Advanced liver surgery training in collaborative VR environments
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97 MATHEMATICS AND COMPUTING↗
SEARCH · Engineering Papers
Results for “virtual training”
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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Semi-supervised Machine Learning Enables the Robust Detection of Multireference Character at Low Cost
Multireference (MR) diagnostics are common tools for identifying strongly correlated electronic structure that makes single-reference (SR) methods (e.g., density functional theory or DFT) insufficient for accurate property prediction. However, MR diagnostics typically require computationally demanding correlated wave function theory (WFT) calculations, and diagnostics often disagree or fail to predict MR effects on properties. To overcome these challenges, we introduce a semi-supervised machine learning (ML) approach with virtual adversarial training (VAT) of an MR classifier using 15 WFT and DFT MR diagnostics as inputs. In semi-supervised learning, only the most extreme SR or MR points are labeled, and the remaining point labels are learned. The resulting VAT model outperforms the alternatives, as quantified by the distinct property distributions of SR- and MR-classified molecules. Additionally, to reduce the cost of generating inputs to the VAT model, we leverage the VAT model’s robustness to noisy inputs by replacing WFT MR diagnostics with regression predictions in an MR decision engine workflow that preserves excellent performance. We demonstrate the transferability of our approach to larger molecules and those with distinct chemical composition from the training set. This MR decision engine demonstrates promise as a low-cost, high-accuracy approach to the automatic detection of strong correlation for predictive high-throughput screening.
36 MATERIALS SCIENCE↗
Scaling Up: Demonstrating Risk Reduction and Cost Compression for Commercial Heat Pump Water Heaters - CRADA 625 (Abstract)
Commercial heat pump water heater (CHPWH) systems significantly decarbonize the commercial and multifamily sectors by eliminating the reliance on gas-fired water heating. CHPWH systems are also well suited to include load shift controls that enable load-up and shed commands for supporting grid reliability and time-of-use pricing structure. However, they have not had wide adoption due to factors including price, complexity, and perceived risk. Although CHPWHs have been available in the US for decades, they have not made significant market gains in part because the systems have required significant and costly engineering design expertise and proved lackluster performance. Successful widespread market adoption requires a different approach; a shift from the current custom specialized expertise project design and installation to a repeatable approach that requires little specialized knowledge or expertise and can deliver persistent performance. Using this type of holistic systems approach requires effectively integrating four CHPWH system key components: primary air-to-water heat pumps; primary thermal storage tanks, a temperature maintenance system, and a control system which has capabilities to manage the primary heat pump cycles, any back-up, supplemental, or temperature maintenance heating, alarms, and grid connectivity allowing for demand response (DR), and/or load shifting. The project team has developed and will implement a suite of tools to support faster, less expensive, and more reliable field installations of CHPWH technology and with the resulting data used to further improve the tool set. These tools include: (1) A tool for optimizing system size and costs. (2) A tool that predicts annual energy use and overall system efficiency. (3) The Advanced Water Heater Specification (AWHS 8.0) defining the components of a full CHPWH system addressing performance requirements by climate zone. (4) The Qualified Products List: (QPL) of approved products that meet the specifications requirements. (5) Training materials including online on-demand modules, instructor-led training, and virtual interactive video tours of CHPWH installations in multifamily buildings. Demonstration site identification in low-income buildings in underserved communities is currently underway. Preliminarily, the team anticipates having three demonstrations in the Pacific Northwest and three in the Northeast for a total of six sites. After the demonstration sites are finalized, and M&V instrumentation installations are complete, the team will gather performance data and confirm whether the CHPWH systems perform as predicted and use the data to improve the existing tools.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Best Practices for NERSC Training
The National Energy Research Supercomputing Center (NERSC) at Lawrence Berkeley National Laboratory (LBNL) organizes approximately 20 training events per year for its 8,000 users from 800 projects, who have varying levels of High Performance Computing (HPC) knowledge and familiarity with NERSC's HPC resources. Due to the novel circumstances of the pandemic, NERSC began transforming our traditional smaller-scale, on-site training events to larger-scale, fully virtual sessions in March 2020. We treated this as an opportunity to try new approaches and improve our training best practices. This paper describes the key practices we have developed since the start of this transformation, including considerations for organizing events; collaboration with other HPC centers and the DOE ECP Program to increase reach and impact of events; targeted emails to users to increase attendance; efficient management of user accounts for computational resource access; strategies for preventing Zoombombing; streamlining the publication of professional-quality, closed-captioned videos on the NERSC YouTube channel for accessibility; effective communication channels for Q&A; tailoring training contents to NERSC user needs via close collaboration with vendors and presenters; standardized training procedures and publishing of training materials; and considerations for planning HPC training topics. Additionally, most of these practices will be continued after the pandemic as effective norms for training.
97 MATHEMATICS AND COMPUTING↗
Virtual and Augmented Realities to Support Development, Training, Deployment, and Operations of Remote Systems - 20516
Throughout the U.S. Department of Energy complex, there is a need to deploy new, unique equipment and systems for cleanup activities, waste processing, infrastructure repair, and decommissioning and demolition in radioactive and hazardous environments. The new equipment often consists of custom systems created by integrating and modifying commercial technologies. In addition, the systems are deployed in unique environments due to limited access, waste properties, and the presence of previously installed equipment or infrastructure that has been left in place. The deployment of custom systems that are 'turnkey' ready for first-time installation and application has its risks. Use of test platforms and mockups for cold (i.e., non-radioactive) testing is one method to mitigate that risk: however, full-scale test mockups for operations such as waste retrieval and tank inspection can be expensive and must be modified to incorporate the uniqueness of each application. An alternative to full-scale mockups is to use three-dimensional (3D) virtual environments and virtual reality (VR) technology to develop interactive virtual test beds. These can be used throughout the down-selection, development, system integration, operational planning, operator training, and mission execution phases of a task. Virtual test beds are readily reconfigured, can be used for multiple tasks, support multiple users in parallel, and can accommodate enhancements and changes not originally planned or conceived in the initial design. Virtual test beds also provide a platform for developing and testing augmented reality (AR) tools for improving operator efficiency. A VR experience has been generated for waste retrieval from a Hanford waste tank to present the potential applications of a virtual test bed. Features of the VR experience are presented, potential applications of VR and AR for remote applications are discussed, and development areas for improving future application are considered. (authors)
12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
In Silico Versus In Situ: The Challenging Landscape of Nuclear Criticality Safety Training
Nuclear criticality safety grew out of the ranks of experimentalists studying the physics of chain-reacting systems at critical experiment facilities. Consequently, critical experiment facilities provide the best forum for conducting training in nuclear criticality safety. As the nuclear renaissance gains traction, there is an increased demand to train personnel in nuclear criticality safety (NCS). Strides have been made in both on-line and virtual reality based NCS training. While these are perhaps a necessary component of NCS training, hands-on training at critical experiments facilities remains the most effective means of developing competency for fissionable material handlers, managers of fissionable material operations, criticality safety analysts, and experimentalists. This paper explores the challenges of developing and maintaining workforce competency in nuclear criticality safety to support safe and efficient fissionable material operations.
73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
A Physics-Informed Reinforcement Learning Framework for Economic-Thermal Co-Optimization of Crypto Mining Data Centers: Preprint
The rapid expansion of cryptocurrency mining has created a new class of high-density data centers characterized by extreme thermal flux and high sensitivity to volatile economic markets. Traditional thermal management strategies, typically reliant on rule-based control, maintain static setpoints that fail to account for fluctuating electricity prices and cryptocurrency values - factors critical to mining profitability. To address this, we present a physics-informed reinforcement learning (PIRL) framework for economic-thermal co-optimization in crypto mining data centers. This framework consists of a proximal policy optimization (PPO) agent, a virtual testbed powered by high-fidelity physics-based models, and an interactive frontend dashboard. The PPO agent is trained using the virtual testbed and strict hardware safety limits. This physics-informed approach allows the agent to learn a stochastic policy that dynamically balances mining revenue against operational costs by co-optimizing HVAC cooling setpoints and IT computational hashrate. The simulation results demonstrate that the integrated framework achieved an 8.62% increase in net operational profit compared to traditional baseline strategies while strictly adhering to safety-critical temperature constraints (coolant supply temperature < 32 degrees C). This work provides a scalable template for the deployment of reinforcement learning in mission critical facilities where economic volatility and physical safety must be managed simultaneously.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Science-informed Virtual Digital Twin for an Integrated Energy System with Carbon Capture: Research, Training, and Education
AMPc 2022 Presentation on "Science-informed Virtual Digital Twin for an Integrated Energy System with Carbon Capture: Research, Training, and Education"
Zitney, Steve↗
International Radiological/Nuclear Training for Emergency Response - Major Public Events Virtual Workshop: Nuclear Security Planning and Operations (Day 3) [Slides]
This module will provide a basic overview of radiological/nuclear search operations, including the application of radiological detection instrumentation and best practices for operational tactics, techniques and procedures. This module will introduce basic radiological/nuclear search operations and include the following: (1) A review of the principles of optimizing detection, (2) An understanding of radiation hazards and alarms, and (3) An examination of nuclear security measures best practices for buildings and stadium complexes, large areas, roadways and parking areas, and pedestrian and vehicle entrance portals.
61 RADIATION PROTECTION AND DOSIMETRY↗
International Radiological/Nuclear Training for Emergency Response - Major Public Events Virtual Workshop: Radiation Detection and Emergency Response Equipment (Day 2) [Slides]
The objective of this presentation is to familiarize participants with the different types of radiation detection systems and their practical applications for radiological emergency response. The specific goals are for participants to: (1) Understand the Three Step Process for Radiological Response of (i) Search and/or Survey, (ii) Radioisotope Identification, and (iii) Source Recovery, (2) Recognize the types of radiation detection equipment and their applications, and (3) View examples of common radiation detection instrumentation with operational videos.
61 RADIATION PROTECTION AND DOSIMETRY↗
International Radiological/Nuclear Training for Emergency Response - Major Public Events Virtual Workshop: Nuclear Security Planning and Operations (Day 3) [Slides]
This module will provide an overview of best practices for nuclear security planning requirements to include building a nuclear security plan and a concept of operations. It will introduce best practices for nuclear security planning to include the following: (1) Develop a Nuclear Security Plan, (2) Review 10 key elements of a Concept of Operations, (3) Estimate resources for an MPE, and (4) Capture lessons learned and best practices.
61 RADIATION PROTECTION AND DOSIMETRY↗
International Radiological/Nuclear Training for Emergency Response - Major Public Events Virtual Workshop: Alarm Interdiction and Adjudication and Source Recovery (Day 4) [Slides]
The objective of this presentation is for participants to understand the process for interdicting and adjudicating radiation alarms and review operational scenarios for radiological emergency response best practices. The specific goal is for participants to: (1) Become familiar with the Primary and Secondary Inspection process for alarm interdiction, investigation, and adjudication, (2) Gain knowledge of common operational scenarios where alarm interdiction, investigation and adjudication are conducted as best practices, and (3) Review several scenarios that could be encountered as part of the Nuclear Security measures for an MPE.
61 RADIATION PROTECTION AND DOSIMETRY↗
CRCNS21 Computational Models of Multisensory Integration by Upper Limb in Humanoids and Amputees
This international collaborative research project between Johns Hopkins University (JHU) and the Technical University of Munich (TUM) investigated how the human brain processes and integrates multiple types of sensory information, such as touch and force, with the goal of improving prosthetic limbs for amputees and advancing sensory capabilities in humanoid robots. The research advanced our understanding of how the brain responds to sensory feedback in upper-limb amputees. Through experiments in which amputees received electrical stimulation while performing phantom hand movements, we demonstrated that sensory feedback activates the cortical sensorimotor and multisensory regions, and that these regions communicate dynamically during stimulation. Experiments with intact-limb participants explored the integration of visual, haptic, and force feedback, as well as in virtual reality motor training, further showing how the brain processes multimodal sensory information. In addition, this research inspired work on examining the reliability of where amputees perceive sensations over time, which contributed to a successful doctoral fellowship for continued investigation. Our collaborators at TUM improved multimodal sensor technology combining tactile and thermal feedback for humanoid robots, demonstrating the feasibility of integrating multiple sensor types into a unified system for detecting and responding to environmental stimuli. The experimental methods and analysis techniques developed across both teams, including functional network analysis and multimodal sensor integration, provide a foundation for future research in prosthetics and robotics. This research benefits the public by generating knowledge about how amputees process restored sensory information. Advances in humanoid sensing contribute to safer human-robot interaction. The project also fostered international collaboration and cross-disciplinary training: one TUM doctoral student spent a summer at JHU working on multimodal sensor integration, while two JHU students traveled to TUM to host workshops on neuromorphic sensory encoding and sensory integration.
42 ENGINEERING↗
Object manipulation in immersive virtual environments: Hand Motion tracking technology and snap-to-fit function
There have been recent efforts to use virtual reality and manipulation to train construction workers and inspection. However, there is a lack of research efforts comparing and evaluating virtual manipulation hardware for construction tasks. Moreover, the current practice of virtual manipulation has limited functionality to guide users with the placement of objects in virtual environments. To address these issues, this paper presents 1) a detailed case study that compares three types of manipulation hardware (image-based, infrared-based, and magnetic-based) for construction applications and 2) a snap-to-fit method that improves the VM through solving limitations of the advanced virtual reality interaction metaphors. The latter enhances the placement process for manipulation by comparing two models (e.g., scan and BIM models) for proper placement in assembly scenarios. Finally, the case study results show that magnetic-based systems outperform others in construction scenarios. Lastly, the snap-to-fit function was validated in terms of accuracy and time performance.
42 ENGINEERING↗
NFPA Distributed Energy Resources Safety Training (DERST) For Emergency Responders
The National Fire Protection Association, with support from the Department of Energy, executed a multi-year initiative to develop, enhance, and disseminate Distributed Energy Resources Safety Training (DERST) tools for U.S. emergency responders. As Distributed Energy Resources (DER)—such as solar photovoltaics, battery energy storage systems (ESS), electric vehicles (EVs), and associated infrastructure—become increasingly prevalent, the NFPA identified a critical need for up-to-date standardized, accessible, and effective safety training tailored for the fire service and related public safety professionals. The project delivered a comprehensive suite of educational resources to improve responders’ abilities to safely manage DER-related incidents. This included: • Revised Modular Training Courses: Updated classroom-based DER safety courses, now modular and accessible nationwide through fire academies and the North American Fire Training Directors (NAFTD) network. • Live Burn Testing & Research: A full-scale controlled burn of a DER-equipped residential structure provided real-world data and insights, forming the basis for updated best practices. • A Gamified Simulation Tool – Firefighters Incident Response Simulation Tool (FIRST): A first-of-its-kind, multiplayer, scenario-based simulation using the Unreal Engine 5.0 to train responders in a realistic virtual, multi-DER incident environment. • Field Familiarization Software Tools & Prop Guide: Digital DER field familiarization evolutions software guide and a prop development manual to support field-based DER training exercises, enhancing responders' hands-on familiarity with DER infrastructure and collaboration on virtual incident responses. • National Dissemination Strategy: Strategic partnerships with NAFTD, Vector Solutions, and others enabled wide-scale distribution, with over 5,000 departments accessing resources and 1,100+ departments adopting the simulator in the first seven months. Also provided a web portal for easy access to all training and simulation programs developed under this grant for the U.S. responder community. Key findings from the project—particularly from the burn test—led to paradigm shifts in fire response tactics. For example, traditional approaches to garage fires may be hazardous if DERs are present, due to explosive off gassing and thermal runaway risks. The new training emphasizes scene assessment, stand-off approaches, thermal imaging verification, and careful post-incident cooling of DER components to prevent reignition. This initiative has had a significant national impact, raising awareness, enhancing preparedness, and supporting safer DER incident response practices. Significant engagement from the media, public safety organizations, and PBS coverage has further amplified the reach and adoption of NFPA’s DER safety training, tools, and simulations.
14 SOLAR ENERGY↗
Predicting turbulent wake flow of marine hydrokinetic turbine arrays in large-scale waterways via physics-enhanced convolutional neural networks
We present a physics-enhanced convolutional neural network (PECNN) algorithm for reconstructing the mean flow and turbulence statistics in the wake of marine hydrokinetic (MHK) turbine arrays installed in large-scale meandering rivers. The algorithm embeds the mass and momentum conservation equations into the loss function of the PECNN algorithm to improve the physical realism of the reconstructed flow fields. The PECNN is trained using large eddy simulation (LES) results of the wake flow of a single row of turbines in a virtual meandering river. Subsequently, the trained PECNN is applied to predict the wake flow of MHK turbines with arrangements and positionings different than those considered during the training process. The PECNN predictions are validated using the results of separately performed LES. The results show that the PECNN algorithm can accurately predict the wake flow of MHK turbine farms at a small fraction of the cost of LES. The PECNN can improve the accuracy by around 1% and reduce the physical constraint indices by around 50% compared to the CNN without physical constraints. This work underscores the potential of PECNN to develop reduced-order models for control co-design and optimization of MHK turbine arrays in natural riverine environments.
Mechanics↗
High-Fidelity Multiphysics Modeling of a Heat Pipe Microreactor Using BlueCrab
Researchers who are actively developing nuclear microreactors are planning to employ innovative designs and features using traditional commercial modeling tools that may be inadequate for their design and licensing activities. The codes developed under the U.S. Department of Energy Office of Nuclear Energy Advanced Modeling and Simulation (NEAMS) program provide flexibility in terms of geometry modeling and multiphysics coupling and are particularly well suited for modeling novel microreactor concepts. To test the maturity of these codes, this paper introduces a conceptual heat pipe microreactor (HP-MR) designed to gather various technologies of interest to microreactor developers such as control drums, heat pipes, and hydride moderators. Here, the objective of this effort is to demonstrate NEAMS tools capability to perform high-fidelity multiphysics simulations, using coupled neutronics (via the Griffin code), heat conduction (via the BISON code), heat pipe modeling (via the Sockeye code), and hydrogen redistribution in hydride metal moderator (via the SWIFT code). Codes are coupled in-memory through the Multiphysics Object-Oriented Simulation Environment (MOOSE) framework, which permits flexible multiphysics data transfer schemes. The analysis confirmed two key aspects of the HP-MR concept: (1) its ability to follow the power load requested from the heat pipe and (2) its ability to avoid heat pipe cascading failure unless designed with high power close to operating failure limits of its heat pipes. The developed computational model was distributed publicly on the Virtual Test Bed for training purposes to accelerate adoption by industry and to provide a high-fidelity multiphysics solution for benchmarking against other tools. Additional multiphysics analyses including other transients and coupled physics were identified as necessary future work, together with a focus on validating multiphysics behavior against experiments.
Microreactor↗
Final Report in Response to ARPA-E Contract Award Number DE-AR0001157
Autonomous operations and maintenance (O&M) by robots has been identified as a key technology to facilitate both the safe operation, and reduced operational costs associated with future nuclear reactors. Radiation and thermal environments, particularly associated with molten salt reactor designs, excludes the use of human proximity and so there is a need to train robots for tasks that have not yet been fully identified, or for accidents that may occur in the future. This program sought to develop a generic methodology that could be applied to train ‘any’ robot, to perform ‘any’ task, through the use of machine learning (ML) training performed in a virtual reality (VR) environment that simulates the physically perceived task(s). The VR environment allows us to construct ‘any’ future task and the ML approach, which included reinforcement learning (RL), allowed us to generate extensive data sets that can be used to establish control algorithms to thereby control the physical robot in the physical environment.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗