Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Virtualization”

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 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Arm DevSummit Keynote: Environment for Data Engineering in Virtual Reality: Ethical Considerations

Currently, the US Government is going through a large-scale data Transformation effort, where the GSA playbook is guiding all agencies to make their processes data-driven with the help of emerging technologies. To address concerns about algorithm sharing, AI adoption, and vendor lock-in, the NASA Langley Research Center Digital Transformation Group has developed the Environment for Data Engineering in Virtual Reality (EnDEVR), a data science ecosystem that allows users to command and investigate customizable data analyses from a VR environment. The system has been evaluated in the Oculus Rift S and Quest environments, two popular VR systems powered by the ARM architecture. Current and future development will employ several AI capabilities to guide research and automation within the environment and to facilitate algorithm sharing. In this talk, we will discuss the results of an initial ethical investigation and recommended considerations for the use of AI within the system.

Artificial Intelligence↗
NASA NTRS2022

Cockpit Interface for Locomotion and Manipulation Control of the NASA Valkyrie Humanoid in Virtual Reality (VR)

A virtual reality (VR) interface is presented for controlling NASA’s Valkyrie humanoid robot with flexible locomotion control options and intuitive teleoperation. Locomotion modes include navigating to a specified waypoint, sending desired velocities with a joystick, or manually placing a sequence of footsteps. On the other hand, teleoperation modes include voice commands to toggle relative whole-body tracking or high-level commandeering, and key bindings for common stored poses such as power grasps. The primary novelties of the interface are in the implementation of the cockpit mode and a floating augmented reality (AR) screen fixed with respect to the robot. The former enables embodied teleoperation and increased awareness during navigation in a mixed-reality setting. The latter preserves depth perception without the disparity clutter from a stereo point cloud. The interface is demonstrated on two real humanoids performing common explosive ordnance disposal (EOD) tasks such as door opening, vehicle inspection, and disruptor placement. Notably, the interface enables a single operator to control multiple humanoids. While the interface is motivated by EOD missions, the presented ideas are usable for other robots employing VR-based control.

Virtual-reality↗
NASA NTRS2023

Using Virtual Reality to Envision Deployment of Spacesuit-Compatible Augmented Reality Displays for Lunar Surface Operations

The National Aeronautics and Space Administration (NASA) will soon land crew on the lunar surface to establish a sustainable presence and develop operational concepts for future long-duration missions. New technologies will be necessary to extend planning and execution capabilities for lunar surface activities. The Joint Augmented Reality Visual Informatics System (Joint AR) at NASA Johnson Space Center (JSC) is one such technology. Joint AR is a suit-mounted augmented reality (AR) display and compute system which facilitates unprecedented information exchange and data visualization capabilities between mission support operators and suited crew. This paper describes challenges associated with developing an AR technology for an envisioned work domain by applying a sociotechnical lens to the iterative testing and development of novel AR technology through virtual reality (VR). A foundational VR testbed was established, providing a high-fidelity approximation of the lunar surface and enabling testing of envisioned AR system features in parallel with real world product development. Using this environment, a series of human-in-the-loop experiments were conducted using VR to deploy a notional AR system supporting use cases envisioned for lunar exploration extravehicular activity (xEVA). Our findings indicate VR is a powerful and immersive tool for testing capabilities which extend beyond the limitations of current AR technology. Our VR testbed enables early testing of proposed system features, advancing identification of high-value features and driving present-day development of Joint AR. Future work directions are discussed, including lessons learned and a roadmap for an iterative research and development approach applying VR (among other testbeds) to accelerate Joint AR system maturation.

Augmented reality↗
NASA NTRSDate not supplied

Development of a Combined Cohesive and Virtual Crack-Closure Technique Approach to Represent R-Curves

Resistance curve (R-curve) effects due to fiber bridging, crack migration, and other blunting mechanisms are common in composite laminates. These mechanisms can dramatically increase the resistance to crack propagation but can be challenging to simulate. Delamination with R-curves can be analyzed using cohesive zone modeling (CZM) or the virtual crack closure technique (VCCT). Large fracture process zones can be simulated with CZM, but they require highly refined meshes. Coarser meshes can be used with VCCT, but this method is only applicable to small fracture process zones with R-curves defined as functions of position. Therefore, a technique with the computational efficiency of VCCT and the natural ability of cohesive elements to represent large fracture process zones is desirable. An approach is proposed that starts as a CZM in which the cohesive traction separation law (TSL) is separated into high-strength (HS) and low-strength (LS) components. The HS part, which is responsible for the mesh requirements of the CZM analysis, is replaced by VCCT. The combined method is evaluated by analyzing the response of double cantilevered beam specimens. Two specimen layup configurations, [0/90/90/0] 3s and [0/90/90/0] 9s , are evaluated. The R-curve response from each configuration is determined and applied to the constitutive properties of models built with CZM and with VCCT. The results and computational efficiency of the CZM, VCCT, and combined approaches are compared. The results are indicative that a combined cohesive/VCCT approach can enable progressive failure analyses to retain the computational efficiency of VCCT with the ability of the cohesive elements to capture R-curve effects.

Virtual Crack-Closure Technique↗
DOE OSTI2020

Stochastic Virtual Battery Modeling of Uncertain Electrical Loads using Variational Autoencoder

Effective utilization of flexible loads for grid services, while satisfying end-user preferences and constraints, requires an accurate estimation of the aggregated predictive flexibility offered by the electrical loads. Recently, there have been efforts to quantify the predictive flexibility of thermostatic loads (e.g. residential air-conditioners, electric water-heaters) using the notion of virtual battery (VB), whose state evolution is governed by a first order dynamics including self-dissipation rate, and power and energy capacities. Identifying the VB model parameters for a collection of thermostatic loads, however, is challenging primarily due to uncertainties and lack of information regarding the end-user behavior, underlying device models and parameters. In this paper, we propose a \textit{variational autoencoder}-based deep learning algorithm to identify the parameters of the VB model. Using available sensors and meters data, the proposed algorithm generates not only point estimates of the VB parameters, but also confidence intervals around those values. Effectiveness of the proposed frameworks is demonstrated on a collection of electric water-heater loads, whose operation is driven by uncertain water usage profiles.

virtual battery, deep learning algorithms↗
DOE OSTI2023

Virtual Engineering Software Framework for Integrated Biomass Conversion Modeling

This presentation covers the design and implementation of a software tool to systematically connect computational models of unit operations to simulate an integrated process of low-temperature conversion of biomass to fuel. This virtual engineering (VE) software was designed with the overarching goal of connecting unit models written in various programming languages and requiring different computational resources within a single, flexible framework. The models and features currently considered for the VE library include mechanistic models for pretreatment, enzymatic hydrolysis, and aerobic bioreaction; high-fidelity computational fluid dynamics (CFD) simulations for enzymatic hydrolysis and aerobic bioreaction; and the capability to perform techno-economic analyses (TEA) using Aspen Plus, a commercial software package. The CFD models require access to high-performance computing (HPC) resources, so in addition to handling multiple programming languages and interfaces, the VE software must also be capable of interacting with an HPC scheduler to submit, run, and post-process jobs. Using the Python programming language, a new VE software package has been developed that contains functionality to manage the input-output communication between various unit models, schedule simulations to run on NREL's HPC and analyze those results, and interface with existing TEA software workflows. A Jupyter-notebook GUI was also created to solicit user input and provide documentation. In cases where multiple models for a particular unit-operation exist, selection between models is accomplished through a simple checkbox, with the appropriate inputs and outputs being parsed and converted seamlessly in the background. Each operation makes use of a different programming language, but the flow of information from pretreatment to enzymatic hydrolysis to bioreaction is managed with an intuitive, centralized file-communication strategy. In this talk, the programming approach and implementation details of the notebook are presented for multiple possibilities of the conversion process, including a demonstration of the ability to manage HPC resources. Additionally, an example of a sensitivity study of treatment parameters governing the overall conversion outcome is shown which highlights the ease of defining new problems using the VE Notebook workflow and leads into a discussion of ongoing work to enable outer-loop optimization studies.

biofuel↗
DOE OSTI2023

3D Virtual Simulation for Radiation Safety and Hazards Identification Training

This presentation will be presented at CAES codebreaker session on May 4th. The slides include two projects that demonstrate how virtual training can benefit from visualization technology. One is a collaboration project between the Applied Visualization Laboratory and College of Eastern Idaho for radiation safety and survey training. The other one is Idaho National Laboratory's hazards identification online training that utilized LiDAR technology.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Overview of Virtual Test Bed (VTB)

The Virtual Test Bed (VTB) was established to support the deployment of advanced reactors by facilitating the use of state-of-the-art modeling and simulation capabilities. These tools can solve previously prohibitive challenge problems with greater levels of fidelity and in a multiphysics framework. A VTB repository was recently set up to host models for a wide variety of reactor types and using a range of Nuclear Energy Advanced Modeling and Simulation-developed software. The repository currently includes models for High-Temperature Gas-Cooled Reactors, Sodium Fast Reactors, Fluoride-salt-cooled High-temperature Reactors, Molten Salt Reactors, and Heat-Pipe Microreactors. This article provides an overview of the capabilities currently in the VTB repository.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2022

The Virtual Test Bed (VTB) Repository: A Library of Multiphysics Reference Reactor Models using NEAMS Tools

With the next generation of nuclear reactors under development, modeling and simulation (M&S) tools are being developed by the Nuclear Energy Advanced Modeling and Simulation (NEAMS) program in order to support their design, licensing, and future operation. Mirroring the physical test beds currently under construction (i.e., EBR-II and ZPPR), the Virtual Test Bed (VTB) was launched by the National Reactor Innovation Center (NRIC) in collaboration with NEAMS to support the advanced reactor community. This collaborative effort, which involves multiple teams at both Idaho National Laboratory and Argonne National Laboratory aims to use NEAMS tools to model a wide range of reactor designs. Those models are automatically tested to ensure their continued functionality as the tools are further developed. Examples are extensively documented, each acting as a tutorial for applying the relevant NEAMS tools to that reactor design. Currently, five advanced reactor types (with a total of eight specific design variants) are simulated by a variety of different models. These models range from steady-state, core multiphysics simulations to integrated plant analysis during loss-of-flow transients.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2025

Immersive Scientific Visualization of Molten-Salt Reactor Waste Characteristics Using Virtual Reality

Immersive visualization is changing how we explore, communicate, and understand complex scientific systems. In nuclear energy, an area in which data are often multidimensional, time-dependent, and difficult to interpret, virtual reality (VR) represents a powerful and intuitive informational medium. This work introduces a VR-based platform that visualizes the post-shutdown behavior and waste management lifecycle of molten-salt reactors (MSRs), a next-generation reactor type with unique operational and safety characteristics. The platform, built in Unity, is streamed on the Meta Quest 3 headset. It transforms high-fidelity simulation data into an interactive, immersive experience. Users can explore time-dependent reactor characteristics such as nuclide decay, which is a key factor for evaluating reactor waste strategies. The datasets were generated using the MOOSE (Multiphysics Object-Oriented Simulation Environment) framework and then processed through ParaView scripting for smooth integration into Unity. From a visualization standpoint, the platform emphasizes spatial storytelling, temporal exploration, and user-centered interaction. Users can navigate 3D reactor geometries, slice through volumetric data, and manipulate time to observe how physical phenomena evolve. Real-scale rendering and embodied interaction make the experience feel tangible. The interface is designed to be accessible, even to those without nuclear or simulation expertise. This lowers the barrier for stakeholders, policymakers, and the general public, while still supporting expert analysis and collaborative decision-making. This work shows how immersive visualization can function as both a scientific tool and a communication interface. By integrating simulation, processing, and visualization into a cohesive workflow, we offer a scalable framework for immersive scientific storytelling. The modular design supports future extensions to other reactor types and lifecycle stages, from shutdown to long-term storage, making the platform adaptable for both research and outreach.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Renormalization group improved photon impact factors and the high energy virtual photon scattering

We perform the renormalization group improved collinear resummation of the photon-gluon impact factors. We construct the resummed cross section for virtual photon-photon (γ ∗ γ ∗ ) scattering which incorporates the impact factors and BFKL gluon Green’s function up to the next-to-leading logarithmic accuracy in energy. The impact factors include important kinematical effects which are responsible for the most singular poles in Mellin space at next-to-leading order. Further conditions on the resummed cross section are obtained by requiring the consistency with the collinear limits. Our analysis is consistent with previous impact factor calculations at NLO, apart from a new term proportional to C F that we find for the longitudinal polarization. Finally, we use the resummed cross section to compare with the LEP data on the γ ∗ γ ∗ cross section and with previous calculations. The resummed result is lower than the leading logarithmic approximation but higher than the pure next-to-leading one, and is consistent with the experimental data.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Twist-three cross-sections in deeply virtual Compton scattering

We study the deeply virtual Compton scattering process with both twist-two and twist-three Compton form factors and present our cross-sections formulas with all polarization configurations. While the twist-three contributions are generally assumed to be negligible in the literature due to the kinematical suppression, we compare them with the twist-two ones at typical JLab 6 GeV and 12 GeV kinematics as well as EIC kinematics and show their kinematical suppression explicitly, justifying the leading-twist approximation made in the literature. In addition, we also estimate the twist-three Compton form factors using Wandzura-Wilczek relations and inputs of twist-two generalized parton distributions based on a reggeized spectator model. With those estimated Compton form factors, we analyze the kinematical behavior of twist-two and twist-three cross-sections in a wide range of kinematics, and discuss the optimal regions for separating the leading-twist effects from the higher-twist ones.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2024

DK/Dπ scattering and an exotic virtual bound state at the SU(3) flavour symmetric point from lattice QCD

Abstract ElasticS-wave scattering of a charm meson with a light pseudoscalar meson inJ P = 0 + is investigated in the flavour$$ \overline{\textbf{3}} $$ 3 ¯ ,6and$$ \overline{\textbf{15}} $$ 15 ¯ sectors at the SU(3) f flavour point using lattice QCD, working on three volumes withm π ≈ 700 MeV. Large bases of interpolating operators are employed to extract finite-volume spectra, which are subsequently used with the Lüscher method to provide constraints on infinite-volume scattering amplitudes. Examining the singularities of the amplitudes, theS-wave amplitude in the flavour$$ \overline{\textbf{3}} $$ 3 ¯ sector is found to contain a deeply bound state, strongly coupled to elastic threshold, corresponding to the$$ {J}^P={0}^{+}\ {D}_{s0}^{\ast }(2317) $$ J P = 0 + D s 0 ∗ 2317 . In the exotic flavour6sector a virtual bound state is found at$$ \sqrt{s_{\textrm{pole}}} $$ s pole = 2510 – 2610 MeV, roughly 40 – 140 MeV below threshold, whereas the$$ \overline{\textbf{15}} $$ 15 ¯ channel shows weak repulsion.

Physics↗
DOE OSTI2021

Higher-order kinematical effects in deeply virtual Compton scattering

We study the deeply virtual Compton scattering cross-section in twist-two generalized parton distribution (GPD) approximation, and show that different choices of light-cone vectors and gauges for the final photon polarization will lead to different higher-order kinematical corrections to the cross-section formula. The choice of light-cone vectors affects kinematic corrections at the twist-three level, accounting for the differences between the cross-section formulas in the literature. On the other hand, kinematical corrections from higher-twist GPDs should eliminate the light-cone dependence at twist three. Those light-cone dependencies are studied systematically at JLab 12 GeV and future EIC kinematics. They serve as the intrinsic systematic uncertainties in extracting the Compton form factors through the cross-section formula. More importantly, they are also necessary for understanding cross-section measurements with higher-twist precision and to reconstruct higher-order Compton form factors.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2025

The Dynamic Networks Experiments: Virtual Experiments to Quantify Gains in Nuclear Explosion Monitoring

We describe an ongoing series of virtual experiments conducted collaboratively by four United States National Laboratories: Sandia National Laboratories, Los Alamos National Laboratory, Lawrence Livermore National Laboratory, and Pacific Northwest National Laboratory. These Dynamic Network Experiments (DNEs) provide an experimental framework to evaluate the potential impact of new research tools on nuclear explosion monitoring. The second DNE (DNE2), completed in 2024, exploited waveform data (seismic, infrasound, and electromagnetic) that was recorded by multi-modal sensors within and near the Nevada National Security Site and synthetic radionuclide signatures over multiple time periods. During the execution of DNE2, we processed and analyzed data through a multi-stage event processing pipeline that ingested raw data, performed quality control, detected signals, built events from these signals, located these events, and characterized the events’ source types and sizes. For each stage and over the entire event processing pipeline, we evaluated performance changes by comparing the performance of new data processing methods, models, and algorithms against a baseline. We also performed an additional execution phase to assess event processing pipeline function, speed, and efficiency against that of an expert analyst, including computational and manual efforts. Finally, we assessed the impact and effort of modern computing infrastructure on the monitoring pipeline. This paper describes key elements of the DNEs, from formulation through execution, as demonstrated in DNE2. The DNEs introduce several novel concepts to quantitatively measure the potential impact of new methods on explosion monitoring, including the collaborative design of multi-modal datasets, performance and logistical metrics, and integrated analyses.

42 ENGINEERING↗
DOE OSTI2022

Virtual texture analysis to investigate the deformation mechanisms in metal microstructures at the atomic scale

Understanding the deformation behavior of metallic materials at high strain rates requires the characterization of plasticity contributors such as twins, phase transformed regions, and dislocations. However, predicting the contributions from phase transformation and twinning relies on a complete understanding of the selection of variants for various loading orientations and the evolution of their volume fractions. This manuscript presents a new virtual texture (VirTex) analysis approach to characterize phase transformation and twinning variants in deformed microstructures generated using molecular dynamics (MD) simulations. Furthermore, the VirTex method involves the construction of a rotation matrix to calculate the angle/axis pairs and misorientation angles for each atom in the microstructure. Any changes in the orientation angle from angle/axis pairs and/or structure types are analyzed to determine the nucleation and evolution of variants in the microstructure. The study uses shock deformed single-crystal Fe, Ta, and Cu to analyze the variant selections for phase transformation or twinning or both in BCC and FCC systems. In addition, the VirTex analysis is able to characterize the phase transformation and twinning variants in nanocrystalline Fe and Ta microstructures. Besides characterizing variants, orientation mapping also provides an accelerated and on-the-fly approach for quantifying twin fractions in MD microstructures.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Virtual refrigerant charge sensor for variable-speed heat pumps based on feature selection

The refrigerant charge level in heat pump systems significantly impacts their energy efficiency. Virtual refrigerant charge (VRC) sensing technology has been comprehensively investigated and well-established due to its lower cost compared to physical sensors. However, the previous VRC research often relied on expert judgment and physical reasoning for their variable selection, which can potentially select redundant (or highly correlated) or insignificant features, and it is also primarily focused on single-speed systems. To address these challenges, this study proposes a VRC algorithm for variable-speed heat pumps that selects features through a rigorous feature selection method in combination with physical insights. We also propose a piecewise linear model structure segmented by subcooling temperature to accurately predict charge levels, particularly when subcooling temperatures are substantially low. The proposed algorithm was evaluated using experimental data of a residential R410A heat pump, and the performance was compared with two baseline VRC algorithms. The results are: (1) The proposed algorithm outperforms for the case with subcooling temperature less than 1 °C. (2) The proposed algorithm achieves a tested mean absolute percentage error (MAPE) of 4.23%, and improves the overall accuracy for cooling conditions by approximately 60%, compared with the two baseline algorithms. (3) The proposed algorithm uses two fewer features and improves the accuracy for undercharge cooling conditions by 68.0%, compared with baseline algorithm 2. These improvements enhance prediction accuracy and prevent overfitting, providing a more reliable refrigerant charge level prediction and helping improve the heat pump energy efficiency.

Liang, Chenjiyu↗