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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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At least 163 records · Page 9
All sourcesNASA NTRSDOE OSTI
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 OSTI2025

Fair Concurrent Training of Multiple Models in Federated Learning

Federated learning (FL) enables collaborative learning across multiple clients. In most FL work, all clients train a single learning task. However, the recent proliferation of FL applications may increasingly require multiple FL tasks to be trained simultaneously, sharing clients’ computing resources, which we call Multiple-Model Federated Learning (MMFL). Current MMFL algorithms use naïve average-based client-task allocation schemes that often lead to unfair performance when FL tasks have heterogeneous difficulty levels, as the more difficult tasks may need more client participation to train effectively. Furthermore, in the MMFL setting, we face a further challenge that some clients may prefer training specific tasks to others, and may not even be willing to train other tasks, e.g., due to high computational costs, which may exacerbate unfairness in training outcomes across tasks. We address both challenges by firstly designing FedFairMMFL, a difficulty-aware algorithm that dynamically allocates clients to tasks in each training round, based on the tasks’ current performance levels. We provide guarantees on the resulting task fairness and FedFairMMFL’s convergence rate. We then propose novel auction designs that incentivizes clients to train multiple tasks, so as to fairly distribute clients’ training efforts across the tasks, and extend our convergence guarantees to this setting. Here, we finally evaluate our algorithm with multiple sets of learning tasks on real world datasets, showing that our algorithm improves fairness by improving the final model accuracy and convergence speed of the worst performing tasks, while maintaining the average accuracy across tasks.

Federated learning↗
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.↗
NASA NTRS1990

Elevated central venous pressure: A consequence of exercise training-induced hypervolemia

Resting plasma volumes, and arterial and central venous pressures (CVP) were measured in 16 men before and after exercise training to determine if training-induced hypervolemia could be explained by a change in total vascular capacitance. In addition, resting levels of plasma vasopressin (AVP), atrial natriuretic peptide (ANP), aldosterone (ALD), and norepinephrine (NE) were measured before and after training. The same measurements of vacular volume, pressures, and plasma hormones were measured in 8 subjects who did not undergo exercise and acted as controls. The exercise training program consisted of 10 weeks of controlled cycle exercise for 30 min/d, 4 d/wk at 75 to 80 percent of maximal oxygen uptake (VO2max). A training effect was verified by a 20 percent increase in VO2max, a resting bradycardia, and a 370 ml (9 percent) increase in blood volume. Mean arterial blood pressure was unaltered by exercise training, but resting CVP increased. The percent change in blood volume from before to after training was linearly related to the percent change in CVP. As a consequence of elevations in both blood volume and CVP, the volume-to-pressure ratio was essentially unchanged following exercise training. Plasma AVP, ANP, ALD, and NE were unaltered. Results indicate that elevated CVP is a consequence of training-induced hypervolemia without alteration in total effective venous capacitance. This may represent a resetting of the pressure-volume stimulus-response relation for regulation of blood volume.

Convertino, Victor A.↗
NASA NTRS1995

Locomotion training of legged robots using hybrid machine learning techniques

In this study artificial neural networks and fuzzy logic are used to control the jumping behavior of a three-link uniped robot. The biped locomotion control problem is an increment of the uniped locomotion control. Study of legged locomotion dynamics indicates that a hierarchical controller is required to control the behavior of a legged robot. A structured control strategy is suggested which includes navigator, motion planner, biped coordinator and uniped controllers. A three-link uniped robot simulation is developed to be used as the plant. Neurocontrollers were trained both online and offline. In the case of on-line training, a reinforcement learning technique was used to train the neurocontroller to make the robot jump to a specified height. After several hundred iterations of training, the plant output achieved an accuracy of 7.4%. However, when jump distance and body angular momentum were also included in the control objectives, training time became impractically long. In the case of off-line training, a three-layered backpropagation (BP) network was first used with three inputs, three outputs and 15 to 40 hidden nodes. Pre-generated data were presented to the network with a learning rate as low as 0.003 in order to reach convergence. The low learning rate required for convergence resulted in a very slow training process which took weeks to learn 460 examples. After training, performance of the neurocontroller was rather poor. Consequently, the BP network was replaced by a Cerebeller Model Articulation Controller (CMAC) network. Subsequent experiments described in this document show that the CMAC network is more suitable to the solution of uniped locomotion control problems in terms of both learning efficiency and performance. A new approach is introduced in this report, viz., a self-organizing multiagent cerebeller model for fuzzy-neural control of uniped locomotion is suggested to improve training efficiency. This is currently being evaluated for a possible patent by NASA, Johnson Space Center. An alternative modular approach is also developed which uses separate controllers for each stage of the running stride. A self-organizing fuzzy-neural controller controls the height, distance and angular momentum of the stride. A CMAC-based controller controls the movement of the leg from the time the foot leaves the ground to the time of landing. Because the leg joints are controlled at each time step during flight, movement is smooth and obstacles can be avoided. Initial results indicate that this approach can yield fast, accurate results.

Simon, William E.↗
NASA NTRS1997

Autogenic-Feedback Training Exercise (AFTE) Method and System

The Autogenic-Feedback Training Exercise (AFTE) method of the present invention is a combined application of physiologic and perceptual training techniques. such as autogenic therapy and biofeedback. This combined therapy approach produces a methodology that is appreciably more effective than either of the individual techniques used separately. The AFTE method enables sufficient magnitude of control necessary to significantly reduce the behavioral and physiologic reactions to severe environmental stressors. It produces learned effects that are persistent over time and are resistant to extinction and it can be administered in a short period of time. The AFTE method may be used efficiently in several applications, among which are the following: to improve pilot and crew performance during emergency flying conditions; to train people to prevent the occurrence of nausea and vomiting associated with motion and sea sickness, or morning sickness in early pregnancy; as a training method for preventing or counteracting air-sickness symptoms in high-performance military aircraft; for use as a method for cardiovascular training, as well as for multiple other autonomic responses, which may contribute to the alleviation of Space Motion Sickness (SMS) in astronauts and cosmonauts; training people suffering from migraine or tension headaches to control peripheral blood flow and reduce forehead and/or trapezius muscle tension; training elderly people suffering from fecal incontinence to control their sphincter muscles; training cancer patients to reduce the nauseagenic effects of chemotherapy; and training patients with Chronic Intestinal Pseudo-obstruction (CIP).

Cowings, Patricia S.↗
NASA NTRS2000

What the Logs Can Tell You: Mediation to Implement Feedback in Training

The problem addressed by Mediation to Implement Feedback in Training (MIFT) is to customize the feedback from training exercizes by exploiting knowledge about the training scenario, training objectives, and specific student/teacher needs. We achieve this by inserting an intelligent mediation layer into the information flow from observations collected during training exercises to the display and user interface. Knowledge about training objectives, scenarios, and tasks is maintained in the mediating layer. A designer constraint is that domain experts must be able to extend mediators by adding domain-specific knowledge that supports additional aggregations, abstractions, and views of the results of training exercises. The MIFT mediation concept is intended to be integrated with existing military training exercise management tools and reduce the cost of developing and maintaining separate feedback and evaluation tools for every training simulator and every set of customer needs. The MIFT Architecture is designed as a set of independently reusable components which interact with each other through standardized formalisms such as the Knowledge Interchange Format (KIF) and Knowledge Query and Manipulation Language (KQML).

Maluf, David↗
NASA NTRS2000

SHARED VIRTUAL ENVIRONMENTS FOR COLLECTIVE TRAINING

Historically NASA has trained teams of astronauts by bringing them to the Johnson Space Center in Houston to undergo generic training, followed by mission-specific training. This latter training begins after a crew has been selected for a mission (perhaps two years before the launch of that mission). While some Space Shuttle flights have included an astronaut from a foreign country, the International Space Station will be consistently crewed by teams comprised of astronauts from two or more of the partner nations. The cost of training these international teams continues to grow in both monetary and personal terms. Thus, NASA has been seeking alternative training approaches for the International Space Station program. Since 1994 we have been developing, testing, and refining shared virtual environments for astronaut team training, including the use of virtual environments for use while in or in transit to the task location. In parallel with this effort, we have also been preparing applications for training teams of military personnel engaged in peacekeeping missions. This paper will describe the applications developed to date, some of the technological challenges that have been overcome in their development, and the research performed to guide the development and to measure the efficacy of these shared environments as training tools.

Loftin, R. Bowen↗
NASA NTRS2001

STS-104 Crew Training Clips

The crewmembers of STS-104, Commander Steven Lindsey, Pilot Charles Hobaugh, and Mission Specialists Michael Gernhardt, James Reilly, and Janet Kavandi, are seen during various stages of their training. Footage shows the following: (1) Water Survival Training at the Neutral Buoyancy Laboratory (NBL); (2) Rendezvous and Docking Training in the Shuttle Mission Simulator; (3) Training in the Space Station Airlock; (4) Training in the Virtual Reality Lab; (5) Post-insertion Operations in the Fixed Base Simulator; (6) Extravehicular Activity Training at the NBL; (7) Crew Stowage Training in the Space Station Mock-up Training Facility; and (8) Water Transfer Training in the Crew Compartment Trainer.

Source record↗
NASA NTRS1992

Influence of exercise training on the oxidative capacity of rat abdominal muscles

Our purpose was to determine if endurance exercise training would increase the oxidative capacity of the abdominal expiratory muscles of the rat. Accordingly, 9 male rats were subjected to an endurance training protocol (1 h/day, 6 days/week, 9 weeks) and 9 litter-mates served as controls. Citrate synthase (CS) activity was used as an index of oxidative capacity, and was determined in the following muscles: soleus, plantaris, costal diaphragm, crural diaphragm, and in all four abdominal muscles: rectus abdominis, transversus abdominis, external oblique, and internal oblique. Compared to their non-trained litter-mates, the trained rats had higher peak whole body oxygen consumption rates (+ 16%) and CS activities in plantaris (+34%) and soleus (+36%) muscles. Thus, the training program caused substantial systemic and locomotor muscle adaptations. The CS activity of costal diaphragm was 20% greater in the trained animals, but no difference was observed in crural diaphragm. The CS activity in the abdominal muscles was less than one-half of that in locomotor and diaphragm muscles, and there were no significant changes with training except in the rectus abdominis where a 26% increase was observed. The increase in rectus abdominis CS activity may reflect its role in postural support and/or locomotion, as none of the primary expiratory pumping muscles adapted to the training protocol. The relatively low levels of CS activity in the abdominal muscles suggests that they are not recruited frequently at rest, and the lack of an increase with training indicates that these muscles do not contribute significantly to the increased ventilatory activity accompanying exercise in the rat.

Non-NASA Center↗