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At least 271 records · Page 15

Methane Propulsion Elements for Mars

Human exploration beyond LEO relies on a suite of propulsive elements to: (1) Launch elements into space, (2) Transport crew and cargo to and from various destinations, (3) Provide access to the surface of Mars, (4) Launch crew from the surface of Mars. Oxygen/Methane propulsion systems meet the unique requirements of Mars surface access. A common Oxygen/Methane propulsion system is being considered to reduce development costs and support a wide range of primary & alternative applications.

Percy, Tom↗

Human and Robotic Exploration Missions to Phobos Prior to Crewed Mars Surface Missions

Phobos is a scientifically significant destination that would facilitate the development and operation of the human Mars transportation infrastructure, unmanned cargo delivery systems and other Mars surface systems. In addition to developing systems relevant to Mars surface missions, Phobos offers engineering, operational, and public engagement opportunities that could enhance subsequent Mars surface operations. These opportunities include the use of low latency teleoperations to control Mars surface assets associated with exploration science, human landing-site selection and infrastructure development, which may include in situ resource utilization (ISRU) to provide liquid oxygen for the Mars Ascent Vehicle (MAV). A human mission to Mars' moons would be preceded by a cargo pre-deploy of a surface habitat and a pressurized excursion vehicle (PEV) to Mars orbit. Once in Mars orbit, the habitat and PEV would spiral to Phobos using solar electric propulsion based systems, with the habitat descending to the surface and the PEV remaining in orbit. When a crewed mission is launched to Phobos, it would include the remaining systems to support the crew during the Earth-Mars transit and to reach Phobos after insertion in to Mars orbit. The crew would taxi from Mars orbit to Phobos to join with the predeployed systems in a spacecraft that is based on a MAV, dock with and transfer to the PEV in Phobos orbit, and descend in the PEV to the surface habitat. A static Phobos surface habitat was chosen as a baseline architecture, in combination with the PEV that was used to descend from orbit as the main exploration vehicle. The habitat would, however, have limited capability to relocate on the surface to shorten excursion distances required by the PEV during exploration and to provide rescue capability should the PEV become disabled. To supplement exploration capabilities of the PEV, the surface habitat would utilize deployable EVA support structures that allow astronauts to work from portable foot restraints or body restrain tethers in the vicinity of the habitat. Prototype structures were tested as part of NEEMO 20.

Gernhardt, Michael L.↗

Simulating Fleet Noise for Notional UAM Vehicles and Operations in New York

This paper presents the results of systems-level simulations using Metrosim that were conducted for notional Urban Air Mobility (UAM)-style vehicles analyzed for two different scenarios for New York (NY). UAM is an aviation industry term for passenger or cargo-carrying air transportation services, which are often automated, operating in an urban/city environment. UAM-style vehicles are expected to use vertical takeoff and landing with fixed wing cruise flight. Metrosim is a metroplex-wide route and airport planning tool that can also be used in standalone mode as a simulation tool. The scenarios described and reported in this paper were used to evaluate a fleet noise prediction capability for this tool. The work was a collaborative effort between the National Aeronautics and Space Administration (NASA), Intelligent Automation, Inc (IAI), and the Port Authority of New York and New Jersey (PANYNJ). One scenario was designed to represent an expanded air-taxi operation from existing helipads around Manhattan to the major New York airports. The other case represented a farther term vision case with commuters using personal air vehicles to hub locations just outside New York, with an air-taxi service running frequent connector trips to a few key locations inside Manhattan. For both scenarios, the trajectories created for the entire fleet were passed to the Aircraft Environmental Design Tool (AEDT) to generate Day-Night Level (DNL) noise contours for inspection. Without data for actual UAM vehicles available, surrogate AEDT empirical Noise-Power-Distance (NPD) tables used a similar sized current day helicopter as the Baseline, and a version of that same data linearly scaled as a first guess at possible UAM noise data. Details are provided for each of the two scenario configurations, and the output noise contours are presented for the Baseline and reduced noise DNL cases.

Glaab, Patricia↗

Urban Air Mobility Airspace Dynamic Density Safety Metric

NASA’s Advanced Air Mobility (AAM) project focuses on enabling emerging aviation markets by accelerating development of safe, high-volume flight operations.[1] It involves development and validation of vehicles, airspace, and automation changes required to support concepts such as Urban Air Mobility, a vision for electric or hybrid electric, vertical or short take-off and landing vehicles that can transport passengers and cargo over an urban environment.[2] NASA’s System Wide Safety (SWS) project is coordinating with AAM by understanding how safety could be affected by these emerging operations. We approach the assessment of airspace safety by identifying threats to operations and then monitoring and predicting the evolution of those threats encoded in a set of safety metrics.[3] Toward this end, we have been developing a dynamic density metric to predict the likelihood of vehicle conflicts in the airspace.

Dynamic Density↗

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗

Dynamic Spectrum Allocation in Urban Air Transportation System via Deep Reinforcement Learning

The emerging concepts of Urban Air Mobility (UAM) and Advanced Air Mobility (AAM) open a new paradigm for urban air transportation. A big challenge is that these new aerial vehicles will quickly saturate the already crowded aviation spectrum, which is an essential resource to ensure reliable communications for safe operations. In this paper, we consider an air transportation system where multiple aerial vehicles are operated to transport passengers or cargo from different sources to destinations along their pre-defined paths. During the flight, the minimum communication Quality of Service (QoS) requirement must be achieved to ensure flight safety. Our objective is to minimize the average mission completion time by jointly optimizing the velocity selection and spectrum allocation for all aerial vehicles. We formulate the optimization problem as a multi-stage Markov Decision Process (MDP) where the optimization variables are coupled together. A multi-agent Deep Reinforcement Learning (DRL) based solution is proposed where Value Decomposition Networks (VDN) algorithm is utilized to take discrete actions. Additionally, we propose a heuristic greedy algorithm as a baseline solution. Simulation results show that our learning based solution outperforms the heuristic greedy algorithm and another Orthogonal Multiple Access (OMA) solution in minimizing the mission completion time.

Ruixuan Han↗

National Campaign (NC)-1 Strategic Conflict Management Simulation (X4) Final Report

Urban Air Mobility (UAM) enables highly automated, cooperative, passenger or cargo-carrying air transportation services in and around urban areas. UAM is a subset of the Advanced Air Mobility (AAM) concept under development by the National Aeronautics and Space Administration (NASA), the Federal Aviation Administration (FAA), and industry. The Strategic Conflict Management (SCM) Simulation, dubbed “X4”, was conducted between July 2021 and June 2022 by NASA with the FAA and industry to evolve the Provider of Services for UAM (PSU) that will be needed to ensure initial UAM operations can scale in the National Airspace System (NAS). The FAA UAM Concept of Operations (ConOps) v1 [1] served as an initial guiding document for the X4 airspace management system design to ensure the architecture supports testing of services provided by third-party service providers. To that end, the X4 architecture leveraged concepts and technologies developed for Unmanned Aircraft System (UAS) Traffic Management (UTM) while also developing and testing new capabilities and services needed for UAM. The architecture included an initial prototype of the FAA-Industry Exchange Protocol (FIDXP), third-party services such as the PSUs, and Discovery and Synchronization Service (DSS), and other new services such as Demand-Capacity Balancing (DCB) to facilitate UAM strategic conflict management. During X4, NASA led discussions and collaborated with seven industry airspace partners to develop initial airspace management concepts for UAM that drove what would be tested and evaluated during the simulations. The initial capabilities defined for PSU leveraged UTM UAS Service Supplier (USS) as a starting point and evolved to meet UAM requirements. These discussions were also an opportunity for NASA to collaborate with industry to develop a set of initial Community-Based Rules (CBRs). These CBRs enabled how UAM traffic would be cooperatively managed among UAM operators. The collaborative, iterative process of developing CBRs with industry during X4 provided insight into the challenges involved and identified the need for a suitable and effective forum for future CBR development. In parallel with these discussions, NASA conducted a series of seven software sprints and two collaborative simulations with the airspace partners that built up in complexity. Over the course of the year, all seven partners successfully completed all the sprints by demonstrating the required capabilities. They also participated in the two collaborative simulations to demonstrate how multiple PSUs could work together in a collaborative, more complex environment with higher traffic density. The X4 simulation accomplished NASA's objectives and helped advance the development of the seven participating PSUs. The lessons learned provided insight into key elements of the UAM Notional Architecture from the FAA ConOps v1 [1] and UTM technologies when applied to UAM: - Having a Concept of Use (ConUse) defined prior to the activity would accelerate the time and effort from concept development to testing. - While the UAM Notional Architecture provided a starting point for a federated architecture that support services provided by third-party providers, the USS and PSU differed in their capability definitions for operational intent submission and sharing, conformance monitoring, airspace authorization, strategic conflict management, airspace constraints and dynamic replanning. - While existing DSS developed for UTM provided a way for PSU and other UAM services (such as DCB) to discover relevant operations from each other, additional complexity and challenges were found during X4 testing and illuminated the need for a more suitable solution for UAM. Industry can leverage the results of this demonstration to accelerate UAM requirements, CBRs, and standards development. The FAA and other government municipalities and agencies will be able to leverage results to inform future policies and identify additional gaps that require further analysis, moving toward operationalization of UAM.

Urban Air Mobility↗

High Density Vertiplex: Scalable Autonomous Operations Prototype Assessment Simulation

Urban Air Mobility (UAM) is a rapidly growing topic within the field of aviation because of the impact a refined ecosystem and uncrewed aerial vehicles could have on modern society, such as urban air mobility, cargo, and emergency transport. Before the UAM concept can be actualized, research is needed to understand how to integrate these new classes of vehicles and operations into the National Airspace System. One under-researched but critical piece of infrastructure required for UAM operations is Vertiport operations. Vertiports are the envisioned takeoff and landing locations for these uncrewed aerial vehicles. To accommodate the high use of the vertiport, new technologies and roles will be required for optimal use. At NASA, the High Density Vertiplex sub-project targets research into vertiports. The High Density Vertiplex team created an Urban Air Mobility ecosystem to test and evaluate different concepts and tools used to support higher density operations at vertiports. Part of the test and evaluation included the Prototype Assessment Operations simulation of highdensity operations around a vertiport to study vertiport management and vertiport operations. The research team also evaluated how the prototype Urban Air Mobility ecosystem supported fleet managers, ground control station operators, and vertiport managers in execution of nominal and off-nominal high-density operations. Results from this simulation provided insight regarding UAM ecosystem research and development and vertiport automation systems.

Small Uncrewed Aerial Vehicle (sUAS)↗

High Density Vertiplex: Scalable Autonomous Operations Prototype Assessment Simulation

Urban Air Mobility is a rapidly growing topic within the field of aviation because of the impact a refined ecosystem and uncrewed aerial vehicles could have on modern society, such as urban air, cargo, and emergency transport. Before the UAM concept can be actualized, research is needed to understand how to integrate these new classes of vehicles and operations into the National Airspace System. One under-researched but critical piece of infrastructure required for UAM operations is Vertiport operations. Vertiports are the envisioned takeoff and landing locations for these uncrewed aerial vehicles. To accommodate the high use of the vertiport, new technologies and roles will be required for optimal use. At NASA, the High Density Vertiplex sub-project targets research into vertiports. The High Density Vertiplex team created their own Advanced Air Mobility ecosystem to test and evaluate different concepts and tools used to support higher density operations at vertiports. Part of the test and evaluation included the Prototype Assessment Operations simulation of high-density operations around a vertiport to study vertiport management and vertiport operations. The research team also evaluated how their simulated Urban Air Mobility ecosystem supported fleet managers, ground control station operators, and vertiport managers in execution of nominal and off-nominal high-density operations. Takeaways from this simulation helped the team evaluate well how the users of the ecosystem were able to use the components of the ecosystem to complete Urban Air Mobility missions.

Small Uncrewed Aerial Vehicle (sUAS)↗

Artemis I Orion ESM Propulsion System Engine Performance

NASA's Orion spacecraft transports humans and cargo into cislunar space for the Artemis program. The European Service Module (ESM), supplied by ESA and its European industry partners, provides Orion with power and in-space propulsion. The Orion-ESM propulsion system is a bipropellant hypergolic propulsion system using monomethyl hydrazine (MMH) and nitrogen tetroxide (MON-3). Primary translational propulsion is provided by the Orbital Maneuvering System Engine(OMS-E), with backup translational propulsion provided by eight Auxiliary thrusters (AUX). Attitude control and small translational maneuvers are provided by twenty four Reaction Control System (RCS) engines. The 2022 Artemis I mission was the first integrated flight test of the Orion-ESM spacecraft and its propulsion system. The OMS-E used on Artemis I was a refurbished Space Shuttle OMS-E that previously flew on nineteen missions ranging f rom STS-41G in 1984 to STS-112 in 2002. The Auxiliary engines are modified Aerojet Rocketdyne R4D-11 engines produced specifically for the Orion program. The RCS engines are Ariane Group engines originally used for the Automated Transfer Vehicle (ATV) program. This paper will discuss the unique operational requirements for each engine on Orion and the development and qualification effort sat both the engine and system-level that were completed to enable a successful Artemis I mission. Next the paper will evaluate the in-f light performance of the engines during the Artemis I mission showing nominal performance as expected. Additionally, comparisons to models will be presented showing very good correlation. Finally, the paper will address the plan for the engines on future Orion missions and the evolution of the system operation.

liquid propulsion systems↗

Artemis I Orion ESM Propulsion System Engine Performance

NASA's Orion spacecraft transports humans and cargo into cislunar space for the Artemis program. The European Service Module (ESM), supplied by ESA and its European industry partners, provides Orion with power and in-space propulsion. The Orion-ESM propulsion system is a bipropellant hypergolic propulsion system using monomethyl hydrazine (MMH) and nitrogen tetroxide (MON-3). Primary translational propulsion is provided by the Orbital Maneuvering System Engine (OMS-E), with backup translational propulsion provided by eight Auxiliary thrusters (AUX). Attitude control and small translational maneuvers are provided by twenty-four Reaction Control System (RCS) engines. The 2022 Artemis I mission was the first integrated flight test of the Orion-ESM spacecraft and its propulsion system. The OMS-E used on Artemis I was a refurbished Space Shuttle OMS-E that previously flew on nineteen missions ranging from STS-41G in 1984 to STS-112 in 2002. The Auxiliary engines are modified Aerojet Rocketdyne R4D-11 engines produced specifically for the Orion program. The RCS engines are Ariane Group engines originally used for the Automated Transfer Vehicle (ATV) program. This paper will discuss the unique operational requirements for each engine on Orion and the development and qualification effort sat both the engine and system-level that were completed to enable a successful Artemis I mission. Next the paper will evaluate the in-f light performance of the engines during the Artemis I mission showing nominal performance as expected. Additionally, comparisons to models will be presented showing very good correlation. Finally, the paper will address the plan for the engines on future Orion missions and the evolution of the system operation.

liquid propulsion systems↗

DART: Delta Advanced Reusable Transport. An alternate manned space system proposal

The Delta Advanced Reusable Transport (DART) craft is being developed to add, multiple, rapid, and cost effective space access to the U.S. capability and to further the efforts towards a permanent space presence. The DART craft provides an augmentative and an alternative system to the Shuttle. As a supplement launch vehicle, the DART adds low cost and easily accessible transport of crew and cargo to specific space destinations to the U.S. program. This adds significant opportunities for manned rated missions that do not require Shuttle capabilities. In its alternative role, the DART can provide emergency space access and satellite repair, the continuation of scientific research, and the furthering of U.S. manned efforts in the event of Shuttle incapabilities. In addition, the DART is being designed for Space Station Freedom compatibility, including its use as a 'lifeboat' emergency reentry craft for Freedom astronauts, as well as the transport of crew and cargo for station resupply.

Source record↗

Integrated Logistics and Supportability Challenges of Sustained Human Lunar Exploration

NASA’s Artemis program plans to establish a sustained human presence on the lunar surface. The International Space Station other space station programs have demonstrated long-duration human spaceflight operations that reuse infrastructure in Low Earth Orbit, sometimes including long uncrewed “dormant” periods. In contrast, human lunar exploration to date has consisted solely of relatively short sortie missions, rather than a sustained presence. A sustained human outpost on the Moon that can support month-long crewed exploration missions and be reused by multiple crews will be more challenging than past operations, particularly from the perspective of logistics, supportability, and risk. This paper examines the integrated logistics and supportability challenges of sustained human lunar exploration and provides a review of historical spaceflight experience in terms of crewed mission endurance, uncrewed duration, transportation overhead, and access to abort. Planned Artemis Base Camp crewed mission endurance is approximately 2.5 times longer than past lunar surface crewed mission endurance, but similar to average time between resupply for the International Space Station. Sustained human spacecraft have only twice experienced uncrewed durations longer than the planned interval between Artemis Base Camp missions, and Artemis surface assets will face long uncrewed periods more regularly than any past sustained human spacecraft. Transportation of crew and cargo to and from the Moon will be more difficult and time-consuming than transportation to and from Low Earth Orbit, and crew access to abort will be more limited. The implications of Artemis lunar operations for crewed Mars mission planning are also discussed. Historical approaches to risk management—including logistics, supportability, and abort strategies—should be reexamined and re-optimized for this new mission context. Sustained lunar operations will provide a valuable proving ground for testing new approaches to crewed space exploration.

logistics↗

Integrated Logistics and Supportability Challenges of Sustained Human Lunar Exploration

NASA’s Artemis program plans to establish a sustained human presence on the lunar surface. The International Space Station and other space station programs have demonstrated long-duration human spaceflight operations that reuse infrastructure in Low Earth Orbit, sometimes including long uncrewed “dormant” periods. In contrast, all human exploration beyond Low Earth Orbit to date has consisted solely of relatively short sortie missions, rather than a sustained presence. A sustained human outpost on the Moon that can support month-long crewed exploration missions and be reused by multiple crews will be more challenging than past operations, particularly from the perspective of logistics, supportability, and risk. This paper examines the integrated logistics and supportability challenges of sustained human lunar exploration and provides a review of historical spaceflight experience in terms of crewed mission endurance, uncrewed duration, transportation overhead, and access to abort. Planned Artemis Base Camp crewed mission endurance is approximately 2.5 times longer than past lunar surface crewed mission endurance, but similar to the average time between resupply for the International Space Station. Sustained human spacecraft have only twice experienced uncrewed durations longer than the planned interval between Artemis Base Camp missions, and Artemis surface assets will face long uncrewed periods more regularly than any past sustained human spacecraft. Transportation of crew and cargo to and from the Moon will be more difficult and time-consuming than transportation to and from Low Earth Orbit, and crew access to abort will be more limited. The implications of Artemis lunar operations for crewed Mars mission planning are also discussed. Historical approaches to risk management—including logistics, supportability, and abort strategies—should be reexamined and re-optimized for this new mission context. Sustained lunar operations will provide a valuable proving ground for testing new approaches to crewed space exploration.

logistics↗

Space Shuttle cargo processing.

The spacecraft processing techniques to be used at the Kennedy Space Center for the assembly and check-out of Space Shuttle cargoes are discussed. The processing flow for vertically handled spacecraft, which are inserted into the Shuttle orbiter cargo bay while the orbiter is in the vertical position and often are attached to a separate solid booster stage, includes assembly into unified payloads in the Vertical Processing Facility, followed by Cargo Integration Test Equipment tests to ensure cargo bay compatibility, and transportation to the launch pad. Horizontally handled spacecraft such as Spacelab, which are inserted into the Orbiter cargo bay while it is in the Orbiter Processing Facility in the horizontal position, are assembled and checked out within the Operations and Checkout Building, where the CITE tests are performed and the Spacelab will be disassembled, and then transported to the Orbiter Processing Facility. It is pointed out that during the Space Shuttle era, when the number of spacecraft to be processed simultaneously at Kennedy will double or triple, all spacecraft processing facilities will be required to handle the workload.

Neilon, J. J.↗

Minimum cost criteria for SPS transportation to GEO

The application of cost optimization to vehicle design is discussed relative to establishing ground rules for a minimum cost heavy cargo launch vehicle to transport the Satellite Power System to geosynchronous orbit. Criteria defined include: fully reusable; unmanned; technical simplicity; and operations simplicity. Graphs are presented depicting (1) the launch vehicle concept schematic, (2) the cost comparison, (3) the launch vehicle sizing, and (4) the specific transportation cost.

Koelle, D. E.↗

Energy-based break-even transportation distance of biomass feedstocks

The distance a solid biomass feedstock could be used to transport the feedstock when used as biobased fuel is critical information for transportation analysis. However, this information is not available. The break-even transportation distance (BTD) of various fuels from biomass feedstocks and fossil sources was analyzed for truck, rail, and ship transport modes based on bulk density, moisture content, and specific energy. Fourteen different biomass feedstocks, such as crop residues (e.g., corn stover), woody biomass (e.g., wood chips), including thermally pretreated (torrefied) and densified forms (pellets), cattle feedlot compost, and three standard fossil fuels, namely, coal, lignite, and diesel, were considered for BTD analysis and comparison. The BTD values were derived by comparing the energy content of biomass feedstocks with the energy expended in transporting the fuels through selected transportation modes. For ready reference, an alternative derivation of BTD equations and example calculations were also presented. Among the biomass feedstocks, torrefied pellets had the highest BTD (4.16 × 10 4 , 12.47 × 10 4 , and 54.14 × 10 4 km), and cattle feedlot compost had the lowest BTD (1.29 × 10 4 , 3.88 × 10 4 , and 9.23 × 10 4 km), respectively, for truck, rail, and ship. Higher bulk density and higher specific energy of the biomass feedstocks increased the BTD for all modes of transport. Transport is most efficient when mass-limited. Biomass feedstock bulk densities where transportation becomes mass-limited are 223, 1,480, and 656 kg/m 3 for truck, rail, and ship, respectively. Truck transport is typically mass-limited (payload limit restriction; increased BTD), whereas rail transport is entirely volume-limited (cargo space restriction; decreased BTD), and ship transport is mostly volume-limited for biomass feedstocks and mass-limited for densified biomass feedstocks. Ship transport is the most efficient, followed by rail and truck; on average for the materials (17) studied, rail is 3.1 times and ship is 9.2 times the truck's BTD. Based on the bulk density and higher specific energy of the biomass feedstocks, regardless of the refinery location, interstate truck transport of these feedstocks is not a limiting factor in the bio-refining process., with the studied biomass feedstock BTD per truckload representing between 0.89 and 2.88 times the US perimeter.

09 BIOMASS FUELS↗

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗