Search NASA⌕ Search

SEARCH · Search NASA

Results for “Operator learning”

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.

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

At least 811 records · Page 45
All sourcesNASA NTRSDOE OSTI
NASA NTRS2019

OSIRIS-REx Orbit Determination Performance During the Navigation Campaign

The OSIRIS-REx mission Navigation Campaign consists of three sub-phases: Approach,Preliminary Survey, and Orbital A. Approach was designed for initial characterization ofBennu while matching Bennu's heliocentric velocity. Preliminary Survey provided the firstspacecraft-based estimate of Bennu's mass. This phase consisted of five target flybys witha close approach distance of about 7 km. Orbital A was a two-month phase devoted to theNavigation Team learning the close proximity operations dynamics and environment aroundBennu and transitioning from center-finding optical navigation to landmark feature-basednavigation. This paper provides a detailed summary of the orbit determination performancethroughout the Navigation Campaign.

Leonard, Jason M.↗
NASA NTRSDate not supplied

Update on Feasibility of UV LEDs in a Spacecraft Wastewater Application

As the National Aeronautics and Space Administration (NASA) expands its scope and begins to venture into long-duration, manned space flights, the function and maintenance of spacecraft water systems becomes increasingly critical and difficult. New mission requirements limit opportunities for resupply and demand extended periods of uncrewed operations. Based on lessons learned from the International Space Station (ISS), one particular challenge of future spacecraft water systems will be maintaining adequate microbial control, especially in water subsystems and component-level elements, where effective long-duration biocontrol strategies do not currently exist. To ensure the reliability and redundancy in these systems, innovative technologies will be needed to ensure mission success. After proving feasibility of commercial-off-the-shelf (COTS) devices in flow-through applications in 2018, work focused on utilizing ultraviolet (UV) light emitting diodes (LEDs) in a wastewater tank application. This paper gives an update on the tank testing that has been performed to date. Using this technology may reduce the need for consumable resupply, such as filters or biocides, and minimize crew time to make the repairs on exhausted and/or compromised systems. The ultimate rationale behind developing a UV disinfection system is to increase the stability of water systems as requirements for sterility and microbial control become more stringent for deep space missions. The resulting data from this study will be used to narrow down possible technology demonstrations for selected ISS locations to assess the use of UV technology on future exploration-class spacecraft systems.

Susan N. Gilbert↗
NASA NTRS2018

On-Orbit Results and Lessons Learned from the ASTERIA Space Telescope Mission

The Arcsecond Space Telescope Enabling Research in Astrophysics (ASTERIA) was deployed from the International Space Station (ISS) on 20 November 2017, beginning a technology demonstration and opportunistic science mission to advance the state of the art in nanosatellite performance for astrophysical observations. The goal of ASTERIA is to achieve arcsecond-level line-of-sight pointing error and highly stable focal plane temperature control. These capabilities enable precision photometry—i.e. the careful measurement of stellar brightness over time—which in turn allows investigation of astrophysical phenomena such as transiting exoplanets. By the end of the 90-day prime mission, ASTERIA had achieved line-of-sight pointing stability of approximately 0.5 arcseconds root mean square (RMS) over 20-minute observations, pointing repeatability of 1 milliarcsecond RMS from one observation to the next, and focal plane temperature stability better than ±0.01 K over 20-minute observations. This paper presents an overview of the ASTERIA flight and ground system, summarizes the pre-delivery test campaign, and discusses the on-orbit performance obtained by the pointing and thermal control subsystems. We also describe the process for planning opportunistic science observations and present lessons learned from development and operations. Having successfully operated for over 200 days as of this writing, ASTERIA is currently in an extended mission to observe nearby bright stars for transiting exoplanets.

Seager, Sara↗
NASA NTRS2024

Terrestrial Proving Ground Capabilities Needed for Lunar In Situ Resource Utilization (ISRU) & Construction Concepts of Operation

Incorporating any new technology or system into a human exploration mission or architecture requires development well in advance of the mission to eliminate technology, cost, and schedule risk concerns. It is often stated that technologies need to be at a Technology Readiness Level (TRL) of 6, i.e. ‘system/subsystem model or prototype demonstration in a relevant environment (ground or space)’, by Authority To Proceed (ATP) or by the Preliminary Design Review (PDR) for the mission at the latest. There are two game changing capabilities for sustained human exploration of space that can have a significant effect on the overall exploration architecture and the technologies and systems included in the architecture. The first game changing capability, known as In Situ Resource Utilization (ISRU), involves the search for, acquisition, and processing of resources on the Moon and Mars into mission consumables and usable products, and the second is the ability to utilize space resources in the construction of roads, structures, and surface infrastructure. ISRU and surface construction capabilities have the potential to greatly reduce the cost and risk of human exploration while enabling sustained lunar surface and commercial operations. However, ISRU and surface construction systems are complex and must operate in extremely harsh environments, with abrasive regolith and pervasive dust, for long-periods of time, with potentially limited opportunities for maintenance and repair by humans. The complexity of these capabilities and operations also means that there are a limited number of companies that can design, build, and operate end-to-end systems on their own. The majority of the technologies being developed for these systems are by small companies and at the component or subsystem level. With the overarching strategy of the United States National Aeronautics and Space Administration (NASA) Space Technology Mission Directorate (STMD) to enable industry to implement ISRU and surface infrastructure for Artemis and space commercialization, it is therefore important to establish processes and capabilities to promote and foster collaborations among large and small companies involved in ISRU and surface infrastructure development. For ISRU and infrastructure systems and capabilities to be used in Artemis missions and future commercial lunar surface operations, a coordinated framework with virtual/physical integration and testing locations, or ‘Proving Grounds’, needs to be established and operated on a regular basis and open to all. This paper will discuss the ISRU and surface construction near and long-term concepts of operations, and review operations and lessons-learned from the previous ISRU analog field tests. From this information, requirements and capabilities will be proposed to support and enable the integration and testing of ISRU and construction systems with industry, academia, and international agencies, as well as what facilities and organizations could help establish these Proving Grounds.

ISRU↗
DOE OSTI2023

Towards a Continuous Variable Quantum Compiler

We present initial work towards an experimental implementation of a nonuniversal continuous variable quantum compiler capable of learning a linear phase operation with increased precision when quantum resources are used.

Feldman, Matthew↗
DOE OSTI2023

Detector Related Uncertainties in ICARUS

This paper explores the challenges in neutrino detection and emphasizes the importance of understanding the ICARUS neutrino detector. It delves into the use of cosmic rays to bridge the gap between experimental and theoretical data, offering insights into the behavior of different components of neutrino detectors. This procedure sheds light on the intricate aspects of cosmic ray detection in ICARUS, contributing to an increased understanding of how this detector’s uncertainties operate. More can be learned about neutrino physics in future experiments through this understanding.

43 PARTICLE ACCELERATORS↗
DOE OSTI2024

Detector Related Uncertainties in ICARUS Continued

This paper explores the challenges in neutrino detection and emphasizes the importance of understanding the ICARUS neutrino detector. It delves into the use of cosmic rays to bridge the gap between experimental and theoretical data, offering insights into the behavior of different components of neutrino detectors. This procedure sheds light on the intricate aspects of cosmic ray detection in ICARUS, contributing to an increased understanding of how this detector's uncertainties operate. More can be learned about neutrino physics in future experiments through this understanding.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Detector Related Uncertainties in ICARUS Continued

This paper explores the challenges in neutrino detection and emphasizes the importance of understanding the ICARUS neutrino detector. It delves into the use of cosmic rays to bridge the gap between experimental and theoretical data, offering insights into the behavior of different components of neutrino detectors. This procedure sheds light on the intricate aspects of cosmic ray detection in ICARUS, contributing to an increased understanding of how this detector's uncertainties operate. More can be learned about neutrino physics in future experiments through this understanding.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

A Continuous Variable Quantum Compiler

We implement a continuous-variable quantum compiler that can learn a linear phase operation with a fourfold increase in precision and a factor of 80 reduction in time to solution when quantum resources are used.

Feldman, Matthew↗
DOE OSTI2026

Machine Learning for Multipactor Susceptibility Prediction in Planar RF Gaps

Multipactor discharge is a nonlinear electron avalanche that limits the performance of high-power radio-frequency (RF) and vacuum electronic devices. Predicting multipactor susceptibility traditionally relies on Monte Carlo or particle-in-cell (PIC) simulations, which become computationally expensive for large parametric studies. In this work, we present a supervised machine-learning (ML) framework for prediction of multipactor susceptibility in a two-surface planar geometry. The models are trained using high-fidelity PIC simulation generated susceptibility data and learn the relationship between operational parameters, geometry, and material-dependent secondary electron emission properties. The proposed approach enables rapid reconstruction of susceptibility charts while preserving the physical structure of multipactor growth regions.

43 PARTICLE ACCELERATORS↗
DOE OSTI2026

A Graphical User Interface for the Deep Underground Neutrino Experiment Robotic Test Stand

In preparation for DUNE, Fermilab along with six other institutions are testing cold electronics for quality control before components placed in the far detector. We test them by using a robotic arm that places these chips into sockets on a computer board that will test their functionality. Up until now, the chips have been tested using a command line script that drives a state machine to conduct tests step-by-step. In order to lower the skill barrier to conduct tests and to speed up the quality control process, I was tasked to create a graphical user interface that would allow users to use buttons, text boxes, and drop-down menus to input information and tell the testing state machine how to operate. I had to learn about the Python package Tkinter to start the process of widget placement. I further developed a pause feature unused in the previous command line script that would allow the user to shut down testing gracefully, bring the robotic arm to go back to ground state, and go forward or backward a step in the testing process. After completing the basic functionality of the GUI, I started testing production chips with the GUI to debug. Some issues were found, which required me to further develop parts of the inherited state machine code. The code for the GUI has now been pushed into the copy the DUNE/FD_CE git repository and will soon be merged with the official DUNE/FD_CE repository so that the other institutions testing DUNE cold electronics can use and expand upon it.

Gutierrez Villanueva, Jaziel [Fermilab]↗
NASA NTRSDate not supplied

DeepONet-Assisted Optimization of Surface Topography for Transition Delay in a Mach 4.5 Boundary Layer

We use deep learning, an ensemble variational technique (EnVar), and direct numerical simulations(DNS) to design an optimal topography for a two-dimensional roughness element that delays the on-set of laminar-turbulent transition in a Mach 4.5 flat-plate boundary layer. Deep operator networks (DeepONets), which have the known ability to learn complex nonlinear operators within dynamical systems, are used for machine learning. For the baseline configuration of a smooth flat plate, the second-mode waves at the DNS inflow cause a quick nonlinear breakdown of the high-speed boundary layer within the computational domain. Results reported in the present study validate the ability of DeepONets to model the transition delay via a given topography of the roughness element. The computing cost to optimize the rough-ness element for minimal skin-friction drag is substantially lowered by the DeepONets-based reduced-order model. In comparison to the baseline method of EnVar optimization based on DNS alone, the DeepONets-based EnVar optimizer is able to delay transition past the outflow boundary of the computational domain while utilizing almost 5–6 times fewer DNS.

Machine Learning↗
NASA NTRS2011

Operational Philosophy Concerning Manned Spacecraft Cabin Leaks

The last thirty years have seen the Space Shuttle as the prime United States spacecraft for manned spaceflight missions. Many lessons have been learned about spacecraft design and operation throughout these years. Over the next few decades, a large increase of manned spaceflight in the commercial sector is expected. This will result in the exposure of commercial crews and passengers to many of the same risks crews of the Space Shuttle have encountered. One of the more dire situations that can be encountered is the loss of pressure in the habitable volume of the spacecraft during on orbit operations. This is referred to as a cabin leak. This paper seeks to establish a general cabin leak response philosophy with the intent of educating future spacecraft designers and operators. After establishing a relative definition for a cabin leak, the paper covers general descriptions of detection equipment, detection methods, and general operational methods for management of a cabin leak. Subsequently, all these items are addressed from the perspective of the Space Shuttle Program, as this will be of the most value to future spacecraft due to similar operating profiles. Emphasis here is placed upon why and how these methods and philosophies have evolved to meet the Space Shuttle s needs. This includes the core ideas of: considerations of maintaining higher cabin pressures vs. lower cabin pressures, the pros and cons of a system designed to feed the leak with gas from pressurized tanks vs. using pressure suits to protect against lower cabin pressures, timeline and consumables constraints, re-entry considerations with leaks of unknown origin, and the impact the International Space Station (ISS) has had to the standard Space Shuttle cabin leak response philosophy. This last item in itself includes: procedural management differences, hardware considerations, additional capabilities due to the presence of the ISS and its resource, and ISS docking/undocking considerations with a cabin leak occurring. The paper also offers a look at how different equipment configurations on future spacecraft impact the previously defined cabin leak operational philosophy and includes additional operational methods and considerations that result due to various configurations. The intent is to showcase these various considerations and highlight the variability they allow. The paper concludes with a selection of the author s personal observations from a spacecraft operator's point of view and recommendations with the goal of improving the design and operations of future spacecraft.

DeSimpelaere, Edward↗
DOE OSTI2026

Variational Optical Phase Learning on a Continuous-Variable Quantum Compiler

Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two-mode squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. We further show how our approach can be extended to higher-dimensional compilation tasks. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution.

97 MATHEMATICS AND COMPUTING↗
NASA NTRSDate not supplied

Aircraft Classification Using Radar from Small Unmanned Aerial Systems for Scalable Traffic Management Emergency Response Operations

This work investigates two machine learning techniques: Support Vector Machine (SVM) and Autoencoders (AE)with SVM layer for classification of radar trajectories as General Aviation (GA), fixed-wing small Unmanned Aerial System (sUAS), or not-an-aircraft using radar data recorded from sUAS. Onboard identification of intruder aircraft type is useful for planning avoidance maneuvers and is necessary to provide autonomous systems to meet or exceed the avoidance capability of a human pilot. Aircraft classification can identify intruder aircraft that are not part of the team and may be violating a Temporary Flight Restriction. Aircraft classification is needed in monitoring an airspace where multiple aircraft are teaming on a shared task. Scalable Traffic Management for Emergency Response Operations (STEReO) is a NASA project aimed at improving disaster response by enabling large scale aircraft operations through the teaming of manned aircraft with sUAS to maximize emergency response resources. To this end, this work uses trajectories and radar derived features to classify aircraft from a multirotor sUAS. The AE + SVM generated the strongest classification overall accuracy of 93.5% using the first 4 seconds of radar track data for tracks that activated the avoidance system. Subsampling the available track data increased the available training data with the maximum aircraft recall of 0.94 achieved using the SVM with 1 second track data.

Chester V. Dolph↗