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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Implications of stop-and-go traffic on training learning-based car-following control

Learning-based car-following control (LCC) of connected and autonomous vehicles (CAVs) is gaining significant attention with the advancement of computing power and data accessibility. While the flexibility and large model capacity of model-free architecture enable LCC to potentially outperform the model-based car-following (CF) model in improving traffic efficiency and mitigating congestion, the generalizability of LCC for traffic conditions different from the training environment/dataset is not well-understood. Herein, this study seeks to explore the impact of stop-and-go traffic in the training dataset on the generalizability of LCC. It uses the characteristics of lead vehicle trajectories to describe stop-and-go traffic, and links the theory of identifiability (i.e., obtaining a unique parameter estimation result using sensor measurements) to the generalizability of behavior cloning (BC) and policy-based deep reinforcement learning (DRL). Correspondingly, the study shows theoretically that: (i) stop-and-go traffic can enable the property of identifiability and enhance the control performance of BC-based LCC in different traffic conditions; (ii) stop-and-go traffic is not necessary for DRL-based LCC to generalize to different traffic conditions; (iii) DRL-based LCC trained with only constant-speed lead vehicle trajectories (not sufficient to ensure identifiability) can be generalized to different traffic conditions; and (iv) stop-and-go traffic increases variance in the training dataset, which improves the convergence of parameter estimation while negatively impacting the convergence of DRL to the optimal control policy. Numerical experiments validate the above findings, illustrating that BC-based LCC entails comprehensive training datasets for generalizing to different traffic conditions, while DRL-based LCC can achieve generalization with simple free-flow traffic training environments. This further suggests DRL as a more promising and cost-effective LCC approach to reduce operational costs, mitigate traffic congestion, and enhance safety and mobility, which can accelerate the deployment and acceptance of CAVs.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2023

Utilizing multimodal high-intensity interval training for a firefighter training academy during the COVID-19 pandemic

Firefighters typically undergo a 16–24-week training academy during which they perform a variety of traditional exercise programs such as cardiovascular, resistance, and concurrent training. Because of limited facility access, some fire departments seek alternative exercise programs, such as multimodal high-intensity interval training (MM-HIIT), which essentially combines resistance and interval training. Here, the primary purpose of this study was to assess the effect of MM-HIIT on body composition and physical fitness in firefighter recruits who completed a training academy during the coronavirus (COVID-19) pandemic. A secondary purpose was to compare the effects of MM-HIIT to previous training academies that implemented traditional exercise programs. Healthy and recreationally-trained recruits (n=12) participated in 2-3 days/week of MM-HIIT for 12 weeks and had several components of body composition and physical fitness measured before and after the program. Because of COVID-19-related gym closures, all MM-HIIT sessions were performed outdoors at a fire station with minimal equipment. These data were retroactively compared to a control group (CG) that previously completed training academies with traditional exercise programs. Subjects in the MM-HIIT group significantly improved several components of body composition and fitness, including fat mass, fat-free mass, body fat percentage, aerobic capacity, and muscular endurance (p < 0.005). Moreover, there were no significant differences for any dependent variable when MM-HIIT was compared to the CG (p≥0.005). These results suggest that MM-HIIT may serve as an effective substitute for traditional concurrent training paradigms that are typically used for firefighter academies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Reducing Communication in Graph Neural Network Training

Graph Neural Networks (GNNs) are powerful and flexible neural networks that use the naturally sparse connectivity information of the data. GNNs represent this connectivity as sparse matrices, which have lower arithmetic intensity and thus higher communication costs compared to dense matrices, making GNNs harder to scale to high concurrencies than convolutional or fully-connected neural networks. Here, we introduce a family of parallel algorithms for training GNNs and show that they can asymptotically reduce communication compared to previous parallel GNN training methods. We implement these algorithms, which are based on 1D, 1. 5D, 2D, and 3D sparse-dense matrix multiplication, using torch.distributed on GPU-equipped clusters. Our algorithms optimize communication across the full GNN training pipeline. We train GNNs on over a hundred GPUs on multiple datasets, including a protein network with over a billion edges.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

A pre-training and self-training approach for biomedical named entity recognition

Named entity recognition (NER) is a key component of many scientific literature mining tasks, such as information retrieval, information extraction, and question answering; however, many modern approaches require large amounts of labeled training data in order to be effective. This severely limits the effectiveness of NER models in applications where expert annotations are difficult and expensive to obtain. In this work, we explore the effectiveness of transfer learning and semi-supervised self-training to improve the performance of NER models in biomedical settings with very limited labeled data (250-2000 labeled samples). We first pre-train a BiLSTM-CRF and a BERT model on a very large general biomedical NER corpus such as MedMentions or Semantic Medline, and then we fine-tune the model on a more specific target NER task that has very limited training data; finally, we apply semi-supervised self-training using unlabeled data to further boost model performance. We show that in NER tasks that focus on common biomedical entity types such as those in the Unified Medical Language System (UMLS), combining transfer learning with self-training enables a NER model such as a BiLSTM-CRF or BERT to obtain similar performance with the same model trained on 3x-8x the amount of labeled data. We further show that our approach can also boost performance in a low-resource application where entities types are more rare and not specifically covered in UMLS.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Controllable X-ray Pulse Trains from Enhanced Self-Amplified Spontaneous Emission

We report the demonstration of optical compression of an electron beam and the production of controllable trains of femtosecond, soft x-ray pulses with the Linac Coherent Light Source (LCLS) freeelectron laser (FEL). This is achieved by enhanced self-amplified spontaneous emission with a 2 μm laser and a dechirper device. Optical compression was achieved by modulating the energy of an electron beam with the laser and then compressing with a chicane, resulting in high current spikes on the beam which we observe to lase. A dechirper was then used to selectively control the lasing region of the electron beam. Field autocorrelation measurements indicate a train of pulses, and we find that the number of pulses within the train can be controlled (from 1 to 5 pulses) by varying the dechirper position and undulator taper. These results are a step toward attosecond spectroscopy with x-ray FELs as well as future FEL schemes relying on optical compression of an electron beam.

Duris, Joseph P↗
DOE OSTI2023

Simple Diesel Train Fuel Consumption Model for Real-Time Train Applications

This paper introduces a simple diesel train energy consumption model that calculates the instantaneous energy consumption using vehicle operational input variables, including the instantaneous speed, acceleration, and roadway grade, which can be easily obtained from global positioning system (GPS) loggers. The model was tested against real-world data and produced an error of -1.33% for all data and errors ranging from -12.4% to +8.0% for energy consumption of four train datasets amounting to a total of 5854 km trips. The study also validated the proposed model with separate data that were collected between Valencia and Cuenca, Spain, which had a total length of 198 km and found that the model was accurate, yielding a relative error of -1.55% for the total energy consumption. These results show that the proposed model can be used by train operators, transportation planners, policy makers, and environmental engineers to evaluate the energy consumption effects of train operational projects and train simulation within intermodal transportation planning tools.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Improved Architectures and Training Algorithms for Deep Operator Networks

Operator learning techniques have recently emerged as a powerful tool for learning maps between infinite-dimensional Banach spaces. Trained under appropriate constraints, they can also be effective in learning the solution operator of partial differential equations (PDEs) in an entirely self-supervised manner. In this work we analyze the training dynamics of deep operator networks (DeepONets) through the lens of Neural Tangent Kernel theory, and reveal a bias that favors the approximation of functions with larger magnitudes. To correct this bias we propose to adaptively re-weight the importance of each training example, and demonstrate how this procedure can effectively balance the magnitude of back-propagated gradients during training via gradient descent. We also propose a novel network architecture that is more resilient to vanishing gradient pathologies. Taken together, our developments provide new insights into the training of DeepONets and consistently improve their predictive accuracy by a factor of 10-50x, demonstrated in the challenging setting of learning PDE solution operators in the absence of paired input-output observations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

A nearsighted force-training approach to systematically generate training data for the machine learning of large atomic structures

A challenge of atomistic machine-learning (ML) methods is ensuring that the training data are suitable for the system being simulated, which is particularly challenging for systems with large numbers of atoms. Most atomistic ML approaches rely on the nearsightedness principle (“all chemistry is local”), using information about the position of an atom’s neighbors to predict a per-atom energy. Here, in this work, we develop a framework that exploits the nearsighted nature of ML models to systematically produce an appropriate training set for large structures. We use a per-atom uncertainty estimate to identify the most uncertain atoms and extract chunks centered around these atoms. It is crucial that these small chunks are both large enough to satisfy the ML’s nearsighted principle (that is, filling the cutoff radius) and are large enough to be converged with respect to the electronic structure calculation. We present data indicating when the electronic structure calculations are converged with respect to the structure size, which fundamentally limits the accuracy of any nearsighted ML calculator. These new atomic chunks are calculated in electronic structures, and crucially, only a single force—that of the central atom—is added to the growing training set, preventing the noisy and irrelevant information from the piece’s boundary from interfering with ML training. The resulting ML potentials are robust, despite requiring single-point calculations on only small reference structures and never seeing large training structures. We demonstrated our approach via structure optimization of a 260-atom structure and extended the approach to clusters with up to 1415 atoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Coherent high-power RF wakefield generation by electron bunch trains in a metamaterial structure

In this paper, we present an experimental study of coherent high-power wakefield generation in a metamaterial (MTM) structure at 11.7 GHz by 65 MeV electron bunch trains at the Argonne Wakefield Accelerator (AWA), following a previous experiment, the Stage-I experiment, at the AWA. Both the Stage-II experiment, reported in this paper, and the Stage-I experiment were conducted using MTM structures, which are all-metal periodic structures with the period being much smaller than the wavelength. Differences between the two experiments include (1) structure length (Stage-I 8 cm and Stage-II 20 cm); (2) number of bunches used to excite the structure (Stage-I with two bunches, up to 85 nC of total charge; Stage-II with eight bunches, up to 224 nC of total charge); and (3) highest peak power measured (Stage-I 80 MW in a 2 ns pulse and Stage-II 380 MW in a 10 ns pulse). High-power radio frequency pulses were generated by reversed Cherenkov radiation of the electron beam due to the negative group velocity in the MTM structures. Because the radiation is coherent, a train of bunches with a proper spacing can build up to achieve a high peak power. The observed output power levels are very promising for future applications in direct collinear wakefield acceleration or in transfer to a second accelerator for two-beam acceleration.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Delivering Effective Virtual Energy-Focused Trainings: Successful Strategies and Lessons Learned from the Virtual Cohort In-Plant Training Pilot

While there are always barriers to energy efficiency efforts, few challenges have been as severe as the global COVID-19 pandemic. Many manufacturers throughout the United States and the world have been trying to determine how to safely open and operate their plants. To minimize the number of people working within facilities and maximize the physical distance between workers, only essential facility engineers are allowed into plants to ensure proper operations. The Department of Energy’s Better Plants Program created In-Plant Trainings (INPLTs) to help partners develop in-house expertise in energy efficiency. Delivering INPLTs in the traditional in-person fashion is infeasible under these circumstances. To continue providing INPLTs during this difficult time, the Better Plants Program piloted Virtual Cohort In-Plant Trainings (VINPLTs) on wastewater treatment and ammonia industrial refrigeration systems from October 27 to November 19, 2020. This paper provides an overview of the pilot, then discusses successful strategies and lessons learned from this pilot for delivering effective virtual energy-focused trainings.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2021

Strategic Training in Transdisciplinary Radiation Science for the 21st Century (STARS21): 15-Year Evaluation of an Innovative Research Training Program

To evaluate the 15-year impact of a transdisciplinary research training program for graduate students, postdoctoral fellows, and clinical trainees focused on radiation science, entitled Strategic Training in Transdisciplinary Radiation Science for the 21st Century (STARS21) with a primary objective to build capacity in radiation research.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2022

America’s Cutting Edge CNC machining and metrology training

This paper describes the workforce development activities supported by America’s Cutting Edge (ACE), a national initiative for machine tool technology development and advancement. ACE is supported by the Department of Defense Industrial Base Analysis and Sustainment (IBAS) program from the Office of Industrial Policy. Both the online and in-person components of the computer numerically controlled (CNC) machining and metrology training programs are summarized and participation information is provided.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2020

FPDeep: Scalable Acceleration of CNN Training on Deeply-Pipelined FPGA Clusters

In this paper, we propose a framework called FPDeep, which uses a hybrid of model and layer paral- lelism to configure distributed reconfigurable clusters to train DNNs. This approach has numerous benefits. First, the design does not suffer from batch size growth. Second, novel workload and weight partitioning leads to balanced loads of both among nodes. And third, the entire system is fine-grained pipeline. This leads to high parallelism and utilization and also minimizes the time features need to be cached while waiting for back-propagation.

Wang, Tianqi↗
DOE OSTI2021

Train Like a (Var)Pro: Efficient Training of Neural Networks with Variable Projection

Deep neural networks (DNNs) have achieved state-of-the-art performance across a variety of traditional machine learning tasks, e.g., speech recognition, image classification, and segmentation. The ability of DNNs to efficiently approximate high-dimensional functions has also motivated their use in scientific applications, e.g., to solve partial differential equations and to generate surrogate models. In this paper, we consider the supervised training of DNNs, which arises in many of the above applications. We focus on the central problem of optimizing the weights of the given DNN such that it accurately approximates the relation between observed input and target data. Devising effective solvers for this optimization problem is notoriously challenging due to the large number of weights, nonconvexity, data sparsity, and nontrivial choice of hyperparameters. To solve the optimization problem more efficiently, we propose the use of variable projection (VarPro), a method originally designed for separable nonlinear least-squares problems. Our main contribution is the Gauss--Newton VarPro method (GNvpro) that extends the reach of the VarPro idea to nonquadratic objective functions, most notably cross-entropy loss functions arising in classification. These extensions make GNvpro applicable to all training problems that involve a DNN whose last layer is an affine mapping, which is common in many state-of-the-art architectures. In our four numerical experiments from surrogate modeling, segmentation, and classification, GNvpro solves the optimization problem more efficiently than commonly used stochastic gradient descent (SGD) schemes. Finally, GNvpro finds solutions that generalize well, and in all but one example better than well-tuned SGD methods, to unseen data points.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Cyber-Informed Engineering (CIE) Workbook: End-of-Train (EoT) / Head-of-Train (HoT) Communications

This workbook presents a vulnerability (CVE-2025-1727 ) found in train applications and guides a digital risk assessment and mitigation analysis and application of Cyber-Informed Engineering principles to mitigate the potential consequences and ultimately the hazard through the engineering discipline because of exploiting this vulnerability. Workshop participants are encouraged to use the workbook to capture insights and lessons learned. The workbook guides the participant to: • Understand the HE communication vulnerability • Map digital threats to physical consequences • Use bowtie analysis to illustrate both “security” and “engineering” barriers • Apply CIE principles to ensure that even if communications are compromised, the physical engineered system still behaves safely. • Produce an actionable set of engineered and infosec controls for implementation

42 - ENGINEERING↗
DOE OSTI2022

How Management and Leadership Training Can Impact a Health System: Evaluation Findings From a Public Health Management Training Program in Cambodia

In 2017, the National Institute of Public Health in Cambodia collaborated with the U.S. Centers for Disease Control and Prevention to provide management and leadership training for 20 managers and senior staff from 10 health centers. We conducted a mixed methods evaluation of the program's outcomes and impact on the graduates and health centers. From June 2018 (baseline) to January 2019 (endpoint), we collected data from a competency assessment, observational visits, and interviews. From baseline to endpoint, all 20 participants reported increased competence in seven management areas. Comparing baseline and endpoint observational visits, we found improvements in leadership and governance, health workforce, water, sanitation, and hygiene, and health centers' use of medical products and technologies. When evaluating the improvements made by participants against the World Health Organization's key components of a well-functioning health system, the program positively contributed toward building four of the six components—leadership and governance, health information systems, human resources for health, and service delivery. While these findings are specific to the context of Cambodian health centers, we hope this evaluation adds to the growing body of research around the impact of skilled public health management on health systems.

60 APPLIED LIFE SCIENCES↗