Data-driven reduced-order models via regularised Operator Inference for a single-injector combustion process
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97 MATHEMATICS AND COMPUTING↗
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.
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Novel Data-Driven Distributed Learning Framework for Solving AC Power Flow for Large Interconnected Systems
Not Available
42 ENGINEERING↗
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. Here, an experimental data-driven machine learning (ML) approach is presented that predicts the pedestal heights and widths of electron density (n e ) and electron temperature (T e ) 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 (I p ), toroidal magnetic field (B Φ ), neutral beam heating power (P NBI ) 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↗
Branching Dueling Q-Network Based Online Scheduling of a Microgrid With Distributed Energy Storage Systems
This letter investigates a Branching Dueling Q-Network (BDQ) based online operation strategy for a microgrid with distributed battery energy storage systems (BESSs) operating under uncertainties. Additionally, the developed deep reinforcement learning (DRL) based microgrid online optimization strategy can achieve a linear increase in the number of neural network outputs with the number of distributed BESSs, which overcomes the curse of dimensionality caused by the charge and discharge decisions of multiple BESSs. Numerical simulations validate the effectiveness of the proposed method.
24 POWER TRANSMISSION AND DISTRIBUTION↗
Bindee
Bindee is a clang tool that outputs a simple pybind11 template given a C++ file for efficient generation of C++-Python bindings. Bindee is intended to be a helper tool for minimizing initial user effort and safeguarding against common runtime errors. Bindee relies on two open-source software to produce bindings. Clang's LibTooling enables bindee to traverse a C++ file's AST to pick out bindable variables and functions, or "bindees." PyBind11 is templated, header-only library for generating C++-Python bindings for variables and functions. Operating purely in C++, bindee does not require learning any new API for accomplishing its task. Additionally, picking the correct pybind11 API for a given bindable element is handled without interaction from the user. Any user input is denoted by '@TEXT@' string substitution. Bindee is capable of generating modular bindings for public class methods, public class variables, enumerations, and free functions and variables. Bindings for templates are also supported. For those familiar with pybind11, bindee does not handle trampolines, C++ extensions through lambdas, or custom type casters.
Kimkno, JasonS.↗
Final Technical Report: Results of Phase 1
Arizona Public Service Company (APS) expects that by 2027, renewable energy will account for 6,590 GWh in energy consumption by its customers. While much of this future energy will come from large centrally-located power plants, distributed renewable energy, sited at the point of end-use will also play an important role in meeting the needs of APS customers and is expected to provide 1,734 GWh. With increasing penetration of residential and commercial photovoltaic (PV) systems at the point of end-use, PV power generation not only offsets the load, but could also cause significant shifts in power flow patterns through the distribution system, and could possibly cause reversal of flow through some branches of a distribution circuit. Significant changes to power flow introduced into existing distribution systems due to the increased amount of PV systems may cause operational issues, including over-voltage on the distribution feeder (loss of voltage regulation) and incorrect operation of control equipment, which may lead to an increase in the number of operations and related equipment wear that could affect equipment reliability and customer power quality. Additionally, connecting generation resources to a distribution feeder can introduce additional sources of short-circuit current to the distribution system. This could potentially result in increased short-circuit currents, potentially reaching damaging levels, causing protection desensitization and a potential loss of protection coordination. These effects may be further compounded by variability of PV production due to shading by clouds. The effects of these phenomena in distributed PV applications are not well understood, and there is a great need to characterize this variability. This project will contribute to understanding the effects of high-penetration solar electricity on the design and operation of distribution systems by demonstrating how a high penetration of PV systems affects grid operations of a working, utility distribution feeder. To address the technical challenges related to the integration of distributed PV when PV penetration levels reach or exceed 30% of the total load, technologies and methods to ensure the stable and safe operation of the feeder will be evaluated. Lessons learned will enable APS to improve the framework for future PV integration on its system and may also aid other utilities across the United States energy sector in accelerating the adoption of distributed photovoltaic generation.
14 SOLAR ENERGY↗
Non-destructive Quantitative Phase Analysis and Microstructural Characterization of Zirconium Coated U-10Mo Fuel Foils via Neutron Diffraction
This report uses neutron diffraction to investigate the crystal phase composition of uranium-molybdenum alloy foils (U-10Mo) for the CONVERT MP-1 Reactor Conversion Project, and determines the effect on alpha-uranium contamination following the deposition of a Zr metal diffusion layer by various methods: plasma spray deposition of Zr powders at LANL and hot co-rolling with Zr foils at BWXT. In summary, there is minimal decomposition of the gamma phase U-10Mo foil to alpha phase contamination following both plasma spraying and hot co-rolling. The average unit cell volume, i.e. lattice spacing, of the Zr layer can be mathematically extracted from the diffraction data; co-rolled Zr matches well with literature values of bulk Zr, while plasma sprayed Zr shows a slight increase in the lattice spacing, indicative of interstitial oxygen in the lattice. Neutron diffraction is a beneficial alternative to conventional methods of phase composition, i.e. x ray diffraction (XRD) and destructive metallography. XRD has minimal penetration depth in high atomic number materials, particularly uranium, and can only probe the first few microns of the fuel plate; neutrons pass completely through the foil, allowing for bulk analysis of the foil composition and no issues with addition of cladding layers, as in the final, aluminum-clad reactor fuel plates. Destructive metallography requires skilled technicians, cutting of the foil into small sections, hazardous etching conditions, long polishing and microscopy times, etc.; the neutron diffraction system has an automated sample loader and can fit larger foils, so there is minimal analysis preparation; the total spectrum acquisition time is ~ 1 hour per sample. The neutron diffraction results are limited by spectra refinement/calculation times and the availability of the neutron beam source. In the case of LANSCE at Los Alamos, the beam operates ~50% of the year. Following the lessons learned from these preliminary results, optimizations to the process and analysis can be made, and neutron diffraction can become a viable and efficient technique for gamma/alpha phase composition determination for nuclear fuels.
11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
Alaska Wind Energy Project
The Alaska Wind Energy Research Project will support studies that provide new learning on wind-diesel modeling techniques and operating wind turbine systems in arctic environments
17 WIND ENERGY↗
Underground Natural Gas Storage - Analog Studies to Geologic Storage of CO 2
The purpose of this report is to compile a stand-alone body of knowledge regarding historical and current underground natural gas storage operations that may be directly or indirectly relevant to carbon dioxide (CO 2 ) geologic storage in saline-bearing formations. This is the first of three planned documents that evaluate analog industries of CO 2 storage (the second focuses on wastewater disposal using United States [U.S.] Environmental Protection Agency [EPA] Underground Injection Control [UIC] Class I disposal wells, and the third on CO 2 enhanced oil recovery). Natural gas has been stored underground for over 100 years. This type of storage is necessary for meeting seasonal demand requirements as well as insuring against unforeseen supply disruptions. There are significant similarities that exist between natural gas storage and CO 2 geologic storage (and full-scale carbon capture and storage [CCS]) in terms of site selection and characterization, as well as operational procedures, and the equipment used. Therefore, the extensive operational history of underground natural gas storage operations provides a wealth of knowledge and lessons learned from which CO 2 storage stakeholders in industry, academia, and policy can benefit.
03 NATURAL GAS↗
Module 10 Reactor Physics
Students will gain a understanding of some basic reactor physics concepts, which will prepare them for learning more detailed concepts about the design and operations of the experiments performed using the NCERC critical assemblies.
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms
Deployment and execution of machine learning tasks on extreme-scale computing platforms face several significant technical challenges: 1) High computing cost incurred by dense networks – The computing workload of deep networks with densely-connected topology increases rapidly with the network size, imposing a non-scalable computing model of extreme-scale computing platforms; 2) Non-optimized workload distribution – Many advanced deep learning algorithms, e.g., sparsification and irregular net-work topology, produce very unbalanced workload distribution on extreme-scale computing platforms. The computation efficiency is greatly hindered by the incurred data and computation redundancies as well as long tails of the node with extensive workload; 3) Constraints in data movement and I/O bottle-neck – Inter-node data movement in extreme-scale computing platforms are associated with high energy and latency costs, and subject to the constraints of I/O bandwidth; and 4) Generalization of algorithm realization and acceleration on computing platforms – The large varieties of machine learning algorithms and structures of extreme-scale computing platforms make the derivation of a generalized algorithm realization and acceleration method very challenging, which, however, is the requirement by domain scientists and interested users. We call the above challenges Smale’s Problems in Machine Learning and Understanding for High-Performance Computing Scientific Discovery. The objective of our three-year research project is to develop a holistic innovation set at structure, assembly, and acceleration layers of machine learning algorithms to address the above challenges in algorithm deployment and execution. Three tasks are particularly performed, including: At the algorithm structure level, we investigate the techniques that can structurally sparsify on the topology of deep networks for computing workload reduction. We also study clustering and pruning techniques that can optimize the workload distributions over the extreme-scale computing platforms; At the algorithm assembly level, we derive a unified learning framework for unsupervised transfer learning and dynamic growing capabilities. Novel training methods are also exploited to enhance the training efficiency of the proposed framework; At the algorithm acceleration level, we will develop a series of techniques that can accelerate the computation of sparse matrix operations, which are one of the core executions in deep learning and optimize memory access of the concerned platforms. Our proposed techniques attack the fundamental problems in machine learning algorithms running on extreme-scale computing platforms by vertically integrating the solutions at three closely entangled layers, paving the long-term scaling path of machine learning applications under DOE context. Three tasks corresponding to the above respective research orientations are performed during the three-year project period with our collaborators at ORNL. The outcome of the proposed project is anticipated to form a holistic solution set of novel algorithms and network topologies, efficient training techniques, and fast acceleration methods to promote the computing scalability of the machine learning applications of particular interest to DOE.
97 MATHEMATICS AND COMPUTING↗
BETO Visit: Investigating Large-Scale Cultivation Processes [Slides]
Questions: How important is density management in a scaled system?; What processes are currently proposed/used, and how are they deployed?; What sort of information (tools/experimentation) would be helpful?; What can be learned that can inform better strain, cultivation systems, operations and tools development for increased productivities?; Beginning with the end in mind?
59 BASIC BIOLOGICAL SCIENCES↗
Development and Validation of Home Comfort System for Total Performance Deficiency/Fault Detection and Optimal Comfort Control
In this project, we developed and tested a learning-based home thermal model that facilitates the operation of a model predictive control (MPC)-based optimization agent and an automated fault detection and diagnosis (AFDD) agent. The home thermal model was constructed using a two-node resistor-capacitor model. Moreover, two accompanying parameter identification methods were introduced, least-squares and optimization. Based on the home thermal model, the MPC-based optimization agent was developed to optimize residential HVAC operation. Using two FDD methods, the AFDD agent was constructed to detect and diagnose two prevalent residential AC faults, airflow reduction and refrigerant undercharge. The home thermal model, along with the MPC-based optimization agent and AFDD agent, were tested at the Norman Test House, Miami Test House, Pacific Northwest National Laboratory (PNNL) Test House A, and PNNL Test House B. Finally, they were also field tested in nine demonstration homes with real occupants.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Team Dynamics and Development in Chemical Emergency Response
This presentation is focused on Team Dynamics and Development in Chemical Emergency Response. While regulatory requirements focus on defining the performance requirements associated with this category of response team, there is very little information and guidance provided to team leaders on the criticality of team dynamics of development in performance. These elements are essential to achieving successfully integrated team operations. NREL has worked to expand chemical response team participation to include operational personnel from research, support operations, and various technical areas. Since this team expansion has encompassed integration from numerous levels and areas within the organization, a systematic approach was adopted to better understand how organization culture, dynamics, and diversity can be used to be more effective. Benchmarking other organizations and reviewing relevant literature assisted in laying the foundation to develop a strategy that incorporates team development principles, dynamics, and leadership practices to increase the effectiveness of emergency response operations. This presentation focuses on the valuable lessons learned and the team-based approaches that can be used to develop a successful response team.
emergency response↗
Data-driven estimation of energy consumption for electric bus under real-world driving conditions
Reliable and accurate estimation of an electric bus’s instantaneous energy consumption is critical in evaluating energy impacts of planning and control of electric bus operations. In this study, we developed machine learning-based long short-term memory (LSTM) and artificial neural network (ANN) models to estimate 1 Hz energy consumption of electric buses based on continuous monitoring data of electric buses in Chattanooga, Tennessee, in 2019 and 2020. We propose a data-partitioning algorithm to separate energy charging and discharging modes before applying data-driven estimation models. Here, a K-fold cross-validation-based model selection process was conducted to identify the optimal model structure and input variables in terms of prediction accuracy. The estimation results show the predicted mean absolute percentage error rates of LSTM and ANN models were 3% and 5%, respectively. We compared the proposed models with existing models in the literature based on the same testing data to demonstrate the predictability of our models.
Artificial neural network↗
FY23 ION Based Approaches to Address Labor and Knowledge Retention
This study will seek to outline the current problem facing the nuclear industry related to staffing, training, and retaining the necessary workforce for long-term sustainability. A review of published reports and knowledge gained by working in the nuclear energy sector will form the basis for this scoping study. This study will not seek to draw any conclusions regarding the underlying causes of these personnel issues but will attempt to ask questions and help outline future research. The Integrated Operations for Nuclear business model provides opportunities to partially mitigate some of the current issues facing staffing the nuclear fleet. Upgrading safety and control systems from analog to digital as advocated by the Integrated Operations for Nuclear process allows the nuclear facility to compete for the best and brightest candidates and offers them a broad and sustainable career path where their skills can be valued and utilized in the broader industrial sectors. This study will seek to identify the primary reasons that the nuclear industry is facing this labor crisis and seek to identify possible solutions that will be fully evaluated in future research.
42 ENGINEERING↗
Post-Fukushima Safety Enhancement Activities in Canada
In the immediate aftermath of the common-cause seismic and tsunami events that triggered the Fukushima Daiichi nuclear accident on March 11, 2011, the nuclear regulator, the Canadian Nuclear Safety Commission (CNSC), requested that all Cass I nuclear facilities re-examine the safety cases for their nuclear facilities. Lessons-learned task forces were established by the CNSC, and the nuclear utilities, Ontario Power Generation (OPG), Bruce Power (BP), and New Brunswick Power (NBP). The purposes of these independent task forces was to develop relevant lessons learned issues for the Candu nuclear power plants operating in Canada and to identify actions that could enhance safety. Subsequently, a CNSC Integrated Action Plan was developed to address the recommendations arising from the CNSC Task Force Report, and the licensees aligned their actions with this plan. The actions were grouped into four categories, as follows. 1. Strengthening defence in depth; Actions included re-assessment of a site-specific external hazards using PSA; physical plant changes to add further cooling water make-up capability to heat sinks; development of new Canadian standards and updated regulatory documents that address requirements for Beyond Design Basis Accidents (BDBA), Probabilistic Safety Assessment (PSA) and Period Safety Review (PSR); development of a new and novel methodology for assessing multi-unit risk that is consistent with the IAEA construct of defence-in-depth (including a safety goal hierarchy and complementary risk analysis methods that address 'Black Swan' events); enhanced management of hydrogen combustion challenges; and enhanced Severe Accident Management Guidelines (SAMG). 2. Enhancing emergency response; Actions included provision of Emergency Mitigation Equipment (EME) such as transportable diesel-driven pumps or fire engines, transportable diesel generators for AC and instrument power; pre-installed connections to in-plant equipment via easily accessible and protected quick-connect points for water and electrical power; upgraded emergency response centres; enhanced communication systems and protocols; and enhanced emergency response programs, training and exercises. 3. Improving the regulatory framework and processes; 4. Enhancing international collaboration. Actions assigned under categories 3 and 4 were placed upon the regulator. This presentation will focus on the assigned actions in categories 1 and 2. The underlying motivation, objectives, and safety benefits associated with the actions will be described within the context of broader aspects of severe accident mitigation in water-cooled reactors. (author)
21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
Hierarchical Flexibility Offering Strategy for Integrated Hybrid Resources in Real-time Energy Markets
This paper proposes a hierarchical model for determining the energy flexibility offering strategy of integrated hybrid resources (IHRs) in power distribution systems to participate in real-time energy markets. The proposed model utilizes the scalability, fast response time, and uncertainty observation of deep reinforcement learning (DRL) to overcome the scalability issue of operating numerous flexible resources and deliverability of energy flexibility to the real-time markets in the presence of the network constraints. To that end, the power distribution system is divided into multiple IHRs, where different types of flexible loads, energy storage systems, and solar plants with controllable inverters are operated through local IHR controllers, trained by deep deterministic policy gradient (DDPG) algorithm. Active power request and reactive power capacity of IHRs are then transmitted to a central flexibility controller, where a quadratic optimization model ensures the deliverability of the energy flexibility to the real-time energy market by satisfying the distribution network constraints. The proposed model is implemented on the 123-bus test power distribution system, demonstrating the capability of DRL-based hierarchical model for scalable operation of IHRs in order to offer deliverable energy flexibility to the real-time energy market.
Majidi, Majid↗