Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “Operator learning”

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 1,405 records · Page 78
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Quantum optical classifier with superexponential speedup

Abstract Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational operations. Here, we present a quantum optical pattern recognition method for binary classification tasks. It classifies objects without reconstructing their images, using the rate of two-photon coincidences at the output of a Hong-Ou-Mandel interferometer, where both the input and the classifier parameters are encoded into single-photon states. Our method exhibits the behaviour of a classical neuron of unit depth. Once trained, it shows a constant $${{\mathcal{O}}}(1)$$ O ( 1 ) complexity in the number of computational operations and photons required by a single classification. This is a superexponential advantage over a classical artificial neuron.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2019

Unveiling the principle descriptor for predicting the electron inelastic mean free path based on a machine learning framework

The TPP-2M formula is the most popular empirical formula for the estimation of the electron inelastic mean free paths (IMFPs) in solids from several simple material parameters. The TPP-2M formula, however, poorly describes several materials because it relies heavily on the traditional least-squares analysis. Herein, we propose a new framework based on machine learning to overcome the weakness. This framework allows a selection from an enormous number of combined terms (descriptors) to build a new formula that describes the electron IMFPs. The resulting framework not only provides higher average accuracy and stability but also reveals the physics meanings of several newly found descriptors. Using the identified principle descriptors, a complete physics picture of electron IMFPs is obtained, including both single and collective electron behaviors of inelastic scattering. Our findings suggest that machine learning is robust and efficient to predict the IMFP and has great potential in building a regression framework for data-driven problems. Furthermore, this method could be applicable to find empirical formula for given experimental data using a series of parameters given a priori, holds potential to find a deeper connection between experimental data and a priori parameters.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Scenario adaptive disruption prediction study for next generation burning-plasma tokamaks

Next generation High Performance (HP) tokamaks risk damage from unmitigated disruptions at high current and power. Achieving reliable disruption prediction for a device's HP operation based on its Low Performance (LP) data is a key to its success. In this letter, through explorative data analysis and dedicated numerical experiments on multiple existing tokamaks, we demonstrate how the operational regimes of tokamaks can affect the power of a trained disruption predictor. First, our results suggest data-driven disruption predictors trained on abundant LP discharges work poorly on the HP regime of the same tokamak, which is a consequence of the distinct distributions of the tightly correlated signals related to disruptions in these two regimes. Second, we find that matching operational parameters among tokamaks strongly improves cross-machine accuracy which implies our model learns from the underlying scalings of dimensionless physics parameters like q 95 , β p and confirms the importance of these parameters in disruption physics and cross machine domain matching from the data-driven perspective. Finally, our results show in the absence of HP data from the target devices, the best predictivity of the HP regime for the target machine can be achieved by combining LP data from the target with HP data from other machines. Furthermore, these results provide a possible disruption predictor development strategy for next generation tokamaks, such as ITER and SPARC, and highlight the importance of developing baseline scenario discharges of future tokamaks on existing machines to collect more relevant disruptive data.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2017

Cyber-attack detection and accommodation algorithm for energy delivery systems

Cyber-attack accommodation in a cyber-physical system is to ensure system operation, integrity and availability while maintaining a reasonable operational performance under attack. In this paper, we present a novel cyber-attack accommodation algorithm by estimating the true operational states of the system with new boundary & performance constrained resilient estimators while the system is continuously operating and is under attack. Our approach is based on combining data driven machine learning and physics based domain knowledge with traditional resilient estimation. The results were evaluated using a high fidelity model-based simulation environment.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Data Science in Chemical Engineering: Applications to Molecular Science

Chemical engineering is being rapidly transformed by the tools of data science. On the horizon, artificial intelligence (AI) applications will impact a huge swath of our work, ranging from the discovery and design of new molecules to operations and manufacturing and many areas in between. Early adoption of data science, machine learning, and early examples of AI in chemical engineering has been rich with examples of molecular data science—the application tools for molecular discovery and property optimization at the atomic scale. Here, we summarize key advances in this nascent subfield while introducing molecular data science for a broad chemical engineering readership. We introduce the field through the concept of a molecular data science life cycle and discuss relevant aspects of five distinct phases of this process: creation of curated data sets, molecular representations, data-driven property prediction, generation of new molecules, and feasibility and synthesizability considerations.

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

tesuract v.1.0

SAND2021-14370 O Tesuract (tensor surrogate approximation and computation) provides Python tools to build machine learning regressors that include polynomial chaos expansions. The software also offers methods for creation regressors for tensor outputs such as spatially varying fields and using a reduced order modeling methodology. The library is built on and compatible with the scikit-learn API, which allows integration with one of the most ubiquitous machine learning Python libraries out there. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Chowdhary, Kamaljit↗
DOE OSTI2022

Emulating the Android Boot Process

Critical vulnerabilities continue to be discovered in the boot process of Android smartphones used around the world. The entire device's security is compromised if boot security is compromised, so any weakness presents undue risk to users. Vulnerabilities persist, in part, because independent security analysts lack access and appropriate tools. In response to this gap, we implemented a procedure for emulating the early phase of the Android boot process. This work demonstrated feasibility and utility of emulation in this space. By using HALucinator, we derived execution context and data flow, as well as incorporated peripheral hardware behavior. While smartphones with shared processors have substantial code overlap regardless of vendor, generational changes can have a significant impact. By applying our approach to older and modern devices, we learned interesting characteristics about the system. Such capabilities introduce new levels of introspection and operation understanding not previously available to mobile researchers.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Immersive Industrialized Construction Environments for Energy Efficiency Construction Workforce

The National Renewable Energy Laboratory is actively developing and testing Immersive Industrialized Construction Environments (IICE) for construction automation and worker-machine interaction to investigate possible solutions and increase workforce productivity. At full scope and matured functionality, IICE allows us to accelerate the development of and better explore industrialized construction approaches such as prefabrication. IICE also enables wider adoption of energy-efficient products and Industry 4.0 construction automation through worker-machine interaction pilots. Industry 4.0 and industrialized construction approaches can encourage workforce specialization in energy efficiency construction, address the lack of multi-skilled workers, and increase workforce productivity with construction automation. However, recent attempts to integrate these concepts with the industry have only been moderately successful. To address this, focusing the pedagogy on using a digital twin, its digital models, and virtual reality could make the experience of continuing education on construction automation more affordable, accessible, scalable, immersive, and safer, and could greatly improve the efficiency and robustness of the building and construction industry. IICE accurately represents the realities of construction uncertainties without having to create full scale physical prototypes of machines. In this paper, we address the following research question: How can a digital twin and its models in virtual reality enhance the learning experience and productivity of energy efficiency construction workers to gain the skills in operating Industry 4.0 components such as construction automation and handling energy-efficient products in industrialized construction factories and on-site? We introduce original research on developing IICE and present preliminary findings from time and motion pilot studies.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2025

Predicting cutoff L-shells of solar protons using the GPPSn particle dataset

Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for > 30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.

58 GEOSCIENCES↗
DOE OSTI2026

Analysis of Power Electronic Solutions for Wide-Range Wave Energy Input

Wave energy is a largely untapped energy source with the potential to provide 290 TWh per year to the continental United States. As a new technology, wave energy converters are less efficient and reliable compared to established energy sources, leaving the vast energy of the sea largely unutilized. Wave energy is uniquely inconsistent, with large fluctuations. Paired with harsh operating environments and difficulty in repair, the power electronics designed to harvest wave energy need to be reliable, cost-effective, and able to work in a wide operating band. The literature on existing power electronics in wave energy converters and lessons learned across other industries provide a framework of topologies to simulate. WEC-Sim, an open-source hydrodynamic platform in MATLAB Simulink developed by the National Laboratory of the Rockies, was used to simulate a wide range of power electronics under a variety of wave conditions. This paper provides a comprehensive investigation into existing wave energy power electronics converters, state-of-the-art topologies that can be applied to wave energy, and novel solutions to wave conversion.

16 TIDAL AND WAVE POWER↗
DOE OSTI2022

1.2.4.404 - Data-Driven Approach for Hydropower Plant Controller Prototyping Using Remote Hardware in the Loop (DR-HIL)

Real-time prototyping of hydropower plant controls is important for reducing the cost and the risk of field deployment. This project will 1) collect design and operational data from actual hydro plants and 2) use a physics-informed machine learning approach for real-time emulation of hydropower plants, including hydro turbine and hydrodynamics. The data-driven models will be interfaced with digital real-time simulation at NREL’s Flatirons campus for hardware-in-the-loop (HIL) testing of the governor hardware device or controller-HIL (CHIL). The proposed approach will also establish the connectivity based remote CHIL testing capability using real-time data streams from an actual hydro plant. This integrated hydro-plant emulation with CHIL will be used to prototype hydro-governor controls and eventually provide an opportunity to test hydropower integrated with various technologies (e.g. conventional and renewable generation, energy conversion, etc.) as HIL.

controls prototyping↗
DOE OSTI2013

Graphite Waste Tank Cleanup and Decontamination under the Marcoule UP1 D and D Program - 13166

The UP1 plant in Marcoule reprocessed nearly 20,000 tons of used natural uranium gas cooled reactor fuel coming from the first generation of civil nuclear reactors in France. During more than 40 years, the decladding operations produced thousands of tons of processed waste, mainly magnesium and graphite fragments. In the absence of a French repository for the graphite waste, the graphite sludge content of the storage pits had to be retrieved and transferred into a newer and safer pit. After an extensive R and D program, the equipment and process necessary for retrieval operations were designed, built and tested. The innovative process is mainly based on the use of two pumps (one to capture and the other one to transfer the sludge) working one after the other and a robotic arm mounted on a telescopic mast. A dedicated process was also set up for the removal of the biggest fragments. The retrieval of the most irradiating fragments was a challenge. Today, the first pit is totally empty and its stainless steel walls have been decontaminated using gels. In the second pit, the sludge retrieval and transfer operations have been almost completed. Most of the non-pumpable graphite fragments has been removed and transferred to a new storage pit. After more than 6 years of operations in sludge retrieval, a lot of experience was acquired from which important 'lessons learned' could be shared. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2017

Integrity Assessment of Hanford 242-A Evaporator Facility E-A-1 Re-boiler Vessel via Fluorescein Liquid Tracer Test Methods - 17357

The 242-A Evaporator Facility is critical to the United States Department of Energy's (DOE) Hanford Site clean-up mission due to limited space within the existing Double-Shell Tanks (DSTs) used to store nuclear and chemical waste until future processing. The 242-A Evaporator Facility is currently the sole waste volume reduction process used at Hanford, and the E-A-1 Re-boiler is essential to the Evaporator process. Due to the potential for cross-contamination between the Re-boiler tubes through which waste passes and the tube sheets of uncontaminated steam, an assessment of the Re-boiler vessel's integrity is required for continuing operations of the facility. In order to meet these requirements, an independent integrity assessment was designed to use fluorescein liquid tracer (LT) to verify the leak-tightness of the Re-boiler vessel in accordance with Washington River Protection Solutions, LLC (WRPS) tightness test standards, as well as, the ASME Boiler and Pressure Vessel (B and PV) Code, Section III, Division 1, NB-6300. LT test methodologies were evaluated and determined to be appropriate for a vessel with restricted access, such as the E-A-1 Re-boiler, as it provided the needed sensitivity, reliability, and cost; as well as, exceeded the leak-tightness specifications required by both WRPS and ASME B and PV Code standards. The integrity test requires water laced with fluorescein be circulated through the waste processing loop of the 242-A Evaporator and samples of steam condensate be collected and analyzed for traces of fluorescein to determine leak-tightness. Liquid condensate sampling was conducted prior to and during testing and the samples were analyzed for fluorescein tracer to determine the amount of fluorescein that flowed out of the tubes of the Re-boiler into the steam side of the vessel. Given the established criteria for 'leak-tight,' test results determined that the Re-boiler passed the integrity assessment. After further scrutiny of the 2014 test and data results, it was determined that although the Re-boiler was 'leak-tight,' as established by the test criteria, there was concern of future leakage. The DOE and the DOE Office of River Protection (ORP) requested that WRPS repeat the integrity assessment in two and a half years to determine if the potential leak was growing, which could threaten continuing operations of the 242-A Evaporator. The 2017 test has been redesigned using lessons learned from the 2014 test and was modified to account for ergonomic and safety considerations, ease of repeatability, and accuracy of results. The liquid tracer methodology and test plan developed for the Re-boiler will continue to be implemented in 5 year increments to monitor current and future integrity. The method may also prove useful for other aging components across the Hanford Site, where appropriate. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2019

Panel Session 62: Progress on Deep Repository Programs Around the World

This panel session focused on the progress of deep repository programs, worldwide. While efforts on a deep geological repository for SNF/UNF and HLW in the USA were terminated in 2010, several other repository programs continue to make considerable progress and deserve to be recognized. Panelists were invited from national, multi-national, and international repository programs and organizations to report upon the status and future as well as the perceived reasons underlying the success and the failure of components of their respective repository program. This panel provided a forum for interested parties to present, exchange, and take advantage of, as appropriate, repository progress, challenges, concepts/designs and other lessons learned around the world, including site characterization, site selection, research and development, licensing, construction, operation, and public acceptance and participation. Presentations proposed include: 1 - Cigeo (French Deep Geological Repository Project) update 2018 (Daniel Delort, Andra (France)); 2 - Progress on Deep Geological Repository Programs Around the World (Bruce McKirdy, Radioactive Waste Management (United Kingdom)); 3 - Geological disposal facility in Belgium (Didier Leonard, ONDRAF/NIRAS (Belgium))

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2020

Panel Session 89: Progress on Deep Repository Around the World

This panel focused on the progress of deep repository programs, worldwide. While efforts on a deep geological repository for SNF/UNF and HLW in the USA were terminated in 2010, several other repository programs continue to make considerable progress 10 and deserve to be recognized. Panelists from national, multi-national, and international repository programs and organizations reported on the status and future plans as well as the perceived reasons underlying the success and the failure of components of their respective repository program. This session provided a forum for interested parties to present, exchange, and take advantage of, as appropriate, repository progress, challenges, concepts/designs and other lessons learned around the world, including site characterization, site selection, research and development, licensing, construction, operation, and public acceptance and participation. Panelists with presentations: Switzerland's Geological Repository Program for Radioactive Waste (Tim Vietor); Update on NWMO's Progress in Implementing Canada's Plan (Sarah Hirschorn); Cigeo Project Progress and Next Milestones (Corinne Bauer)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2025

Ensuring Safe, Effective, and Reliable Use of Artificial Intelligence-Based Applications for Nuclear Electricity Generation through a Systems Approach

There is a significant potential to reduce operating and maintenance cost at nuclear power plants using artificial intelligence (AI) and machine learning (ML). For instance, AI/ML has the potential to significant improve work management processes, condition reporting, and plant surveillance activities. However, the nuclear industry has been slow in adopting AI/ML due to several multifaceted barriers discussed in this paper. This work presents such multidisciplinary approach intended to 1) accelerate industry adoption of AI/ML-based applications at nuclear power plants and 2) ensure their safe, reliable, and effective use. This approach is discussed further in this work and will be used to address overarching challenges associated with AI deployment at scale to provide industry guidance that support accelerated adoption of AI/ML technologies throughout the industry.

99 - GENERAL AND MISCELLANEOUS↗
DOE OSTI2021

Luna

SAND2021-15051 O Luna is an experimental cognitive architecture that seeks to enable continuous learning in an embodied agent. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Dellana, Ryan↗