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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 1,315 records · Page 73
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

NASA Station Explorer for X-Ray Timing and Navigation Technology (SEXTANT) Mission Operations Architecture

"The Station Explorer for X-Ray Timing and Navigation (SEXTANT) mission is a technology demonstrationenhancement to the Neutron Star Interior Composition Explorer (NICER) mission, a NASA AstrophysicsExplorer Mission of Opportunity to the International Space Station (ISS) that was launched in June of 2017.The NICER instrument is a precision pointing X-ray telescope that provides measurements of neutron stars,which the SEXTANT mission uses to perform autonomous onboard X-ray Pulsar Navigation (XNAV) by usingmilli-second pulsars (MSPs), a category of neutron stars, as timing sources for navigation. By comparing thedetected time of arrival of X-ray photons to a reference of expected pulsar lightcurve timing models, one caninfer a range and range rate measurement based on light time delay. Since both timing and orientationinformation comes from a celestial source, this technology could provide a GPS-like navigation capabilityavailable throughout our Solar System and beyond. Applications that XNAV can support include outer planetand interstellar missions, manned missions, libration orbit missions, and current infrastructure such as the DeepSpace Network (DSN). The SEXTANT team successfully completed a rst demonstration of in-space andautonomous XNAV in November 2017. NICER and SEXTANT have separate teams, with NICER being theprimary team with its own science objectives. Operational modes for both missions must have concurrent andindependent components as well as an integrated ground system. Within this joint mission pro le, SEXTANToperations requires an infrastructure and cadence that is exible to handle concurrent science operations fromthe NICER team, independent autonomous navigation demonstrations, and events within the extensive ISSoperations environment. This paper rst details the infrastructure implemented and its concept of operations.It then describes the operations for the SEXTANT demonstration and lessons learned."

Yu, Wayne H.↗
NASA NTRS2010

TPSAS-NF1676L-11132-DND

The Orion Pad Abort 1 Flight Test occurred on May 6th, 2010 at White Sands Missile Range in New Mexico. The test was the first for the Orion vehicle and was primarily a test of the Orion Launch Abort System’s ability to execute an abort from the launch pad. The test was successful, and, in most cases, results were well within predicted outcomes. For this test, the thermal control subsystem was an inherent part of the flight operations team. The thermal team provided pre-launch predictions of component temperatures, set component temperature limits, and monitored those limits during the flight operations process as part of the primary flight operations team. The thermal team was also responsible for day-of-flight predictions of the main abort motor’s propellant mean bulk temperature, a key launch constraint for the test. After the test, thermal data was collected, reviewed, and compared against pre-launch predictions showing very good agreement in most areas. Based on the pre-test and flight operations experience, a detailed list of lessons learned has been developed and will be reviewed prior to potential future Orion or other such flight tests.

Joseph F Gasbarre↗
NASA NTRSDate not supplied

TPSAS-NF1676L-11021-DND

The Orion Pad Abort 1 Flight Test occurred on May 6th, 2010 at White Sands Missile Range in New Mexico. The test was the first for the Orion vehicle and was primarily a test of the Orion Launch Abort System’s ability to execute an abort from the launch pad. The test was successful and in most cases results were well within predicted outcomes. For this test, the thermal control subsystem was an inherent part of the flight operations team. The thermal team provided pre-launch predictions of component temperatures, set component temperature limits, and monitored those limits during the flight operations process as part of the primary flight operations team. The thermal team was also responsible for day-of-flight predictions of the main abort motor’s propellant mean bulk temperature, a key launch constraint for the test. After the test, thermal data was collected, reviewed, and compared against pre-launch predictions showing very good agreement in most areas. Based on the pre-test and flight operations experience, a detailed list of lessons learned has been developed and will be reviewed prior to potential future Orion or other such flight tests.

Joseph F Gasbarre↗
NASA NTRS2020

Development and Evaluation of the Active Response Gravity Offload System as a Lunar and Martian EVA Simulation Environment

In preparation for future exploration missions, NASA seeks the ability to simulate partial-gravity operations for use in ground-based research, crew training, and engineering design evaluations. The Active Response Gravity Offload System (ARGOS) at the Johnson Space Center (JSC) is designed to simulate reduced gravity environments, such as lunar, Martian, or microgravity, using a robotic system similar to an overhead bridge crane. ARGOS continuously offloads a portion of a suited human’s weight during all dynamic motions within the test facility, which can include basic functional movements such as walking, running, and jumping, as well as a wide range of planetary surface activities. This system will be used as part of a metabolic-rate task characterization study to determine the workload associated with partial-gravity extravehicular activity (EVA). Pilot testing was conducted using the MKIII prototype planetary space suit and two gimbal designs to determine the ability of the ARGOS test environment to simulate planetary EVA operations. This paper will describe the lessons learned from the feasibility testing, simulation-environment mockup design, and the results from the pilot tests and their influence into the final study design. Being able to effectively simulate partial-gravity environments and characterize the performance of crewmembers will have an impact on multiple domains including suit design, task design, thermal models, and life-support-system capacity verification plans, among others.

Omar S Bekdash↗
NASA NTRS2003

Atmospheric sounding at JPL: current and future technologies

We discuss lessons learned on AIRS in the development and operations as well as plans for next generation systems including SIRAS, a wide field hyperspectral infrared imaging spectrometer which offers AIRS spectral performance at 24x the spatial resolution.

infrared↗
NASA NTRS2022

Electrical Ground Support Equipment for the Sampling Caching System of the Mars 2020 Rover

In this work we describe in detail the architecture, design, testing and operation of the Electrical Ground Support Equipment (EGSE) “Blue Box” used to test and validate the Sampling Caching System (SCS) of the Mars 2020 Perseverance rover. The Blue Box architecture is centered around COTS motor controllers and COTS input-output modules communicating over an EtherCAT bus. A custom, low-level safety subsystem ensures no harm can be done to the flight articles. The modular architecture of the EGSE reduces cost and complexity while expediting assembly time. The Blue Box drives the 19 actuators of the SCS which span the main robotic arm, the corer system, the internal sample handling arm, the sample tube sealing system and the gas dust removal tool; mimicking the Rover Motor Control Assembly (RMCA). Due to the limited availability of RMCA’s, the EGSE enabled and performed the bulk of testing activities for SCS. The majority of the SCS actuators are composed of a 3-phase DC brushless motors, hall sensors for commutation, dual resolvers for output angular measurement, brakes, heaters and platinum thermistors. Additionally, the EGSE read 12 strain gauges forming part of a force torque sensor, and switches used for external positioning references. Over the 3-year span of the V&V campaign for the SCS, over 32 EGSE systems were built, tested and deployed to test venues at JPL and externally. The EGSE tested several families of the SCS subsystem, ranging from engineering units, life test units and two flight units. Test venues that this EGSE supported included lab benches, ultra-clean cleanrooms, ATLO facilities, and thermal vacuum chambers. Together with the test software systems, SSDEV and SSDEV-ECAT, the Blue Box EGSE enabled the team to efficiently test flight hardware and flight software together. We go over the safety features and fault management techniques employed to protect flight hardware. The effects of the long, 50-feet, EGSE harnesses on motor performance, EMI, electrical noise, and motor control performance are explained. Mitigations to these unwanted effects, including shielding strategy and inductance compensation, are summarized. We go over an excerpt of notable anomalies that this EGSE suffered through its operation, along with investigations and resolutions. Lessons learned, areas of improvement as part of future work, and recommendations for future implementations for similar EGSE’s, are shared.

Levine, Dan↗
NASA NTRS2024

Physiological and Subjective Responses of Pilots during Advanced Air Mobility Flight Testing with Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA’s Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This paper investigates into pilots’ physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The study provides insights into pilots’ workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this study enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

Kevin J. Monk↗
NASA NTRSDate not supplied

Physiological and Subjective Responses of Pilots During Advanced Air Mobility Flight Testing With Automated Systems

Aviation is constantly evolving, mostly due to the integration of automated systems into the National Airspace System. This presents a host of challenges and opportunities. NASA's Advanced Air Mobility (AAM) project has taken a significant leap forward with a research flight test led by the Integration of Automated Systems (IAS) sub-project. This effort focused on assessing automated flight deck algorithms essential for supporting high-density Urban Air Mobility (UAM) operations. Carrying out a two-ship flight test in UAM Maturity Level 4 scenarios, the team evaluated state-of-the-art algorithms, including Hazard Perception and Avoidance (HPA) and flight path management (FPM) systems. This presentation focuses on results of pilots' physiological and subjective responses during the flight scenarios conducted. In collaboration with Lockheed Martin Advanced Technology Laboratories (ATL), we collected and analyzed live-flight biometric data using eye tracking, mobile brain imaging, and heart rate sensors. The data provides insights into pilots' workload and cognitive engagement while navigating automated tools, leveraging both biometric data and post-encounter subjective assessments. The research highlights the interaction between human operators and automated systems, contributing valuable lessons learned about gathering human data in live-flight environments. The knowledge acquired from this endeavor enhances our understanding of human factors in automated flight and informs future studies attempting to undertake similar feats.

air mobility↗
NASA NTRS2024

ECI Final Report: Closing of Critical Technology Gaps for Rotating Detonation Rocket Engines

NASA has successfully fired several configurations of a dual regenerative 10K lbf class Rotating Detonation Rocket Engines using cryogenic liquid methane, liquid hydrogen, and RP-1 as fuel and liquid or gaseous oxygen as the oxidizer. In addition, 3 hot fire test phases were conducted to evaluate parametrically varying combustor geometries using a subscale 500-1K lbf class thruster RDRE. Several studies were successfully completed to evaluate the impacts of contraction ratio, annulus L’, heat transfer through calorimetry, and varying injector designs. Laser powder bed fusion GRCop-42, GRX-810, and C-103 alloys were used for the outer bodies, inner bodies, nozzles, and injector variations. The development of RDRE technology is only now possible with additive manufacturing techniques and these specialized alloys. Critical design parameters, design rules of thumb, and operability limitations have been identified. Major lessons learned in the development of critical components have also been documented. All critical technology gaps have been demonstrated at one scale or another and closed. A major finding of this work is that the combustion efficiency of the RDRE is far superior to that of the traditional liquid rocket engine requiring an order of magnitude lower chamber volume and residence time to react. Because of this, the design trade space opens up significantly. More compact chamber geometries are now possible with potential mass savings and significant length savings possible to flight geometries. In addition, the ceiling for the technology, in terms of Isp, has not yet been reached. It is likely that an additional ~10% better Isp over equivalent state-of-the-art combustion devices may be achieved over the next decade should the technology continue to be developed. This technology has been shown to be viable for near term infusion (2-5 years) into space missions and enable cost effective space access for US industry. This final report documents all major findings from this early career initiative (ECI) project and lays out what is needed post ECI.

Thomas Teasley↗
DOE OSTI2016

Getting to Net Zero

The technology necessary to build net zero energy buildings (NZEBs) is ready and available today, however, building to net zero energy performance levels can be challenging. Energy efficiency measures, onsite energy generation resources, load matching and grid interaction, climatic factors, and local policies vary from location to location and require unique methods of constructing NZEBs. It is recommended that Components start looking into how to construct and operate NZEBs now as there is a learning curve to net zero construction and FY 2020 is just around the corner.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2016

The Joint Convention, Its Global Impact and U.S Continuing Involvement - 16645

The Joint Convention on the Safety of Spent Fuel Management and on the Safety of Radioactive Waste Management (hereafter called the Joint Convention) is an international convention, conducted under the auspices of the International Atomic Energy Agency (IAEA). It is one of a suite of international instruments dealing with nuclear safety and physical security, which serve to promote a global culture for the safe use of radioactive materials. Unlike other safety or security conventions, however, the Joint Convention deals exclusively with the end of the nuclear fuel cycle. The Joint Convention focuses on the lessons to be learned from past experience and existing facility operations, which can be implemented in the policies and designs for future operating facilities in the nuclear fuel cycle. The U.S. has participated fully in the review process, as well as setting forth initiatives to sponsor increased ratification. The U.S. holds the current Joint Convention presidency for the Fifth Review Cycle. Because of the important benefits associated with active participation, the U.S. has strongly supported a Regional Conference Initiative outreach program to increase membership. We also provided an expert for each of the conferences to assist in advancing the message to non-Member States, in particular developing nations considering ratification. The Joint Convention process also provides opportunities to identify future areas of bilateral and multilateral technical and regulatory cooperation with other Parties. Furthermore, the Joint Convention is consistent with U.S. foreign policy considerations to support, as a priority, the strengthening of the worldwide safety culture in the use of nuclear energy. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Full-Scale Development and Piloting of a Hybrid Digital Twin for Wastewater Operations Optimization

This full-scale pilot of a machine learning based nutrient controller/digital twin is being done as part of The Water Research Foundation (WRF) project 5121: Development of Innovative Predictive Control Strategies for Nutrient Removal. The overall goal of this project is to develop and full-scale test a hybrid (machine learning + mechanistic model) nutrient management controller at four different water resource recovery facilities (WRRFs). The controller demonstrates both short-term optimization functions and predictive capabilities. This project includes the design of the controller, its performance at the WRRFs, and a description of the future improvements.

Johnson, Bruce↗
DOE OSTI2024

Updates and Lessons Learned from NuMI Beamline at Fermilab

The Neutrinos at the Main Injector (NuMI) beamline at Fermilab generates an intense muon neutrino beam for the NOvA (NuMI Off-axis $\nu_e$ Appearance) long-baseline neutrino experiment. Over the years, the NuMI beamline has been pivotal in advancing neutrino physics, providing invaluable data and insights. This proceeding paper discusses updates and the lessons learned from recent experiences during the beam operations, maintenance, and monitoring of the NuMI beamline. Key topics include the optimization of beam performance and challenges in maintaining beamline stability. The paper aims to share best practices and provide a road-map for future beamline projects, including the Long-Baseline Neutrino Facility (LBNF).

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Independent malware detection architecture

A system and method (referred to as the system) detect malware by training a rule-based model, a functional based model, and a deep learning-based model from a memory snapshot of a malware free operating state of a monitored device. The system extracts a feature set from a second memory snapshot captured from an operating state of the monitored device and processes the feature set by the rule-based model, the functional-based model, and the deep learning-based model. The system identifies identifying instances of malware on the monitored device without processing data identifying an operating system of the monitored device, data associated with a prior identification of the malware, data identifying a source of the malware, data identifying a location of the malware on the monitored device, or any operating system specific data contained within the monitored device.

Smith, Jared M.↗
NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗
NASA NTRSDate not supplied

Airport Runway Configuration Management with Offline Model-free Reinforcement Learning

Runway configuration management (RCM) deals with the optimal selection of runways to operate on (for arrivals and departures) based on traffic, surface wind speed, wind direction and other environmental variables. RCM is one of the most challenging tasks in air traffic management, as it relies on operational and environmental variables (e.g., weather forecast) that are highly uncertain and complex to model. In this paper, an innovative and automated approach is deployed using offline model-free reinforcement learning to provide decision-support for RCM. The proposed technology processes historical data about variables of interest, decisions made regarding RCM, and their subsequent outcome, to identify a policy that would encourage good decisions and avoid the poor ones. The policy search is guided by an appropriately chosen weighted utility function (e.g., based on minimizing delays and go-arounds). Finally, the performance of the proposed tool is validated using Charlotte Douglas International Airport as the case study, which shows that the proposed method is superior to other conventional rule-based approaches.

Milad Memarzadeh↗
DOE OSTI2023

Two-Stage Deep Reinforcement Learning for Distribution System Voltage Regulation and Peak Load Management: Preprint

The growing integration of distributed solar photovoltaic (PV) in distribution systems could result in adverse effects during grid operation. This paper develops a soft actor critic-based deep reinforcement learning (SAC-DRL) solution to simultaneously control PV inverters and battery energy storage systems for voltage regulation and peak load demand shaving. The novel two-stage framework, featured with two different control agents, is applied for daytime and nighttime operation to enhance the control performance. Comparison results with other control methods on a real feeder in Western Colorado demonstrate that the proposed method can provide advanced voltage regulation with modest active power curtailment for peak demand reduction.

deep reinforcement learning↗