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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 OSTI2022

Enhancing Local Grid Resilience with Small Hydropower Hybrids: Proving the concept through demonstration, simulation, and analysis with Idaho Falls Power

Large hydropower, connected to the transmission system and typically possessing significant ability to balance grid frequency, has long been central to black start plans in regions where it is present. Small hydropower possesses most of the attributes required for black start but is often connected to distribution or sub-transmission systems and has less ability to balance grid frequency. Integrating energy storage such as batteries or ultracapacitors increases the combined asset’s ability to balance frequency. This asset integration enables a bottom-up grid restoration paradigm in which critical electric loads on the local distribution system can be powered even when the regional transmission system is down. This report presents a field demonstration conducted with Idaho Falls Power that proved this concept. The contribution of energy storage in restoring the small hydro-dominated distribution grid and its operational sensitivity across different control settings are further analyzed using high-fidelity simulations. Readers will learn about technical details on the field demonstration setup, energy storage contribution to black start, detailed simulation steps of islanded distribution grid restoration, and usage of field demonstration measurements for transient model refinements. Collectively this report points to a great opportunity for small hydropower to enhance resilience of local electric grids.

13 HYDRO ENERGY↗
DOE OSTI2025

Fermilab Booster loss modelling and rebalancing using Bayesian methods

Fermilab Booster is being upgraded for the PIP-II project to support 20Hz ramp rate at higher intensities. Loss trip limits determine the achievable peak power. To meet PIP-II requirements, losses need to be halved as compared to current levels. Losses primarily occur at injection and transition crossing, with both gradually increasing and threshold-like intensity-dependent behaviors. The existing simulation models are not yet good enough for quantitative loss predictions. In practice, it will be necessary to tune up the Booster using iterative methods and operator intuition. In this paper we present an effort to systematically model Booster losses using active learning (Bayesian exploration) techniques, and subsequently to rebalance them for higher trip limit margins. We first created several sets of spatially and temporally isolated orbit and optics knobs, and trained Gaussian process models for each beam loss monitor as well as beam current. This is a complex task due to safety and timing requirements – we discuss mitigations such as uncertainty constraints and approximate fitting. Once models are stable, we perform large-scale single and multi-objective tuning using scalarized objectives made up of critical beam loss locations. Our results demonstrate significant rebalancing of losses, increasing trip margins, as well as an overall improvement in beam transmission efficiency. We are exploring how to combine existing simulations with experimental data and automate the collection procedure so that more advanced surrogate models can be created over time.

Kuklev, Nikita [Fermilab]↗
DOE OSTI2025

Evaluation of fluxon synapse device based on superconducting loops for energy efficient neuromorphic computing

With Moore’s law nearing its end due to the physical scaling limitations of CMOS technology, alternative computing approaches have gained considerable attention as ways to improve computing performance. Here, we evaluate performance prospects of a new approach based on disordered superconducting loops with Josephson-junctions for energy efficient neuromorphic computing. Synaptic weights can be stored as internal trapped fluxon states of three superconducting loops connected with multiple Josephson-junctions (JJ) and modulated by input signals applied in the form of discrete fluxons (quantized flux) in a controlled manner. The stable trapped fluxon state directs the incoming flux through different pathways with the flow statistics representing different synaptic weights. We explore implementation of matrix–vector-multiplication (MVM) operations using arrays of these fluxon synapse devices. We investigate the energy efficiency of online-learning of MNIST dataset. Our results suggest that the fluxon synapse array can provide ~100× reduction in energy consumption compared to other state-of-the-art synaptic devices. This work presents a proof-of-concept that will pave the way for development of high-speed and highly energy efficient neuromorphic computing systems based on superconducting materials.

42 ENGINEERING↗
DOE OSTI2023

Code for BALDR Study 07.05 v.1.0.0

SAND2024-01293O The Code for BALDR Study 07.05 software reproduces results from the BALDR study concerning "A Semi-Supervised Learning Method to Produce Explainable Radioisotope Proportion Estimates for NaI-based Synthetic and Measured Gamma Spectra." The code for BALDR Study 07.05 can reproduce a scientific study following the step numbers within the file names. Using synthetic and measured data to find the best model for the radioisotope proportion estimation task of interest, the study generated results for inclusion in a paper. The high-level methods involved are neural networks, semi-supervised learning, and out-of-distribution detection. Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-NA0003525.

Morrow, Tyler↗
DOE OSTI2017

King County Metro Battery Electric Bus Demonstration: Preliminary Project Results

The U.S. Federal Transit Administration (FTA) funds a variety of research projects that support the commercialization of zero-emission bus technology. To evaluate projects funded through these programs, FTA has enlisted the help of the National Renewable Energy Laboratory (NREL) to conduct third-party evaluations of the technologies deployed under the FTA programs. NREL works with the selected agencies to evaluate the performance of the zero-emission buses compared to baseline conventional buses in similar service. The evaluation effort will advance the knowledge base of zero-emission technologies in transit bus applications and provide 'lessons learned' to aid other fleets in incrementally introducing next generation zero-emission buses into their operations. This report provides preliminary performance evaluation results from a demonstration of three zero-emission battery electric buses at King County Metro in King County, Washington. NREL developed this preliminary results report to quickly disseminate evaluation results to stakeholders. Detailed evaluation results will be published in future reports.

25 ENERGY STORAGE↗
DOE OSTI2018

County Connection Battery Electric Bus Demonstration: Preliminary Project Results

The U.S. Federal Transit Administration (FTA) funds a variety of research projects that support the commercialization of zero-emission bus technology. To evaluate projects funded through these programs, FTA has enlisted the help of the National Renewable Energy Laboratory (NREL) to conduct third-party evaluations of the technologies deployed under the FTA programs. NREL works with the selected agencies to evaluate the performance of the zero-emission buses compared to baseline conventional buses in similar service. The evaluation effort will advance the knowledge base of zero-emission technologies in transit bus applications and provide 'lessons learned' to aid other fleets in incrementally introducing next generation zero-emission buses into their operations. This report provides preliminary performance evaluation results from a demonstration of four all-electric trolley-replica buses and two electric vehicle charging stations at County Connection in the San Francisco Bay Area. NREL developed this preliminary results report to quickly disseminate evaluation results to stakeholders. Detailed evaluation results will be published in future reports.

25 ENERGY STORAGE↗
DOE OSTI2021

Domain knowledge-informed, process-mapping AI graph for designing Fe-based alloys

<span style="font-family: Calibri, sans-serif; font-size: 12pt;">Continuous improvement in efficiency of a power plant relies on designing materials for use at increasingly higher temperature and/or pressure, for 100,000s hours of operation. Due to complexity, non-linearity and high-dimensionality of the problem, traditional Machine Learning (ML) approaches require unreasonably large datasets for the data-driven model development. Science-based material and process engineering complements hard data with, sometimes soft and intuitive, empirical domain knowledge. Artificial Intelligence (AI) was used in this study to incorporate such knowledge into computational graph architecture (process-mimicking artificial neuron design, causal layer and graph structures, ensemble modeling of latent states) and learning procedures (variable transformation, fuzzy physics pre-training and freezing of deep layers, virtual microstructure representation, and adversarial multi-objective optimization). The first alloys design pathways suggested by the AI tool (pyroMind) passed a preliminary engineering review on soundness and transparency.</span>

Romanov, Vyacheslav↗
DOE OSTI2012

A preliminary user-friendly, digital console for the control room parameters supervision in old-generation Nuclear Plants

Improvements in the awareness of a system status is an essential requirement to achieve safety in every kind of plant. In particular, in the case of Nuclear Power Plants (NPPs), a progress is crucial to enhance the Human Machine Interface (HMI) in order to optimize monitoring and analyzing processes of NPP operational states. Firstly, as old-fashioned plants are concerned, an upgrading of the whole console instrumentation is desirable in order to replace an analog visualization with a full-digital system. In this work, we present a novel instrument able to interface the control console of a nuclear reactor, developed by using CompactRio, a National Instruments embedded architecture and its dedicated programming language. This real-time industrial controller composed by a real-time processor and FPGA modules has been programmed to visualize the parameters coming from the reactor, and to storage and reproduce significant conditions anytime. This choice has been made on the basis of the FPGA properties: high reliability, determinism, true parallelism and re-configurability, achieved by a simple programming method, based on LabVIEW real-time environment. The system architecture exploits the FPGA capabilities of implementing custom timing and triggering, hardware-based analysis and co-processing, and highest performance control algorithms. Data stored during the supervisory phase can be reproduced by loading data from a measurement file, re-enacting worthwhile operations or conditions. The system has been thought to be used in three different modes, namely Log File Mode, Supervisory Mode and Simulation Mode. The proposed system can be considered as a first step to develop a more complete Decision Support System (DSS): indeed this work is part of a wider project that includes the elaboration of intelligent agents and meta-theory approaches. A synoptic has been created to monitor every kind of action on the plant through an intuitive sight. Furthermore, another important aim of this work is the possibility to have a front panel available on a web interface: CompactRio acts as a remote server and it is accessible on a dedicated LAN. This supervisory system has been tested and validated on the basis of the real control console for the 1-MW TRIGA reactor RC-1 at the ENEA, Casaccia Research Center. In this paper we show some results obtained by recording each variable as the reactor reaches its maximum level of power. The choice of a research reactor for testing the developed system relies on its training and didactic importance for the education of plant operators: in this context a digital instrument can offer a better user-friendly tool for learning and training. It is worthwhile to remark that such a system does not interfere with the console instrumentation, the latter continuing to preserve the total control. (authors)

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2015

Mega salt-stone Disposal Units at the Savannah River site - 15322

The Liquid Waste (LW) System at Savannah River Site (SRS) is a highly integrated operation involving safely storing radioactive liquid waste in underground storage tanks; vitrifying the higher activity waste; and storing the vitrified waste in stainless steel canisters until permanent disposition is provided; and removing, treating, and dispositioning the low-level waste (LLW) fraction in concrete with final disposition being the salt-stone Disposal Units (SDUs) at the SRS salt-stone Disposal Facility (SDF). Treatment and disposition of salt waste is the critical path to completion of the SRS Liquid Waste (LW) Disposition Program. Successful and timely salt waste removal and disposal is integral to efforts by SRS to proceed with all aspects of tank cleanup and removal from service. The construction and implementation of the first-of-a-kind Mega SDU (MSDU) is underway to provide the most environmentally safe and cost-effective solution for salt processing and long term storage of the LLW at SRS. The MSDU design is a duplicate of the Water Treatment Tanks located in Syracuse, New York. This commercial design, developed by an affiliate of Savannah River Remediation LLC (SRR), was slightly modified to meet the requirements of the nuclear industry. The MSDU is a critical link in salt disposition. In comparison to the current SDU design, approximately 46 meters (150 feet) in diameter and 7 meters (22 feet) tall with a capacity of approximately 11,000 m{sup 3} (3.0 million gallons), the MSDU is 114 meters (375 feet) in diameter and 13 meters (43 feet) tall which will provide a capacity of approximately 121,000 m{sup 3} (32.0 million gallons) to receive the LLW fraction embedded in a grout-like form. The MSDU will provide the taxpayers an estimated $300 M in life cycle savings at SRS by reducing the number of SDUs from the estimated 72 smaller vessels to 7 of the new larger vessels. Additionally, based on the reduced number of SDUs, the environmental footprint is significantly reduced as well. Due to the similarities between the existing SDU design and the MSDU design, efforts to integrate the MSDU into the LW system are minimal. Required infrastructure modifications will consist of tying into the existing grout and drain lines, as well as the installation of new electrical and instrumentation services for the MSDU. Grout flow testing has been successfully completed, with results showing the MSDU will be a safe and cost-effective replacement for the existing SDUs. The MSDU design is complete, Critical Decision (CD)-3 was approved on June 17, 2014, and the first MSDU, salt-stone Disposal Unit n.6 (SDU 6), is being constructed in the existing LW SDF operated by SRR as a Capital Line Item project. As an added benefit, once constructed, lessons learned will be available for future MSDUs at SRS and throughout the complex as necessary. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2016

The Ambivalent Role of Participants in Nuclear Waste Decisions - 16360

Today's global nuclear sector has entered a critical juncture. Waste management solutions have the potential to influence the prospects of whether new plants will be built and when aging plants will be decommissioned. Even in countries without nuclear power plants, the siting of nuclear waste can present communication challenges and opportunities for public involvement. As new nuclear programs withstand scrutiny, local communities share their want for solutions to nuclear waste before new plant construction commences. Developing nations have begun to explore the potential of adding nuclear power production to their energy mix. They carefully watch established nuclear players to accelerate their plans with lessons learned elsewhere. Some newcomers to nuclear energy will make decisions on new construction based on other operators' abilities to reconcile public concerns regarding radioactive waste. Distinct forms of government, cultural norms and means of public discourse present innovative approaches and changing expectations worldwide. This paper explores how practitioners are evaluating ways to activate stakeholders on nuclear waste siting in nations embarking on new nuclear programs. It investigates the incentives that people have to participate and legitimize the formation of laws, regulations and standards for nuclear materials management. Finally, it highlights examples of public participation techniques from established operating environments and posits that overly prescriptive requirements for stakeholder engagement can reduce the authenticity of public involvement and weaken confidence among participants in the decision making process. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

Autonomous Utility Pole Identification

The implementation of small unmanned aerial systems (sUAS) for the purpose of powerline inspection is an emerging concept among utility companies. Such operations can produce a significant amount of useful data for the purpose of training different machine learning models or keep track of the integrity of electrical infrastructure. As such, this system is developed for the purpose of properly cataloging and leveraging this collected data.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

AUTOMATIC GENERATION OF EVENT TREES AND FAULT TREES: A MODEL-BASED APPROACH

In the past few decades, increasing complexity in modern engineering systems has been driven by the integration of a large number of components and by the fact that the system operations involve many disciplines (e.g., thermal-hydraulics, plant operations, cyber-security). Current safety/reliability modeling approaches to such systems are labor intensive, difficult to learn, and rely heavily on simplistic Boolean logic to depict failure propagation and accident progression. While these methods serve well for simple systems (i.e., linear causal systems with limited small inter- and intra-system interactions), their results are difficult to verify when modeling complex systems (typically performed through the extensive use of modeling assumptions). The development of new methods is addressed to meet these challenges through a model-based system engineering (MBSE) lens. Under MBSE philosophy, every aspect of the system (form or function) is represented by a model that completely characterizes its architecture or behavior. MBSE approach greatly improves the management of design, analysis and verification of complex systems. An integration of Dynamic Probabilistic Risk Assessment (DPRA) methods with MBSE models is proposed to perform safety/reliability analyses of engineering systems. In particular, MBSE representation of the system (performed using Systems Modeling Language [SysML]) is coupled with DPRA methods to automatically generate event trees and fault trees.

97 - MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Augmented cognition tool for rapid military decision making.

This report describes the laboratory directed research and development work to model relevant areas of the brain that associate multi-modal information for long-term storage for the purpose of creating a more effective, and more automated, association mechanism to support rapid decision making. Using the biology and functionality of the hippocampus as an analogy or inspiration, we have developed an artificial neural network architecture to associate k-tuples (paired associates) of multimodal input records. The architecture is composed of coupled unimodal self-organizing neural modules that learn generalizations of unimodal components of the input record. Cross modal associations, stored as a higher-order tensor, are learned incrementally as these generalizations form. Graph algorithms are then applied to the tensor to extract multi-modal association networks formed during learning. Doing so yields a novel approach to data mining for knowledge discovery. This report describes the neurobiological inspiration, architecture, and operational characteristics of our model, and also provides a real world terrorist network example to illustrate the model's functionality.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2025

Detecting Process Equipment Failures Using Acoustic Data and Machine Learning

Nuclear power plant (NPP) process equipment such as fans, motors, valves, and pumps generate frequent or continuous noise, and deviations from the normal operational sounds made by this equipment can indicate potential issues. These deviations can be identified via automated acoustic anomaly detection, which involves using acoustic sensors (i.e., microphones) alongside detection algorithms to continuously monitor for changes in acoustic signatures. This task is made challenging by the substantial background noise that exists, such as operators opening and closing doors, manipulating valves, and conversing—in addition to typical plant noises. In collaboration with a nuclear power utility partner, this effort assessed the efficacy of acoustic anomaly detection when using a specific acoustic sensor that compresses data into a fixed set of features that are transferable over a standard Internet of Things communication protocol, thereby improving usability but potentially degrading detection performance. Two methods of performing automated acoustic anomaly detection were evaluated: one-class support vector machine (OC-SVM) and isolation forest (iForest). To enable the use of high-quality acoustic data encompassing both normal and anomalous conditions, the study utilized the publicly available Malfunctioning Industrial Machine Investigation and Inspection dataset, which includes real measured acoustic sensor data for a range of equipment types, model numbers, and signal-to-noise ratios (SNRs), along with a benchmark set of detection results. Using this dataset, the methods were tested and then compared against the benchmark results. The results indicated that although the specific acoustic sensor did not enable as rich a feature set extraction, the proposed methods with the limited feature set performed just as well. This provides solid justification for both the methods and the use of the proposed acoustic sensor.

46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AN↗
DOE OSTI2024

Situational awareness-enhancing community-level load mapping with opportunistic machine learning

Motivated by present and forthcoming challenges in the adoption and integration of distributed renewable energy, we develop a machine learning (ML) approach that builds short-fuse mappings connecting the occasionally-unobservable true load in one target community with information-rich signals collected from relatively more instrumented reference communities. Our setting is inspired by and tailored to target communities with significant unobservable behind-the-meter solar generation, where true load (a relatively well-behaved quantity of interest to grid operators) is hard to discern during daytime due to insufficient instrumentation and/or privacy reasons, but that can be related to reference communities with low unobservable distributed variable generation or with sufficient instrumentation. The developed mapping, herein realized with Support Vector Machine regression, is built using nighttime data from all communities, when their distributed generation is low or zero. Our ML algorithm opportunistically learns to correlate signals of interest and then is operationally used the next day to shed light into target community load evolution. The mapping is subsequently rebuilt, rolling its short-fuse scope perpetually forward in time. Here, we demonstrate the efficacy of our approach on nine synthetically generated topologies and associated timeseries stemming from real-world data, on which we observe cumulative error performance that yields lower than 10% and 15% daily-averaged mean absolute percentage errors in target community load estimation on more than about 75% and 90% of days, respectively, in multiple yearly evaluations that shed light on long-term performance also under seasonal and one-off effects. The proposed ML-powered methodology can offer grid operators much-improved visibility into a previously obscure space and can also serve as an additional source of information in broader, multi-modal solar disaggregation solutions.

14 SOLAR ENERGY↗
DOE OSTI2015

Crystallization in high level waste (HLW) glass melters: Savannah River Site operational experience

This paper provides a review of the scaled melter testing that was completed for design input to the Defense Waste Processing Facility (DWPF) melter. Testing with prototype melters provided the data to define the DWPF operating limits to avoid bulk (volume) crystallization in the un-agitated DWPF melter and provided the data to distinguish between spinels generated by refractory corrosion versus spinels that precipitated from the HLW glass melt pool. A review of the crystallization observed with the prototype melters and the full-scale DWPF melters (DWPF Melter 1 and DWPF Melter 2) is included. Examples of actual DWPF melter attainment with Melter 2 are given. The intent is to provide an overview of lessons learned, including some example data, that can be used to advance the development and implementation of an empirical model and operating limit for crystal accumulation for a waste treatment and immobilization plant.

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

DECT-MULTRA: Dual-Energy CT Image Decomposition With Learned Mixed Material Models and Efficient Clustering

Dual-energy computed tomography (DECT) imaging plays an important role in advanced imaging applications due to its material decomposition capability. Image-domain decomposition operates directly on CT images using linear matrix inversion, but the decomposed material images can be severely degraded by noise and artifacts. This paper proposes a new method dubbed DECT-MULTRA for image-domain DECT material decomposition that combines conventional penalized weighted-least squares (PWLS) estimation with regularization based on a mixed union of learned transforms (MULTRA) model. Our proposed approach pre-learns a union of common-material sparsifying transforms from patches extracted from all the basis materials, and a union of cross-material sparsifying transforms from multi-material patches. The common-material transforms capture the common properties among different material images, while the cross-material transforms capture the cross-dependencies. The proposed PWLS formulation is optimized efficiently by alternating between an image update step and a sparse coding and clustering step, with both of these steps having closed-form solutions. The effectiveness of our method is validated with both XCAT phantom and clinical head data. The results demonstrate that our proposed method provides superior material image quality and decomposition accuracy compared to other competing methods.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Intelligent Optimization of the Digital Low Level RF Control System for LANSCE LINAC

The LINAC at the Los Alamos Neutron Science Center (LANSCE) accelerates protons from 750 keV to its final energy at 800 MeV via 48 radio frequency (RF) modules. However, the startup and recovery process of the low-level RF (LLRF) systems, the primary controls for the RF modules, cost significant time for the beam operation, while the process itself is highly prone to human errors. With the new conversion from the analog LLRF (aLLRF) to digital LLRF (dLLRF) system under the recent LANSCE Modernization Project, new approaches with the new dLLRF capabilities can be achieved to address this issue. We propose to develop an intelligent optimization scheme that can significantly lower the downtime caused by the LLRF systems. This directly address the MFR problem statement that asks for “innovative engineering improvements to ancillary systems such as RF and pulsed power that improve reliability, maintainability, and/or performance.”

43 PARTICLE ACCELERATORS↗