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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Confinement performance predictions for a high field axisymmetric tandem mirror

This paper presents Hammir tandem mirror confinement performance analysis based on Realta Fusion's first-of-a-kind model for axisymmetric magnetic mirror fusion performance. This model uses an integrated end plug simulation model including, heating, equilibrium, and transport combined with a new formulation of the plasma operation contours (POPCONs) technique for the tandem mirror central cell. Using this model in concert with machine learning optimization techniques, it is shown that an end plug utilizing high temperature superconducting magnets and modern neutral beams enables a classical tandem mirror pilot plant producing a fusion gain Q > 5. The approach here represents an important advance in tandem mirror design. The high fidelity end plug model enables calculations of heating and transport in the highly non-Maxwellian end plug to be made more accurately. The detailed end plug modelling performed in this work has highlighted the importance of classical radial transport and neutral beam absorption efficiency on end plug viability. The central cell POPCON technique allows consideration of a wide range of parameters in the relatively simple near-Maxwellian central cell, facilitating the selection of more optimal central cell plasmas. These advances make it possible to find more conservative classical tandem mirror fusion pilot plant operating points with lower temperatures, neutral beam energies, and end plug performance requirements than designs in the literature. Despite being more conservative, it is shown that these operating points have sufficient confinement performance to serve as the basis of a viable fusion pilot plant provided that they can be stabilized against MHD and trapped particle modes.

FOS: Physical sciences↗
DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Confinement performance predictions for a high field axisymmetric tandem mirror

This paper presents a Hammir tandem mirror confinement performance analysis based on Realta Fusion’s first-of-a-kind model for axisymmetric magnetic mirror fusion performance. This model uses an integrated end plug simulation model including, heating, equilibrium and transport combined with a new formulation of the plasma operation contours (POPCONs) technique for the tandem mirror central cell. Using this model in concert with machine learning optimization techniques, it is shown that an end plug utilizing high temperature superconducting magnets and modern neutral beams enables a classical tandem mirror pilot plant producing a fusion gain Q > 5. The approach here represents an important advance in tandem mirror design. The high-fidelity end plug model enables calculations of heating and transport in the highly non-Maxwellian end plug to be made more accurately. The detailed end plug modelling performed in this work has highlighted the importance of classical radial transport and neutral beam absorption efficiency on end plug viability. The central cell POPCON technique allows consideration of a wide range of parameters in the relatively simple near-Maxwellian central cell, facilitating the selection of more optimal central cell plasmas. These advances make it possible to find more conservative classical tandem mirror fusion pilot plant operating points with lower temperatures, neutral beam energies and end plug performance requirements than designs in the literature. Despite being more conservative, it is shown that these operating points have sufficient confinement performance to serve as the basis of a viable fusion pilot plant provided that they can be stabilized against magnetohydrodynamic and trapped particle modes.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Reinforcement Learning‐Based Adaptation of Grid Following Inverter's Internal Controller to Networked Microgrids' Strengths

The varying topological configurations, generator commitments and dispatches, and dynamic load demand lead to changing system's strengths during the operations of networked microgrids. When the system's strengths significantly change, the fixed control gains at large devices may result in unsatisfactory system performance; this necessitates the tuning of the control gains at large devices to adapt to the changing system's strengths. In this paper, observer-based reinforcement learning (RL) is utilised to automatically tune the proportional-integral (PI) gains of phase lock loop (PLL) controller of grid-following (GFL) inverters to adapt to the changing strengths of microgrids and networked microgrids. The RL agent in this framework augments an observer predicting system's strengths, from which the RL control policy will adjust accordingly to tune the PLL controller's gains towards the system's strengths. Also, to enhance the control performance, the recently introduced Barrier function-based RL framework is leveraged for the design of reward function to prevent the high frequency nadir. An operational 26 kV electric distribution system, which is modelled as networked microgrids, is used to illustrate the need and effectiveness of the proposed RL-tuned control.

frequency response↗
DOE OSTI2023

Conduct of Operations and Nuclear Criticality Safety Standards

Since the beginning of the nuclear age in 1943, there have been 22 criticality accidents in process facilities worldwide in which fissionable materials were processed by hand for various purposes. Of these 22 process criticality accidents, 16 involved faulty or flawed conduct of operations. Most of these process accidents (17 of 22) occurred before 1970, mostly due to the implementation of formal conduct of operations—such as the use of operating procedures and training—and the use of consensus standards for nuclear criticality safety (NCS). Obviously, the nuclear facilities applied lessons learned from their accident history, and the rate of criticality accidents was significantly reduced as a result. Over the years, it has been emphasized in NCS training courses and other venues in the United States that the consensus standards, such as ASA N6.1-1964, were the key contributors to this reduction in accidents. The formality of operations required to implement and apply the standards at a facility is also a crucial prerequisite that must be sufficiently robust to ensure NCS. This prerequisite is crucial to success fully implement the NCS consensus and other safety standards at a site.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2026

Feedback, physics, and forecasts: The emerging paradigm of machine learning-driven battery research

Machine learning (ML) is reshaping how we understand, predict, and optimize electrochemical systems. In batteries, ML accelerates discovery across chemistry, design, and operation by transforming massive experimental and simulated datasets into predictive, interpretable models. This review consolidates a decade of progress in ML-driven battery innovation, from early-cycle feature extraction to operando image analysis and physics-informed modeling. We categorize approaches by data domain and physical fidelity, emphasizing interpretable ML for diagnostics, reinforcement learning for control, and multi-objective optimization for lifetime extension strategies. Additionally, we demonstrate how integrated models accelerate discovery, reduce testing time, and guide sustainable design. Economic analyses furthermore illustrate how these advances can lower cost per cycle and improve circularity. Together, these developments chart a path toward self-optimizing, sustainable battery technologies.

artificial intelligence↗
DOE OSTI2016

UMo Solutions Processing in La Hague Cold Crucible Induction Melter: the Feedback From the First Years of Operation - 16376

250 cubic meters of high-level liquid waste from reprocessed U-Mo-Sn-Al spent fuel, used in Gas Cooled Reactors (GCR), were produced during the mid-1960's at La Hague facility. These 'UMo' solutions are less radioactive than the current fission product concentrates coming from ongoing reprocessing activities, but are very rich in molybdenum and phosphorus whose contents make the molten glass quite corrosive and require a high-temperature glass formulation to obtain sufficiently high waste loading factors (12% in molybdenum oxide). Hence the use of the Cold Crucible Induction Melter (CCIM) technology to process such solutions has been deemed a good opportunity for AREVA to meet its performance expectations. In addition to being very corrosive, the UMo waste is quite challenging to process as the molybdenum has a strong tendency to stick in the calciner and causes clogging issues in off-gas treatment equipment. Therefore, the process and technological qualifications were deployed in order to address these specific issues. UMo solutions processing in the La Hague CCIM has started in January 2013 and is currently ongoing. During this period (from 2013 to 2015), 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, operations teams experience along with engineering and R and D support allowed managing them. This paper presents the start-up methodology and the feedback from the first years of UMo solutions processing with the CCIM technology at La Hague site. Lessons learned are presented with the difficulties encountered and the solutions implemented, emphasizing the benefits of a close integration between R and D, engineering and operations teams. (authors)

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

Fast, Simple Calcium Imaging Segmentation with Fully Convolutional Networks

Calcium imaging is a technique for observing neuron activity as a series of images showing indicator fluorescence over time. Manually segmenting neurons is time-consuming, leading to research on automated calcium imaging segmentation (ACIS). We evaluated several deep learning models for ACIS on the Neurofinder competition datasets and report our best model: U-Net2DS, a fully convolutional network that operates on 2D mean summary images. U-Net2DS requires minimal domain-specific pre/post-processing and parameter adjustment, and predictions are made on full 512×512 images at ≈9K images per minute. It ranks third in the Neurofinder competition (F1=0.57) and is the best model to exclusively use deep learning. We also demonstrate useful segmentations on data from outside the competition. The model’s simplicity, speed, and quality results make it a practical choice for ACIS and a strong baseline for more complex models in the future.

Klibisz, Alex↗
DOE OSTI2018

A Study of Clustering Techniques and Hierarchical Matrix Formats for Kernel Ridge Regression

We present memory-efficient and scalable algorithms for kernel methods used in machine learning. Using hierarchical matrix approximations for the kernel matrix the memory requirements, the number of floating point operations, and the execution time are drastically reduced compared to standard dense linear algebra routines. We consider both the general H matrix hierarchical format as well as Hierarchically Semi-Separable (HSS) matrices. Furthermore, we investigate the impact of several preprocessing and clustering techniques on the hierarchical matrix compression. Effective clustering of the input leads to a ten-fold increase in efficiency of the compression. The algorithms are implemented using the STRUMPACK solver library. These results confirm that - with correct tuning of the hyperparameters - classification using kernel ridge regression with the compressed matrix does not lose prediction accuracy compared to the exact - not compressed - kernel matrix and that our approach can be extended to O(1M) datasets, for which computation with the full kernel matrix becomes prohibitively expensive. We present numerical experiments in a distributed memory environment up to 1,024 processors of the NERSC's Cori supercomputer using well-known datasets to the machine learning community that range from dimension 8 up to 784.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Multi-Source Data Aggregation and Real-Time Anomaly Classification and Localization in Power Distribution Systems

This paper proposes a real-time anomaly location and classification framework for power distribution systems to simultaneously determine the type of anomaly (i.e., short-circuit fault, cyber attack, DER switching) and its location. The proposed framework employs the data aggregation module to collect the measurement data from multiple field devices operating at different sampling rates, such as protection relays and D-PMUs. The output of the data aggregation is then fed into a multi-task learning-based long-based short-term memory (MTL-LSTM) to classify the type of anomaly and the location in two separate tasks. The proposed MTL-LSTM approach can be utilized in real-time operation in order to distinguish between normal and several anomalous operations and locate the anomaly. The proposed framework is tested on a modified IEEE 33-bus test feeder benchmark that integrates solar generation and energy storage. Furthermore, the results show that the proposed framework can locate and classify anomalies for several operation conditions with more than 96% accuracy. Further experiments highlight the impact of aggregating multiple sources of data on the performance of the proposed model.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2018

Generation Forecasting for Small Run-of-the-River Hydroelectric Systems Using Statistical Learning Models

With an increasing interest in constructing new run-of-the-river (ROR) hydroelectric generation over the more traditional reservoir-based hydroelectric systems, there is an increasing operational challenge due to the volatility of stream flow. The Snohomish County Public Utilities District (SnoPUD) has recently invested in the construction and operation of 3 new run-of-the-river projects in Northwestern Washington along Calligan Creek, Hancock Creek, and Youngs Creek. In order to effectively plan generation dispatch, SnoPUD has expressed interest in the development of an accurate forecasting tool to predict the generation capacity for these ROR systems. The following research project aims to use statistical learning models, namely Hidden Markov Models (HMMs), to predict day-ahead generation capacities for the aforementioned ROR systems. These models are constructed using 12 years of historical stream flow data collected at the intake sites and precipitation data recorded at the National Oceanic and Atmospheric Administration (NOAA) Alpine Meadows station. Four methods of constructing the models are studied for their forecast accuracies, and are compared with the persistence model. Despite using only one set of observable variables, the HMMs are shown to have slight improvements in accuracy over the persistence model approach, which shows great optimism for future work.

13 HYDRO ENERGY↗
DOE OSTI2023

Machine-learning-assisted material discovery of oxygen-rich highly porous carbon active materials for aqueous supercapacitors

Abstract Porous carbons are the active materials of choice for supercapacitor applications because of their power capability, long-term cycle stability, and wide operating temperatures. However, the development of carbon active materials with improved physicochemical and electrochemical properties is generally carried out via time-consuming and cost-ineffective experimental processes. In this regard, machine-learning technology provides a data-driven approach to examine previously reported research works to find the critical features for developing ideal carbon materials for supercapacitors. Here, we report the design of a machine-learning-derived activation strategy that uses sodium amide and cross-linked polymer precursors to synthesize highly porous carbons (i.e., with specific surface areas > 4000 m 2 /g). Tuning the pore size and oxygen content of the carbonaceous materials, we report a highly porous carbon-base electrode with 0.7 mg/cm 2 of electrode mass loading that exhibits a high specific capacitance of 610 F/g in 1 M H 2 SO 4 . This result approaches the specific capacitance of a porous carbon electrode predicted by the machine learning approach. We also investigate the charge storage mechanism and electrolyte transport properties via step potential electrochemical spectroscopy and quasielastic neutron scattering measurements.

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

Nowcasting Earthquakes: Imaging the Earthquake Cycle in California With Machine Learning

We propose a new machine learning-based method for nowcasting earthquakes to image the time-dependent earthquake cycle. The result is a timeseries that may correspond to the process of stress accumulation and release. The timeseries are constructed by using principal component analysis of regional seismicity. The patterns are found as eigenvectors of the cross-correlation matrix of a collection of seismicity timeseries in a coarse grained regional spatial grid (pattern recognition via unsupervised machine learning). The eigenvalues of this matrix represent the relative importance of the various eigenpatterns. Using the eigenvectors and eigenvalues, we compute the weighted correlation timeseries of the regional seismicity. This timeseries has the property that the weighted correlation generally decreases prior to major earthquakes in the region, and increases suddenly just after a major earthquake occurs. As in a previous paper, we find that this method produces a nowcasting timeseries that resembles the hypothesized regional stress accumulation and release process characterizing the earthquake cycle. We then address the problem of whether the timeseries contain information regarding future large earthquakes. For this, we compute a receiver operating characteristic and determine the decision thresholds for several future time periods of interest (optimization via supervised machine learning). We find that signals can be detected that can be used to characterize the information content of the timeseries. These signals may be useful in assessing present and near-future seismic hazards.

58 GEOSCIENCES↗
DOE OSTI2012

Lessons from Iowa: Development of a 270 Megawatt Compressed Air Energy Storage Project in Midwest Independent System Operator

The Iowa Stored Energy Park was an innovative, 270 Megawatt, $400 million compressed air energy storage (CAES) project proposed for in-service near Des Moines, Iowa, in 2015. After eight years in development the project was terminated because of site geological limitations. However, much was learned in the development process regarding what it takes to do a utility-scale, bulk energy storage facility and coordinate it with regional renewable wind energy resources in an Independent System Operator (ISO) marketplace. Lessons include the costs and long-term economics of a CAES facility compared to conventional natural gas-fired generation alternatives; market, legislative, and contract issues related to enabling energy storage in an ISO market; the importance of due diligence in project management; and community relations and marketing for siting of large energy projects. Although many of the lessons relate to CAES applications in particular, most of the lessons learned are independent of site location or geology, or even the particular energy storage technology involved.

25 ENERGY STORAGE↗
DOE OSTI2024

Advancing spatiotemporal forecasts of CO 2 plume migration using deep learning networks with transfer learning and interpretation analysis

Accurate and timely forecasts of CO 2 plume distribution throughout the injection and post-injection phases are crucial for detecting plume migration, assessing leakage risks, and supporting operational decisions in geologic carbon storage (GCS). Current convolutional neural network-based approaches primarily focus on spatial information and overlook temporal dependencies in plume distributions, thus limiting their ability to capture dynamic movement effects and provide accurate predictions of plume migration. In this work, we propose two deep learning models, Auto-Encoder (AE)-LSTM and Encoder-Decoder (ED)-ConvLSTM, each uniquely designed to capture both spatial and temporal features. We apply the proposed methods to forecast the dynamic distribution of CO 2 plumes based on 108 reservoir simulations over a 30-year injection and a 30-year post-injection period. The results indicate that the ED-ConvLSTM model outperforms the AE-LSTM model in accurately predicting the spatiotemporal dynamics of CO 2 plume migration, achieving R 2 values above 0.99. To provide a deeper understanding of these model predictions, we employ a gradient-based explanation method on the trained models. This approach provides insights into the influence of input variables on plume migration forecasts and uncovers the underlying prediction mechanisms of the proposed models. Furthermore, we introduce a transfer learning technique, enabling fast and accurate plume migration forecasting in the post-injection phase by leveraging the trained model during the injection phase. This reduces the necessity for extensive data collection or re-training. In conclusion, the methods proposed in our work enhances the performance and interpretability of CO 2 plume migration forecasts, thereby facilitating informed decision-making throughout the entire lifecycle of GCS applications.

58 GEOSCIENCES↗
DOE OSTI2019

Coordination of Behind-the-Meter Energy Storage and Building Loads: Optimization with Deep Learning Models*

With the increasing penetration of renewable energy systems and energy storage systems in buildings, it is critical to optimize system operation. In this paper, we develop an optimization strategy to minimize the operation cost as well as maintain indoor thermal comfort, for a building integrated with battery and PV. A Recurrent Neural Network (RNN) is used to predict building thermal load and zone temperatures. A black-box optimization algorithm known as nonlinear optimization by mesh adaptive direct search (NOMADS) is employed in simulation to provide look-ahead optimal battery dispatch and zone temperature set-point schedules so that the operation cost is minimized. Field data collected from a medium sized office building at Pacific Northwest National Laboratory (PNNL) integrated with Photovoltaic (PV) system model and battery storage system model is used to demonstrate the proposed methodology. Compared with rule based methods, the optimization strategy obtained a lower cost of operation while satisfying comfort constraints.

Chen, Yimin↗
DOE OSTI2024

Savings in Action: Lessons Learned from a Vermont Community with Solar Plus Storage

As the U.S. works to meet emissions reduction goals and modernize power sector operations, residential buildings - which account for 21% of the total U.S. electricity consumption - will play a key role. Residential buildings with energy efficiency and distributed energy resources are able to provide value to multiple stakeholders, including the occupant, utility, and society at large. To account for this, multiple metrics that capture the value each stakeholder gets are required. This report contains analysis of a unique solar plus storage community in Vermont, including the calculation of multiple metrics and a unique "score card" that succinctly captures all of the value in a single figure.

14 SOLAR ENERGY↗
DOE OSTI2020

Facility Cybersecurity Framework Best Practices

Federal facilities are increasingly adopting automation and connecting to the Internet creating an energy-internet-of-things environment that converges operational technology (OT) and information technology (IT). Today's buildings increasingly weave together networked sensors and cyber and physical systems that enable data to be collected, aggregated, exchanged, stored and monetized in new ways. Building technological advances have created new energy technology, services, markets and value creation opportunities (e.g. transactive energy, two-way grid communications, machine learning, and increased use of renewable and distributed energy resources). But as larger data sets are being exchanged at faster speeds between an increasing number of OT systems, it becomes more difficult to protect the security of the data lifecycle and the physical equipment it interacts with. These challenges are especially difficult to overcome because the economic and environmental gain (interoperability, big data, social networks and ubiquitous information sharing) are driving these prominent trends in the digital age. Often cybersecurity is an afterthought. The U.S. Department of Energy’s (DOE) Federal Energy Management Program (FEMP) funded the Pacific Northwest National Laboratory (PNNL) to develop various cybersecurity tools, trainings, and reports to aid federal facility managers – and other building owners and operators – in better applying frameworks and lessons learned from the National Institute of Standards and Technology (NIST) Cybersecurity Framework (CSF), risk management framework (RMF), DOE’s cybersecurity capability maturity model (C2M2), and a wide variety of industry best practices and guidance documents (i.e., NIST 800 series, Department of Defense United Facilities Criteria). This set of tools, collectively known as the FEMP Facility-Related Control System Cyber Toolkit (FRCS Cyber Toolkit)2, is focused on cybersecurity concerns from facility-related control systems and other operational technology (OT), such as industrial control systems (ICS). The FRCS Cyber Toolkit can be applied across six of the sixteen critical infrastructure sectors designated by the Department of Homeland Security, including government facilities, healthcare and public health, commercial facilities (e.g., public assembly, offices, lodging), financial services (e.g., banking and insurance), emergency services (e.g., fire and police stations), and information technology. With increasingly converged IT and OT systems, it is crucial to address OT cybersecurity considerations and assess how the seam of these two systems could impact the overall cybersecurity posture of a facility. The objective of this report is to provide an overview of the best possible method to use FRCS Cyber Toolkit (section 2.0) and distilled cybersecurity best practices for the federal facilities to address growing non-linear cyber threats (section 3.0). Recommendations in this document are aggregated from several NIST and other documents (see Appendix A for additional details).

97 MATHEMATICS AND COMPUTING↗