SEARCH · Engineering Papers
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
INL installed a new controls system and a flame detection system on TEDS as part of upgrades that will eventually allow unattended operation and integration with accessory systems, such as digital twins, or facilities in other locations. These upgrades resolve some corrective actions that came about when oil was spilled from a high-point vent during system startup testing.
The Spallation Neutron Source (SNS) at Oak Ridge National Laboratory, produces the world’s most intense pulse neutrons beams. An accelerated proton beam is directed into a mercury target to generate neutrons via spallation. The target system accounted for over 40% of the overall downtime of the facility in 2022. Thus, early detection in anomalies in the target systems can enable taking corrective actions to avoid failures and reduce downtime. Fault prognostics and anomaly detection in accelerators, both at SNS and outside, has largely focused on the beam side. This paper presents one the first studies exploring leveraging machine learning to automate the detection of anomalies in the target system. The target system consists of over 30 different interconnected subsystems, and the present work focuses on the mercury process system as a use case. Analyzing data from 28 process variables from 2022 and 2023, tree-based and reconstruction-based algorithms are employed to detect anomalies in archived data. The algorithms detected previously unreported anomalies, several of which were deemed alert worthy by human experts, particularly those found by reconstruction-based algorithms. Using data from each production run in the accelerator increased the generalizability of the models in time. Efforts are now underway to implement a workflow for incorporating human feedback to update the models and evaluating performance on unseen data. The models will eventually be integrated into the existing System Tracking and Reliability system with a web interface for automated anomaly detection and reporting along with a pathway for incorporating human feedback for model updates.
Determine the most probable root cause of the LaSalle fuel rod failure, distinguish primary failure mechanisms from post-failure degradation, and assess whether the condition is isolated or indicative of a broader issue affecting similar rods or assemblies. Results will support decisions related to core operation, fuel management, and corrective actions.
SRNL has developed instrument control software for diode array spectrophotometers that can be used for real time monitoring of dynamic systems. This software has evolved through several iterations as instruments have been developed for use in processing facilities at Savannah River Site. There are several noteworthy features of the software. The wavelength calibration is checked and updated in real time against reference emission lines that are designed into the instrument light source. Nonlinearities and offsets associated with the intensity response of the instruments are characterized, and absorbance spectra are appropriately corrected. These actions are necessary for accurate calculation of an absorbance spectrum from the intensity responses of two individual diode array detectors. The corrections also result in the spectra obtained for a particular sample by two (or more) different diode array detector pairs being nearly identical. This property assures consistent measurements at multiple locations with different instruments. It also permits the use of a single analyte calibration across many instruments without modification and without loss of accuracy
The purpose of this research is to develop a first-of-a-kind framework for integrating Big Data capability into the daily activities of our current fleet of nuclear power plants. Big Data is traditionally defined as data sets with high volume, velocity, and heterogeneity, and the existing Big Data analytics capabilities are now widely popular in fields such as finance, weather, e-commerce, healthcare and sports. In the nuclear industry, while the volume and velocity of data may present computational challenges for existing analytics capabilities, data heterogeneity are seen to present the major challenge. This research project mainly focuses on incorporating the wide range of data heterogeneities in nuclear power plants into an integrated Big Data Analytics capability. The primary end-product of this project is a Big Data framework that is capable of dealing with the large volume and heterogeneity of the data found in nuclear power plants to extract timely and valuable information on equipment performance. The framework can generate system insights that are actionable relations between measurable impacts and the corresponding maintenance action plans and enable optimization of plant operation and maintenance based on the extracted information. The developed framework is capable of handling heterogeneous data including both image data and time-series sensor data. Specifically, this developed framework includes the following components. The first component is an overarching maintenance ontology which includes system insights required by maintenance optimization. The maintenance ontology interacts with other components in the developed framework. The second component handles Piping & Instrumentation Diagram (P&ID) data. It can be used to extract system components and their relations automatically from the P&IDs. This extracted information is stored in the first component, i.e., maintenance ontology, and is also used as input to the third component, i.e., a tool for generating the fault tree for the corresponding system. The generated fault tree in turn is stored in the ontology for assessing risk that is used as a criterion in maintenance policy optimization. The fourth component is a tool for inferring the parameters in the Markov degradation model for a nuclear system. It uses basic information from the ontology. The fifth component is a tool for assessing the degradation level using sensor measurement data, for example, pressure, flowrate. This tool can be used for determining corrective maintenance actions. The results obtained from components four and five are returned to the ontology. The sixth component of the framework is a tool for optimizing the maintenance policy for a nuclear system of interest. It takes certain basic information from the ontology, e.g., costs of maintenance actions and system failures, as input, and returns the optimal maintenance policy to the ontology. This tool can be used for determining predictive maintenance actions. A set of experiments have also been conducted to verify the algorithms developed in this project for nuclear system degradation monitoring. The experiments are based on four solenoid valves, similar to the ones used in nuclear power plants. The analyses based on the experimental data using two algorithms, i.e., the Randomized Window Decomposition (RWD) algorithm and the particle filtering algorithm, and the results are introduced in the report. The Big Data framework developed in this project can be used as a support tool in daily activities of plant operation and maintenance and will reduce current costs while maintaining or improving safety levels. Overall, the project will not only benefit existing reactors, however it will open new frontiers to realize the long overdue value of Big Data Analytics in the nuclear sphere.
Not Available
Abstract not provided.
Abstract not provided.
We consider the shift of charge-to-mass ratio for extremal black holes in the context of effective field theory, motivated by the Weak Gravity Conjecture. We constrain extremality corrections in different regimes subject to unitarity and causality constraints. In the asymptotic IR, we demonstrate that for any supersymmetric theory in flat space, and for all minimally coupled theories, logarithmic running at one loop pushes the Wilson coefficient of certain four-derivative operators to be larger at lower energies, guaranteeing the existence of sufficiently large black holes with Q > M. We identify two exceptional cases of nonsupersymmetric theories involving large numbers of light states and Planck-scale nonminimal couplings, in which the sign of the running is reversed, leading to black holes with negative corrections to Q/M in the deep IR, but argue that these do not rule out extremal black holes as the requisite charged states for the WGC. We separately show that causality and unitarity imply that the leading threshold corrections to the effective action from integrating out massive states, in any weakly coupled theory, can be written as a sum of squares and is manifestly positive for black hole backgrounds. Quite beautifully, the shift in the extremal Q/M ratio is directly proportional to the shift in the on-shell action, guaranteeing that these threshold corrections push Q > M in compliance with the WGC. Our results apply for black holes with or without dilatonic coupling and charged under any number of U(1)s.
Distributed energy resources (DERs) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline controller.
Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.
Distributed energy resources (DER) in distribution systems, including renewable generation, micro-turbine, and energy storage, can be used to restore critical loads following extreme events to increase grid resiliency. However, properly coordinating multiple DERs in the system for multi-step restoration process under renewable uncertainty and fuel availability is a complicated sequential optimal control problem. Due to its capability to handle system non-linearity and uncertainty, reinforcement learning (RL) stands out as a potentially powerful candidate in solving complex sequential control problems. Moreover, the offline training of RL provides excellent action readiness during online operation, making it suitable to problems such as load restoration, where in-time, correct and coordinated actions are needed. In this study, a distribution system prioritized load restoration based on a simplified single-bus system is studied: with imperfect renewable generation forecast, the performance of an RL controller is compared with that of a deterministic model predictive control (MPC). Our experiment results show that the RL controller is able to learn from experience, adapt to the imperfect forecast information and provide a more reliable restoration process when compared with the baseline MPC controller.
Quantum computers are highly susceptible to errors due to unintended interactions with their environment. It is crucial to correct these errors without gaining information about the quantum state, which would result in its destruction through back-action. Quantum Error Correction (QEC) provides information about occurred errors without compromising the quantum state of the system. However, the implementation of QEC has proven to be challenging due to the current performance levels of qubits -- break-even requires fabrication and operation quality that is beyond the state-of-the-art. Understanding how qubit performance factors into the success of a QEC code is a valuable exercise for tracking progress towards fault-tolerant quantum computing. Here we present \texttt{qec\_code\_sim}, an open-source, lightweight Python framework for studying the performance of small quantum error correcting codes under the influence of a realistic error model appropriate for superconducting transmon qubits, with the goal of enabling useful hardware studies and experiments. \texttt{qec\_code\_sim} requires minimal software dependencies and prioritizes ease of use, ease of change, and pedagogy over execution speed. As such, it is a tool well-suited to small teams studying systems on the order of one dozen qubits.
We study the quantum error correction properties of the black hole interior in a toy model for an evaporating black hole: Jackiw-Teitelboim gravity entangled with a non-gravitational bath. After the Page time, the black hole interior degrees of freedom in this system are encoded in the bath Hilbert space. We use the gravitational path integral to show that the interior density matrix is correctable against the action of quantum operations on the bath which (i) do not have prior access to details of the black hole microstates, and (ii) do not have a large, negative coherent information with respect to the maximally mixed state on the bath, with the lower bound controlled by the black hole entropy and code subspace dimension. Thus, the encoding of the black hole interior in the radiation is robust against generic, low-rank quantum operations. For erasure errors, gravity comes within an O (1) distance of saturating the Singleton bound on the tolerance of error correcting codes. For typical errors in the bath to corrupt the interior, they must have a rank that is a large multiple of the bath Hilbert space dimension, with the precise coefficient set by the black hole entropy and code subspace dimension.
The accumulation of dust on the surface of photovoltaic modules can reduce their performance and affect the cost competitiveness of this technology. This phenomenon is known as soiling and can be mitigated through appropriate corrective and/or preventive actions. In order to maximize its effectiveness, it is important to plan the soiling mitigation strategy even before the PV system is operational. This is typically done through a nearest neighbor approach, by estimating soiling using data from the nearest operational photovoltaic system. This work focuses on understanding the uncertainty related to this practice. For this purpose, the semi-variance function is used to study the dissimilarity between the soiling losses of two locations in California depending on their distance. The results show that, when the soiling loss at a nearby system is used to estimate soiling of a site, the uncertainty can be approximated to increase linearly at a rate of 0.08-0.10%/km up to 60 or 80 km. After this distance, the use of a nearest neighbor approach is no longer justified, as it produces an uncertainty as big as the average soiling loss of the sites in the dataset used in this study. In some conditions, uncertainties > 0% are found also for sites located within 25 km, meaning that even close-by systems might soil differently.
Domestic extremism has been a growing concern in the United States in recent months, as illustrated in multiple bulletins from the Department of Homeland Security (DHS) warning law enforcement partners of the heightened threat. As concerns about these actors grows, it is important that facilities in the U.S. and internationally that protect critical assets, such as sensitive information, hazardous materials, or critical infrastructure, have effective methods in place to secure those assets. DE has challenged security systems through the threat of insider attack and violence, creating a new threat to be countered in the Office of Radiological Security’s radiological source security mission. In this effort, we used a literature review and focus group discussions with experts in critical asset security and extremism to understand the nature of the domestic extremist threat, to identify best practices in securing assets, recognize potential gaps in security measures to be corrected, and recommend actions and next steps. Twenty-two subject matter experts participated in a series of five focus group sessions. Questions focused on definitions of domestic extremism, potential changes in the threat, best practices in securing facilities, assets, and personnel, and any perceived gaps. Upon completion of the focus groups, notes were analyzed thematically to identify any recurring patterns in the results. In addition, a review of academic, industry, and government literature was conducted to understand the threat, describe the process of radicalization to extremism, and to identify empirically informed practices in prevention and response. Results of this project demonstrated that further work is needed to define domestic extremism in law, regulation, and policy, to help the U.S. develop a consistent response to the threat within organizations. This is especially important, as SMEs emphasized the need for early intervention in prevention efforts, noting that organizations need clear guidance on when and how to intervene. In addition, the need for social media monitoring was discussed, although challenges remain to do so with appropriate respect for privacy and civil liberties concerns.
Domestic extremism (DE) has been a growing concern in the U.S. in recent months, as illustrated in multiple bulletins from the Department of Homeland Security (DHS) warning law enforcement partners of the heightened threat. As concerns about these actors grows, it is important that facilities in the U.S. and internationally that protect critical assets, such as sensitive information, hazardous materials, or critical infrastructure, have effective methods in place to secure those assets. DE has challenged security systems through the danger of insider attack and violence, creating a new threat to be countered. In this effort, therefore, we used a literature review and focus group discussions with experts in critical asset security and extremism to understand the nature of the domestic extremist threat, to identify best practices in securing assets, recognize potential gaps in security measures to be corrected, and recommend actions for the Office of Radiological Security (ORS) to address DE with its partners.
Following recent work on heavy-light correlators in higher-dimensional conformal field theories (CFTs) with a large central charge C T , we clarify the properties of stress tensor composite primary operators of minimal twist, [ T m ], using arguments in both CFT and gravity. We provide an efficient proof that the three-point coupling $\left\langle {\mathcal{O}}_L{\mathcal{O}}_L\left[{T}^m\right]\right\rangle$, where ${\mathcal{O}}_L$ is any light primary operator, is independent of the purely gravitational action. Next, we consider corrections to this coupling due to additional interactions in AdS effective field theory and the corresponding dual CFT. When the CFT contains a non-zero three-point coupling $\left\langle TT{\mathcal{O}}_L\right\rangle$, the three-point coupling $\left\langle {\mathcal{O}}_L{\mathcal{O}}_L\left[{T}^2\right]\right\rangle$ is modified at large C T if $\left\langle TT{\mathcal{O}}_L\right\rangle \sim \sqrt{C_T}$. This scaling is obeyed by the dilaton, by Kaluza-Klein modes of prototypical supergravity compactifications, and by scalars in stress tensor multiplets of supersymmetric CFTs. Quartic derivative interactions involving the graviton and the light probe field dual to ${\mathcal{O}}_L $ can also modify the minimal-twist couplings; these local interactions may be generated by integrating out a spin- ℓ ≥ 2 bulk field at tree level, or any spin ℓ at loop level. These results show how the minimal-twist OPE coefficients can depend on the higher-spin gap scale, even perturbatively.