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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 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Applications of physics informed neural operators

Abstract We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code , an interactive website to visualize the predictions of our PINOs, and a tutorial for their use at the Data and Learning Hub for Science .

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

A review of artificial intelligence applications in manufacturing operations

Abstract Artificial intelligence (AI) and machine learning (ML) can improve manufacturing efficiency, productivity, and sustainability. However, using AI in manufacturing also presents several challenges, including issues with data acquisition and management, human resources, infrastructure, as well as security risks, trust, and implementation challenges. For example, getting the data needed to train AI models can be difficult for rare events or costly for large datasets that need labeling. AI models can also pose security risks when integrated into industrial control systems. In addition, some industry players may be hesitant to use AI due to a lack of trust or understanding of how it works. Despite these challenges, AI has the potential to be extremely helpful in manufacturing, particularly in applications such as predictive maintenance, quality assurance, and process optimization. It is important to consider the specific needs and capabilities of each manufacturing scenario when deciding whether and how to use AI in manufacturing. This review identifies current developments, challenges, and future directions in AI/ML relevant to manufacturing, with the goal of improving understanding of AI/ML technologies available for solving manufacturing problems, providing decision‐support for prioritizing and selecting appropriate AI/ML technologies, and identifying areas where further research can yield transformational returns for the industry. Early experience suggests that AI/ML can have significant cost and efficiency benefits in manufacturing, especially when combined with the ability to capture enormous amounts of data from manufacturing systems.

Plathottam, Siby Jose↗
DOE OSTI2021

An optimization-based approach to parameter learning for fractional type nonlocal models

Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this study we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Review and Status of Wind Integration and Transmission in the United States. Key Issues and Lessons Learned

The objective in electric power system operation is to use generation and transmission resources within organizational constraints and operational rules and regulations to reliably and costeffectively balance load and generation. To meet this objective, system operational practices have been created to accommodate the innate variability and uncertainty that comes from a variety of sources, such as uncertainty of demand forecasts, whether a specific generating unit will be available when called upon, the variability of demand from many different types of customers, and others. As more wind power is connected to the power system, operating experiences acquired during the past several years have generally confirmed the findings of wind integration studies: wind energy increases the level of variability and uncertainty that a system operator must manage.

17 WIND ENERGY↗
DOE OSTI2020

An optimization-based approach to parameter learning for fractional type nonlocal models

Nonlocal operators of fractional type are a popular modeling choice for applications that do not adhere to classical diffusive behavior; however, one major challenge in nonlocal simulations is the selection of model parameters. In this work we propose an optimization-based approach to parameter identification for fractional models with an optional truncation radius. We formulate the inference problem as an optimal control problem where the objective is to minimize the discrepancy between observed data and an approximate solution of the model, and the control variables are the fractional order and the truncation length. For the numerical solution of the minimization problem we propose a gradient-based approach, where we enhance the numerical performance by an approximation of the bilinear form of the state equation and its derivative with respect to the fractional order. Several numerical tests in one and two dimensions illustrate the theoretical results and show the robustness and applicability of our method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2015

Cold crucible deployment in La Hague facility: the feedback from the first four years of operation - 15119

In April 2010, a Cold Crucible Induction Melter (CCIM) started hot operation for the first time ever in an existing very high active facility (R7) at La Hague. This was the culmination of more than two decades of R and D involving progressive process and technological development. The industrial deployment included extensive cleaning of the hot cell, removal of the previous melter, exchange of several mechanical and process components in the cell, and implementation of the various additional utilities and components necessary for operation of the new CCIM. The design and implementation phases for this deployment were described in a previous paper at WM. The CCIM has now been operated for more than four years, essentially processing active effluents from D and D operations and high-level liquid waste from reprocessed U-Mo-Sn-Al spent fuel. During this period, many data have been collected to confirm the process parameters that were defined during the qualification of this innovating process. Even if some difficulties occurred, the experience of the operating teams with the support of engineering and R and D allow managing them. This paper presents the start-up methodology and history of the cold crucible deployment in La Hague facility with the feedback from the first years of operation. The production records with the different liquid solutions treated in the process are itemized. The lessons learned during the first four years of operation of the CCIM are presented with the difficulties encountered and the solutions implemented, emphasizing the benefits of a close integration between RD, engineering, and operation teams. (authors)

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

Improving Gas Reactor Design with Complex Non-Standard Reaction Mechanisms in a Reactive Flow Model

Fluorinated gases are critical to a number of high-technology industries including semiconductor manufacturing and related processes such as flat panel display, LED, and photovoltaic manufacturing. These fluorinated gases have very high global warming potentials (GWPs) and, unfortunately are chemically very stable, in some cases lasting for thousands of years in the atmosphere. For environmental reasons, it is imperative that the release of these gases be minimized, but existing methods to destroy them requires a significant amount of energy. The goal of this project was to develop a more energy efficient method of destroying these high GWP gases using computational fluid dynamics, but the computational requirements are significant. In this project, we bring together resources available at the national labs, including high-performance computing hardware and modern computational methods, in order to explore an industrially important problem with key environmental and economic impact. In this project, we examined the state-of-the-art models for the physical processes involved, and made subtle changes to arrive at alternative formulations considerably better suited for computation and study. We examined several representative scenarios and developed a set of analyses techniques and tools that could be used to explore the systems in much greater detail. This exploratory project has demonstrated to ALZETA that HPC can indeed provide a unique view into their complex engineering problem - one that is simply inaccessible by any other means. The tools allow us to explore the details of the chemical process, and allow one to construct "what-if" scenarios to address shortcomings of the physical device in its current operating modes. However, as with many sources of completely new information, many new questions are illuminated with this capability. It will take considerable time, effort and experience before we learn to probe the solutions, pose operating scenarios and confirm modifications to the device that lead to real engineering advances.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

The Modular Borehole Monitoring Program. A research program to optimize well-based monitoring for geologic carbon sequestration

Understanding the impacts caused by injection of large volumes of CO 2 in the deep subsurface necessitates a comprehensive monitoring strategy. While surface-based and other remote geophysical methods can provide information on the general morphology of a CO 2 plume, verification of the geochemical conditions and validation of the remote sensing data requires measurements from boreholes that penetrate the storage formation. Unfortunately, the high cost of drilling deep wellbores and deploying instrumentation systems constrains the number of dedicated monitoring borings as well as limits the technologies that can be incorporated in a borehole completion. The objective of the Modular Borehole Monitoring (MBM) Program was to develop a robust suite of well-based tools optimized for subsurface monitoring of CO 2 that could meet the needs of a comprehensive well-based monitoring program. It should have enough flexibility to be easily reconfigured for various reservoir geometries and geologies. The MBM Program sought to provide storage operators with a turn-key fully engineered design that incorporated key technologies, function over the decades long time-span necessary for post-closure reservoir monitoring, and meet industry acceptable risk profiles for deep-well installations. While still within the conceptual design phase of the MBM program, the SECARB Anthropogenic Test in Citronelle, Alabama, USA was identified as a deployment site for our engineered monitoring systems. The initial step in designing the Citronelle MBM system was to down-select from the various monitoring tools available to include technologies that we considered essential to any program. Monitoring methods selected included U-tube geochemical sampling, discrete quartz pressure and temperature gauges, an integrated fibre-optic bundle consisting of distributed temperature and heat-pulse sensing, and a sparse string of conventional 3C-geophones. While not originally planned within the initial MBM work scope, the fibre-optic cable was able to also be used for the emergent technology of distributed acoustic sensing. The MBM monitoring string was installed in March, 2012. To date, the Citronelle MBM instruments continue to operate reliably. Results and lessons learned from the Citronelle MBM deployment are addressed along with examples of data being collected.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

The 6 GeV TMD Program at Jefferson Lab

The study of the transverse momentum dependent parton distributions (TMDs) of the nucleon in semi-inclusive deep-inelastic scattering (SIDIS) has emerged as one of the major physics motivations driving the experimental program using the upgraded 11 GeV electron beam at Jefferson Lab’s Continuous Electron Beam Accelerator Facility (CEBAF). The accelerator construction phase of the CEBAF upgrade is essentially complete and commissioning of the accelerator has begun as of April, 2014. As the new era of CEBAF operations begins, it is appropriate to review the body of published and forthcoming results on TMDs from the 6 GeV era of CEBAF operations, discuss what has been learned, and discuss the key challenges and opportunities for the 11 GeV SIDIS program of CEBAF.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Real-Time Evolution and Deployment of Neuromorphic Computing at The Edge

Extremely low power neuromorphic systems are well-suited for deployment to the edge for many applications. In many use cases of neuromorphic computing for control, a spiking neural network is trained off-line using a simulation and then deployed to a neuromorphic system at the edge, where it will operate without ongoing training or learning. However, it may be desirable to continue training or learning at the edge to refine or adapt to the real-world system. In this work, we propose an approach for performing real-time evolutionary optimization for spiking neural networks for neuromorphic deployment at the edge. In particular, we propose a combination of simulation and real-world evaluations, along with feedback from the real-world environment, to train spiking neural networks for continuous deployment to the edge. We show that the real-time evolution at the edge approach achieves comparable performance to an evolution approach that requires constant evaluation in the realworld environment.

Schuman, Catherine↗
DOE OSTI2022

The COVID-19 High-Performance Computing Consortium

In March of 2020, recognizing the potential of High Performance Computing (HPC) to accelerate understanding and the pace of scientific discovery in the fight to stop COVID-19, the HPC community assembled the largest collection of worldwide HPC resources to enable COVID-19 researchers worldwide to advance their critical efforts. Amazingly, the COVID-19 HPC Consortium was formed within one week through the joint effort of the Office of Science and Technology Policy (OSTP), the U.S. Department of Energy (DOE), the National Science Foundation (NSF), and IBM to create a unique public–private partnership between government, industry, and academic leaders. This article is the Consortium's story–how the Consortium was created, its founding members, what it provides, how it works, and its accomplishments. We will reflect on the lessons learned from the creation and operation of the Consortium and describe how the features of the Consortium could be sustained as a National Strategic Computing Reserve to ensure the nation is prepared for future crises.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Exploring Capability of Multimodal Foundation Model for Image-based Fault Detection of Photovoltaic Modules

Multimodal Foundation Model (MFM), like ChatGPT and Gemini, have emerged as powerful tools for their exceptional natural language processing capabilities and their emerging potential in image analysis. This paper investigates the application of MFMs for photovoltaic (PV) fault detection through image analysis, focusing on ChatGPT 4.0 and Gemini 1.5 Pro. Three types of PV images and the corresponding common PV faults are detected: bird droppings using visible images, cell cracks via electroluminescence (EL) images, and hotspots using infrared (IR) images. Among the two models, Gemini 1.5 Pro demonstrated superior performance, achieving near-perfect results with an average F1 score of 0.97, consistently outperforming ChatGPT 4.0 in accuracy and reliability. Unlike traditional machine learning (ML) models, MFMs can operate in a zero shot manner that does not require additional training by the user, and the input images are not limited by size, angle, scope, or PV technology. The strong adaptability and user-friendliness make MFM a promising tool for analyzing PV images and advancing health monitoring for PV modules.

Li, Baojie↗
DOE OSTI2021

Improving the Accuracy of Clustering Electric Utility Net Load Data using Dynamic Time Warping

Identifying patterns in electric utility net load data in a time-series format is very useful in preparing the operation for next day. Machine learning algorithms have been used in other domains and those concepts are applied in this paper on real-world net load measurement data. Clustering is the practice of grouping data with similar characteristics as determined by the distance measure. The K-means clustering algorithm is utilized here with actual electric utility data. The paper uses the standard distance measure, Euclidean distance (ED), and compares its performance against the dynamic time warping (DTW) measure. An actual case study with real data is presented, and DTW distance measure-based method observed to result better accuracy compared to the ED based method for substation net load measurements predominantly with residential customers.

clustering↗
DOE OSTI2020

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations

42 ENGINEERING↗
DOE OSTI2014

SU-E-I-42: Normalized Embryo/fetus Doses for Fluoroscopically Guided Pacemaker Implantation Procedures Calculated Using a Monte Carlo Technique

Purpose: It is well known that pacemaker implantation is sometimes needed in pregnant patients with symptomatic bradycardia. To our knowledge, there is no reported experience regarding radiation doses to the unborn child resulting from fluoroscopy during pacemaker implantation. The purpose of the current study was to develop a method for estimating embryo/fetus dose from fluoroscopically guided pacemaker implantation procedures performed on pregnant patients during all trimesters of gestation. Methods: The Monte Carlo N-Particle (MCNP) radiation transport code was employed in this study. Three mathematical anthropomorphic phantoms representing the average pregnant patient at the first, second and third trimesters of gestation were generated using Bodybuilder software (White Rock science, White Rock, NM). The normalized embryo/fetus dose from the posteroanterior (PA), the 30° left-anterior oblique (LAO) and the 30° right-anterior oblique (RAO) projections were calculated for a wide range of kVp (50–120 kVp) and total filtration values (2.5–9.0 mm Al). Results: The results consist of radiation doses normalized to a) entrance skin dose (ESD) and b) dose area product (DAP) so that the dose to the unborn child from any fluoroscopic technique and x-ray device used can be calculated. ESD normalized doses ranged from 0.008 (PA, first trimester) to 2.519 μGy/mGy (RAO, third trimester). DAP normalized doses ranged from 0.051 (PA, first trimester) to 12.852 μGy/Gycm2 (RAO, third trimester). Conclusion: Embryo/fetus doses from fluoroscopically guided pacemaker implantation procedures performed on pregnant patients during all stages of gestation can be estimated using the method developed in this study. This study was supported by the Greek Ministry of Education and Religious Affairs, General Secretariat for Research and Technology, Operational Program ‘Education and Lifelong Learning’, ARISTIA (Research project: CONCERT)

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-E-T-580: On the Significance of Model Based Dosimetry for Breast and Head and Neck 192Ir HDR Brachytherapy

Purpose: To assess the dosimetric and radiobiological differences between TG43-based and model-based dosimetry in the treatment planning of {sup 192}Ir HDR brachytherapy for breast and head and neck cancer. Methods: Two cohorts of 57 Accelerated Partial Breast Irradiation (APBI) and 22 head and neck (H&N) patients with oral cavity carcinoma were studied. Dosimetry for the treatment plans was performed using the TG43 algorithm of the Oncentra Brachy v4.4 treatment planning system (TPS). Corresponding Monte Carlo (MC) simulations were performed using MCNP6 with input files automatically prepared by the BrachyGuide software tool from DICOM RT plan data. TG43 and MC data were compared in terms of % dose differences, Dose Volume Histograms (DVHs) and related indices of clinical interest for the Planning Target Volume (PTV) and the Organs-At-Risk (OARs). A radiobiological analysis was also performed using the Equivalent Uniform Dose (EUD), mean survival fraction (S) and Tumor Control Probability (TCP) for the PTV, and the Normal Tissue Control Probability (N TCP) and the generalized EUD (gEUD) for the OARs. Significance testing of the observed differences performed using the Wilcoxon paired sample test. Results: Differences between TG43 and MC DVH indices, associated with the increased corresponding local % dose differences observed, were statistically significant. This is mainly attributed to their consistency however, since TG43 agrees closely with MC for the majority of DVH and radiobiological parameters in both patient cohorts. Differences varied considerably among patients only for the ipsilateral lung and ribs in the APBI cohort, with a strong correlation to target location. Conclusion: While the consistency and magnitude of differences in the majority of clinically relevant DVH indices imply that no change is needed in the treatment planning practice, individualized dosimetry improves accuracy and addresses instances of inter-patient variability observed. Research co-financed by the ESF and Greek funds through the Operational Program Education and Lifelong Learning Investing in Knowledge Society of the NSRF. Research Funding Program Aristeia. Nucletron, an Elekta company (Veenendaal, The Netherlands) is gratefully acknowledged for providing Oncentra Brachy v4.4 for research purposes.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2015

SU-F-BRA-11: An Experimental Commissioning Test of Brachytherapy MBDCA Dosimetry, Based On a Commercial Radiochromic Gel/optical CT System

Purpose: To implement a 3D dose verification procedure of Model-Based Dose Calculation Algorithms (MBDCAs) for {sup 192}Ir HDR brachytherapy, based on a novel Ferrous Xylenol-orange gel (FXG) and optical CT read-out. Methods: The TruView gel was employed for absolute dosimetry in conjunction with cone-beam optical CT read-out with the VISTA scanner (both from Modus Medical Inc, London, ON, Canada). A multi-catheter skin flap was attached to a cylindrical PETE jar (d=9.6cm, h=16cm) filled with FXG, which served as both the dosimeter and the water equivalent phantom of bounded dimensions. X- ray CT image series of the jar with flap attached was imported to Oncentra Brachy v.4.5. A treatment plan consisting of 8 catheters and 56 dwell positions was generated, and Oncentra-ACE MBDCA as well as TG43 dose results were exported for further evaluation. The irradiation was carried out with a microSelecton v2 source. The FXG dose-response, measured via an electron irradiation of a second dosimeter from the same batch, was linear (R2>0.999) at least up to 12Gy. A MCNP6 input file was prepared from the DICOM-RT plan data using BrachyGuide to facilitate Monte Carlo (MC) simulation dosimetry in the actual experimental geometry. Agreement between experimental (reference) and calculated dose distributions was evaluated using the 3D gamma index (GI) method with criteria (5%-2mm applied locally) determined from uncertainty analysis. Results: The TG-43 GI failed, as expected, in the majority of voxels away from the flap (pass rate 59% for D>0.8Gy, corresponding to 10% of prescribed dose). ACE performed significantly better (corresponding pass rate 92%). The GI evaluation for the MC data (corresponding pass rate 97%) failed mainly at low dose points of increased uncertainty. Conclusion: FXG gel/optical CT is an efficient method for level-2 commissioning of brachytherapy MBDCAs. Target dosimetry is not affected from uncertainty introduced by TG43 assumptions in 192Ir skin brachytherapy. Research co-financed by the ESF and Greek funds through the Operational Program Education and Lifelong Learning Investing in Knowledge Society of the NSRF. Research Funding Program: Aristeia. Modus Medical Devices Inc. provided a TruView dosimeter batch and Nucletron, and Elekta company, provided access to Oncentra Brachy v4.5, for research purposes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2015

SU-F-BRA-12: End-User Oriented Tools and Procedures for Testing Brachytherapy TPSs Employing MBDCAs

Purpose: To develop user-oriented tools for commissioning and dosimetry testing of {sup 192}Ir brachytherapy treatment planning systems (TPSs) employing model based dose calculation algorithms (MBDCAs). Methods: A software tool (BrachyGuide) has been developed for the automatic generation of MCNP6 input files from any CT based plan exported in DICOM RT format from Elekta and Varian TPSs. BrachyGuide also facilitates the evaluation of imported Monte Carlo (MC) and TPS dose distributions in terms of % dose differences and gamma index (CT overlaid colormaps or relative frequency plots) as well as DVHs and related indices. For users not equipped to perform MC, a set of computational models was prepared in DICOM format, accompanied by treatment plans and corresponding MCNP6 generated reference data. BrachyGuide can then be used to compare institutional and reference data as per TG186. The model set includes a water sphere with the MBDCA WG {sup 192}Ir source placed centrically and in two eccentric positions, a water sphere with cubic bone and lung inhomogeneities and a five source dwells plan, and a patient equivalent model with an Accelerated Partial Breast Irradiation (APBI) plan. Results: The tools developed were used for the dosimetry testing of the Acuros and ACE MBDCAs implemented in BrachyVision v.13 and Oncentra Brachy v.4.5, respectively. Findings were consistent with previous results in the literature. Besides points close to the source dwells, Acuros was found to agree within type A uncertainties with the reference MC results. Differences greater than MC type A uncertainty were observed for ACE at distances >5cm from the source dwells and in bone. Conclusion: The tools developed are efficient for brachytherapy MBDCA planning commissioning and testing. Since they are appropriate for distribution over the web, they will be put at the AAPM WG MBDCA’s disposal. Research co-financed by the ESF and Greek funds. NSRF operational Program: Education and Lifelong Learning Investing in Knowledge Society-Aristeia. Varian Medical Systems and Nucletron, an Elekta company provided access to TPSs for research purposes. Miss Peppa was supported by IKY-fellowships of excellence for postgraduate studies in Greece,Siemens Program.

60 APPLIED LIFE SCIENCES↗