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At least 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
NASA NTRS2018

Augmented Reality Data Generation for Training Deep Learning Neural Network

One of the major challenges in deep learning is retrieving sufficiently large labeled training datasets, which can become expensive and time consuming to collect. A unique approach to training segmentation is to use Deep Neural Network (DNN) models with a minimal amount of initial labeled training samples. The procedure involves creating synthetic data and using image registration to calculate affine transformations to apply to the synthetic data. The method takes a small dataset and generates a highquality augmented reality synthetic dataset with strong variance while maintaining consistency with real cases. Results illustrate segmentation improvements in various target features and increased average target confidence.

Torres, Gil↗
NASA NTRS2022

FY22 X-Hab: Rochester Institute of Technology: Gamification and Performance-Based Monitoring of Sensorimotor Training Activities

Proprioception is the body’s ability to sense movement, action, and location. Proprioceptive training is crucial for astronauts because humans balance system, cognitive functions, and muscle tone heavily rely on Earth’s gravity. The goal of this project is to create a training system that strengthens those characteristics to ensure astronauts not only maintain their coordination and cognitive skills, but improve upon them as well. Furthermore, the system’s live biofeedback provides astronauts with sensorimotor feedback to correct any balance dysfunction. The completed system prototype and data collected from it will support NASA’s knowledge base about the use of gamification for sensorimotor training activities.

Griffin Brown↗
NASA NTRSDate not supplied

Increased Treadmill Running and Lower-Body Resistance Training Intensity May Attenuate Aerobic Capacity Loss During Spaceflight

BACKGROUND: In-flight exercise training is the primary countermeasure to mitigate spaceflight-induced decrements in strength and aerobic capacity. Despite high-quality exercise capabilities and prescriptions aboard the International Space Station (ISS), most astronauts experience strength and aerobic capacity loss; however, significant individual variability exists. Recent findings suggest that greater in-flight running (i.e., distance per session) and lower-body resistance exercise intensity (i.e., load relative to body weight) help preserve strength. Whether similar variables contribute to the preservation of aerobic capacity during spaceflight is unknown. METHODS: A NASA database of exercise performance metrics was used to identify long-duration (≥ 90 days) ISS crew who completed pre- and post-flight aerobic capacity testing (VO2peak, cycle ergometry, n = 44). “Responders” (R) were classified as those that maintained or improved VO2peak (n = 13; 1.4 ± 3.3%), and “non-responders” (NR) were those with the most severe decrements (n = 13; -20.0 ± 3.4%) from pre- to post-flight. In-flight exercise records (cycling, treadmill, resistance exercise) were aggregated across flight weeks. Independent t-tests or Mann-Whitney U tests were used to compare training variables (i.e., frequency, time, intensity, volume) between R and NR. RESULTS: Consistent with the variables contributing to strength protection, in-flight running distance (R: 5.0 ± 0.7; NR: 4.2 ± 1.0 km/session, p = 0.020) and lower-body resistance exercise intensity (R: 110.9 ± 27.3; NR: 91.7 ± 16.5% body weight, p = 0.040) were greater in R than NR. Active running time was also greater in R than NR (R: 101.1 ± 20.4; NR: 82.3 ± 23.5 min/wk; p = 0.039). However, no group differences in cycling frequency, intensity, or time, as well as upper body or core resistance exercise intensity, volume, or frequency were observed. CONCLUSIONS: Although physiologically distinct stimuli, the exercise training variables contributing to the preservation of both strength and aerobic capacity during microgravity exposure appear to be consistent. Increased running time and distance, but not cycling, combined with higher-intensity lower-body resistance exercise, may help mitigate losses in both aerobic capacity and strength during long-duration spaceflight.

Alyssa N Varanoske↗
NASA NTRSDate not supplied

Probabilistic Calibration of Expensive Models using Efficiently Trained Surrogates

Calibration of computational models in the presence of uncertainty is often cast as a Bayesian inference problem and solved via sampling methods, e.g., Markov chain Monte Carlo. When the computational model is expensive, this task becomes intractable due to the large number of samples required to accurately estimate the posterior distribution of the calibration parameters. A popular solution to this problem is to use machine learning to develop a faster-to-evaluate, lower-fidelity substitute for the original model to serve as a surrogate while solving the inference problem. Although considered an offline cost, generating training data to construct this surrogate model can still be an expensive task in practice. An active learning algorithm is presented that focuses training on improving surrogate accuracy specifically in and around the bulk of the posterior distribution, as this is where the model is exercised during calibration. Candidate samples are drawn from families of distributions related to an approximation of the posterior. The sample maximizing predictive variance is then selected for evaluation by the original computational model, yielding a label for the training point. Iterating this approach increases efficiency relative to space filling designs (e.g., Latin hypercube sampling) by avoiding low probability points. Practical considerations are discussed, including the benefits of using a sequential Monte Carlo sampling approach, convergence heuristics, and the importance of both exploration and exploitation given that the true posterior is unknown a priori.

uncertainty quantification↗
NASA NTRSDate not supplied

Research Needs for Developing Qualification Standards for Flight Simulation Training Devices with Head Mounted Displays

This presentation discusses research needs to support evidence-based qualification standards for Flight Simulation Training Devices (FSTDs) with head mounted displays (HMDs). Currently, the FAA and EASA have no qualification standards specifically for FSTDs using HMDs and extended reality (XR) visuals, and existing qualification standards may not be sufficient. Special Conditions can be prescribed for the evaluation and qualification of individual devices, but these Special Conditions need to be assessed and adjusted on a case-by-case basis resulting in an elaborate process. Furthermore, test tolerances used in Special Conditions are often based on conventional visual systems and may not be adequate. This presentation first identifies gaps in the current knowledge of using HMDs in pilot training, and, more specifically, using HMDs in the training of manual aircraft control with motion cues. Next, research is proposed to help close these gaps, inform the development of qualification criteria and tolerances, and guide efforts to update regulations. The Vertical Motion Simulator (VMS) at NASA Ames Research Center, with its large motion capability, is currently undergoing significant upgrades including a state-of-the-art out-the-window visual system. In addition, an HMD capability will be introduced to use in conjunction with the conventional visual system. With its long history of setting standards in the simulation industry, the VMS is the ultimate tool to conduct research that could help accelerate the development of standards for the use of HMDs in FSTDs. The goal of the talk is ultimately to spark a discussion about the research needs and priorities to best serve the development of new qualification standards.

XR↗
DOE OSTI2015

The Science Training Program for Young Italian Physicists and Engineers at Fermilab

Since 1984 Fermilab has been hosting a two-month summer training program for selected undergraduate and graduate Italian students in physics and engineering. Building on the traditional close collaboration between the Italian National Institute of Nuclear Physics (INFN) and Fermilab, the program is supported by INFN, by the DOE and by the Scuola Superiore di Sant`Anna of Pisa (SSSA), and is run by the Cultural Association of Italians at Fermilab (CAIF). This year the University of Pisa has qualified it as a “University of Pisa Summer School”, and will grant successful students with European Supplementary Credits. Physics students join the Fermilab HEP research groups, while engineers join the Particle Physics, Accelerator, Technical, and Computing Divisions. Some students have also been sent to other U.S. laboratories and universities for special trainings. The programs cover topics of great interest for science and for social applications in general, like advanced computing, distributed data analysis, nanoelectronics, particle detectors for earth and space experiments, high precision mechanics, applied superconductivity. In the years, over 350 students have been trained and are now employed in the most diverse fields in Italy, Europe, and the U.S. In addition, the existing Laurea Program in Fermilab Technical Division was extended to the whole laboratory, with presently two students in Master’s thesis programs on neutrino physics and detectors in the Neutrino Division. And finally, a joint venture with the Italian Scientists and Scholars North-America Foundation (ISSNAF) provided this year 4 professional engineers free of charge for Fermilab. More details on all of the above can be found below.

Barzi, Emanuela↗
DOE OSTI2015

Neutron Source Facility Training Simulator Based on EPICS

A plant operator training simulator is developed for training the plant operators as well as for design verification of plant control system (PCS) and plant protection system (PPS) for the Kharkov Institute of Physics and Technology Neutron Source Facility. The simulator provides the operator interface for the whole plant including the sub-critical assembly coolant loop, target coolant loop, secondary coolant loop, and other facility systems. The operator interface is implemented based on Experimental Physics and Industrial Control System (EPICS), which is a comprehensive software development platform for distributed control systems. Since its development at Argonne National Laboratory, it has been widely adopted in the experimental physics community, e.g. for control of accelerator facilities. This work is the first implementation for a nuclear facility. The main parts of the operator interface are the plant control panel and plant protection panel. The development involved implementation of process variable database, sequence logic, and graphical user interface (GUI) for the PCS and PPS utilizing EPICS and related software tools, e.g. sequencer for sequence logic, and control system studio (CSS-BOY) for graphical use interface. For functional verification of the PCS and PPS, a plant model is interfaced, which is a physics-based model of the facility coolant loops implemented as a numerical computer code. The training simulator is tested and demonstrated its effectiveness in various plant operation sequences, e.g. start-up, shut-down, maintenance, and refueling. It was also tested for verification of the plant protection system under various trip conditions.

Park, Young Soo↗
DOE OSTI2017

Trip optimization system and method for a train

A system for operating a train having one or more locomotive consists with each locomotive consist comprising one or more locomotives, the system including a locator element to determine a location of the train, a track characterization element to provide information about a track, a sensor for measuring an operating condition of the locomotive consist, a processor operable to receive information from the locator element, the track characterizing element, and the sensor, and an algorithm embodied within the processor having access to the information to create a trip plan that optimizes performance of the locomotive consist in accordance with one or more operational criteria for the train.

42 ENGINEERING↗
DOE OSTI2018

International MNSR Training Center in Ghana Provides HEU Removal Expertise

Building on our years of cooperation, the United States and Ghana worked together to construct the International MNSR Training Center (IMTC) at the National Nuclear Research Institute of the Ghana Atomic Energy Commission (NNRI/GAEC) in Accra, Ghana. Here, the IMTC was used for training operators in Ghana to discharge the reactor’s HEU core, load it into a shielded transportation cask, and secure the cask for transport. Moving forward, the IMTC will be used to train personnel from the remaining countries with MNSRs to conduct these operations.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2022

Developing a New Criticality Safety Hands-On Training Utilizing ZPPR Plates

Nuclear criticality safety is an extremely important part of the work at Los Alamos National Laboratory (LANL). As part of the work LANL performs to continue to keep criticality safety a top priority, LANL has developed and regularly teaches nuclear criticality safety training classes for both the United States Department of Energy Nuclear Criticality Safety Program as well as internal trainings for LANL employees. A portion of the training classes is comprised of hands-on demonstrations, where students get the opportunity to handle special nuclear material at the National Criticality Experiments Research Center (NCERC). One hands-on demonstration uses the “Class foils,” thin HEU foils which are stacked with lucite moderator plates. A hand-stack is performed until the multiplication reaches the “three-quarters rule,” where the demonstration is continued remotely on a vertical lift assembly up until the system is critical. This hands-on demonstration eventually achieves a critical configuration and follows the ANS-1 guidelines on an approach to critical. Another hands-on demonstration involves handling clad plutonium and neptunium spheres, and follows procedures using criticality safety evaluations to ensure that the hands-on demonstrations remain subcritical.This hands-on demonstration also involves the use of polyethylene shells around the plutonium sphere to demonstrate how additional reflector increases the criticality of a system. This paper is focused on developing a new hands-on demonstration using Zero Power Physics Reactor (ZPPR) plates. This new hands-on demonstration will follow the ANS-8 standards as it is not desired to achieve criticality with the ZPPR plates during the hands-on demonstration. A hands-on demonstration using multiple plutonium parts will likely be more applicable to personnel who handle plutonium on a daily basis, such as LANL glovebox operators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2021

Development of MCNP Training Modules for Safeguards Practitioners [Abstract]

The Monte-Carlo N-Particle (MCNP) software developed at LANL is the most widely used neutron transport code in the world. It is an essential tool for a variety of applications including detector development and design, nuclear fuel burnup simulation, criticality safety, and nondestructive assay system optimization. For this reason, it is indispensable within the safeguards and materials control & accountability (MC&A) communities. Multiple MCNP training courses have been created and taught over the last several decades by the MCNP development team at LANL, however there are no existing courses that cover specialized topics considered fundamental to NDA and safeguards models. To fill this gap, the MCNP team and Safeguards Science and Technology group at LANL have co-created a set of training modules customized to meet the specialized needs of the safeguards and MC&A communities. The basic modules cover concepts such as NDA system optimization, He-specific and other capture tallies, and tools for improved theoretical understanding. An advanced module was also created to cover topics including variance reduction for active interrogation simulations, use of the LANL MCNPTools post-processor, PTRAC (particle tracking) and list-mode data simulations, and fuel burnup simulations. The training modules teach to the latest and most state-of-the-art MCNP features and tools released by the development team at LANL and are intended to be taught jointly by the developers and safeguards experts. Ultimately, we hope that creation of these modules will serve to capture and convey the safeguards modeling and MCNP expertise at LANL, and that we will be able to share the modules more broadly with the MC&A and safeguards communities.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2024

Smart Building Technology Training Modules for Academic and Professional Education

Smart building technologies are a new suite of resources that improve building energy efficiency and resilience, reduce carbon emissions, and provide load flexibility to the grid. However, in both college curricula and building professionals’ continuing education, there is a lack of systematic instruction on smart building technologies–topics that include smart building concepts, key components, smart building controls, “Internet of Things” (IoT) devices, and how to integrate multiple energy systems including distributed energy resources (DER). This major gap in smart building education prevents stakeholders from understanding and adopting smart building technologies in building design and operations. Slipstream leads a DOE-funded project developing a semester-long smart building curriculum for college students and adapting the contents into 16 training videos for building professionals and the general public. The education and training cover the drivers and benefits of smart building technologies, key building energy systems, the latest sensor technologies and IoT devices, and focus on topics related to smart building controls (i.e., energy management information systems, smart building control platforms, cybersecurity, grid-interactive-efficient buildings (GEBs), smart building control methods, and occupant-centric control. This paper describes the project approach, provides outlines of the training materials, and identifies lessons learned in creating the content. We also suggest ways to scale the instruction of smart building concepts to empower the workforce to accelerate the adoption of smart building technologies in the real world.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2024

Challenges in Training PINNs: A Loss Landscape Perspective

This paper explores challenges in training Physics Informed Neural Networks (PINNs), emphasizing the role of the loss landscape in the training process. We examine difficulties in minimizing the PINN loss function, particularly due to ill conditioning caused by differential operators in the residual term. We compare gradient-based optimizers Adam, L-BFGS, and their combination Adam+L-FGS, showing the superiority of Adam+L-BFGS, and introduce a novel secondorder optimizer, NysNewton-CG (NNCG), which significantly improves PINN performance. Theoretically, our work elucidates the connection between ill-conditioned differential operators and ill-conditioning in the PINN loss and shows the benefits of combining first- and second-order optimization methods. Our work presents valuable insights and more powerful optimization strategies for training PINNs, which could improve the utility of PINNs for solving difficult partial differential equations.

Rathore, Pratik↗
DOE OSTI2026

Lessons learned from the development and implementation of a workforce training curriculum for advanced controls for high performance HVAC systems

Over the past decade, academic research on advanced controls has slowly transitioned into new software platforms, giving rise to various companies developing and deploying these innovative products, including solutions for light commercial HVAC systems. However, the current workforce remains widely unprepared to install, maintain and operate these systems, particularly complex software-based control platforms, as most workforce training programs still focus on traditional building automation for large commercial buildings. This paper presents the development and piloting of curriculum for three key types of professionals: ● Technicians (trade-level): installing and maintaining modern high-performance HVAC systems and controls ● Programmers (undergrad-level): developing and implementing advanced controls ● Engineers and energy professionals (undergrad/grad-level): managing and evaluating system performance We share details of the material developed including training videos, open-source software, instruction manuals. We also present the results of a pilot implementation of the training materials with real students.

Casillas, Armando↗
DOE OSTI2017

Electric train energy consumption modeling

For this paper we develop an electric train energy consumption modeling framework considering instantaneous regenerative braking efficiency in support of a rail simulation system. The model is calibrated with data from Portland, Oregon using an unconstrained non-linear optimization procedure, and validated using data from Chicago, Illinois by comparing model predictions against the National Transit Database (NTD) estimates. The results demonstrate that regenerative braking efficiency varies as an exponential function of the deceleration level, rather than an average constant as assumed in previous studies. The model predictions are demonstrated to be consistent with the NTD estimates, producing a predicted error of 1.87% and -2.31%. The paper demonstrates that energy recovery reduces the overall power consumption by 20% for the tested Chicago route. Furthermore, the paper demonstrates that the proposed modeling approach is able to capture energy consumption differences associated with train, route and operational parameters, and thus is applicable for project-level analysis. The model can be easily implemented in traffic simulation software, used in smartphone applications and eco-transit programs given its fast execution time and easy integration in complex frameworks.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Operation of helium sub-atmospheric multistage cryogenic centrifugal compressor trains: Part 1 – Steady state modeling and speed ratio selection

Helium cryogenic systems which can provide cooling below the normal boiling point of helium (approximately 4.2 K) are often required by superconducting radio-frequency niobium resonators utilized in modern high-energy particle accelerators. Achieving temperatures below 4.2 K generally involves operating a cryogenic vessel with liquid helium under sub-atmospheric conditions, thereby lowering the saturation pressure and corresponding saturation temperature. Over the last several decades, multi-stage cryogenic centrifugal compressor trains (CC’s) have been operated efficiently and reliably within large-scale cryogenic systems to continuously evacuate helium vapor generated by a device within the vessel, maintaining sub-atmospheric conditions in the vessel while pressurizing the return vapor to above atmospheric conditions. Traditionally, these CC systems have been operated using empirically derived control philosophies and insight gathered from previous operational experience. Recent efforts at the Facility for Rare Isotope Beams (FRIB) have been aimed at the development of a theoretical basis to characterize the operation of multi-stage cryogenic centrifugal compressor train and utilizing predictive model results to generate control parameters. The objective of this research was identifying operational points which adequately balance cryogenic system efficiency, stability, and overall ease of operation. Furthermore, this manuscript provides an overview of the predictive model development, characterization of the FRIB cryogenic centrifugal compressors and implementation of the predicted performance results during steady-state system operation.

Compressor train control↗
DOE OSTI2016

Prostate Brachytherapy Case Volumes by Academic and Nonacademic Practices: Implications for Future Residency Training

Purpose: The use of prostate brachytherapy has continued to decline in the United States. We examined the national practice patterns of both academic and nonacademic practices performing prostate brachytherapy by case volume per year to further characterize the decline and postulate the effect this trend might have on training the next generation of residents. Methods and Materials: Men diagnosed with prostate cancer who had undergone radiation therapy in 2004 to 2012 were identified. The annual brachytherapy case volume at each facility was determined and further categorized into ≤12 cases per year (ie, an average of ≤1 cases per month), 13 to 52 cases per year, and ≥53 cases per year (ie, an average of ≥1 cases per week) in academic practices versus nonacademic practices. Results: In 2004 to 2012, academic practices performing an average of ≤1 brachytherapy cases per month increased from 56.4% to 73.7%. In nonacademic practices, this percentage increased from 60.2% to 77.4% (P<.0001 for both). Practices performing an average of ≥1 cases per week decreased among both academic practices (from 6.7% to 1.5%) and nonacademic practices (from 4.5% to 2.7%). Conclusions: Both academic and nonacademic radiation oncology practices have demonstrated a significant reduction in the use of prostate brachytherapy from 2004 to 2012. With the case volume continuing to decline, it is unclear whether we are prepared to train the next generation of residents in this critical modality.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2026

Rank-Limiting Strategies for Optimizing Tensor-Train Finite-Difference Time-Domain Simulations

We introduce rank-limiting strategies to optimize tensor-train decompositions for three-dimensional finite-difference time-domain simulations using the relationship between the tensors and their specific dimensionality. These include the use of hard caps on the inner ranks of the tensor train decomposition and the use of a group rounding algorithm taking into account all field components simultaneously. Here, several numerical examples are considered to verify the efficacy of the proposed optimization strategies.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗