting biomedical knowledgebases with small spaceflight datasets to understand gene-to-gene interaction networks, and 5) a suite of benchmarked open science datasets (spaceflight mouse liver; radiation DNA damage) enabling programmers to identify the best ML algorithms to answer space biological science questions.

space biology↗