Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Trains”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

A Hands-On Curriculum for Training in HPC Cluster Deployment and Management

This paper presents the design, methodology, and outcomes of the High-Performance Computing Technologies (HPCT) course, a hands-on training program focused on the system-side of HPC cluster deployment and administration. Delivered as part of the Master in High Performance Computing (MHPC) program, the course introduces students to key concepts in cluster configuration, including networking, software stack provisioning, job scheduling, and monitoring. Initially taught in person, the course was transitioned to an online format during the COVID-19 pandemic. This shift led to the development of openly available instructional material and a flipped-classroom approach that continues to support both in-person and hybrid delivery. All course materials are publicly available at www.hpc.temple.edu/mhpc/hpc-technology/index.html. By documenting the structure, infrastructure, and evolution of HPCT, this paper offers a model for accessible HPC system training that supports workforce development in computational science.

Posada Correa, Fernando [ORNL] (ORCID:000000022565↗
DOE OSTI2021

ESPC ESA Training Series: Photovoltaic (PV) Project Considerations

This training covers technical, economic and other photovoltaic (PV) project considerations. Technical topics include cost-effectiveness, resilience, cybersecurity, and electrical considerations. In addition, other topics, such as compatibility with agency mission, site plans, coordination with the serving utility, the National Environmental Policy Act, and agency approval requirements. Considerations specific to third-party owned/third-party financed PV projects are also covered, as well as available resources and templates for project development. The training also includes a recap of the first session in the Energy Savings Performance Contracts Energy Sales Agreements (ESPC ESA) Webinar Series.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Confronting Domain Shift in Trained Neural Networks

Neural networks (NNs) are known as universal function approximators and can interpolate nonlinear functions between observed data points. However, when the target domain for deployment shifts from the training domain and NNs must extrapolate, the results are notoriously poor. Prior work Martinez et al. (2019) has shown that NN uncertainty estimates can be used to correct binary predictions in shifted domains without retraining the model. We hypothesize that this approach can be extended to correct real-valued time series predictions. As an exemplar, we consider two mechanical systems with nonlinear dynamics. The first system consists of a spring-mass system where the stiffness changes abruptly, and the second is a real experimental system with a frictional joint that is an open challenge for structural dynamicists to model efficiently. Our experiments will test whether 1) NN uncertainty estimates can identify when the input domain has shifted from the training domain and 2) whether the information used to calculate uncertainty estimates can be used to correct the NN’s time series predictions. While the method as proposed did not significantly improve predictions, our results did show potential for modifications that could improve models’ predictions and play a role in structural health monitoring systems that directly impact public safety.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Designing alloys with process-mapping AI pre-trained on empirical knowledge

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Accelerated materials design should match the recent trends in the product development cycles. Materials data analytics can be used to significantly shorten development time of specialized alloys needed for next generation energy applications. However, it faces a challenge of scarce data available for training ML models. Incorporation of the domain knowledge into deep-learning graph structure via fuzzy pre-training and causal process imitation presents a viable approach to developing accurate data-driven models and reliable alloy design tools, with limited datasets. Artificial Intelligence (AI) was used in this study to incorporate such knowledge in the domain-specific computational tool, pyroMind. The tool provides not only novel design ideas but also their interpretation via physics and engineering concepts.</span>

Romanov, Vyacheslav↗
DOE OSTI2020

Radiation Signature Training Device Gamma Equivalent Spheres

Radiation signature training device sources are a continuation of a previous effort to produce a highly enriched uranium equivalent sphere. The original research was designed and motivated to produce a Department of Transportation special form source that emulated the passive gamma signature of a solid metal highly enriched uranium sphere. In addition, the mass reduction (i.e., of the 235 U) minimized logistical costs (security, criticality safety, radiological safety and transportation requirements). Radiation signature training device sources expand on the original concept, redesigning the encapsulation in a modular fashion that allows a variety of loadings. The result is a reconfigurable special nuclear material source set that can vary greatly in the magnitude of the gamma flux and the isotopic distribution. Use of the source to test a variety of standards (N42.34-2015, N42.38-2015, N42.43-2015, N42.53-2018, N42.48-2018, DHS-TCS Backpack Based Radiation Detection Systems, DHS-TCS Handheld Instruments Used for the Detection and Identification of Radionuclides, DHS-TCS Vehicle Mounted Mobile Systems) will be discussed.

Blessinger, Christopher S.↗
DOE OSTI2023

Pulsed Power Supply for Magnet Quench Training

Superconducting magnets typically go through aseries of spontaneous “training” quenches as their current isincreased towards its design value. To decrease the number ofthese quenches, a device has been designed to boost the magnetcurrent at the moment a quench has been detected and beforemagnet protection has been initiated. The aim of the boost is todrive the magnet coil further along towards its maximum currentwith each training quench, and hence reduce the number ofquenches required.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Privacy Amplification for Episodic Training Methods

It has been shown that differential privacy bounds improve when subsampling within a randomized mechanism. Episodic training, utilized in many standard machine learning techniques, uses a multistage subsampling procedure which has not been previously analyzed for privacy bound amplification. In this paper, we focus on improving the calculation of privacy bounds in episodic training by thoroughly analyzing privacy amplification due to subsampling with a multi-stage subsampling procedure. The newly developed bound can be incorporated into existing privacy accounting methods.

Tombs, Vandy↗
DOE OSTI2024

Refining Absorber Shroud Geometry to Maximize Power Output and Reduce Power Peaking in ATF Test Train

In the wake of the Fukushima Daiichi nuclear power plant accident in 2011, the accident tolerant fuels (ATF) program was initiated to enhance the safety of light-water reactor fuels, placing significant emphasis on cladding. A crucial step for the broad implementation of ATF in commercial reactors involves irradiation testing of the fuel designs. ATF-2D, the latest experiment in the ATF series, is slated to undergo irradiation in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). ATF-2D is a joint effort of the INL with industry partners General Electric Global Research; Framatome; General Atomics; the Japan Atomic Energy Agency; Hitachi-GE Nuclear Energy, Ltd; Global Nuclear Fuel-Japan Co., Ltd; Nippon Nuclear Fuel Development Co., Ltd; and Mitsubishi Heavy Industries, Ltd. The ATF-2D test train design consists of four tiers, each housing six rodlets. The device will be inserted in Loop 2A within the central flux trap of the ATR, and is anticipated to undergo irradiation throughout three 60-day cycles. Typical pressurized water reactor conditions will be emulated during the irradiation. The objective of the work presented here is to dimension neutron-absorbing hafnium (Hf) components surrounding the fuel rodlets, such that the axial power profile is flattened, while simultaneously ensuring that the total fission power output of the entire test train remains below 200 kW.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Refining Absorber Shroud Geometry to Maximize Power Output and Reduce Power Peaking in ATF Test Train

In the wake of the Fukushima Daiichi nuclear power plant accident in 2011, the accident tolerant fuels (ATF) program was initiated to enhance the safety of light-water reactor fuels, placing significant emphasis on cladding. A crucial step for the broad implementation of ATF in commercial reactors involves irradiation testing of the fuel designs. ATF-2D, the latest experiment in the ATF series, is slated to undergo irradiation in the Advanced Test Reactor (ATR) at Idaho National Laboratory (INL). ATF-2D is a joint effort of the INL with industry partners General Electric Global Research; Framatome; General Atomics; the Japan Atomic Energy Agency; Hitachi-GE Nuclear Energy, Ltd; Global Nuclear Fuel-Japan Co., Ltd; Nippon Nuclear Fuel Development Co., Ltd; and Mitsubishi Heavy Industries, Ltd. The ATF-2D test train design consists of four tiers, each housing six rodlets. The device will be inserted in Loop 2A within the central flux trap of the ATR, and is anticipated to undergo irradiation throughout three 60-day cycles. Typical pressurized water reactor conditions will be emulated during the irradiation. The objective of the work presented here is to dimension neutron-absorbing hafnium (Hf) components surrounding the fuel rodlets, such that the axial power profile is flattened, while simultaneously ensuring that the total fission power output of the entire test train remains below 200 kW.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2024

Accelerating Resonance Searches via Signature-Oriented Pre-training

The search for heavy resonances beyond the Standard Model (BSM) is a key objective at the LHC. While the recent use of advanced deep neural networks for boosted-jet tagging significantly enhances the sensitivity of dedicated searches, it is limited to specific final states, leaving vast potential BSM phase space underexplored. We introduce a novel experimental method, Signature-Oriented Pre-training for Heavy-resonance ObservatioN (Sophon), which leverages deep learning to cover an extensive number of boosted final states. Pre-trained on the comprehensive JetClass-II dataset, the Sophon model learns intricate jet signatures, ensuring the optimal constructions of various jet tagging discriminates and enabling high-performance transfer learning capabilities. We show that the method can not only push widespread model-specific searches to their sensitivity frontier, but also greatly improve model-agnostic approaches, accelerating LHC resonance searches in a broad sense.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2024

Leveraging Military Veteran Talent Through SkillBridge Training: Recommendations for the Solar Industry

Solar Energy International (SEI), in partnership with IREC, has published a white paper, Leveraging Military Veteran Talent Through SkillBridge Training: Recommendations for the Solar Industry. This resource presents SkillBridge as a key strategy for solar employers to attract, develop, and retain qualified veteran talent, and provides model training frameworks aligned with four key occupations, including solar installation, operations and maintenance, technical sales, and solar design.

Howes, Megan↗
DOE OSTI2024

Recruiting Military Talent through Solar Ready Vets Training Partnerships Webinar

SkillBridge training programs provide opportunities for service members to gain valuable civilian work experience through career training prior to separation. Employers are invited to join this webinar hosted by Solar Energy Industries Association to learn more about these programs, featuring success stories from across the industry.

Cohen Hall, E'Lon↗
DOE OSTI2025

Development of a New Criticality Safety Training Program for College Students

Nuclear criticality safety (NCS) expertise remains a crucial workforce need within the US Department of Energy (DOE) laboratory complex. To address this challenge, a novel university/laboratory-based nuclear criticality training certificate program is being developed through a collaborative effort between the Georgia Institute of Technology, Texas A&M University, and Oak Ridge National Laboratory. This comprehensive program implements a two-tiered certification approach that combines online theoretical coursework with hands-on experimental training to create a sustainable pipeline of nuclear criticality specialists. The program specifically targets undergraduate and graduate students in engineering, physics, and mathematics disciplines across the United States. Through integration of fundamental nuclear physics principles, practical safety applications, and experiential learning opportunities, this initiative aims to establish a standardized pathway for developing the next generation of NCS professionals.

K-Effective↗
DOE OSTI2026

Lessons Learned from Open-Source Software Training Toward Expanding the Fusion Workforce

Within the United States (U.S.), the Fusion Innovation Research Engine (FIRE) Collaboratives seek to accelerate fusion technology development through wide-ranging community-driven research activities that bring together industry, laboratories, research institutes, and academia. Increasing the maturity of key components and systems for future fusion power plants (FPPs) toward commercialization, will, by necessity, increase the need for knowledgeable engineers, designers, and researchers in industry capable of integrating and further improving these technologies within industry FPP concepts. Further, various modeling and simulation packages support these programs and are under-development within them to drive design iteration and the development of FPP digital twins. To derive the greatest benefit from model output and capabilities, training these same specialists will be vital. Thus, the development of effective and accessible software onboarding and professional development opportunities focused on fusion will be key to keeping the pace of growth high in the coming decade and beyond. A similar need exists within the advanced fission reactor community, driven by an ever-growing need for carbon-free baseload power for industrial and data center applications. Within the U.S. and around the world, several open-source packages and frameworks exist to support this endeavor, and two we will highlight here are the Multiphysics Object Oriented Simulation Environment (MOOSE) framework and OpenMC. These packages, notably, are also being utilized in the FIRE Collaboratives program. In this presentation, we will highlight the lessons learned from over 30 years of combined software development and training experience, focused on developing strong technical software foundations within the nuclear workforce, from students to professional engineers & scientists. We will connect this experience to present and emerging needs in the fusion energy community, and, finally, will outline possible paths forward to develop a large, robust, global fusion workforce.

70 - PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

Creating a Training Dataset for Semantic Segmentation of Canal Networks for Irrigation Modernization

Canal infrastructure has provided critical irrigation water to the western United States for over a century. To continue providing vital water resources to the semi-arid West, irrigation systems must undergo maintenance and modernization. Many canal companies are resource-constrained, and because funding opportunities often require detailed knowledge of existing infrastructure, they can struggle to secure financial capital. We address this problem by creating training data for a semantic segmentation deep learning model to map canal networks throughout the western United States. To create a diverse and robust training dataset, we labelled 1-m NAIP imagery with the locations of no canals, wet canals, and dry/vegetated canals. Since creating these datasets is time consuming, we first developed a preprocessing methodology to identify canals within our four study areas. We used NAIP imagery and provided canal centerline data to buffer, standardize, and cluster the imagery, automating the labeling process as much as possible. However, this still required manual cleaning and manual classification of canal type. Challenges arose when canals were interrupted (e.g., road culverts or piped sections) or when nearby features shared similar characteristics (e.g., irrigated fields, trees, and shadows). Combining automated preprocessing with manual refinement produced four detailed canal masks to be used in the semantic segmentation model developed by Richard Tapia.

13 - HYDRO ENERGY↗
DOE OSTI2021

DNTTD (Distributed Non-Negative Tensor Train Decomposition)

The era of exascale computing opens new venues for innovations and discoveries in many scientific, engineering, and commercial fields. However, with the exa flops also come the extra-large high-dimensional data generated by high performance computing. High-dimensional data is presented as multidimensional arrays, aka tensors. The presence of latent (not directly observable) structures in the tensor allows a unique representation and compression of the data by classical tensor factorization techniques. However, the classical tensor methods are not always stable or they can be exponential in their memory requirements, which makes them not suitable for high-dimensional tensors. Tensor train (TT) is a state-of-the-art tensor network introduced for factorization of high-dimensional tensors. TT transforms the initial high-dimensional tensor in a network of three-dimensional tensors that requires only a linear storage. Many real-world data, such as, density, temperature, population, probability, etc., are non-negative and for an easy interpretation, the algorithms preserving non-negativity are preferred. Here, we introduce a distributed non-negative tensor-train and demonstrate its scalability and the compression on synthetic and real world big datasets.

Bhattarai, Manish↗
DOE OSTI2021

General Employee Radiological Training Refresher 47968

This training is designed to refresh knowledge of basic radiological principles and terms, the risks from exposure to ionizing radiation, LANL’s radiological hazards and postings, ALARA methods, your responsibilities during emergencies, and how to obtain your personal radiological dose records.

61 RADIATION PROTECTION AND DOSIMETRY↗