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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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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

VTXO: The Virtual Telescope for X-ray Observations

The Virtual Telescope for X-ray Observations (VTXO) will use lightweight Phase Frensel Lenses (PFLs) in a virtual X-ray telescope with⇠1 km focal length and with⇠50 milli-arc second angular resolution. VTXO is formed by using precision formation flying of two SmallSats: a smaller OpticsSat that houses the PFLs and navigation beacons while a larger DetectorSat contains an X-ray camera, a precision start tracker, and the propulsion for the formation flying. The baseline flight dynamics uses a highly elliptical super synchronous orbit allow the formation to hold in an inertial frame around the 90,000 km apogee for 10 hours of the 32.5 hour orbit with nearly a year mission lifetime. VTXO’s fine angular resolution enables measuring the environments close to the central engines of bright compact X-ray sources. This X-ray imaging capability allows for the study of the effects of dust scattering near to the central objects such as Cyg X-3 and GX 5-1, for the search for jet structure near to the compact object in X-ray novae such as Cyg X-1 and GRS 1915+105, and for the search for structure in the termination shock of in the Crab pulsar wind nebula. The VTXO SmallSat and instrument designs, mission parameters, and science performance are described. VTXO development was supported as one of the selected 2018 NASA Astrophysics SmallSat Study (AS3) missions

John F Krizmanic↗
NASA NTRS2018

Virtual Sensors Determined Through Machine Learning

We propose a method that increases the capability of a conventional sensor, transforming it into an enhanced virtual sensor. This paper focuses on a virtual thermal Infrared Radiation (IR) sensor based on a conventional visual (RGB) sensor. The estimation of thermal IR images can enhance the ability of terrain classification, which is crucial for autonomous navigation of rovers. The estimate in IR from visual band has inherent limitations, as these are different bands, yet correlations between visual RGB and thermal IR images exist, as different terrains, which visually may appear different, also have different thermal inertia. This paper describes the developed deep learning-based algorithm that estimates thermal IR images from RGB images of terrains, providing the feasibility of the idea with average 1.21 error [degree Celsius].

Torresen, Jim↗
NASA NTRS2018

Virtual Sensors Determined Through Machine Learning

We propose a method that increases the capability of a conventional sensor/instrument, transforming it into an enhanced virtual sensor/instrument. This paper focuses on a virtual thermal IR sensor based on a conventional visual (RGB) sensor. The estimation of thermal IR images can enhance the ability of terrain classification, which is crucial for autonomous navigation of rovers. The estimate in IR from visual band has inherent limitations, as these are different bands, yet correlations between visual/RGB and thermal IR images exist, as different terrains, which visually may appear different, also have different thermal inertia. We developed a deep learning-based algorithm that estimates thermal IR images from RGB images of terrains, providing the feasibility of the idea with average 1.21 error [degree].

Kurazume, Ryo↗
NASA NTRS2021

Virtual Reality Training Lab

This video highlights the Virtual Reality Laboratory. It shows off the different systems the lab supports for crew training including Charlotte Mass Handling, Robotics and hardware checkout and SAFER. It focuses on the SAFER (Simplified Aid for EVA Rescue) system and shows briefly how to perform a SAFER rescue in Virtual Reality.

Simulation↗
NASA NTRSDate not supplied

Virtual International Satellite Training Experiences for Weather, Marine, Climate, and Environmental Applications

Since January 2020, National Oceanic and Atmospheric Administration, its Cooperative Institutes, and international partners provided over 15,000 hours of virtual satellite focused on user engagement. The training provides and guides users to analyze a large variety of satellite data for weather, climate, and environmental applications and decision support. The training team reached users in over 50 countries via monthly Regional Focus Group and special topic sessions, World Meteorological Organization Regional Association III and IV Workshops, and American Meteorological Society Short Courses. The training sessions were offered in both English and Spanish covering such themes as GOES-R, JPSS and other satellite capabilities, data access using GEONET Cast, data display, and user applications. The training also emphasized the operational application of satellite observations for cross-disciplinary topics like heavy rain and hail events, aviation hazards, fire and smoke detection, volcanic eruptions and ash monitoring, climate indices, monitoring coral reef health, and in tsunami preparedness. The presentation will highlight the methods used and challenges posed by the various training approaches and future modifications to adapt to the virtual environment during training sessions planned for the next year.

Satellite Training↗
NASA NTRSDate not supplied

A Virtual Reality Planning Environment for High-Risk, High-Latency Teleoperation

Teleoperation of robots in space is challenging due to high latency and limited workspace visibility. Previously, the Interactive Planning and Supervised Execution (IPSE) and Augmented Virtuality systems were developed to reduce failure risk. These tools were visualized on a 3D da Vinci surgical console and operated using the da Vinci manipulators or visualized on conventional monitors and operated with a keyboard and mouse. Experimental studies indicated operator preference for the latter. In this work, we develop a 3D virtual reality (VR) interface for IPSE, implemented on a Meta Quest 2 head-mounted display (HMD), and evaluate it against the prior 2D, keyboard-and-mouse-based interface. The results demonstrate improved operator load with the 3D VR interface, with no decrease in task performance, while also providing cost and portability benefits compared to the conventional 2D interface.

Will Pryor↗
NASA NTRS2022

Virtual Assistant for First Responders Using Natural Language Understanding and Optical Character Recognition

Commercial deep learning capabilities are available for many applications such as computer vision processing and intelligent chat bots. The Google Cloud Platform product Google Dialogflow provides lifelike conversational artificial intelligence (AI) using machine learning (ML) to generate natural conversations between computers and humans. This ML utilizes natural language understanding (NLU) to recognize a user’s intent and extracts key information into a form of entities. We have developed a user-friendly application through understanding the hazardous material database, first aid safety guidelines and observing the process of first responders who access this information in the field. We created the Trusted and Explainable Artificial Intelligence for Saving Lives (TruePAL) virtual assistant using Dialogflow1 and TensorFlow2 paired with EasyOCR.3 The chatbot supports first responders by providing voice interaction which helps limit additional steps such as browsing through multiple categories when searching for information. Using feedback from our field interviews, the voice interface has been developed to enable the first responder to focus on the immediate emergency. With less distractions, the first responder is able to engage the incident more effectively. The partial hands-free TruePAL chatbot assistant improves the accessibility to the correct guidance by an average of 1.9 seconds compared to the widely used application, NIH WISER, which requires full attention to operate. We combined this intelligent chatbot with a separate visual processing capability to produce hazardous signage analysis and generate the proper guidance for first responders. With the evolving functionality of AI tools, the use of virtual assistants in first responder technology will be an advancement, benefiting the safety of both first responders and civilians.

Chow, Edward↗
DOE OSTI2012

Virtually simulating the next generation of clean energy technologies: NETL's AVESTAR Center is dedicated to the safe, reliable and efficient operation of advanced energy plants with carbon capture

Imagine using a real-time virtual simulator to learn to fly a space shuttle or rebuild your car's transmission without touching a piece of equipment or getting your hands dirty. Now, apply this concept to learning how to operate and control a state-of-the-art, electricity-producing power plant capable of carbon dioxide (CO{sub 2}) capture. That's what the National Energy Technology Laboratory's (NETL) Advanced Virtual Energy Simulation Training and Research (AVESTAR) Center (www.netl.doe.gov/avestar) is designed to do. Established as part of the Department of Energy's (DOE) initiative to advance new clean energy technology for power generation, the AVESTAR Center focuses primarily on providing simulation-based training for process engineers and energy plant operators, starting with the deployment of a first-of-a-kind operator training simulator for an integrated gasification combined cycle (IGCC) power plant with CO{sub 2} capture. The IGCC dynamic simulator builds on, and reaches beyond, conventional power plant simulators to merge, for the first time, a 'gasification with CO{sub 2} capture' process simulator with a 'combined-cycle' power simulator. Based on Invensys Operations Management's SimSci-Esscor DYNSIM software, the high-fidelity dynamic simulator provides realistic training on IGCC plant operations, including normal and faulted operations, as well as plant start-up, shutdown and power demand load changes. The highly flexible simulator also allows for testing of different types of fuel sources, such as petcoke and biomass, as well as co-firing fuel mixtures. The IGCC dynamic simulator is available at AVESTAR's two locations, NETL (Figure 1) and West Virginia University's National Research Center for Coal and Energy (www.nrcce.wvu.edu), both in Morgantown, W.Va. By offering a comprehensive IGCC training program, AVESTAR aims to develop a workforce well prepared to operate, control and manage commercial-scale gasification-based power plants with CO{sub 2} capture. The facility and simulator at West Virginia University promotes NETL's outreach mission by offering hands-on simulator training and education to researchers and university students.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2013

Optimized Hypervisor Scheduler for Parallel Discrete Event Simulations on Virtual Machine Platforms

With the advent of virtual machine (VM)-based platforms for parallel computing, it is now possible to execute parallel discrete event simulations (PDES) over multiple virtual machines, in contrast to executing in native mode directly over hardware as is traditionally done over the past decades. While mature VM-based parallel systems now offer new, compelling benefits such as serviceability, dynamic reconfigurability and overall cost effectiveness, the runtime performance of parallel applications can be significantly affected. In particular, most VM-based platforms are optimized for general workloads, but PDES execution exhibits unique dynamics significantly different from other workloads. Here we first present results from experiments that highlight the gross deterioration of the runtime performance of VM-based PDES simulations when executed using traditional VM schedulers, quantitatively showing the bad scaling properties of the scheduler as the number of VMs is increased. The mismatch is fundamental in nature in the sense that any fairness-based VM scheduler implementation would exhibit this mismatch with PDES runs. We also present a new scheduler optimized specifically for PDES applications, and describe its design and implementation. Experimental results obtained from running PDES benchmarks (PHOLD and vehicular traffic simulations) over VMs show over an order of magnitude improvement in the run time of the PDES-optimized scheduler relative to the regular VM scheduler, with over 20 reduction in run time of simulations using up to 64 VMs. The observations and results are timely in the context of emerging systems such as cloud platforms and VM-based high performance computing installations, highlighting to the community the need for PDES-specific support, and the feasibility of significantly reducing the runtime overhead for scalable PDES on VM platforms.

Yoginath, Srikanth B↗
DOE OSTI2016

Virtual substrates for epitaxial growth and methods of making the same

A virtual substrate includes a handle support and a strain-relieved single crystalline layer on the handle support. A method of making the virtual substrate includes growing a coherently-strained single crystalline layer on an initial growth substrate, removing the initial growth substrate to relieve the strain on the single crystalline layer, and applying the strain-relieved single crystalline layer on a handle support.

36 MATERIALS SCIENCE↗
DOE OSTI2016

Quantum Virtual Machine (QVM)

There is a lack of state-of-the-art HPC simulation tools for simulating general quantum computing. Furthermore, there are no real software tools that integrate current quantum computers into existing classical HPC workflows. This product, the Quantum Virtual Machine (QVM), solves this problem by providing an extensible framework for pluggable virtual, or physical, quantum processing units (QPUs). It enables the execution of low level quantum assembly codes and returns the results of such executions.

McCaskey, Alexander J.↗
DOE OSTI2014

Methods and devices for immobilization of single particles in a virtual channel in a hydrodynamic trap

Disclosed herein are methods of immobilizing a particle which comprise focusing the flow of a sample fluid containing the particle into a virtual channel which flows towards an unoccupied hydrodynamic trap in a microfluidic channel such that the particle flows into the hydrodynamic trap and becomes immobilized therein. Also disclosed are microfluidic devices which comprise at least one microchannel having at least one hydrodynamic trap, at least one focusing fluid inlet, said focusing fluid inlet is upstream of the hydrodynamic trap such that a focusing fluid introduced therein results in a virtual channel of a sample fluid when present which preferentially flows toward the hydrodynamic trap.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2020

Virtual Slit for Improved Resolution in Longitudinal Emittance Measurement

A technique to reduce point-spread originating from physical slit width in emittance measurements is described. This technique is developed to improve phase resolution in a longitudinal emittance apparatus consisting of a dipole magnet, energy-selecting slit and bunch shape monitor. In this apparatus, the energy and phase resolutions are directly proportional to the width of the slit, but the virtual slit method enables sub-slit resolution. The bunch phase profile is measured at two points in the energy distribution with a separation less than the physical slit width. The difference of these two profiles is used to reconstruct the profile from a virtual slit of width equal to the separation.

Ruisard, Kiersten↗
DOE OSTI2021

Employer Info Session: Virtual Solar Career Fair

Leading up to the 4/08 virtual career fair, this session featured an overview of the Solar Ready Vets program, a presentation by Hiring Our Heroes staff with tips on navigating the virtual career fair platform, and Q&A with solar employers interested in participating in the event.

Long, Becky↗
DOE OSTI2021

Q-VR: System-Level Design for Future Mobile Collaborative Virtual Reality

High Quality Mobile Virtual Reality (VR) is what the incoming graphics technology era demands: users around the world, regardless of their hardware and network conditions, can all enjoy the immersive virtual experience. However, the state-of-the-art software-based mobile VR designs cannot fully satisfy the realtime performance requirements due to the highly interactive nature of user's actions and complex environmental constraints during VR execution. Inspired by the unique human visual system effects and the strong correlation between VR motion features and realtime hardware-level information, we propose Q-VR, a novel dynamic collaborative rendering solution via software-hardware co-design for enabling future low-latency high-quality mobile VR. At software-level, Q-VR provides flexible high-level tuning interface to reduce network latency while maintaining user perception. At hardware-level, Q-VR accommodates a wide spectrum of hardware and network conditions across users by effectively leveraging the computing capability of the increasingly powerful VR hardware. Extensive evaluation on real-world games demonstrates that Q-VR can achieve an average end-to-end performance speedup of 3.4x (up to 6.7x) over the traditional local rendering design in commercial VR devices, and a 4.1x frame rate improvement over the state-of-the-art static collaborative rendering.

Xie, Chenhao↗
DOE OSTI2021

Virtual Framework for Science Federations with Instruments Access and Control

Experimental science workflows require federations of geographically dispersed science instruments and computing systems connected over a wide-area network. We develop a Virtual Infrastructure Twin (VIT) framework, which is a digital twin of the physical infrastructure that utilizes network virtualization and containerization technologies to support the development and testing of science workflow codes. Using VIT, we illustrate the access to instruments via EPICS system and the orchestration of containerized computations across federation computing systems. We also present a machine learning method to convert VIT throughput measurements to closely match the corresponding physical testbed measurements.

Rao, Nageswara↗
DOE OSTI2022

MEASUREMENT OF THE GENERALIZED POLARIZABILITIES OF THE PROTON IN VIRTUAL COMPTON SCATTERING

Understanding how the visible matter in the universe arises from its elementary quark and gluon constituents is a central question for science. The visible world is founded on the proton, the only composite building block of matter that is stable in nature. Consequently, understanding the formation of matter relies on explaining the dynamics and the properties of the proton?s bound state. A fundamental property of the proton involves the system?s response to an external electromagnetic (EM) field. It is characterized by the EM polarizabilities that describe how easily the charge and magnetization distributions inside the system are distorted by the EM field. When the polarizabilities are generalized to finite momentum transfer, their Fourier transform can map out the spatial distribution of the polarization densities in a proton subject to an EM field. This thesis focuses on the measurement of the proton generalized polarizabilities (GPs) at low four-momentum transfer in experimental Hall C at the Thomas Jefferson National Accelerator Facility (TJNAF). Among the six independent GPs, we will focus on the electric (aE1) and the magnetic (bM1) scalar GPs. The GPs can be accessed through measurements of the Virtual Compton Scattering reaction, by replacing the incoming real photon of the Compton scattering process with a space-like virtual photon. The outgoing real photon provides the EM perturbation to the system. In addition, the Dispersion Relation Formalism is used for generalized polarizability extraction. In this work, the two scalar GPs measured with unprecedented precision, and the measurements help explore a momentum transfer region where an anomalous enhancement of the electric GP that contradicts the predictions of nuclear theory has been observed.

Li, Ruonan↗
DOE OSTI2013

Feasibility study of noise analysis methods on virtual thermal reactor subcriticality monitoring

This paper presents the analysis results of Rossi-alpha, cross-correlation, Feynman-alpha, and Feynman difference methods applied to the subcriticality monitoring of nuclear reactors. A thermal spectrum Godiva model has been designed for the analysis of the four methods. This Godiva geometry consists of a spherical core containing the isotopes of H-l, U-235 and U-238, and the H{sub 2}O reflector outside the core. A Monte Carlo code, McCARD, is used in real time mode to generate virtual detector signals to analyze the feasibility of the four methods. The analysis results indicate that the four methods can be used with high accuracy for the continuous monitoring of subcriticality. In addition to that, in order to analyze the impact of the random noise contamination on the accuracy of the noise analysis, the McCARD-generated signals are contaminated with arbitrary noise. It is noticed that, even when the detector signals are contaminated, the four methods can predict the subcriticality with reasonable accuracy. Nonetheless, in order to reduce the adverse impact of the random noise, eight detector signals, rather than a single signal, are generated from the core, one signal from each equally divided eighth part of the core. The preliminary analysis with multiple virtual detector signals indicates that the approach of using many detectors is promising to improve the accuracy of criticality prediction and further study will be performed in this regard. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗