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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Scalable Traffic Management for Emergency Response Operations (STEReO)Airspace Operations Laboratory (AOL) Unmanned Aircraft Systems (UAS)Traffic Management (UTM) (AOLUSS)Tabletop Overview

The Scalable Traffic Management for Emergency Response Operations (STEReO) project requires prototyping software to extend existing Airspace Operations Laboratory (AOL) software capabilities to support research in integrating drone flight activities within an emergency response airspace environment. A presentation encompassing project AOLUSS details, communications and network design will be presented at a Workshop.

STEReO software↗
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

A Common Habitat Deep Space Exploration Vehicle for Transit and Orbital Operations

When outfitted as a habitat, the SLS Core Stage Liquid Oxygen Tank is a pressure vessel that can be used to support human exploration in deep space. An exploration spacecraft can be constructed with this habitat, known as the Common Habitat, as its central element. More than just a transit vehicle, this spacecraft is a Deep Space Exploration Vehicle – a microgravity science laboratory capable of conducting research with onboard human crews throughout the inner solar system. Supplied with propellant by LEO depots, preliminary trajectory and v estimates indicate that the spacecraft can perform fly-by or orbital missions with trajectories close enough to the sun to intersect the orbit of Mercury or far enough away to fly by the main belt asteroid Vesta. Its primary mission, however, is to support human expeditions to Mars. Many, though not all, of the pressurized and unpressurized elements that compose the Deep Space Exploration Vehicle can also be used in surface base camps on the Moon and Mars. In additional to traditional space science disciplines, the spacecraft offers unique potential for small asteroid retrieval and for artificial gravity research. Three launches are used to deploy the spacecraft, but thirty-nine launches are used to deliver propellant to orbit to fully fuel the spacecraft for deep space missions. Key operations in a Mars crewed mission are described to illustrate how the vehicle is used and forward work is listed to mature the spacecraft concept.

Robert L. Howard Jr.↗
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

Lunar Transport Vehicle Ocupant Protection Requirements

INTRODUCTION The National Aeronautics and Space Administration (NASA) is preparing for future Artemis missions that will return humans to the lunar surface. A critical piece of these future missions is the Lunar Transport Vehicle (LTV), a transportation device to be used on the lunar surface. A vehicular method of transportation will allow for longer duration missions with increased capabilities to conduct research and transport soil, geological samples, or other materials of interest. The objective of this project is to provide occupant protection guidance for the design of the LTV. METHODS At the time of writing of occupant protection requirements, much of the LTV design is unknown. However, several general assumptions have been established. It is assumed that the LTV will be an unpressurized crewed rover with the occupant(s) wearing an EVA suit. Additionally, the occupant must be protected using established health limits as governing criteria to protect the occupant from hazardous levels of acceleration, jerk, and vibration. In addition to the health limits, it is expected that the LTV shall provide a comfortable transportation solution to Lunar operations. Several restraint configurations have been considered to allow for seated and standing operation of the vehicle. RESULTS Preliminary recommendations and metrics were put forward by a cross functional working group with members from Johnson Space Center and Ames Research Center. Exposure to acceleration, vibration, and jerk metrics above the proposed limits could create a risk of occupant injury. In addition to satisfying crew health needs, these limits permit the occupant to meet performance objectives including operating the vehicle and reading displays while traversing the Lunar surface. Initial guidance was refined during a Technical Interchange Meeting with external subject matter experts in industry, academia, and United States Air Force. CONCLUSION Operating a crewed vehicle in a lunar environment is a novel problem with few analogs to provide data. Based on terrestrial data from ISO & MIL Standards, occupant protection recommendations have been identified with several assumptions made about reduced gravity and deconditioned occupants. If the design of the vehicle attenuates energetics to levels below those put forward, there is high confidence the occupant will be able to repetitively operate the vehicle without physical harm or operational issues.

K Dolick↗
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 NTRS2022

The Cooling Loop A Anomaly of 2013: A Case Study in Human-Systems Resilience

Throughout the history of human spaceflight, NASA has employed an operational paradigm of 24/7 dependence on experts in Mission Control Center (MCC). In addition to nominal flight control and mission operations, these 85+ experts per shift manage anomaly detection, diagnosis, and response, and support the crew in real-time in performing maintenance and repair, procedure execution, and other complex mission operations. Future long-duration exploration missions (LDEMs) beyond low-Earth orbit (LEO) will not operate successfully using this same Human-Systems Integration Architecture (HSIA) where crew rely on ground controllers, have ready access to resupply, and have a fallback plan of evacuation. As distance from Earth increases and the communication delay grows, crews will need to respond independently and adequately to time-critical vehicle malfunctions. It will not always be sufficient or even possible to ‘safe the system’ and then wait upon ground intervention. A new and radically different HSIA is needed to accommodate the paradigm shift of deep-space travel. Historical International Space Station (ISS) data show that for a 30-day mission, the likelihood of a high-consequence vehicle anomaly of uncertain origin that requires rapid response is greater than 10%. The likelihood of such an event is 50% by the fourth month of the mission, and it grows exponentially with time. Our team has conducted in-depth investigations into these events and their corresponding anomaly resolution activities. Using MCC and Mission Evaluation Room (MER) anomaly resolution artifacts (including meeting summaries, caution and warning data, and ISS daily summaries), we created timelines detailing ground actions and in-orbit events for two significant anomalies. We then mapped these timelines onto Mars transit conditions, introducing a ground-crew communications time delay and shifting immediate response, time-critical task execution, and vehicle commanding to the crew. In detailing successful anomaly resolution in transit to Mars, the timelines highlight where effective resolution requires drastically evolved onboard capabilities. Though this research has yielded a rich data set based on ground response in past missions, there is still insufficient knowledge to assess the potential impact of inflight anomalies on a small autonomous crew on future LDEMs beyond LEO. To begin building an evidence base that will inform future HSIA standards and requirements, we are developing an approach to systematically capture crew anomaly response and procedure execution during early Artemis missions. Being the first human spaceflight beyond LEO since Apollo, early Artemis missions provide a rare and unique opportunity to serve as a testbed for Mars missions. Our work aims to capitalize on planned data collection to derive crew operational responses to anomalous events in real-time. Our team is also researching the level of simulation fidelity required for empirically validating proposed HSIA standards and evaluating HSIA implementations for LDEMs beyond LEO. This work will produce a trade space study of HSIA simulation objectives and fidelity requirements. Ultimately, these research efforts will assist in developing the standards and technologies needed to build a next-generation HSIA for LDEMs beyond LEO.

human-systems integration architecture↗
NASA NTRSDate not supplied

Assessment of Next Generation Airframe Noise Prediction Methods with PAA and ASN Flight Test Data

This paper presents an assessment of the airframe noise prediction capability in the research version of the NASA Aircraft Noise Prediction Program (ANOPP) with the objective of validating the accuracy of the current prediction methods, and more importantly, identifying the potential improvements to update the methods. The assessment compares predictions of the individual noise components currently implemented in ANOPP, including the landing gear, the slat, the flap, and the trailing edge noise, with the data from the Propulsion Airframe Aeroacoustics and Aircraft System Noise flight test conducted as part of the Boeing 2020 ecoDemonstrator program on a Boeing 787-10 Etihad Airways aircraft. For each component, prediction errors and discussions are presented for the noise characteristics, covering not only the source features but also the installation effects. It is shown that the predictions by the research level methods in ANOPP capture the major noise features, but improvement potential is identified in two main categories. The first includes minor features that have not been sufficiently modeled in the current methods, the noise due to interactions of the landing gear wake with the flaps and the slat bracket noise, for example. The second is related to changes in modern aircraft designs, which may alter the noise source mechanisms, and thus, calls for major improvements and updates on the current prediction models, examples including the flap side edge noise for modern flap systems and the trailing edge noise for realistic wings with taper and sweep. To illustrate the progress of the method development, previous generations of airframe noise models in ANOPP are also compared with the flight test data.

Yueping Guo↗
NASA NTRSDate not supplied

Close out the Critical Commitment for System Wide Safety’s Technical Challenge 3 (TC 3)

- Deliver validated methods and recommended practices to reduce certification costs for ground and onboard vehicle guidance and control systems through expanded acceptability of analysis as evidence for certification FY 2022. - Communicate results to maximize public awareness of the research and transfer capabilities via a set of on-demand training seminars and supporting materials including reports, publications and open source software codes where applicable.

Joseph C Coughlan↗
NASA NTRS2022

Lunar Transport Vehicle Occupant Protection Requirements

The National Aeronautics and Space Administration (NASA) is preparing for future Artemis missions that will return humans to the lunar surface. A critical piece of these future missions is the Lunar Transport Vehicle (LTV), a transportation device to be used on the lunar surface. A vehicular method of transportation will allow for longer duration missions with increased capabilities to conduct research and transport soil, geological samples, or other materials of interest. Multiple missions to the lunar surface throughout the Apollo program also deployed the use of a lunar vehicle, known as the Lunar Roving Vehicle (LRV). The LRV was used during three of the six Apollo missions that successfully landed humans on the moon. Unfortunately, the LRV did not have any onboard instrumentation, and a large portion of the lunar performance evaluation relied on photographic documentation and crew reports. Therefore, we do not know the actual accelerations and vibrations experienced by the crew when driving on the lunar surface. The objective of this document is to provide occupant protection guidance when designing the LTV. Acceleration, vibration, and jerk metrics imparted by the vehicle relative to the occupant shall not exceed those listed in this document. At the time of writing of these requirements, much of the LTV design is unknown. This document details requirements based on multiple possible restraint configurations and crew orientations.

Kevin R. Dolick↗
NASA NTRSDate not supplied

Generating Cryogenic Lunar Simulants Within the Planetary Exploration & Astromaterials Research Laboratory

The Planetary Exploration & Astromaterials Research Laboratory (PEARL) aims to provides capabilities for the creation of—and research on—cryogenic lunar regolith simulants (CRS) containing surface volatile analytes. CRS represent regolith samples that might be collected from lunar Permanently Shadowed Regions (PSRs) during future missions. Handling CRS requires procedural and engineering development due to extremely cold (i.e. ≤ - 196°C) working temperatures. However, there is an imperative to understand the physical and chemical alteration of samples during collection, transport, and curation processes throughout the Artemis missions. The chemistries that will be encountered within the PSRs need to be understood based upon prior mission data analysis. The ability to utilize these techniques provides future tests that could be relevant to planetary protection applications regarding future robotic sample return.

Lunar↗
NASA NTRS2013

Horizontal Habitability Layout Studies

As our efforts in the 2011-2012 X-Hab Academic Innovation Challenge we designed, developed, constructed and tested a deployable horizontally oriented habitat system for use as an Earth analog. The goals of the project included a short-term goal of an interdisciplinary senior design project to design, build and evaluate a horizontally oriented habitat and a long-term goal to develop capabilities in education, research, and outreach in field of space habitat design. This included both technical engineering and outreach efforts. In the 2011-2012 year, the program involved the following schools at OSU: The Schools of Mechanical and Aerospace Engineering (MAE) and Industrial and Engineering Management (IEM) within the College of Engineering, Architecture and Technology and Design, Housing and Merchandising (DHM) in the College of Human Sciences. We also included the School of Aviation Education (AvED) in the College of Education through the ISNPIRE (Interdisciplinary National Science Project Incorporating Research and Education Experience) program of as part of our outreach efforts.

Jamey D. Jacob↗
NASA NTRS2023

Thermomechanical Real-Time Hybrid Simulation: Conceptual Framework and Control Requirements

Real-time hybrid simulation (RTHS) is an enabling technology that has transformed engineering experimentation and helped researchers expand modeling capabilities. However, breakthroughs are necessary to expand the range of hybrid simulation methods and, thus, enable experiments with loading conditions representing multiple hazards. This paper discusses the development of a new thermomechanical RTHS framework and a systematic approach to determining RTHS control requirements. First, the framework is established using a representative finite element model of a layered structural system subjected to thermal loading. A complete two-layer system model serves as the reference system, and it is then partitioned into a numerical layer and an experimental layer that share interface conditions. Next, a thermal actuator is introduced to impose dynamic thermal loading on the experimental subsystem, serving as a transfer system. Finally, control and performance metrics are defined to evaluate the realization of interface boundary conditions and map this to the RTHS execution. Through an illustrative example considering the influence of temperature on a lunar habitat, we demonstrate how to establish controller requirements for RTHS and demonstrate that this approach can be used to conduct RTHS on structures with thermomechanical loading.

Herta Montoya↗
NASA NTRS1995

Data handling and visualization for NASA's science programs

Advanced information systems capabilities are essential to conducting NASA's scientific research mission. Access to these capabilities is no longer a luxury for a select few within the science community, but rather an absolute necessity for carrying out scientific investigations. The dependence on high performance computing and networking, as well as ready and expedient access to science data, metadata, and analysis tools is the fundamental underpinning for the entire research endeavor. At the same time, advances in the whole range of information technologies continues on an almost explosive growth path, reaching beyond the research community to affect the population as a whole. Capitalizing on and exploiting these advances are critical to the continued success of space science investigations. NASA must remain abreast of developments in the field and strike an appropriate balance between being a smart buyer and a direct investor in the technology which serves its unique requirements. Another key theme deals with the need for the space and computer science communities to collaborate as partners to more fully realize the potential of information technology in the space science research environment.

Bredekamp, Joseph H.↗
NASA NTRS2018

Unmanned Aircraft System Traffic Management (UTM) Research Transition Team (RTT) Concept Working Group - Concept & Use Cases Package #2 Addendum: Technical Capability Level 3

This document is a product of the joint NASA and FAA Research Transition Team's (RTT) Concept Working Group (CWG) as part of the UAS Traffic Management (UTM) project. The scope of the document covers Technical Capability Level (TCL) 3 of the UTM research path and presents the 1) Terms and Definitions, 2) Foundational Principles, 3) Concept Narratives, 4) Use Cases, 5) Operational Views (OVs), and 6) Roles and Responsibilities of actors interacting within a TCL3 environment. The document includes additional use cases to accompany the RTT CWG Package #2 document.

UTM↗
NASA NTRS1994

F-18 high alpha research vehicle: Lessons learned

The F-18 High Alpha Research Vehicle has proven to be a useful research tool with many unique capabilities. Many of these capabilities are to assist in characterizing flight at high angles of attack, while some provide significant research in their own right. Of these, the thrust vectoring system, the unique ability to rapidly reprogram flight controls, the reprogrammable mission computer, and a reprogrammable onboard excitation system have allowed an increased utility and versatility of the research being conducted. Because of this multifaceted approach to research in the high angle of attack regime, the capabilities of the F-18 High Alpha Research Vehicle were designed to cover as many high alpha technology bases as the program would allow. These areas include aerodynamics, controls, handling qualities, and propulsion.

Bowers, Albion H.↗
NASA NTRS2005

National Research Council Dialogue to Assess Progress on NASA's Transformational Spaceport and Range Technologies Capability Roadmap Development: General Background and Introduction

Viewgraphs on the National Research Council's diaglog to assess progress on NASA's transformational spaceport and range technologies capability roadmap development is presented. The topics include: 1) Agency Goals and Objectives; 2) Strategic Planning Transformation; 3) Advanced Planning Organizational Roles; 4) Public Involvement in Strategic Planning; 5) Strategic Roadmaps; 6) Strategic Roadmaps Schedule; 7) Capability Roadmaps; 8) Capability Charter; 9) Process for Team Selection; 10) Capability Roadmap Development Schedule Overview; 11) Purpose of NRC Review; 12) Technology Readiness Levels; 13) Capability Readiness Levels; 14) Crosswalk Matrix Trans Spaceport & Range; 15) Example linkage to other roadmaps; 16) Capability Readiness Levels Defined; and 17) Crosswalk Matrix Ratings Work In-progress.

Skelly, Darin M.↗
NASA NTRS2023

Exploration Medical Capability Clinical Decision Support System Architecture Recommendation

A new era in space exploration has arrived with the goal of establishing a long-term presence on the Moon and using those learning to take the next giant leap: sending the first astronauts to Mars. These ambitious goals will require significant changes in in-flight medical care due to constraints on mass, volume, power, crew time, and medical evacuation capabilities. Furthermore, while crews currently rely on real-time communications with ground-based medical providers, as distance from Earth increases, so do communication delays and disruptions. The crew will need to autonomously detect, diagnose, treat, and prevent medical events. These constraints require development of transformative solutions and new technologies. Through participation in the Human Research Program’s (HRP) Exploration Medical Capability (ExMC) research, NASA has developed and demonstrated a Medical Data Architecture (MDA), a platform upon which a robust capability of Clinical Decision Support (CDS) can be built. As a result, NASA is now positioned to build an advanced autonomous CDS System (CDSS) which will aid in crew health decision-making. The CDSS combines data management aspects of a system that would lead to the autonomy required by NASA-STD-3001, Section 3, Health and Medical Care Standards. The project addresses the ExMC gap Medical-701: We need to increase inflight medical capabilities and identify new capabilities that (a) maximize benefit and/or (b) reduce “costs” on human system/mission/vehicle resources. This architecture recommendation document introduces an integrated data architecture that provides self-sufficient medical care for crews on long-duration spaceflight missions.

ExMC↗