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↗
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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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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↗
Moving the Mission Forward with Renewable Energy
Presentation gives an overview of proven examples of renewable energy technologies. Learn how to properly utilize renewable energy technologies; research, select, fund, and deploy renewable technologies; best operate and maintain the federal government’s fleet of on-site solar photovoltaic (PV) systems; and learn best practices to detect and address performance issues with a PV system.
distributed energy↗
A Scalable Method for Decarbonizing Modular Building Solutions: Preprint
The decarbonization movement emphasizes the shift in focus from energy efficiency to directly reducing global-warming impact. Blokable, LLC, a vertically integrated modular builder with an all-electric portfolio, worked with NREL on a roadmap to decarbonize its high-performance building product at a relative cost advantage by utilizing the learning curves of mass production. Previous decarbonization literature focused on either i) lifecycle assessments, or ii) efficiency measures. These decarbonization exercises were bespoke to individual building projects and did not consider positive feedback loops of builder experience or process repetition. Vertically integrated, prefab builders possess the unique ability to leverage learning and repetition to decarbonize their design-build-operate process. This collaboration between Blokable and NREL resulted in a decarbonization strategy utilizing the company's scaling and production efficiencies, on-site renewable energy and storage, and the projected evolution of building components over time based on trends and emerging legislation. The method developed here encompasses a growing business model, lifecycle carbon assessment, and projected changes in product and grid emissions over time due to existing trends and emerging legislation. This methodology incorporates learning-curve efficiencies gleaned from scaled manufacturing, as well as open-source tools integration for energy and carbon accounting. The output projects and compares cost and carbon savings per modular unit as production increases to 10,000 dwelling units annually over 15 years. The resulting roadmap illustrates a path to roughly 60% carbon savings and beyond-net-zero-energy performance at no incremental cost by 2030. The methodology can be mapped to other integrated or productized builders for methodical decarbonization.
affordable housing↗
A Scalable Method for Decarbonizing Modular Building Solutions
The decarbonization movement emphasizes the shift in focus from energy efficiency to directly reducing global-warming impact. Blokable, LLC, a vertically integrated modular builder with an all-electric portfolio, worked with NREL on a roadmap to decarbonize its high-performance building product at a relative cost advantage by utilizing the learning curves of mass production. Previous decarbonization literature focused on either i) lifecycle assessments, or ii) efficiency measures. These decarbonization exercises were bespoke to individual building projects and did not consider positive feedback loops of builder experience or process repetition. Vertically integrated, prefab builders possess the unique ability to leverage learning and repetition to decarbonize their design-build-operate process. This collaboration between Blokable and NREL resulted in a decarbonization strategy utilizing the company's scaling and production efficiencies, on-site renewable energy and storage, and the projected evolution of building components over time based on trends and emerging legislation. The method developed here encompasses a growing business model, lifecycle carbon assessment, and projected changes in product and grid emissions over time due to existing trends and emerging legislation. This methodology incorporates learning-curve efficiencies gleaned from scaled manufacturing, as well as open-source tools integration for energy and carbon accounting. The output projects and compares cost and carbon savings per modular unit as production increases to 10,000 dwelling units annually over 15 years. The resulting roadmap illustrates a path to roughly 60% carbon savings and beyond-net-zero-energy performance at no incremental cost by 2030. The methodology can be mapped to other integrated or productized builders for methodical decarbonization.
affordable housing↗
ION Work Reduction Opportunity Realization Demonstration
The purpose of this research was to realize one of the advanced training work reduction opportunities first presented in the Idaho National Laboratory (INL) report, “Process for Significant Nuclear Work Function Innovation Based on Integrated Operations Concepts” (INL/EXT-21-64134) [1], with a nuclear power plant (NPP) research partner. Researchers modernized two trainings: (1) an accredited instructor-led training (ILT) overview course on Westinghouse DS 480-volt (V) circuit breakers to a multimedia-focused computer-based-training (CBT) learning module, and (2) an on-demand chaptered video on how to properly rack and un-rack a Westinghouse DS 480-V circuit breaker. These modernized work products were developed and implemented in a manner consistent with the industry guidelines found in Institution of Nuclear Power Operations (INPO) Teaching and Learning 23-001 [2]. Researchers calculated that the modernized accredited training course reduced the time necessary to prepare and deliver the training material by a factor of 8:1. The amount of time learners spend in class could be reduced by this same factor. In other words, if a course took 8 hours to deliver a class, the new CBT instruction would take just over 1 hour. The researchers noted that the requirement for any practicum training by the learners with the instructor(s) would remain in place. But through interviews with new and experienced learners, the researchers discovered that the confidence of these learners in performing the racking and un-racking of the circuit breaker improved as a result of using the new modernized CBT process. Additionally, the learners who tested the modernized work products enjoyed the modernized CBT and the learning video significantly more than current in-class learning methods. These are encouraging results for the nuclear industry, as this modernization of training can be applied to other classes and is scalable across the industry. In line with the Integrated Operations for Nuclear (ION) model, positive workload analysis supports the investment of resources in modernizing NPP training processes and infrastructure. Implementation of the advanced training technologies in this report is likely to result in substantive long-term workload benefits to instructors and learners and result in hard-dollar savings on contractor spends. Additionally, investment in these modernized training processes will result in improved learner proficiency. The results of this research can be applied to additional operator, technical, and general training topics to provide additional workload and learning benefits in addition to what was explored.
42 ENGINEERING↗
Considerations for using Privacy Preserving Machine Learning Techniques for Safeguards
In international nuclear safeguards, the International Atomic Energy Agency (IAEA) is tasked with inspecting and verifying nuclear facilities and their activities. Data analytics and machine learning to support inspections require large amounts of data that nuclear facility operators may consider proprietary or sensitive, so the IAEA may not have full access. Allowing computation over private data without compromising its security therefore has value for safeguards inspections and analysis. Privacy-preserving machine learning (PPML) consists of security-focused techniques that allow data analytics and machine learning algorithms to run on sensitive data without revealing it. This includes ideas like homomorphic encryption (HE), secure multiparty computation (SMPC), and secure enclaves. HE allows algorithms and mathematical operations to be conducted directly on the encrypted data instead of first decrypting it. With SMPC, multiple entities collaboratively compute over distributed data such that no party is able to directly view any others’ original data. Secure enclaves allow computation to take place in a separate and heavily blocked-off section of a CPU. Techniques like these allow for several potential use cases in which the security of data is essential. With SMPC, machine learning models can be trained over the input data from multiple entities, resulting in a model that all users can benefit from without leaking the input data from any particular entity. With SMPC or a zero-knowledge proof (ZKP), an algorithm returning some single answer or truth value can be run on someone else’s data without ever needing to see that data, potentially allowing for verification or proof of some underlying question. HE can allow for outsourcing computation on data to a hostile or untrusted environment. Although most of the research in this field resides within the health and financial domains, tools from PPML may have similar applications in nuclear safeguards. Allowing the IAEA to compute over proprietary information, such as process models and raw sensor data using PPML techniques, provides the baseline for running complex analytics without needing direct unencrypted access to the underlying data, maintaining its privacy. Important limitations to consider for these techniques include the efficiency and level of security required. The security of HE and SMPC come at the cost of speed—the significant amount of overhead means that algorithms implemented in these protocols and encryption schemes are slower than when run on plaintext. Additionally, several important parameters determine what techniques or protocols are used based on the security requirements. SMPC protocols may need to be selected for resistance against a party that attempts to deviate from the protocol to distort the result or gain access to additional information, and a protocol secure against these attacks may further increase the overhead of the algorithm.
97 MATHEMATICS AND COMPUTING↗
Integrated Predictive Systems for Solar Energy with Modeling, Post Processing and Machine Learning
Explore the source record for details and available documents.
14 SOLAR ENERGY↗
Deep Reinforcement Learning for Residential HVAC Control with Consideration of Human Occupancy
The Artificial Intelligence (AI) development described herein uses model-free Deep Reinforcement Learning (DRL) to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. Building cooling loads and HVAC operation are difficult to accurately model due to complexity, lack of measurements and data, and model specific performance, so online machine learning is used to allow for real-time readjustment in performance. Energy costs for the multi-zone cooling unit shown in this work are minimized by scheduling on/off commands around dynamic prices. By taking advantage of precooling events that take place when the price is low, the agent is able to reduce operational cost without violating user comfort. The DRL controller was tested in simulation where the learner achieved a 43.89% cost reduction when compared to traditional, fixed-setpoint operation. The system is now ready for the next phase of testing in a live, real-time home environment.
Mckee, Evan↗