SEARCH · Search NASA

Results for “LiAISON”

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.

SourceResearch connectionFrom yearThrough year

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

At least 19 records

All sources NASA NTRS DOE OSTI

NASA NTRS2012

LiAISON-Supplemented Navigation for Geosynchronous and Lunar L1 Orbiter

A high-fidelity simulation is developed to test the theory that relative range and range-rate measurements between a satellite in a geosynchronous orbit and a satellite in orbit about the Earth-Moon L1 point may be used to improve the navigation accuracy of both satellites, compared with ground-only tracking. Recent research has determined that satellite-to-satellite range measurements between these two orbits are su cient to perform absolute state estimation as well as relative state estimation of both satellites - a technology known as LiAISON (Linked Autonomous Interplanetary Satellite Orbit Navigation). This study quanties the improvement in accuracy when LiAISON is used to supplement ground tracking of one or both satellites in a very realistic simulation. Orbit determination techniques show that the inclusion of LiAISON measurements with ground tracking observations can greatly improve absolute position and velocity estimates for both satellites in geosynchronous orbit and about the Earth-Moon L1 point.

satellite-to-satellite tracking (SST)↗

NASA NTRS2013

Interplanetary Departure Stage Navigation by Means of Liaison Orbit Determination Architecture

Autonomous orbit determination for departure stages of interplanetary trajectories is conducted by means of realistic radiometric observations between the departing spacecraft and a satellite orbiting the first lunar libration point. Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) is used to estimate the orbit solution. This paper uses high-fidelity simulations to explore the utilization of LiAISON in providing improved accuracy for interplanetary departure missions. The use of autonomous navigation to supplement current techniques for interplanetary spacecraft is assessed using comparisons with groundbased navigation. Results from simulations including the Mars Science Laboratory, Mars Exploration Rover, and Cassini are presented. It is shown that observations from a dedicated LiAISON navigation satellite could be used to supplement ground-based measurements and significantly improve tracking performance.

interplanetary↗

NASA NTRS2013

Interplanetary Departure Stage Navigation by Means of Liaison Orbit Determination Architecture

Linked Autonomous Interplanetary Satellite Orbit N↗

NASA NTRS2013

Supporting Crewed Lunar Exploration with LiAISON Navigation

This paper examines the benefits of navigating a crewed vehicle between the Earth and the Moon using both ground tracking and satellite-to-satellite tracking. Linked Autonomous Interplanetary Satellite Orbit Navigation (LiAISON) is a new technique that has been shown to dramatically improve the navigation of lunar satellites, libration orbiters, and Earth orbiting satellites using scalar intersatellite observations. In this paper, LiAISON is applied to the problem of navigating a crewed vehicle to the Moon. It has been found that LiAISON observations improve the navigation accuracy enough to reduce the number of active ground tracking stations from six to three.

low-energy↗

NASA NTRS2012

LiAISON-supplemented navigation for geosynchronous and Lunar L1 orbiters

A high-fidelity simulation is developed to test the theory that relative range and range-rate measurements between a satellite in a geosynchronous orbit and a satellite in orbit about the Earth-Moon L₁ point may be used to improve the navigation accuracy of both satellites, compared with ground-only tracking. Recent research has determined that satellite-to-satellite range measurements between these two orbits are sufficient to perform absolute state estimation as well as relative state estimation of both satellites–a technology known as LiAISON(Linked Autonomous Interplanetary Satellite Orbit Navigation). This study quantifies the improvement in accuracy when LiAISON is used to supplement ground tracking of one or both satellites in a very realistic simulation. Orbit determination techniques show that the inclusion of LiAISON measurements with ground tracking observations can greatly improve absolute position and velocity estimates for both satellites in geosynchronous orbit and about the Earth-Moon L₁ point.

Anderson, Rodney L.↗

NASA NTRS2012

Simulating the Liaison Navigation Concept in a Geo + Earth-Moon Halo Constellation

Linked Autonomous Interplanetary Satellite Orbit Navigation, or LiAISON, is a novel satellite navigation technique where relative radiometric measurements between two or more spacecraft in a constellation are processed to obtain the absolute state of all spacecraft. The method leverages the asymmetry of the gravity field that the constellation exists in. This paper takes a step forward in developing a high fidelity navigation simulation for the LiAISON concept in an Earth-Moon constellation. In particular, we aim to process two-way Doppler measurements between a satellite in GEO orbit and another in a halo orbit about the Earth-Moon L1 point.

Linked Autonomous Interplanetary Satellite Orbit N↗

NASA NTRS1990

Industry liaison section implementation plan

The Industry Liaison Section is a new function of the Army/NASA Aircrew-Aircraft Integration (AAAI) Program that is intended to bridge an existing gap between Government developers (including contractors) and outside organizations who are potential users of products and services developed by the AAAI Program. Currently in its sixth year, the Program is experiencing considerable pull from industry and other government organizations to disseminate products. Since the AAAI Program's charter is exploratory and research in nature, and satisfying proper dissemination requirements is in conflict with the rapid prototyping approach utilized by the design team, the AAAI Program has elected to create an Industry Liaison Section (ILS) to serve as the Program's technology transfer focal point. The process by which the ILS may be established, organized and managed is described, including the baseline organizational structure, duties, functions, authority, responsibilities, relations and policies and procedures relevant to the conduct of the ILS.

Lakowske, Stephen↗

DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗

DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗

DOE OSTI2024

LiAISON (Life-cycle Assessment Integration into Scalable Open-source Numerical models) [SWR-24-01]

We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE)7. We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming. Scientific publication available here: https://pubs.acs.org/doi/full/10.1021/acs.est.2c04246

Ghosh, Tapajyoti↗

NASA NTRS1951

Analysis of the effects of boundary-layer control in the take-off and power-off landing performance characteristics of a liaison type of airplane

A performance analysis has been made to determine whether boundary-layer control by suction might reduce the minimum take-off and landing distances of a four-place or five-place airplane or a liaison type of airplane below those obtainable with conventional high-lift devices. The airplane was assumed to have a cruise duration of 5 hours at 60-percent power and to be operating from airstrips having a ground friction coefficient of 0.2 or a combined ground and braking coefficient of 0.4. The payload was fixed at 1500 pounds, the wing span was varied from 25 to 100 feet, the aspect ratio was varied from 5 to 15, and the power was varied from 300 to 1300 horsepower. Maximum lift coefficients of 5.0 and 2.8 were assumed for the airplanes with and without boundary-layer-control --equipment weight was included. The effects of the boundary-layer control on total take-off distance, total power-off landing distance, landing and take-off ground run, stalling speed, sinking speed, and gliding speed were determined.

Horton, Elmer A↗

NASA NTRS2013

Liaison-Supplemented Navigation of a Crewed Vehicle in a Lunar Halo Orbit

This paper offers an early examination of the challenges of navigating a crewed vehicle, with all of the associated unmodeled accelerations that arise from the crew's activities, in an orbit about the Earth-Moon L2 point. The combination of the unstable nature of libration orbits with the lack of acceleration knowledge makes the station keeping strategy challenging. It is found that a combination of ground tracking and satellite-to-satellite tracking produces the most favorable navigation accuracy. This paper examines the costs and benefits of applying LiAISON (Linked Autonomous Interplanetary Satellite Orbit Navigation) to a crewed mission in an unstable L2 orbit.

ARTEMIS↗

DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗

DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 degrees C or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Under baseline projections (i.e., no decarbonization goals), neither process reaches parity with the incumbent technology across several environmental metrics. Under the decarbonization scenarios, the underlying sectoral shifts result in declining impacts over time, compared to 2020 levels, except for metal depletion levels, which increase. The background shifts postulate a heavily decarbonized economy and energy system, which help technologies reach parity with SMR between 2040-2050 (RCP2.6) and 2030-2040 (RCP1.9) for global warming. Despite declines across several other metrics over time, neither PtH2 technology break even with SMR by 2100 besides for global warming.

decarbonizing↗

NASA NTRS2012