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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 1,243 records · Page 69
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Physics-Informed Neural Networks for Heat Transfer Problems

Abstract Physics-informed neural networks (PINNs) have gained popularity across different engineering fields due to their effectiveness in solving realistic problems with noisy data and often partially missing physics. In PINNs, automatic differentiation is leveraged to evaluate differential operators without discretization errors, and a multitask learning problem is defined in order to simultaneously fit observed data while respecting the underlying governing laws of physics. Here, we present applications of PINNs to various prototype heat transfer problems, targeting in particular realistic conditions not readily tackled with traditional computational methods. To this end, we first consider forced and mixed convection with unknown thermal boundary conditions on the heated surfaces and aim to obtain the temperature and velocity fields everywhere in the domain, including the boundaries, given some sparse temperature measurements. We also consider the prototype Stefan problem for two-phase flow, aiming to infer the moving interface, the velocity and temperature fields everywhere as well as the different conductivities of a solid and a liquid phase, given a few temperature measurements inside the domain. Finally, we present some realistic industrial applications related to power electronics to highlight the practicality of PINNs as well as the effective use of neural networks in solving general heat transfer problems of industrial complexity. Taken together, the results presented herein demonstrate that PINNs not only can solve ill-posed problems, which are beyond the reach of traditional computational methods, but they can also bridge the gap between computational and experimental heat transfer.

Engineering↗
DOE OSTI2020

Initial Implementation Of Machine Learning System for SRF Cavity Fault Classification at CEBAF

The Continuous Electron Beam Accelerator Facility (CEBAF) at Jefferson Laboratory is high power CW electron accelerator. It uses a mixture of two types of SRF modules, older, lower energy C20/C50 modules and newer higher energy C100 modules, arrayed in two anti-parallel linear accelerators. Accurately classifying the type of cavity faults is essential to maintaining and improving accelerator performance. Each C100 cryomodule contains eight 7-cell cavities. When a cavity fault occurs within a cryomodule, all eight cavities generate 17 waveforms each containing 8192 points. This data is exported from EPICS and saved for review. Analysis of these waveforms is time intensive and requires subject matter expertise (SME). SMEs examine the data from each event and label it according to one of several known cavity fault types. Multiple machine learning models have been developed on this labeled dataset with sufficient performance to warrant the creation of a limited machine learning software system for use by accelerator operations staff. This paper discusses the transition from model development to implementation of a prototype system.

Carpenter, A.↗
DOE OSTI2021

Bridging Power System Protection Gaps with Data-driven Approaches

Protection is a critical function in power systems to avoid equipment damage, maintain personnel safety, and support system reliability. However, current protective relay technology cannot adequately protect equipment and personnel from effects of some events; these deficiencies are termed protection gaps. In this research, a data-driven approach is proposed to complement traditional protection technology and distinguish fault conditions from transients caused by normal operations. A convolutional neural network (CNN) based fault detection approach is implemented to achieve data translation invariance of the time-series input data. As a result, the data-driven method can accurately detect system faults despite variation and noise in the input data. In addition, using the CNN–based method avoids the complicated manual feature extraction procedure required by many traditional data-driven methods. The effectiveness of the proposed approach is tested on four kinds of protection gaps: high impedance faults, transformer/generator inter-turn faults, distribution system PV circuit faults, and the mis-operation situations of Zone 3 line protection relays operating under system stress. Finally, a transfer learning method is also proposed to address the common issue of data-driven methods for which real-world training data are scarce. Extensive study results demonstrate that the proposed approach can accurately bridge power system protection gaps.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

H2@Scale Program Multi-Party Cooperative Research and Development Agreement: California Hydrogen Infrastructure Research Consortium Task (Cooperative Research and Development Final Report, CRADA Number CRD-18-00754)

Many stakeholders are working on hydrogen and fuel cell products, markets, requirements, mandates, and policies. California has been leading the way for hydrogen infrastructure and fuel cell electric vehicle (FCEV) deployment. This leadership has advanced a hydrogen network that is not duplicated anywhere in the United States and is unique in the world for its focus on providing a retail fueling experience. In addition, the advancements have identified many lessons learned for hydrogen infrastructure development, deployment, and operation. Other interested states and countries are using California's experience as a model case, making success in California paramount to enabling market acceleration and uptake in the United States. The technical research capability of the national laboratories can be used to assist California in decisions and evaluations, as well as to verify solutions to problems impacting the industry. Because these challenges cannot be addressed by one agency or one laboratory, a hydrogen research consortium has been organized to combine and collaborate. The collaboration aims to ensure that data are available to evaluate projects and inform decision makers, independently verify and validate component solutions, provide experimental results for future hydrogen infrastructure, increase the availability of technical experts for quick-need issues for California hydrogen infrastructure development, deployment, operation, and technology advances. The proposed tasks include data collection from operational stations, component failure fix verification (i.e. nozzle freeze lock), new fueling methods for medium and heavy duty applications, and ensuring hydrogen quality is maintained. U.S. leadership for hydrogen technologies is rooted in California, a location for implementing many H2@Scale pathways such as reducing curtailment and stranded resources, reducing petroleum use and emissions, and developing and creating jobs.

08 HYDROGEN↗
DOE OSTI2023

Deep Learning Accelerated History Matching and Forecasting in Geologic CO 2 Sequestration [Slides]

We have successfully developed a predictive workflow for the Geologic CO 2 Sequestration using a deep learning model based on the Fourier Neural Operator. The workflow has high accuracy for predicting pressure & saturation during the injection and post-injection periods and has decent accuracy for predicting water and CO 2 production rates during injection period. It is necessary to train exclusive DNN models for pressure prediction in long term GCS, but for saturation prediction, we can use a single model to predict it.

58 GEOSCIENCES↗
DOE OSTI2020

Coastal Wetland Carbon Processes in a Warmer Climate (Final Report)

Coastal wetlands are global hotspots of carbon storage. Marshes, mangroves, and seagrass meadows account for about half of the total marine soil carbon budget and bury carbon at rates roughly equivalent to terrestrial forests despite occupying just 2.5% of Earth’s land area. Coastal wetlands have recently been recognized as important for storing carbon, but the future sink strength and carbon stock stability of these systems is uncertain because global change drivers such as temperature and elevated CO 2 disturb the complex ecology that drives high rates of soil carbon sequestration. The dynamics of coastal wetland carbon pools are not presently represented in earth system models. Our goals are to understand the influence of warming and elevated CO 2 on C cycling and to forecast the stability of tidal wetland soil carbon pools at the terrestrial-aquatic interface. We designed and implemented a novel field manipulation of elevated CO 2 and temperature and operated it from 2016 to 2020. We learned that plant communities and microbial communities respond differently to rising temperature, and that the net effects of these responses means that a modest amount of warming will increase tidal marsh carbon sequestration, while warming above that level will diminish the potential for additional carbon sequestration. In mathematical terms the system showed non-linear responses which are important for building forecast models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

Evaluating Operators in Deep Neural Networks for Improving Performance Portability of SYCL

SYCL is a portable programming model for heterogeneous computing, so it is important to obtain reasonable performance portability of SYCL. Towards the goal of better understanding and improving performance portability of SYCL for machine learning workloads, we have been developing benchmarks for basic operators in deep neural networks (DNNs). These operators could be offloaded to heterogeneous computing devices such as graphics processing units (GPUs) to speed up computation. In this work, we introduce the benchmarks, evaluate the performance of the operators on GPU-based systems, and describe the causes of the performance gap between the SYCL and Compute Unified Device Architecture (CUDA) kernels. We find that the causes are related to the utilization of the texture cache for read-only data, optimization of the memory accesses with strength reduction, shared local memory accesses, and register usage per thread. We hope that the efforts of developing benchmarks for studying performance portability will stimulate discussion and interactions within the community.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Determining SMEFT and PDF parameters simultaneously based on the CTEQ-TEA framework

The SM effective field theory (SMEFT) provides a model-independent and systematically improvable framework for new physics searches. In this talk, we outline our approach of simultaneously fitting SMEFT parameters and Probability Density Functions (PDFs) in an extension of the CT18 global analysis framework. To enhance the efficiency of our global fitting and Lagrange multiplier scans, we leverage machine-learning techniques. We focus on several representative operators relevant to top-quark pair production and jet production. Through this approach, we establish self-consistent limitations on the associated Wilson coefficients, and explore the correlations between these Wilson coefficients and the PDFs.

Shen, XiaoMin↗
DOE OSTI2018

Field performance of a district central ground source heat pump system in the US

This case study discusses the field performance of a district central ground source heat pump (GSHP) system installed at Ball State University (BSU) in Muncie, IN., USA. This district GSHP system replaces the existing central steam plant and water-cooled chiller plants and designed to serve 47 major buildings in BSU. The field performance of the GSHP system was analyzed based on measured data from August 2015 through July 2016, construction drawings, maintenance records, personal communications, and construction costs. It was compared with the performance of a baseline scenario- a conventional water-cooled chiller and natural-gas-fired boiler system, both of which meet the minimum energy efficiencies allowed by the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE 90.1-2013). The comparison was made to determine source energy savings, energy cost savings, and CO2 emission reductions achieved by the GSHP system. This paper presents the results of the analysis, the lessons learned, and recommendations for improvement in the operation of this district central GSHP system.

Im, Piljae↗
DOE OSTI2019

Representing Small Commercial Building Faults in EnergyPlus, Part I: Model Development

Small commercial buildings (those with less than approximately 1000 m2 of total floor area) often do not have access to cost-effective automated fault detection and diagnosis (AFDD) tools for maintaining efficient building operations. AFDD tools based on machine-learning algorithms hold promise for lowering cost barriers for AFDD in small commercial buildings; however, such algorithms require access to high-quality training data that is often difficult to obtain. To fill the gap in this research area, this study covers the development (Part I) and validation (Part II) of fault models that can be used with the building energy modeling software EnergyPlus® and OpenStudio® to generate a cost-effective training data set for developing AFDD algorithms. Part I (this paper) presents a library of fault models, including detailed descriptions of each fault model structure and their implementation with EnergyPlus. This paper also discusses a case study of training data set generation, representing an actual building.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Prediction of DIII-D Pedestal Structure from Externally Controllable Parameters

The sharp increase of pressure at the edge of a high confinement mode (H-mode) plasma, the pedestal, strongly impacts overall plasma performance. Predicting the pedestal is a necessity to control and optimize tokamak operations. An experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (ne) and electron temperature (Te) profiles as well as the separatrix ne from externally controllable parameters such as the plasma shape, heating method and power, and gas puff rate and integrated gas puff. The OMFIT framework was used with DIII-D data to efficiently, robustly, and automatically build a database of pedestal parameters to train machine learning models. Database creation was enabled by the search engine tool for DIII-D data, TokSearch, which parallelizes data fetching, enabling fast searches through basic signals of thousands of DIII-D shots and selection of relevant time intervals. Principal Component Analysis (PCA) separated the database into three clusters that represent classes of plasma shapes that are regularly used in DIII-D. The most important parameters for setting the pedestal structure were plasma current (Ip), toroidal magnetic field (Bφ), neutral beam heating power (PNBI) and shaping quantities. The Deep Jointly Informed Neural Networks (DJINN) algorithm was applied to identify suitable neural network (NN) architectures that appropriately capture the features of the pedestal database. Separate NNs were implemented for each pedestal parameter, and ensembling methods were used to improve the prediction accuracy and allowed estimation of the prediction uncertainty. The pedestal predictions of the test dataset lie within the measurement uncertainties of the pedestal parameters. The NN outperformed simple Linear Regression (LR) analysis, indicating non-linear dependencies in the pedestal structure. The presented achievements illustrate a promising path for future research, using feature extraction to infer experimental trends and thereby improve pedestal models as well as deploying NN for a fast pedestal prediction in DIII-D scenario development.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
NASA NTRS1988

A mathematical theory of learning control for linear discrete multivariable systems

When tracking control systems are used in repetitive operations such as robots in various manufacturing processes, the controller will make the same errors repeatedly. Here consideration is given to learning controllers that look at the tracking errors in each repetition of the process and adjust the control to decrease these errors in the next repetition. A general formalism is developed for learning control of discrete-time (time-varying or time-invariant) linear multivariable systems. Methods of specifying a desired trajectory (such that the trajectory can actually be performed by the discrete system) are discussed, and learning controllers are developed. Stability criteria are obtained which are relatively easy to use to insure convergence of the learning process, and proper gain settings are discussed in light of measurement noise and system uncertainties.

Phan, Minh↗
NASA NTRS1999

An Overview of NASA's Current In-Situ Consumable Production (ISCP) Development Activities and Goals

Utilization of extraterrestrial resources, or In-Situ Resource Utilization (ISRU), is viewed as an enabling technology for the exploration and commercial exploitation of our solar system. It is fundamental to any program of extended human presence and operation on other extraterrestrial bodies that we learn how to utilize the indigenous resources. The chief benefits of ISRU are that it can reduce the mass, cost, and risk of robotic and human exploration while providing capabilities that the enable commercial development of space. A key subset of ISRU which has significant cost and risk reduction benefits for robotic and human exploration, and which requires a minimum of infrastructure, is In-Situ Consumable Production (ISCP). ISCP involves acquiring, manufacturing, and storing propellants for planetary ascent or Earth return vehicles, gases and water for crew and life support, and fuel cell reagents for power generation by using resources available at the site of exploration. Since propellant mass typically makes up 60 to 80% of the ascent or Earth return vehicle mass, In-Situ Propellant Production (ISPP) on the Lunar or Mars surface can significantly reduce the overall mass for the return vehicle needed to be brought from Earth. Systems analyses of human Mars missions have indicated that solely producing propellants on the surface of Mars by processing atmospheric carbon dioxide can reduce the initial mission mass required in low Earth orbit by approximately 20% as compared to carrying all required propellant to the Mars surface from Earth. An even greater leverage can occur for Mars missions when in-situ water can be processed.

Sanders, G. B.↗
NASA NTRS1999

An Overview of NASA's current In-Situ Consumable Production (ISCP) Development Activities and Goals

Utilization of extraterrestrial resources, or In-Situ Resource Utilization (ISRU), is viewed as an enabling technology for the exploration and commercial exploitation of our solar system. It is fundamental to any program of extended human presence and operation on other extraterrestrial bodies that we learn how to utilize the indigenous resources. The chief benefits of ISRU are that it can reduce the mass, cost, and risk of robotic and human exploration while providing capabilities that the enable commercial development of space. A key subset of ISRU which has significant cost and risk reduction benefits for robotic and human exploration, and which requires a minimum of infrastructure, is In-Situ Consumable Production (ISCP). ISCP involves acquiring, manufacturing, and storing propellants for planetary ascent or Earth return vehicles, gases and water for crew and life support, and fuel cell reagents for power generation by using resources available at the site of exploration. Since propellant mass typically makes up 60 to 80% of the ascent or Earth return vehicle mass, In-Situ Propellant Production (ISPP) on the Lunar or Mars surface can significantly reduce the overall mass for the return vehicle needed to be brought from Earth. Systems analyses of human Mars missions have indicated that solely producing propellants on the surface of Mars by processing atmospheric carbon dioxide can reduce the initial mission mass required in low Earth orbit by approximately 20% as compared to carrying all required propellant to the Mars surface from Earth. An even greater leverage can occur for Mars missions when in-situ water can be processed.

Sanders, G. B.↗
NASA NTRS2000

ISRU: An Overview of NASA'S Current Development Activities and Long-Term Goals

The concept of "living off the land" by utilizing the indigenous resources of the Moon, Mars, or other potential sites of robotic and human exploration has been termed In-Situ Resource Utilization (ISRU). It is fundamental to any program of extended human presence and operation on other extraterrestrial bodies that we learn how to utilize the indigenous resources. The chief benefits of ISRU are that it can reduce the mass, cost, and risk of robotic and human exploration while providing capabilities that enable the commercial development of space. In January 1997, the American Institute of Aeronautics and Astronautics (AIAA) Space Processing Technical Committee released a position paper entitled, "Need for A NASA Indigenous Space Resource Utilization (ISRU) Program". Besides outlining some of the potential advantages of incorporating ISRU into Lunar and Mars human mission plans and providing an overview of technologies and processes of interest, the position paper concluded with a list of seven recommendations to NASA. This paper will examine the seven recommendations proposed and provide an overview of NASA's current ISRU development activities and possible long term goals with respect to these recommendations.

Sanders, Gerald B.↗
NASA NTRS2001

Implementation of a General Real-Time Visual Anomaly Detection System Via Soft Computing

The intelligent visual system detects anomalies or defects in real time under normal lighting operating conditions. The application is basically a learning machine that integrates fuzzy logic (FL), artificial neural network (ANN), and generic algorithm (GA) schemes to process the image, run the learning process, and finally detect the anomalies or defects. The system acquires the image, performs segmentation to separate the object being tested from the background, preprocesses the image using fuzzy reasoning, performs the final segmentation using fuzzy reasoning techniques to retrieve regions with potential anomalies or defects, and finally retrieves them using a learning model built via ANN and GA techniques. FL provides a powerful framework for knowledge representation and overcomes uncertainty and vagueness typically found in image analysis. ANN provides learning capabilities, and GA leads to robust learning results. An application prototype currently runs on a regular PC under Windows NT, and preliminary work has been performed to build an embedded version with multiple image processors. The application prototype is being tested at the Kennedy Space Center (KSC), Florida, to visually detect anomalies along slide basket cables utilized by the astronauts to evacuate the NASA Shuttle launch pad in an emergency. The potential applications of this anomaly detection system in an open environment are quite wide. Another current, potentially viable application at NASA is in detecting anomalies of the NASA Space Shuttle Orbiter's radiator panels.

Dominguez, Jesus A.↗
NASA NTRS2004

Gas-Liquid Flows and Phase Separation

Common issues for space system designers include:Ability to Verify Performance in Normal Gravity prior to Deployment; System Stability; Phase Accumulation & Shedding; Phase Separation; Flow Distribution through Tees & Manifolds Boiling Crisis; Heat Transfer Coefficient; and Pressure Drop.The report concludes:Guidance similar to "A design that operates in a single phase is less complex than a design that has two-phase flow" is not always true considering the amount of effort spent on pressurizing, subcooling and phase separators to ensure single phase operation. While there is still much to learn about two-phase flow in reduced gravity, we have a good start. Focus now needs to be directed more towards system level problems .

McQuillen, John↗