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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.

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At least 19 records
All sourcesNASA NTRSDOE OSTI
NASA NTRS1983

Elevated temperature behavior of superplastically formed/weld-brazed titanium compression panels having advanced shaped stiffeners

The 316 C (600 F) buckling behavior of superplastically formed/weld-brazed titanium compression panels having advanced shaped stiffeners was investigated. Fabrication of the advanced shaped stiffeners was made possible by the increased formability afforded by the superplasticity characteristics of the titanium alloy Ti-6Al-4V. Stiffeners having the configurations of a conventional hat, a beaded web, a modified beaded web, a ribbed web, and a stepped web were investigated. The data from the panel tests include load-shortening curves, local buckling strengths, and failure loads. The superplastic formed/weld-brazed panels with the ribbed web and stepped web stiffeners developed 25 and 27 percent higher buckling strengths at 316 C (600 F) than panels with conventionally shaped stiffeners. The buckling load reductions for panels tested at 316 C (600 F), compared with panels tested at room temperature, were in agreement with predictions based on titanium material property data. The advantage that higher buckling loads can be readily achieved by superplastically forming of advanced stiffener shapes was demonstrated. Application of these advanced stiffener shapes offers the potential to achieve substantial weight savings in aerospace vehicles.

Royster, D. M.↗
NASA NTRS2024

Development of a Surface Water Transportation System for ISRU Operations on Mars

NASA is working to define the architecture needed for a Mars exploration campaign. Initial analysis assumptions allow for pre-deployment of essential cargo and equipment to support a crew landing, including the pre-positioning of a Mars Ascent Vehicle (MAV). This MAV is likely to represent the largest single payload that must be landed on the Mars surface. Its size would be influenced by the amount of mass that state-of-the-art Entry, Descent, and Landing (EDL) systems would be capable of placing on Mars. One possible method of increasing the usable size of the MAV without exceeding available EDL capabilities is to land the MAV without ascent propellant on board. Following such a method may necessitate a strategy to acquire sufficient ascent propellant to allow a crew to safely depart the Martian surface. This paper describes a conceptual return propellant strategy that uses a liquid transportation skid, or pallet, to be used in conjunction with a rover mobility system to transfer water across the Martian surface from a source point to an in-situ resource utilization (ISRU) plant that would use this water as a feedstock to generate oxygen and methane to enable launch of the MAV. Design considerations, concept of operations, and rover energetics will be discussed in this paper.

Fluid Structure Interface↗
NASA NTRS1962

BUCKLING AND POST-BUCKLING OF ELASTIC SHELLS

Some theoretical investigations of buckling of elastic shells are surveyed in this report. Only geometrically perfect shells are considered; initial dents and out-of-roundness are not taken into account. Several questions raised by the studies are: (a) Under what conditions is the infinitesimal theory of buckling of shells adequate? (b) How does the energy theory of buckling of shells correlate with the method based on equilibrium equations for bending moments, tensions, and shears in a buckled configuration? (c) How important are nonlinear terms in the tangential displacements u, v in the strain-displacement relations for buckling and post-buckling studies? (d) How important are the boundary conditions for u, v in affecting stability? (e) If a condition of snap-through is approached, how much external work is required to push the shell "over the hump" into the buckled configuration? (f) How reliable are mathematical approximations used previously in the infinitesimal theory of buckling of shells? Tentative and incomplete answers to some of these questions are suggested.

ELASTIC BUCKLING↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU)processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

Lunar Landing and Launch Pad Construction – Concepts and Criteria

When a lander vehicle launches or lands on the Moon, the rocket engine exhaust plume impinges on the surface and interacts with the regolith to create blast ejecta and associated cratering of the surface. Lunar regolith blast ejecta travels at high velocities (>2,000 m/s) for long distances (kilometers) in a vacuum environment [1] creating hazards for surrounding assets and it can also impact the bottom of the lander vehicle, risking damage to the engines, thermal insulation and sensors. Ballistic particles can possibly enter cislunar space and achieve orbit as debris, if the ejecta is sufficiently energetic. The cratering and regolith erosion can endanger the vehicle itself by affecting the soil stability under the landing gear. Landing on unpredictable terrain with varying topography, natural craters and rock hazards is also hazardous risks tipping a lander at dangerous angles in extreme conditions that may also violate maximum slope angles for subsequent launch operations. During launch (Figure 1), an overpressure pulse created by the ignition of the rocket engines can pose significant ejecta risks to the vehicle. Dust clouds raised during landing limit the efficacy of sensors and reduce visibility for the astronaut pilots, creating significant real-time risk during landing site selection by the pilot or computer navigation system. Future lunar spaceports will require mitigations to these launch and landing risks [2]. There are four main objectives of this effort:1) To establish the state of the art in LLP construction methodologies. 2) To propose criteria for trade studies of LLP concepts. 3) To publicly share the authors’ ideas for potential LLP solutions. 4) To serve as the starting point for future development of LLP technologies. Establishing the state of the art in the area of off-Earth Launch & Landing Pad concepts will base-line the work that has been completed thus far and highlight the wide span between current Technology Readiness Levels (TRLs)and operational readiness. The authors aim to communicate the need for funding in this area in the near term by illustrating that there is much work to be completed before a truly viable option exists. Setting forth criteria for trade studies of LLP concepts is important for several reasons. The first and most straightforward is to establish a framework for performing trade studies on LLP concepts. This will enable NASA to select the most promising concepts for continued development, and it will also help technology developers understand how their concepts compare with others and the priorities of development effort.

landing pad↗
NASA NTRSDate not supplied

ICE-RASSOR: Intelligent Capabilities Enhanced Regolith Advanced Surface Systems Operations Robot

NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from on-board sensory. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We apply supervised learning using real data to estimate the soil mass collected without the need for mass flow rate monitors or other explicate sensing techniques. We also create a reduced-order simulation environment to develop autonomous trenching controllers via reinforcement learning and proto-type state estimation architectures. Our initial results suggest that excavated regolith mass can be inferred within 2.9% RMS error of full scale, and reinforcement learning for autonomous operations has learned viable trenching strategies and helped identify desirable sensing capabilities, arrangements, and considerations. Future work includes regolith mass estimation during dynamic operation, expanding our simulation to more complex environments, and transfer learning from simulation to hardware.

machine learning↗
NASA NTRSDate not supplied

Off Earth Landing and Launch Pad Construction – A Critical Technology for Establishing a Long-Term Presence on Extraterrestrial Surfaces

Space Policy Directive 1 has refocused the National Aeronautics and Space Administration (NASA) to lead the return of humans to the Moon for long-term exploration and utilization. A long-term presence on the moon will require numerous lunar landings and launches to build up surface assets, rotate crew, and deliver resources to/from the moon. Interactions between landing/launch plumes and the unprepared surface cause regolith to be eroded and ejected at high velocities from beneath the vehicle. The associated ejecta elevates mission risks by obscuring sensors and human vision during landing; cratering/modifying the surface that the vehicle will land upon; and subjecting the vehicle, surrounding assets, and potentially orbital assets to impacts from high velocity dust particles. A method to mitigate risks stemming from landing/launch plume ejecta is to construct reusable landing/launch pads. A landing/launch pad can provide a known landing/launch surface, minimize ejecta, protect assets from liberated particles, and provide protection in case of a landing/launch anomaly. This presentation will summarize previous work towards construction of off-Earth landing/launch pads and identify key technology gaps. Additionally, this presentation establishes metrics for comparison of pad construction technologies and trades current approaches. The overall objective of this paper is to baseline the state of the art of off-Earth landing/launch pad construction technologies and serve as the starting point for further technical development.

Landing & Launch Pad↗
NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of how much mass is in the drums, this type of high-level planning is not possible. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All take in system states, such as arm/drum positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums.1) A neural network model that takes a vector of normalized system states; 2) A model that uses the integrated power consumption of an arm-raise (normalized by velocity); 3) A model that uses average drum current over a variable length interval of the drum disengaged from the surface; and 4) A real-time estimation model that aggregates excavation drum current. The developed models run in real time, outputting predictions for the front and rear drums, timestamp of the last prediction, and total mass in RASSOR’s drums. Further testing is required to validate the arm-raise model (2), though initial tests indicate reasonable performance (<10% mean error) on the hardware. The linear fit of average drum-current model (3) had a front value of r^2=0.99 and a rear value of r^2=0.98 on the validation dataset. This model currently has the best performance on unseen data. The real time model (4) is still in development, though initial results on a small subset of the training data show that it has high accuracy in predicting the increase in mass during excavation. Though work remains to be done with deploying a high-fidelity model to the physical system that makes predictions with error below the desired threshold, the modular architecture for model development allows quick adjustment of parameters to increase model fidelity. This architecture can also be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results are promising as it has been shown that models can be developed that accurately estimate excavated regolith mass.

rassor↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Reinforcement Learning

To continue on a sustainable and flexible path, NASA needs to address the challenge of collecting and moving large amounts of regolith at the destination. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for In-Situ Resource Utilization (ISRU) processing. RASSOR’s design enables it to efficiently collect and deposit regolith, return collected material for processing, and myriad related ISRU activities. To reliably perform these operations on the lunar surface, RASSOR software and sensory systems need to be robust and maximize the information extracted from a reduced sensor payload. Herein, we present preliminary findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments to develop autonomous trenching controllers via reinforcement learning and prototype state estimation architectures. The goal of reinforcement learning is for an agent to learn a policy (task strategy) through interactions with an environment. When the agent performs an action, a change occurs in environment state and a numerical reward is received which informs the agent whether the action performed was good or not. Since reinforcement learning algorithms learn through trial-and-error, a simulation is a desirable first environment for development and learning. We developed two simulations, the first is a 2D excavation simulation developed to facilitate parameter selection, and a 3D simulation developed using a game physics engine, to simulate simplified soil interactions and increase the fidelity of the dynamic models of the robotic agents. The development of this 3D simulation has enabled the training of additional sensing capabilities and research both at the granular mechanics and operations levels. We experimented with various virtual sensor payloads to identify a combination that enabled efficient excavation operation and learning. Our reward function is based on how much material is excavated per step. A penalty is also received for leaving the dig site and to smooth the acceleration of the drum arms. We implemented pseudo time-of-flight sensors to report distance from each drum to ground and the height above ground which was found to be more efficient than existing solutions. Our findings suggest that reinforcement learning for autonomous operations has learned viable trenching strategies within 3000 training episodes in our simplified 2D environment and helped identify desirable sensing capabilities, arrangements, and considerations such as the positioning of time-of-flight sensors. Future work includes expanding our simulation to more complex environments and scenarios, and transfer learning from simulation to RASSOR 2.0 hardware for deployment in the Regolith Test Bin at NASA's Kennedy Space Center.

rassor↗
NASA NTRSDate not supplied

In-situ Lunar Launch and Landing Pad Construction with Regolith-Thermoset Polymer Composite Materials

Lunar launch and landing pads are necessary to mitigate risks to lander/ascent vehicles and surrounding surface assets from rocket plume ejected regolith. The current state of the art of landing and launching from the lunar surface is to land/launch on unprepared regolith surfaces. Though this has been a relatively successful approach, significant risks exist for the Artemis Program due to higher thrust levels leading to increased ejecta and cratering, presence of co-located assets in the ejecta path, and potential strict surface levelness requirements. Regolith-thermoset polymer composite materials were developed and evaluated for off Earth launch and landing pad applications. Performance under hot fire conditions was assessed for two simulated Starship lunar launch/landing environments, one targeting thermal conditions and the second targeting pressure conditions. Paver test articles were prepared at 20% and 11% polymer mass fractions. Sintered paver test articles were prepared with 20% polymer grouting filling the seams between pavers. Though significant erosion was experienced during the more extreme thermal testing conditions, all test articles successfully mitigated regolith ejecta from plume effects. A concept for preparing and emplacing the materials was developed and tested in laboratory conditions. The testing evaluated the feasibility of using twin and single screw extruder technology to mix, convey and deposit materials. Both the twin and single screw extruders were capable of processing and extruding the composite with a maximum of 90% mass fraction of regolith achieved by the twin screw extruder.A primary risk for application of regolith-thermoset polymer composites as lunar launch/landingpad is the mass of polymer required to be landed on the moon. Calculations were performed that show that a 100 m diameter, 0.025 m thick pad with polymer mass fraction of 15% will require a payload mass of 72 mt to the Lunar surface. This is below the planned Starship payload capacity to the lunar surface of 100 mt. The regolith-thermoset polymer composite based construction approach proved feasibility in three critical areas: performance under launch/landing conditions, demonstration of a mixing and depositing strategy, and fitting within the planned lunar payload capacity. It is recommended that materials and systems be developed to TRL 6 for a small-scale lunar demonstration of emplacement of a launch/landing pad via a CLPS mission to support Artemis Program roadmap gap closure activities.

ISRU↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗
NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a mobile robotic bucket-drum excavator platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for estimating the quantity of regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete In-Situ Resource Utilization (ISRU) missions. To teleoperate or run autonomously, it is crucial for the amount of regolith mass ingested to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of the total mass in the drums, this type of high-level planning is not possible. Three distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR, none of which require modification to the hardware. All take in system states, such as arm/drum motor positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums. The developed models run in real-time, outputting predictions for the front drum mass, rear drum mass, timestamp of the last prediction, and total drum mass (sum of front and rear) in RASSOR’s drums. Models deployed to the hardware have low error (<7.5% mean error over the mass range, and <2.6% mean error when drums are more than half full) when making predictions in real-time. Our modeling approach can be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results of this work are promising and show that models can be developed to accurately estimate excavated regolith mass.

Bucket Drum Excavators↗
NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a mobile robotic bucket-drum excavator platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for estimating the quantity of regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete In-Situ Resource Utilization (ISRU) missions. To teleoperate or run autonomously, it is crucial for the amount of regolith mass ingested to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of the total mass in the drums, this type of high-level planning is not possible. Three distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR, none of which require modification to the hardware. All take in system states, such as arm/drum motor positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums. The developed models run in real-time, outputting predictions for the front drum mass, rear drum mass, timestamp of the last prediction, and total drum mass (sum of front and rear) in RASSOR’s drums. Models deployed to the hardware have low error (<7.5% mean error over the mass range, and <2.6% mean error when drums are more than half full) when making predictions in real-time. Our modeling approach can be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results of this work are promising and show that models can be developed to accurately estimate excavated regolith mass.

bucket drum excavators↗
NASA NTRSDate not supplied

Towards Autonomous Lunar Resource Excavation via Deep Reinforcement Learning

To support sustainable infrastructure on the Moon, NASA needs to leverage lunar resources for in-situ processing and construction. NASA’s Regolith Advanced Surface Systems Operations Robot (RASSOR) is principally designed to mine and deliver regolith for these tasks. To reliably perform these operations on the lunar surface, RASSOR's sensors and control systems need to be robust and maximize information extracted from a reduced sensor payload. Herein, we present our findings from the Intelligent Capabilities Enhanced RASSOR project. We created reduced-order simulation environments in which we applied reinforcement learning algorithms to learn autonomous trenching controllers and produced state estimation architectures. We developed two simulations: a 2D excavation simulation used to facilitate parameter selection, and a 3D simulation developed using a game physics engine to simulate simplified soil interactions and incorporate robotic agents parameterized by dynamic models. Within these simulations, we learned autonomous excavation routines that exceed excavation efficiency measures as compared against RASSOR's existing control and teleoperation-based methods.

RASSOR↗