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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 NTRS2010

Gait Training Improves Performance in Healthy Adults Exposed to Novel Discordant Conditions

After they return to Earth, astronauts experience sensorimotor disturbances that disrupt their ability to walk. We have previously shown that training with a variety of sensorimotor adaptive challenges enhances the capability of adapting to novel sensorimotor conditions. We are currently developing a sensorimotor adaptability (SA) training program designed to facilitate recovery of function after gravitational transitions. The purpose of this study was to determine whether trained subjects could transfer learned skills from one discordant visuo-proprioceptive environment to another. During three sessions, subjects walked at 2.5 km/h on a treadmill mounted on a motion base platform. Ten subjects trained with a combination of lateral treadmill translation and superimposed sinusoidal lateral optic flow that was presented on a large screen positioned in front of them. Ten controls completed the same training schedule while viewing only the forward optic flow with no visual or physical oscillation. Twenty minutes after the final training session, all subjects completed a 2-minute trial with a novel combination of visual and treadmill roll perturbations not previously experienced during the training (Transfer Test). Compared to the untrained group, participants who received SA training showed faster reaction times and, based on a composite score derived from stride frequency, heart rate, and reaction time, an overall enhanced performance. Our results showed that an SA training program can improve overall walking performance when subjects are exposed to novel incongruent sensory environments. This training has application for both enhancing adaptive responses in astronauts and reducing fall and injury risk in the elderly.

Batson, Crystal D.↗
NASA NTRS2010

NASA-Navy Telemedicine: Autogenic Feedback Training Exercises for Motion Sickness

Airsickness is the most significant medical condition affecting naval aviation training. A 2001 study showed that airsickness was reported in 81% of naval aviation students and was associated with 82% of below average flight scores. The cost to a single training air-wing was over $150,000 annually for fuel and maintenance costs alone. Resistent cases are sent to the Naval Aerospace Medical Institute (NAMI) for evaluation and desensitization in the self-paced airsickness desensitization (SPAD) program. This approach is 75% successful, but can take up to 8 weeks at a significant travel cost. NASA Ames Research Center's Autogenic Feedback Training Exercises (AFTE) uses physiological and biofeedback training for motion sickness prevention. It has a remote capability that has been used from Moffett Field, CA to Atlanta, GA . AFTE is administered in twelve (30-minute) training sessions. The success rate for the NASA AFTE program has been over 85%. Methods: Implementation Phases: Phase I: Transfer NASA AFTE to NAMI; NASA will remotely train aviation students at NAMI. Phase II: NAMI-centered AFTE application with NASA oversight. Phase III: NAMI-centered AFTE to remotely train at various Navy sites. Phase IV: NAMI to offer Tri-service application and examine research opportunities. Results: 1. Use available telemedicine connectivity between NAMI and NASA. 2. Save over $2,000 per student trained. 3. Reduce aviation training attrition. 4. Provide standardization of multi-location motion sickness training. 5. Future tri-service initiatives. 6. Data to NASA and Navy for QA and research opportunities.

Acromite, Michael T.↗
NASA NTRS2012

Integrated Resistance and Aerobic Training Maintains Cardiovascular and Skeletal Muscle Fitness During 14 Days of Bed Rest

Background: Known incompatibilities exist between resistance and aerobic training. Of particular importance are findings that concurrent resistance and aerobic training reduces the effectiveness of the resistance training and limits skeletal muscle adaptations (example: Dudley & Djamil, 1985). Numerous unloading studies have documented the effectiveness of resistance training alone for the maintenance of skeletal muscle size and strength. However the practical applications of those studies are limited because long ]duration crew members perform both aerobic and resistance exercise throughout missions/spaceflight. To date, such integrated training on the International Space Station (ISS) has not been fully effective in the maintenance of skeletal muscle function. Purpose: The purpose of this study was to evaluate the efficacy of high intensity concurrent resistance and aerobic training for the maintenance of cardiovascular fitness and skeletal muscle strength, power and endurance over 14 days of strict bed rest. Methods: 9 subjects (8 male and 1 female; 34.5 +/- 8.2 years) underwent 14 days of bed rest with concurrent training. Resistance and aerobic training were integrated as shown in table 1. Days that included 2 exercise sessions had a 4-8 hour rest between exercise bouts. The resistance training consisted of 3 sets of 12 repetitions of squat, heel raise, leg press and hamstring curl exercise. Aerobic exercise consisted of periodized interval training that included 30 sec, 2 min and 4 min intervals alternating by day with continuous aerobic exercise.

Ploutz-Snyder, Lori↗
NASA NTRS2015

Portable Virtual Training Units

The Mission Operations Lab initiated a project to design, develop, deliver, test, and validate a unique training system for astronaut and ground support personnel. In an effort to keep training costs low, virtual training units (VTUs) have been designed based on images of actual hardware and manipulated by a touch screen style interface for ground support personnel training. This project helped modernized the training system and materials by integrating them with mobile devices for training when operators or crew are unavailable to physically train in the facility. This project also tested the concept of a handheld remote device to control integrated trainers using International Space Station (ISS) training simulators as a platform. The portable VTU can interface with the full-sized VTU, allowing a trainer co-located with a trainee to remotely manipulate a VTU and evaluate a trainee's response. This project helped determine if it is useful, cost effective, and beneficial for the instructor to have a portable handheld device to control the behavior of the models during training. This project has advanced NASA Marshall Space Flight Center's (MSFC's) VTU capabilities with modern and relevant technology to support space flight training needs of today and tomorrow.

Malone, Reagan↗
NASA NTRSDate not supplied

Neo – Mars Adaptive Training Integrative Knowledge System (MATRIKS) to Improve Operational Performance and its Neural Basis for Spaceflight

With prolonged mission durations, spaceflight crews will become increasingly dependent on onboard technologies for knowledge acquisition and maintenance. It is expected that not all skills and knowledge required for these missions can be retained and retrieved based on pre-mission training alone. Limited and delayed communication will significantly constrain support from Mission Control and crews will increasingly rely on autonomous onboard technologies to successfully perform post-landing operations. With the present project we will target NASA’s particular interest in developing and assessing an adaptive, just-in-time countermeasure that will consolidate and improve skills that are most relevant to space flight operations. To achieve this aim, NASA established a Virtual NASA Specialized Center of Research (VNSCOR)referred to as “Mars Adaptive Training Integrative Knowledge System (MATRIKS)”, comprising the following three projects: (1) “Trinity–Multi-Environment Virtual Training for Long Duration Exploration Missions”, PI: A. Anderson (UC Boulder); (2) “Morpheus–A Haptic Sensory Supplement to Optimize In-Flight Adaptive Training for Human Control of Spacecraft Robotic Arms”, PI: S. Robinson, UC Davis); and the present project “Neo–Adaptive Training integrative knowledge System to Improve Operational Performance and its Neural Basis for Spaceflight” (UPenn, PI: A.C. Stahn). Neo leverages a validated workstation called 6DF that simulates a rendezvous and docking maneuver using real spacecraft flight dynamics. It is designed to (1) train and improve sensorimotor skills relevant for inflight and post-landing operational tasks; (2) feature an autonomous and adaptive training approach that does not rely on feedback from flight operations on the ground; (3) maximize the transfer of mission-relevant motor skills; (4) allow the assessment of the neural circuitry underlying the task; and (5) deliver the training in a motivating and meaningful way to astronauts. Neocomprises two overarching aims: First, we will identify the neural circuitry underlying spaceflight relevant tasks by performing a subset of the 6DFtaskduring functional magnetic resonance imaging (MRI)in a total of up to N=30 subjects with varying levels of 6DF training experience. Second, as part of the above-mentioned VNSCOR MATRIKS the proposed 6DF autonomous intelligent tutor system will be integrated in an additive manner with a haptic feedback intervention (Morpheus), and a multi-environment virtual trainer(Trinity).It is expected that Neo, Morpheus and Trinity mutually complement each other to facilitate an effective countermeasure tool to acquire and retain operational skills that are critical for exploration class missions. To assess the efficacy of this combined effort, the VNSCOR MATRIKS will collect data inN=16 crew members in one HERA campaign of 45 days duration with N=16 crew members(four missions with N=4 crew member seach).The primary goal is to identify changes in operational performance as assessed by NASA’s simulator of Canadarm2 operations, i.e., Robotic On-board Trainer (ROBoT-r) in response to MATRIKS. As part of Neo we will also identify if, and to what extent MATRIKS will promote transfer to general cognitive performance (Cognition battery), distinctive visuo-spatial tasks critical for telerobotic tasks (Spatial Cognition battery), and affect brain structural changes and the neural circuitry of key brain networks expected to be relevant for spaceflight-related performance. At the conclusion of the research, we will have defined and demonstrated the use of a neuroscience-based, adaptive training integrative knowledge system to potentially mitigate visuo-spatial and sensorimotor brain changes associated with prolonged isolation and confinement to reduce the likelihood or impact of potential decrements in human performance capabilities during long-duration space missions. The expected significance of this 4-year project relates to its relevance for facilitating effective countermeasure tools to acquire and retain operational skills that are critical for exploration class missions. This will support the development of necessary countermeasures and technologies in support of human space exploration, focusing on mitigating operational performance risks.

A C Stahn↗
NASA NTRSDate not supplied

Crew Medical Training to Progressively Enable EIMO

Background. Onboard medical capabilities have greatly expanded over the history of the US space program. Newly identified space-related medical conditions, technological advances, and longer mission durations have led to an increasing need for on-demand medical expertise. Lengthy communications delays, lack of resupply and evacuation opportunities on exploration-class missions place an ever-increasing burden on the crew to provide medical care. Having adequate knowledge, skills, and abilities (KSA) available is an essential component of successful Earth Independent Medical Operations (EIMO). Without appropriate crew training and KSA, cutting-edge medical equipment has little value. Presumably, the crew will include a qualified physician; however, if the physician is incapacitated, a non-physician crew medical officer (CMO) will be needed. While more crew time is needed for medical training, there will be concomitant increases in preflight training demands for vehicle system management, operations, science, and contingencies. In truly independent operations, onboard resources such as just-in-time training, mixed reality, decision support tools, and AI-enabled chatbot “consultants” will be needed to augment KSA. Overview. Because of crew time constraints, topical priorities must be determined for preflight training. Curricula should be developed that emphasize management of conditions with relatively high incidence and morbidity/mortality. Defining the required KSA levels to treat each condition is essential, but all crewmembers should have basic lifesaving skills. Procedural and diagnostic training on live patients and simulators should be prioritized over classroom lectures. Crews must be trained with onboard equipment, resources, mixed reality, and AI-based decision support tools. Mission simulations should include medical problems with/without ground support and with appropriate communication delays. Certification guidelines for each level of KSA must be established. Skills rapidly decay for non-physician CMO’s; both pre-flight and in-flight refresher training will be needed. During spaceflight just-in-time training, simulations, and onboard CME with crew physician can help retain skills. Discussion. Medical technology, simulation design, mixed reality, and AI are advancing at a dizzying rate. Recognizing the severe constraints on crew time, it is critical that astronaut training is highly efficient and adapted to keep pace with new innovations both pre-flight and during exploration missions. These challenges will be discussed during this panel session.

Jay Lemery↗
DOE OSTI2020

FLEET: Flexible Efficient Ensemble Training for Heterogeneous Deep Neural Networks

Parallel training of an ensemble of Deep Neural Networks (DNN) on a cluster of nodes is an effective approach to shorten the process of neural network architecture search and hyper-parameter tuning for a given learning task. Prior efforts have shown that data sharing, where the common preprocessing operation is shared across the DNN training pipelines, saves computational resources and improves pipeline efficiency. Data sharing strategy, however, performs poorly for a heterogeneous set of DNNs where each DNN has varying computational needs and thus different training rate and convergence speed. This paper proposes FLEET, a flexible ensemble DNN training framework for efficiently training a heterogeneous set of DNNs. We build FLEET via several technical innovations. We theoretically prove that an optimal resource allocation is NP-hard and propose a greedy algorithm to efficiently allocate resources for training each DNN with data sharing. We integrate data-parallel DNN training into ensemble training to mitigate the differences in training rates and introduce checkpointing into this context to address the issue of different convergence speeds. Experiments show that FLEET significantly improves the training efficiency of DNN ensembles without compromising the quality of the result.

Guan, Hui↗
DOE OSTI2022

Current Status of the DOE/NNSA Nuclear Criticality Safety Program Hands-On Criticality Safety Training

The U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) Nuclear Criticality Safety Program (NCSP) has conducted two-week Nuclear Criticality Safety (NCS) Practitioner courses since 2011 to support the training and qualification of new NCS staff. The course was developed in accordance with the American National Standard Institute/American Nuclear Society (ANSI/ANS) standard for NCS training and qualifications (ANSI/ANS-8.26-2007). In 2013, an NCS Manager’s course was developed for process supervisors, managers, regulators, and other professionals with NCS-related responsibilities. This course was revised in 2019 for Criticality Safety Officers (CSOs) based on an NCSP Criticality Safety Support Group tasking (2018-01). This course was piloted at the Nevada Field Office and the National Criticality Experiments Research Center (NCERC) in June 2021. These courses consist of the following training components: classroom education, facility training, and hands-on subcritical and critical experiments training. The two-week Practitioner course offers a week of classroom training, with practical workshops and exercises focused on teaching students how to perform an NCS evaluation. The second week of training involves hands-on critical and subcritical experiments and measurements. The first week is offered in Las Vegas, Nevada, at the DOE Nevada Field Office or the National Atomic Testing Museum. Depending on the student’s clearance level, the second week is offered at Sandia National Laboratory (SNL) (uncleared and L-cleared students) or at the National Criticality Experiments Research Center (Q-cleared students). The one-week Manager’s course is offered at SNL or NCERC, depending on clearance or interest, and includes classroom and hands- on critical and subcritical experiments and measurements. This paper provides an overview and status report for the DOE/NNSA NCSP training courses in NCS and to provide information about future course offerings. This paper discusses the challenges associated with executing the training courses during the COVID-19 pandemic. The 2-week Practitioner and 1-week manager courses are currently offered twice per year and adjustments are made based upon demand.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Current Status of the DOE/NNSA Nuclear Criticality Safety Program Hands-On Criticality Safety Training [Abstract]

The U.S. Department of Energy/National Nuclear Security Administration (DOE/NNSA) Nuclear Criticality Safety Program (NCSP) has conducted two-week Nuclear Criticality Safety (NCS) Practitioner courses since 2011 to support the training and qualification of new NCS staff. The course was developed in accordance with the American National Standard Institute/American Nuclear Society (ANSI/ANS) standard for NCS training and qualifications (ANSI/ANS-8.26-2007). In 2013, an NCS Manager’s course was developed for process supervisors, managers, regulators, and other professionals with NCS-related responsibilities. This course was revised in 2019 for Criticality Safety Officers (CSOs) based on an NCSP Criticality Safety Support Group tasking (2018-01). This course was piloted at the Nevada Field Office and the National Criticality Experiments Research Center (NCERC) in June 2021. These courses consist of the following training components: classroom education, facility training, and hands-on subcritical and critical experiments training. The two-week Practitioner course offers a week of classroom training, with practical workshops and exercises focused on teaching students how to perform an NCS evaluation. The second week of training involves hands-on critical and subcritical experiments and measurements. The first week is offered in Las Vegas, Nevada, at the DOE Nevada Field Office or the National Atomic Testing Museum. Depending on the student’s clearance level, the second week is offered at Sandia National Laboratory (SNL) (uncleared and L-cleared students) or at the National Criticality Experiments Research Center (Q-cleared students). The one-week Manager’s course is offered at SNL or NCERC, depending on clearance or interest, and includes classroom and hands-on critical and subcritical experiments and measurements. This paper provides an overview and status report for the DOE/NNSA NCSP training courses in NCS and to provide information about future course offerings. This paper will also discuss the challenges associated with executing the training courses during the COVID-19 pandemic. The 2-week Practitioner and 1-week manager courses are currently offered twice per year and adjustments are made based upon demand.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

On the Training and Generalization of Deep Operator Networks

Here, we present a novel training method for deep operator networks (DeepONets), one of the most popular neural network models for operators. DeepONets are constructed by two subnetworks, namely the branch and trunk networks. Typically, the two subnetworks are trained simultaneously, which amounts to solving a complex optimization problem in a high dimensional space. In addition, the nonconvex and nonlinear nature makes training very challenging. To tackle such a challenge, we propose a two-step training method that trains the trunk network first and then sequentially trains the branch network. The core mechanism is motivated by the divide-and-conquer paradigm and is the decomposition of the entire complex training task into two subtasks with reduced complexity. Therein the Gram–Schmidt orthonormalization process is introduced which significantly improves stability and generalization ability. On the theoretical side, we establish a generalization error estimate in terms of the number of training data, the width of DeepONets, and the number of input and output sensors. Numerical examples are presented to demonstrate the effectiveness of the two-step training method, including Darcy flow in heterogeneous porous media.

deep operator networks↗
DOE OSTI2025

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↗
NASA NTRSDate not supplied

Training Artemis Astronauts to Explore the Moon

To prepare for human exploration of the Moon in the mid-2020s, NASA is developing a program to provide geoscience and planetary science classroom training and geologic eld training to astronauts and other mission personnel. The Artemis geoscience training plan builds on a long legacy: NASA has trained astronauts in geology, planetary science, and geological eld work for more than 50 years. From ~1965-1972, the geology focus for astronauts included orbital Earth observations during the early NASA missions, but the endgame was preparation for the Apollo missions to the Lunar surface [see Phinney (2015), NASA-SP-2015-626; Lofgren et al.(2011);Schmitt, et al. (2011); and El Baz (2011), all from GSA SP483]. From the mid 1970s through2017, the primary purpose for geoscience training was to prepare astronauts to observe theEarth and document a wide variety of Earth processes from orbital platforms (Skylab, SpaceShuttle and International Space Station). Astronaut observations of Earth from the InternationalSpace Station remain an important activity today, but the prospect of exploration of the lunar surface through the Artemis Program lends additional purpose to the geoscience content and eld experiences that we provide the astronauts. We recently completed the geology and eld training with Astronaut Class 22 and are now planning both focused eld exercises and simulations for future Artemis crews, as well as the accompanying classroom lessons on lunar and planetary sciences. The training we offer extends beyond the astronaut office – we partner across operational and engineering organizations at NASA and provide eld work exercises and opportunities to personnel in the Flight Operations Directorate (responsible for the overall training and certification), as well as the exploration Extravehicular Activity Oce (developing spacewalk suits and tools). These partnerships contribute to building a common language and sense of purpose, and also help to ensure that NASA’s geoscience training and science operations are fully integrated with the teams and systems responsible for getting the next generation of astronauts to the surface of the Moon.

Artemis↗
NASA NTRSDate not supplied

Physics and Modeling of Quasi-Steady and Forced Shock Trains

The dual-mode scramjet is a propulsive concept for contemporary hypersonic vehicles promising flight cruise speeds upwards of Mach 5. Despite decades of development and a recent resurgence in interest, such engines are not routinely deployed for commercial or military purposes. Instead, they have been confined to the realm of ground-test research and limited one-off flight test missions. One of the key stumbling blocks on the critical path to deployment of a dual-mode scramjet is the optimal design and stable operation of the isolator, a typically constant area duct that separates the scramjet inlet from the combustor. During ramjet mode at Mach numbers from 3 to 5 and during the transition to scramjet mode at higher Mach numbers, the isolator contains the pseudo-shock. Necessary to meet the back-pressure in the combustor, the pseudo-shock is typically divided into a shock dominated shock train region followed by a turbulence driven mixing region. Fluctuations in combustor pressure due to vehicle maneuvering, throttling, or combustion instabilities drive the shock train to shift streamwise within the isolator. In response to sharp pressure fluctuations, the shock train is unable to stabilize within the isolator, and the scramjet unstarts by ejecting the shock train out the inlet. This work addresses three key gaps in understanding of the isolator pseudo-shock. The phenomenon is studied in three experimental facilities to investigate the quasi-steady and unsteady fluid dynamics of the shock train in fueled and un-fueled test conditions. The strong link between boundary layer morphology and shock train structure is studied in detail and used as a tool to evaluate the state-of-the-art definitions and models of the pseudo-shock. Identifying the limitations of these models, a new process driven flux-conserved pseudo-shock model is proposed. This provides a computationally efficient model grounded in the fluid dynamic mechanisms of the pseudo-shock with better predictive capability than the current state-of-the-art across all facilities tested. The unsteady behavior of the shock train is further investigated to identify and understand two distinct forced dynamic modes: mechanically induced, low-rate forcing and high-rate combustion driven unsteadiness that sheds upstream propagating shocks. The proposed process driven model is found to be effective in the the limiting case of high-rate combustion driven unsteadiness by evaluating the model in a shock stationary reference frame. Further analysis identifies the approximately rigid chain motion of the shock train structure to mechanically induced forcing. The time delay between the onset of forced dynamics is found to saturate to a constant value and maximum shock train speed along the isolator with respect to the perturbation rate of change. The result of this work provides dual-mode scramjet designers with improved tools and physical understanding to develop more efficient, robust, and capable propulsion systems for hypersonic vehicles.

JAXA↗
DOE OSTI2022

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↗
DOE OSTI2022

Training data selection for event classification in a highly variable environment

A problem of interest for nuclear nonproliferation is monitoring activities at nuclear facilities, where proliferation events may only take place a few times and often under variable conditions. Machine learning has revolutionized data analytics by enabling the use of measurable signatures to generate predictive models of facility operations. However, traditional methods for training these models require large, reliable data sets with labeled observations, a challenge for nonproliferation. Highly variable conditions further complicate this as events from training data may have occurred in conditions quite different from the event of interest. Our hypothesis is that when events occur in a highly variable environment, careful training data selection for each test event could outperform the standard approach of using all available training data. We developed a method to optimize training data selection for the given test event and applied it to predicting the power level of the High Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory. In this study, the reactor startup exhibits variability between occurrences due to natural variability in environmental conditions and operational procedures. Using a combination of analysis techniques, a similitude assessment was performed on data collected from HFIR to isolate clusters that were optimal for training a predictive model. Concepts such as dynamic time warping and Jaccard similarity were used in conjunction with clustering analysis. In order to validate this approach, the model was trained on every combination of unique training events and the predictive performance was compared to the performance using a subset of the training data selected by isolated clusters found through the similitude assessment.

Iyer, A↗
DOE OSTI2020

Final Evaluation Report for the Sandia National Laboratories Recorded Information Management Training Program

The Recorded Information Management (RIM) training and guidance materials were originally evaluated to determine the current status of the training program. Specifically, how and where are training materials implemented and are members of the workforce (MOWs) regularly accessing them. The evaluation highlighted that there is a breadth of training materials and guidance available; however, the materials are often outdated, point to old Sandia Policies, and/or are in outdated templates. Additionally, there is currently no single-entry point for training, meaning that training is available via the RIM homepage, TEDs, etc. This means that MOWs must search in various webpages or systems for training. To ensure RIM training materials are regularly updated and are accessible for MOWs it is recommended to assign one staff member to manage the program training materials.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2024

Optimizing Distributed Training on Frontier for Large Language Models

Large language models (LLMs) have demonstrated remarkable success as foundational models, benefiting various downstream applications through fine-tuning. Loss scaling studies have demonstrated the superior performance of larger LLMs compared to their smaller counterparts. Nevertheless, training LLMs with billions of parameters poses significant challenges and requires considerable computational resources. For example, training a one trillion parameter GPT-style model on 20 trillion tokens requires a staggering 120 million exaflops. This research explores efficient distributed training strategies to extract this computation from Frontier, the world's first exascale supercomputer. We enable and investigate various model and data parallel training techniques, such as tensor parallelism, pipeline parallelism, and sharded data parallelism, to facilitate training a trillion-parameter model on Frontier. We empirically assess these techniques and their associated parameters to determine their impact on memory footprint, communication latency, and GPU's computational efficiency. We analyze the complex interplay among these techniques and find a strategy to combine them to achieve high throughput through hyperparameter tuning. We have identified efficient strategies for training large LLMs of varying sizes through empirical analysis and hyperparameter tuning. For 22 Billion, 175 Billion, and 1 Trillion parameters, we achieved GPU throughputs of 38.38%, 36.14%, and 31.96%, respectively. For the training of the 175 Billion parameter model and the 1 Trillion parameter model, we achieved 100% weak scaling efficiency on 1024 and 3072 Mi250X GPUs, respectively. We also achieved strong scaling efficiencies of 89% and 87% for these two models. We trained these models only tens of iterations instead of training till completion.

Yin, Junqi↗
NASA NTRS1989

The flights before the flight - An overview of shuttle astronaut training

Space shuttle astronaut training is centered at NASA's Johnson Space Center in Houston, Texas. Each astronaut receives many different types of training from many sources. This training includes simulator training in the Shuttle Mission Simulator, in-flight simulator training in the Shuttle Training Aircraft, Extravehicular Activity training in the Weightless Environment Training Facility and a variety of lectures and briefings. Once the training program is completed each shuttle flight crew is well-prepared to perform the normal operations required for their flight and deal with any shuttle system malfunctions that might occur.

Sims, John T.↗