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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 235 records · Page 13

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NASA NTRS1993

Effect of exercise training on cardiopulmonary baroreflex control of forearm vascular resistance in humans

We studied the stimulus-response characteristics of cardiopulmonary baroreflex control of forearm vascular resistance (FVR) in four groups of male volunteer subjects: i) unfit, ii) physically fit, iii) before and after 10 wk of endurance training (chronic blood volume expansion), and iv) before and after acute blood volume expansion. We assessed the relationship between reflex stimulus, i.e., changes in central venous pressure and response, i.e., FVR, during unloading of cardiopulmonary mechanoreceptors with lower body negative pressure (LBNP, 0 to -20 mm Hg). The slope of the linear relationship between FVR and CVP, the index of the responsiveness of this baroreflex, was significantly diminished (> 50%) in the fit subjects compared with the unfit. The slope of the FVR-CVP relationship was inversely correlated with the subject's total blood volume, suggesting that blood volume expansion was related to the attenuated CP baroreflex. In the exercise training study, maximal oxygen consumption and blood volume increased following 10 wk of endurance training (N = 14) but were unchanged in the time control group (N = 7). The slope of the FVR-CVP relationship was significantly reduced (32%) following 10 wk of training but was unchanged in the time control group. The reduction in slope of the FVR-CVP relationship was inversely related to the increase in blood volume associated with exercise training. Acute blood volume expansion 8 ml.kg-1 body weight with 5% human serum albumin solution) significantly reduced the slope of the FVR-CVP relationship. These data support the hypothesis that the attenuated forearm vascular reflex in physically fit individuals is related to a training-induced hypervolemia.(ABSTRACT TRUNCATED AT 250 WORDS).

Review↗

NASA NTRS2017

Training, Retention, and Transfer of Data Entry Perceptual and Motoric Processes Over Long Retention Intervals

Subjects trained in a standard data entry task, which involved typing numbers (e.g., 5421) using their right hands. At test (6 months post-training), subjects completed the standard task, followed by a left-hand variant (typing with their left hands) that involved the same perceptual, but different motoric, processes as the standard task. At a second test (8 months post-training), subjects completed the standard task, followed by a code variant (translating letters into digits, then typing the digits with their right hands) that involved different perceptual, but the same motoric, processes as the standard task. For each of the three tasks, half the trials were trained numbers (old) and half were new. Repetition priming (faster response times to old than new numbers) was found for each task. Repetition priming for the standard task reflects retention of trained numbers; for the left-hand variant reflects transfer of perceptual processes; and for the code variant reflects transfer of motoric processes. There was thus evidence for both specificity and generalizability of training data entry perceptual and motoric processes over very long retention intervals.

dat↗

NASA NTRS2019

Training, Retention, and Transfer of Data Entry Perceptual and Motor Processes Over Short and Long Retention Intervals

In 2 experiments, subjects trained in a standard data entry task, which involved typing numbers (e.g., 2147) using their right hands. At an initial test (20 min or 6 months after training), subjects completed the standard task, followed by a left-hand variant (typing with their left hands) that involved the same perceptual, but different motoric, processes as the standard task. At a second test (2 days or 8 months after training), subjects completed the standard task, followed by a code variant (translating letters into digits, then typing the digits with their right hands) that involved different perceptual, but the same motoric, processes as the standard task. At test, for each of the three tasks, half the trials were trained numbers (old) and half were new. Repetition priming (faster response times to old than new numbers) was found for each task, with extended delays only slightly decreasing the magnitude of the effect. Repetition priming for the standard task reflects retention of trained numbers, for the left-hand variant reflects transfer of perceptual processes, and for the code variant reflects transfer of motoric processes. There was, thus, evidence for both specificity and generalizability of training data entry perceptual and motoric processes even over very long retention intervals.

transfer↗

NASA NTRSDate not supplied

Training Airline Pilots for Improved Flight Path Monitoring: The Sensemaking Model Framework

The importance and benefit of improved monitoring is increasingly recognized. Improved training may be a valuable intervention. Our study (conducted 2019) assessed and trained airline First Officers on flight path monitoring skills. The exploratory study assessed monitoring pre-training in a simulator session that included monitoring challenges (8 or 7 events). A 1-hour interactive training followed, based on the Sensemaking Model of Monitoring; it presented concepts and examples using a slide deck, discussion, and simple activities. Post-training assessment used scenarios with analogous monitoring challenges (7 or 8 events) but a different setting. Performance showed significant and relatively consistent improvement. Training monitoring as sensemaking merits further investigation.

training↗

NASA NTRSDate not supplied

Virtual International Satellite Training Experiences for Weather, Marine, Climate, and Environmental Applications

Since January 2020, National Oceanic and Atmospheric Administration, its Cooperative Institutes, and international partners provided over 15,000 hours of virtual satellite focused on user engagement. The training provides and guides users to analyze a large variety of satellite data for weather, climate, and environmental applications and decision support. The training team reached users in over 50 countries via monthly Regional Focus Group and special topic sessions, World Meteorological Organization Regional Association III and IV Workshops, and American Meteorological Society Short Courses. The training sessions were offered in both English and Spanish covering such themes as GOES-R, JPSS and other satellite capabilities, data access using GEONET Cast, data display, and user applications. The training also emphasized the operational application of satellite observations for cross-disciplinary topics like heavy rain and hail events, aviation hazards, fire and smoke detection, volcanic eruptions and ash monitoring, climate indices, monitoring coral reef health, and in tsunami preparedness. The presentation will highlight the methods used and challenges posed by the various training approaches and future modifications to adapt to the virtual environment during training sessions planned for the next year.

Satellite Training↗

NASA NTRSDate not supplied

The Examination of Individual Factors and AFTE Training Outcomes

Introduction: Motion sickness is common among military aviators. It describes a specific group of symptoms that include epigastric awareness, nausea, pallor, sweating, salivation, and fatigue. Occurrences of these symptoms can pose a significant risk to safety and adversely impact mission success. Accordingly, researchers have developed specific interventions to act as countermeasures. For example, Autogenic Feedback Training Exercise has been empirically validated as a training method that mitigates the impact of motion sickness. However, it remains unclear the extent to which individual factors moderate (or mediate) the effects of this intervention. The examination of individual factors such as interoceptive accuracy (IA) and specific personality traits might provide insight into whom may benefit most from AFTE. Methods: Participants were administered the Big Five Inventory (BFI) and State Trait Anxiety Inventory (STAI). In addition, they were given an interoceptive accuracy task (i.e., heartbeat counting task). The participants’ reported value was then compared to the actual number of heart beats obtained via ECG. Completion of the IA task was followed by pre- AFTE and Post-AFTE rotating chair tests to evaluate motion sickness and the effects of AFTE training. Results: Participants (n=16) were evaluated on IA, personality factors (i.e., BFI & STAI) and performance on the rotating chair. IA was unrelated to personality traits (p > .05), but inversely related to state anxiety post training (p < .05). Finally, IA did not predict performance (i.e., rotations or minutes tolerated ?) on rotating chair F (1, 14) = 1.18, p> .05). Discussion:(1) IA is unrelated to personality traits as measured by BFI and STAI. (2) Individual factors are unrelated to AFTE training outcome. (3) AFTE training can be beneficial for all.

motion sickness↗

DOE OSTI2025

Training Evaluation: A Case Study of Measuring Impact and ROI

The purpose of this case study was to evaluate the Compliance Officer/Coordinator (CO/CC) training program using Phillips’ approach to training evaluation. INL Learning Performance Consultants (LPCs) applied the ROI Methodology to measure the effectiveness of the CO/CC training program. INL CO/CCs participated in focus groups to provide feedback on their learning, enablers and barriers to applying what they learned, and opportunities to improve the CO/CC learning experience. With collaboration from the CO/CC training program owner, the LPCs demonstrated the business impact and return on investment (ROI) of the CO/CC training program. Included are training program recommendations and lessons learned using the ROI Methodology.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2026

Temporal multi-omic analysis uncovers sex-biased molecular programs underlying skeletal muscle adaptation to endurance training

Background. Exercise training is known to benefit health and reduce disease risk. While adaptations in skeletal muscles are fundamental to many of the health benefits of exercise training, the common and sex-specific molecular regulators that mediate these adaptations remain to be fully elucidated. Methods. To this end, we leveraged skeletal muscle multi-omics data generated by the Molecular Transducers of Physical Activity Consortium (MoTrPAC), where 6 month-old male and female rats endurance trained for 1, 2, 4, or 8 weeks. Our objective was to identify shared and sex-specific multi-omic molecular responses to endurance training in skeletal muscle, and relate them to phenotypic adaptations. Results. We identified largely sexually-conserved transcriptomic and proteomic enrichments in the gastrocnemius, which correlated with skeletal muscle responses from a published exercise study in humans. We uncovered sex-consistent post-translational modifications, including decreased oxidation of MYH2 and deacetylation of the ß-oxidation enzyme HADHA. Pathway enrichment analyses revealed sex-specific remodeling across the acetylome, redox proteome, and phosphoproteome; females decreased mitochondrial protein oxidation and increased mitochondrial cristae proteins, indicative of enhanced redox buffering and mitochondrial efficiency. Despite observed decreases in the oxidation of key mitochondrial proteins, females displayed increases in the oxidation of proteins involved in glucose catabolism relative to males after 8 weeks of training, suggestive of sex-biased subcellular reactive oxygen species generation. Conclusions. This work shows a large portion of the adaptive response to endurance training in skeletal muscle is shared between females and males, while there are distinct and nuanced sex-specific adaptations that are evident, particularly at the level of post-translational regulation.

Many, Gina M.↗

DOE OSTI2025

Similarity Metric for Data Optimization and Efficient Training of Reactive Machine Learning Force Fields for Hydrocarbon Radiolysis

Radiolysis is a common approach to sterilize polymers, chemically modify them for upcycling, and accelerate their decomposition for recycling purposes. Reactive molecular dynamics (MD) simulations provide a powerful tool to generate atomic-level trajectories of the reactive processes and quantify radiolytic chemical degradation pathways. For this, machine learning (ML) surrogate models for reactive force fields with quantum mechanical accuracy are now widely used, which require ML training data sets that can provide information on atomic environments for target chemical systems. However, radiolysis chemistry can be highly complex and diverse, which poses significant challenges for generating training data to parametrize ML models. In this regard, we developed a method for optimizing the training data set using a cosine similarity metric to help guide training set selection for radiolysis of polyethylene, a model hydrocarbon polymer, as well as to enhance the transferability of our reactive ML force field (MLFF) to a variety of molecular and polymeric systems. Our approach performs atom-by-atom comparisons between local atomic environments to pinpoint important data points associated with rare and localized events, such as radiolysis damage within structures. We apply this approach to train the Chebyshev Interaction Model for Efficient Simulation (ChIMES) MLFF model, which expresses the atomic interaction potentials in terms of linear combinations of many-body Chebyshev polynomials. We first show that our method can reduce our training set size by ∼70% while improving overall accuracy compared to more standard MD model fitting approaches. We then validate our optimum model against diverse hydrocarbon simulation data, including simple alkanes and systems with unsaturated carbon bonds, over a wide range of thermodynamic conditions. Finally, we use our ChIMES model to perform MD simulations of radiolytic damage with large-scale systems that help avoid system size effects. Overall, our approach yields an MD force field that retains most of the accuracy of the underlying quantum method while yielding many orders of improvement in computational efficiency. In conclusion, our efforts will have impact on future hydrocarbon polymer radiolysis studies, where the chemical details of the polymer–radiation interactions can have a strong effect on the resulting products observed in experiments.

Hydrocarbons↗

DOE OSTI2026

Prediction and Experimental Verification of Electrolyte Solvation Structure from an OMol25-Trained Interatomic Potential

A molecular-level understanding of electrolyte solvation structure and ion–ion correlations is critical to developing next-generation battery chemistries. Atomistic simulation capabilities with sufficient accuracy, speed, and transferability to deliver reliable structural insights while avoiding arduous system-specific reparameterization are thus highly desirable. Machine learning interatomic potentials (MLIPs) trained on large, chemically diverse data sets are revolutionizing computational chemistry, enabling molecular dynamics simulations of battery electrolytes with near-DFT accuracy over 10,000× faster than DFT. While previous MLIP training data sets with suitable elemental coverage for electrolytes have been based on inorganic materials, the Open Molecules 2025 (OMol25) data set provides large-scale molecular DFT MLIP training data with broad elemental coverage and specifically samples tens of millions of electrolyte configurations. Here, we integrate computational modeling with experimental validation to systematically assess the ability of large-scale MLIPs pretrained on materials data or on OMol25 to accurately resolve nanoscale structural organization and ion-solvation characteristics in Na-ion battery electrolytes across diverse physicochemical conditions and compositional regimes. We find that the OMol25-trained Universal Model of Atoms (UMA-OMol) predicts experimentally measured densities and X-ray structure factors in substantially better agreement compared to state-of-the-art models trained only on inorganic materials data. Using UMA-OMol, we further analyze systematic trends in solvation structure as a function of cation identity, anion chemistry, salt concentration, and solvent topology. We observe that increasing system temperature amplifies the heterogeneity within the solvation environment, perturbing cation–solvent interactions and promoting the formation of contact ion pairs (CIPs). Moreover, subtle variations in the solvent topology of glyme-based electrolytes cause pronounced changes in ion correlations and solvation structure. The experimental agreement and microscopic insights shown here position OMol25-trained MLIPs as a practical route to predictive, high-throughput electrolyte simulations beyond the limits of classical force fields and direct DFT molecular dynamics, serving as a powerful tool for accelerating the design of next-generation Na-ion battery electrolytes and beyond.

MLIPs↗

DOE OSTI2025

Stable Simulation of the Community Atmosphere Model Using Machine‐Learning Physical Parameterization Trained With Experience Replay

In recent years, machine learning (ML) models have been used to improve physical parameterizations of general circulation models (GCMs). A significant challenge of integrating ML models into GCMs is the online instability when they are coupled for long‐term simulation. We present a new strategy that demonstrates robust online stability when the physical parameterization package of an atmospheric GCM is replaced by a deep ML model. The method uses experience replay with a multistep training scheme of the ML model in which the model's own output at the previous time step is used in the training. Predicted physics tendencies in the replay buffer with the most recent errors in the training iterations are reused, making the ML model learn from its own errors. The training method reduces the gap between the offline and online environments of the ML model. The method is used to train the ML model as the physical parameterization of the Community Atmosphere Model (CAM5) with training data from the Multi‐scale Modeling Framework high resolution simulations. Three 6‐year online simulations of the CAM5 are carried out by using the ML physics package. The simulated spatial distributions of precipitation, surface temperature and zonally averaged atmospheric fields demonstrate overall better accuracy than that of the standard CAM5 and benchmark model even without the use of additional physical constraints or tuning. This work is the first to demonstrate a solution to address the online instability problem in climate modeling with ML physics by using experience replay.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2026

Pre-training Vision Models for the Classification of Alerts from Wide-field Time-domain Surveys

Modern wide-field time-domain surveys facilitate the study of transient, variable and moving phenomena by conducting image differencing and relaying alerts to their communities. Machine learning tools have been used on data from these surveys and their precursors for more than a decade, and convolutional neural networks (CNNs), which make predictions directly from input images, saw particularly broad adoption through the 2010s. Since then, continually rapid advances in computer vision have transformed the standard practices around using such models. It is now commonplace to use standardized architectures pre-trained on large corpora of everyday images (e.g., ImageNet). In contrast, time-domain astronomy studies still typically design custom CNN architectures and train them from scratch. Here, we explore the effects of adopting various pre-training regimens and standardized model architectures on the performance of alert classification. We find that the resulting models match or outperform a custom, specialized CNN like what is typically used for filtering alerts. Moreover, our results show that pre-training on galaxy images from Galaxy Zoo tends to yield better performance than pre-training on ImageNet or training from scratch. We observe that the design of standardized architectures are much better optimized than the custom CNN baseline, requiring significantly less time and memory for inference despite having more trainable parameters. On the eve of the Legacy Survey of Space and Time and other image-differencing surveys, these findings advocate for a paradigm shift in the creation of vision models for alerts, demonstrating that greater performance and efficiency, in time and in data, can be achieved by adopting the latest practices from the computer vision field.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2026

Tula: Optimizing Time, Cost, and Generalization in Distributed Large-Batch Training

Distributed training increases the number of batches processed per iteration either by scaling-out (adding more nodes) or scaling-up (increasing the batch-size). However, the largest configuration does not necessarily yield the best performance. Horizontal scaling introduces additional communication overhead, while vertical scaling is constrained by computation cost and device memory limits. Thus, simply increasing the batch-size leads to diminishing returns: training time and cost decrease initially but eventually plateaus, creating a knee-point in the time/cost vs. batch-size pareto curve. The optimal batch-size therefore depends on the underlying model, data and available compute resources. Large batches also suffer from worse model quality due to the well-known “generalization gap”. In this paper, we present Tula, an online service that automatically optimizes time, cost, and convergence quality for large-batch training of convolutional models. It combines parallel-systems modeling with statistical performance prediction to identify the optimal batchsize. Tula predicts training time and cost within 7.5−14% error across multiple models, and achieves up to 20× overall speedup and improves test accuracy by ≈9% on average over standard large-batch training on various vision tasks, thus successfully mitigating the generalization gap and accelerating training at the same time.

Tyagi, Sahil [ORNL] (ORCID:0009000783144745)↗

DOE OSTI2025

A Parallel Alternative for Energy-Efficient Neural Network Training and Inferencing

Energy efficiency of training and inferencing with large neural network models is a critical challenge facing the future of sustainable large-scale machine learning workloads. This paper introduces an alternative strategy, called phantom parallelism, to minimize the net energy consumption of traditional tensor (model) parallelism, the most energy-inefficient component of large neural network training. The approach is presented in the context of feed-forward network architectures as a preliminary, but comprehensive, proof-of-principle study of the proposed methodology. We derive new forward and backward propagation operators for phantom parallelism, implement them as custom autograd operations within an end-to-end phantom parallel training pipeline and compare its parallel performance and energy-efficiency against those of conventional tensor parallel training pipelines. Formal analyses that predict lower bandwidth and FLOP counts are presented with supporting empirical results on up to 256 GPUs that corroborate these gains. Experiments are shown to deliver ∼50% reduction in the energy consumed to train FFNs using the proposed phantom parallel approach when compared with conventional tensor parallel methods. Additionally, the proposed approach is shown to train smaller phantom models to the same model loss on smaller GPU counts as larger tensor parallel models on larger GPU counts offering the possibility for even greater energy savings.

Seal, Sudip [ORNL] (ORCID:0000000332330656)↗

DOE OSTI2025

Efficient Training of Deep Neural Operator Networks via Randomized Sampling

Neural operators (NOs) employ deep neural networks to learn the mappings between infinitedimensional function spaces. Deep operator network (DeepONet), a popular NO architecture, has demonstrated success in the real-time prediction of complex dynamics across various scientific and engineering applications. In this work, we introduce a random sampling technique to be adopted during the training of DeepONet, aimed at improving the generalization ability of the model, while significantly reducing the computational time. The proposed approach targets the trunk network of the DeepONet model that outputs the basis functions corresponding to the spatiotemporal locations of the bounded domain on which the physical system is defined. While constructing the loss function, DeepONet training traditionally considers a uniform grid of spatiotemporal points at which all the output functions are evaluated for each iteration. This approach leads to a larger batch size, resulting in poor generalization and increased memory demands, due to the limitations of the stochastic gradient descent (SGD) optimizer. The proposed random sampling over the inputs of the trunk net mitigates these challenges, improving generalization and reducing the memory requirements during training, resulting in significant computational gains. We validate our hypothesis through three benchmark examples, demonstrating substantial reductions in training time while achieving comparable or lower overall test errors relative to the traditional training approach. Our results indicate that incorporating randomization in the trunk network inputs during training enhances the efficiency and robustness of DeepONet, offering a promising avenue for improving the framework’s performance in modeling complex physical systems.

Karumuri, Sharmila [Department of Civil & Systems ↗

DOE OSTI2025

BOPTest As a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Building automation and controls are becoming increasingly complex with the emergence of Grid Integrated Efficient Buildings (GEBs) as well as new highly efficient sequences of operation and data-driven control schemes. However, there remains a significant gap in hands-on training opportunities for building operators and technicians to gain practical experience with advanced control systems in a low-risk environment. This paper presents BOPTEST (Building Optimization Performance Test) as a suitable platform for workforce training in building controls and GEB technologies. BOPTEST provides a suite of standardized building simulation test cases with a REST API, real-time control interfaces through BACnet, semantic models connecting users to building data, and built-in calculation of control metrics and performance indicators. The platform enables trainees to interact with virtual buildings using industry-standard protocols while learning how to implement and innovate control strategies. The training platform is designed to offer a structured and interactive learning experience for building engineers, helping them effectively develop, learn, and retain skills in fault identification, troubleshooting, and correction. The workflow is divided into three main phases: 1) Setup, 2) Exercise, and 3) Review, each comprising specific activities performed by either the instructor or the student. Initial pilot training sessions have yielded positive feedback from instructors and participants and demonstrates that BOPTEST effectively fills an industry need for a low-risk training resource via simulation of real building control systems, allowing trainees to gain practical experience before working in the field. The platform's ability to provide immediate performance feedback while maintaining familiar industry interfaces makes it particularly suitable for workforce development programs. This work provides a replicable model for leveraging building simulation in control education and training.

Paul, Lazlo↗

DOE OSTI2025

BOPTEST as a Platform for Building Controls and Grid-Interactive Buildings Workforce Training

Paul, Lazlo↗

NASA NTRS1976

Development and analysis of a modular approach to payload specialist training

A modular training approach for Spacelab payload crews is described. Representative missions are defined for training requirements analysis, training hardware, and simulations. Training times are projected for each experiment of each representative flight. A parametric analysis of the various flights defines resource requirements for a modular training facility at different flight frequencies. The modular approach is believed to be more flexible, time saving, and economical than previous single high fidelity trainer concepts. Block diagrams of training programs are shown.

Watters, H.↗