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We implement a continuous-variable quantum compiler that can learn a linear phase operation with a fourfold increase in precision and a factor of 80 reduction in time to solution when quantum resources are used.
Multipactor discharge is a nonlinear electron avalanche that limits the performance of high-power radio-frequency (RF) and vacuum electronic devices. Predicting multipactor susceptibility traditionally relies on Monte Carlo or particle-in-cell (PIC) simulations, which become computationally expensive for large parametric studies. In this work, we present a supervised machine-learning (ML) framework for prediction of multipactor susceptibility in a two-surface planar geometry. The models are trained using high-fidelity PIC simulation generated susceptibility data and learn the relationship between operational parameters, geometry, and material-dependent secondary electron emission properties. The proposed approach enables rapid reconstruction of susceptibility charts while preserving the physical structure of multipactor growth regions.
In preparation for DUNE, Fermilab along with six other institutions are testing cold electronics for quality control before components placed in the far detector. We test them by using a robotic arm that places these chips into sockets on a computer board that will test their functionality. Up until now, the chips have been tested using a command line script that drives a state machine to conduct tests step-by-step. In order to lower the skill barrier to conduct tests and to speed up the quality control process, I was tasked to create a graphical user interface that would allow users to use buttons, text boxes, and drop-down menus to input information and tell the testing state machine how to operate. I had to learn about the Python package Tkinter to start the process of widget placement. I further developed a pause feature unused in the previous command line script that would allow the user to shut down testing gracefully, bring the robotic arm to go back to ground state, and go forward or backward a step in the testing process. After completing the basic functionality of the GUI, I started testing production chips with the GUI to debug. Some issues were found, which required me to further develop parts of the inherited state machine code. The code for the GUI has now been pushed into the copy the DUNE/FD_CE git repository and will soon be merged with the official DUNE/FD_CE repository so that the other institutions testing DUNE cold electronics can use and expand upon it.
Quantum process learning is a fundamental primitive that draws inspiration from machine learning with the goal of better studying the dynamics of quantum systems. One approach to quantum process learning is quantum compilation, whereby an analog quantum operation is digitized by compiling it into a series of basic gates. While there has been significant focus on quantum compiling for discrete-variable systems, the continuous-variable (CV) framework has received comparatively less attention. We present an experimental implementation of a CV quantum compiler that uses two-mode squeezed light to learn a Gaussian unitary operation. We demonstrate the compiler by learning a parameterized linear phase unitary through the use of target and control phase unitaries to demonstrate a factor of 5.4 increase in the precision of the phase estimation and a 3.6-fold acceleration in the time-to-solution metric when leveraging quantum resources. We further show how our approach can be extended to higher-dimensional compilation tasks. Our results are enabled by the tunable control of our cost landscape via variable squeezing, thus providing a critical framework to simultaneously increase precision and reduce time-to-solution.
The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.
The Continuous Electron Beam Accelerator Facility (CEBAF) operates hundreds of superconducting radio frequency (SRF) cavities in its two main linear accelerators. Field emission can occur when the cavities are set to high operating RF gradients and is an ongoing operational challenge. This is especially true in newer, higher gradient SRF cavities. Field emission results in damage to accelerator hardware, generates high levels of neutron and gamma radiation, and has deleterious effects on CEBAF operations. So, field emission reduction is imperative for the reliable, high gradient operation of CEBAF that is required by experimenters. Here we explore the use of deep learning architectures via multilayer perceptron to simultaneously model radiation measurements at multiple detectors in response to arbitrary gradient distributions. These models are trained on collected data and could be used to minimize the radiation production through gradient redistribution. This work builds on previous efforts in developing machine learning (ML) models, and is able to produce similar model performance as our previous ML model without requiring knowledge of the field emission onset for each cavity.
Learning how to achieve sustained operation at high plasma pressure is an important goal of the National Spherical Torus Upgrade (NSTX-U) program and is critically important to any future fusion power plant. This research addresses high priority stability and control issues outlined by the NSTX-U Program. Reduced models of the stabilization physics learned continue to be created and tested against actual plasma operation in NSTX, NSTX-U, and other tokamak devices. This research is forming physics-based models for disruption forecasting that aims to unify results across different fusion machines for more confident extrapolation of the techniques studied to future devices including ITER. The Disruption Event Characterization and Forecasting (DECAF) code was expanded to analyze data from the TCV tokamak, including real-time MHD analysis signals. The presence of a mode and likelihood of its m/n numbers can be tracked, as well as the mode frequency, although a limitation at low frequency does not yet allow tracking modes to the locking point in TCV. Initial analyses of NSTX-U operation were conducted using the DECAF code. The timing of sawtooth oscillations was investigated taking advantage of the more extensive L-mode operation of the device in its first run campaign. An initial investigation of thermal collapses in NSTX-U was also conducted with specific comparison to the Greenwald density limit. A local island power balance model to explain the density limit in tokamaks has been tested with NSTX data and is being evaluated as a potential disruption forecaster in the DECAF code, presently showing similarities to the global Greenwald density evolution and limit. A module was written for the DECAF code that generalizes magnetic spectrogram analysis of MHD modes. This new portable code, written in Python, discriminates the toroidal mode number of instabilities and can track each mode as a DECAF event, monitoring the evolution of the mode through bifurcation and locking points. This is a critical new capability to allow disruption event characterization for general tokamak databases.
During this effort, SparkCognition with support from the Electric Power Research Institute (EPRI) was tasked with applying artificial intelligence (AI) to improve the reliability, efficiency, and safety of operations at a coal-fired plant. By implementing AI techniques, like machine learning (ML), it is believed that operators can leverage existing data sources to gain more insights such as advanced warning of machine degradation. With enough lead time, a reliability engineer can take action to minimize, or even avoid, impact to production. To complete this work effort, SparkCognition developed and refined an ML-based model using sensor data for a Steam Turbine unit at a host site. The models were deployed in an online, web-based solution that allows users to visualize model outputs and supporting data. The final solution, based on SparkCognition’s proprietary software platform called SparkPredict®, was shared with EPRI who completed an online evaluation of results to determine the solution’s ability to detect actionable events.
Abstract Nowcasting is a term originating from economics, finance, and meteorology. It refers to the process of determining the uncertain state of the economy, markets or the weather at the current time by indirect means. In this paper, we describe a simple two‐parameter data analysis that reveals hidden order in otherwise seemingly chaotic earthquake seismicity. One of these parameters relates to a mechanism of seismic quiescence arising from the physics of strain‐hardening of the crust prior to major events. We observe an earthquake cycle associated with major earthquakes in California, similar to what has long been postulated. An estimate of the earthquake hazard revealed by this state variable time series can be optimized by the use of machine learning in the form of the Receiver Operating Characteristic skill score. The ROC skill is used here as a loss function in a supervised learning mode. Our analysis is conducted in the region of 5° × 5° in latitude‐longitude centered on Los Angeles, a region which we used in previous papers to build similar time series using more involved methods (Rundle & Donnellan, 2020, https://doi.org/10.1029/2020EA001097 ; Rundle, Donnellan et al., 2021, https://doi.org/10.1029/2021EA001757 ; Rundle, Stein et al., 2021, https://doi.org/10.1088/1361-6633/abf893 ). Here we show that not only does the state variable time series have forecast skill, the associated spatial probability densities have skill as well. In addition, use of the standard ROC and Precision (PPV) metrics allow probabilities of current earthquake hazard to be defined in a simple, straightforward, and rigorous way.
Cyber-physical systems, especially in critical infrastructures, have become primary hacking targets in international conflicts and diplomacy. However, cyber-physical systems present unique challenges to defenders, starting with an inability to communicate. This paper outlines the results of our interviews with information technology (IT) defenders and operational technology (OT) operators and seeks to address lessons learned from them in the structure of our notional solutions. We pre-sent two problems in this paper: (1) the difficulty of coordinat-ing detection and response between defenders who work on the cyber/IT and physical/OT sides of cyber-physical infrastruc-tures, and (2) the difficulty of estimating the safety state of a cyber-physical system while an intrusion is underway but be-fore damage can be effected by the attacker. To meet these chal-lenges, we propose two solutions: (1) a visualization that will enable communication between IT defenders and OT operators, and (2) a machine-learning approach that will estimate the distance from normal the physical system is operating and send information to the visualization.
Salt as a geologic medium has several attributes favorable to long-term isolation of waste placed in mined openings. Salt formations are largely impermeable and induced fractures heal as stress returns to equilibrium. Permanent isolation also depends upon the ability to construct geotechnical barriers that achieve nearly the same high-performance characteristics attributed to the native salt formation. Salt repository seal concepts often include elements of reconstituted granular salt. As a specific case in point, the Waste Isolation Pilot Plant recently received regulatory approval to change the disposal panel closure design from an engineered barrier constructed of a salt-based concrete to one that employs simple run-of-mine salt and temporary bulkheads for isolation from ventilation. The Waste Isolation Pilot Plant is a radioactive waste disposal repository for defense-related transuranic elements mined from the Permian evaporite salt beds in southeast New Mexico. Its approved shaft seal design incorporates barrier components comprising salt-based concrete, bentonite, and substantial depths of crushed salt compacted to enhance reconsolidation. This paper will focus on crushed salt behavior when applied as drift closures to isolate disposal rooms during operations. Scientific aspects of salt reconsolidation have been studied extensively. The technical basis for geotechnical barrier performance has been strengthened by recent experimental findings and analogue comparisons. The panel closure change was accompanied by recognition that granular salt will return to a physical state similar to the halite surrounding it. Use of run-of-mine salt ensures physical and chemical compatibility with the repository environment and simplifies ongoing disposal operations. Our current knowledge and expected outcome of research can be assimilated with lessons learned to put forward designs and operational concepts for the next generation of salt repositories. Mined salt repositories have the potential to isolate permanently vast inventories of radioactive and hazardous wastes.
Salt as a geologic medium has several attributes favorable to long-term isolation of waste placed in mined openings. Salt formations are largely impermeable and induced fractures heal as stress returns to equilibrium. Permanent isolation also depends upon the ability to construct geotechnical barriers that achieve nearly the same high-performance characteristics attributed to the native salt formation. Salt repository seal concepts often include elements of reconstituted granular salt. As a specific case in point, the Waste Isolation Pilot Plant recently received regulatory approval to change the disposal panel closure design from an engineered barrier constructed of a salt-based concrete to one that employs simple run-of-mine salt and temporary bulkheads for isolation from ventilation. The Waste Isolation Pilot Plant is a radioactive waste disposal repository for defense related transuranic elements mined from the Permian evaporite salt beds in southeast New Mexico. Its approved shaft seal design incorporates barrier components comprising salt-based concrete, bentonite, and substantial depths of crushed salt compacted to enhance reconsolidation. This paper will focus on crushed salt behavior when applied as drift closures to isolate disposal rooms during operations. Scientific aspects of salt reconsolidation have been studied extensively. The technical basis for geotechnical barrier performance has been strengthened by recent experimental findings and analogue comparisons. The panel closure change was accompanied by recognition that granular salt will return to a physical state similar to the halite surrounding it. Use of run-of-mine salt ensures physical and chemical compatibility with the repository environment and simplifies ongoing disposal operations. Our current knowledge and expected outcome of research can be assimilated with lessons learned to put forward designs and operational concepts for the next generation of salt repositories. Mined salt repositories have the potential to isolate permanently vast inventories of radioactive and hazardous wastes. (authors)
HPC data centers such as the one at NREL's ESIF will increasingly need to rely on automation to keep pace with exascale growth in compute capability and to manage and optimize the data center environment and facility resources. Artificial intelligence (AI) and machine learning (ML) approaches provide the means to improve HPC data center efficiency (energy, operational, and managerial efficiency) and resiliency by learning historical trends and training models to operate on real-time data collected from both IT and facilities sources. The goal of coupled improvement of data center resiliency and energy efficiency through automated data collection and AI has led to a multi-year, multi-staged collaboration between NREL and Hewlett-Packard Enterprise's Advanced Technology Group, referred to as Artificial Intelligence for Data Center Operations (AIOps). The extended efforts within the AIOps project include a common goal of building capabilities for an advanced smart facility and demonstration of data collection and AI modeling techniques in the ESIF data center.
Active- and transfer-learning are applied to microscale dynamics of polymer flows for the multiscale discovery of effective constitutive approximations required in viscoelastic flow simulation. The result is macroscopic rheology directly connected to a microstructural model. Micro and macroscale simulations are adaptively coupled by means of Gaussian process regression (GPR) to run the expensive microscale computations only as necessary. This multiscale method is demonstrated with flows of a polymer solution as a model system. At the microscale level dissipative particle dynamics (DPD) is employed to model the fluid as a suspension of bead-spring micro-structures subjected to steady shear flow. The results yield the non-Newtonian viscosity and the first normal stress difference at strain rates as training data used in a GPR model. DPD parameters are calibrated with respect to experimental data for a real polymer solution. Compliance with these data requires adjustment of the DPD model's cutoff radius, which then becomes a function of the second invariant of the strain rate tensor. The FENE-P model is chosen for the macroscale description using the spectral element method (SEM) to simulate channel flow and flow past a circular cylinder. The DPD results at the lowest possible shear strain rate yield an estimate of the zero-shear rate viscosity, which allows the initiation of the macroscale flow by SEM as a Newtonian fluid. The resulting strain-rate field is surveyed to determine additional shear strain rate sampling points for the DPD system. This new information allows an initial fitting of parameters of the constitutive equation followed by new SEM simulations at the macroscale. Additionally, guided by active-learning GPR to select new sampling points, this process continues until convergence is achieved. The effectiveness of this new simulation paradigm for viscoelastic flows is tested with different macroscale operating conditions. The effective closure learned in the channel simulation is then transferred directly to the flow past a circular cylinder at low Reynolds number, where the results show that only two additional DPD simulations are required to achieve a satisfactory constitutive model. With an increase of the Reynolds number, the active-learning scheme automatically detects the inaccuracy of the learned constitutive model, and initiates additional DPD simulations for the extra data needed to once again close the microscale-macroscale coupled system. This new paradigm of active- and transfer-learning for multiscale modeling is readily applicable to other microscale-macroscale coupled simulations of complex fluids and other materials. Furthermore, the coupling between microscale and macroscale solvers can be seamlessly implemented with our open source multiscale universal interface (MUI) library.
Commercial nuclear power in the United States has been an unqualified success by any measure, providing low cost, carbon-free, and safe baseload electricity for decades. The industry today is at the peak of its historical performance in terms of generation output, reliable operations, and demonstrated nuclear safety. However, it is no longer among the lowest-cost electric generation sources with the emergence of subsidized renewables and shale gas generation. The business model that has served the operating nuclear fleet so well over its life is now a drag on cost performance due to its reliance on a large highly skilled labor force. In contrast, digital technology and innovation are enabling dramatic efficiencies in energy production, resulting in fierce competition for a commodity such as electricity. The nuclear power industry has responded to this challenge with many initiatives to improve efficiency and modernize plant equipment, especially where reliability and obsolescence issues are pressing. However, it would be a missed opportunity to merely modernize the plant components and the work processes of an outdated business model that was formulated to manage the technology of the 1960s. Rather, the greater opportunity is to transform that business model to one that fully exploits the capabilities of modern digital technology, resulting in substantially lower production costs and sustainable market viability. One successful example of such transformation is the concept of Integrated Operations, which was introduced into the North Sea oil and gas industry a couple of decades ago when the profitability of operating these fields was severely threatened by low world petroleum prices and the high overhead of operating the offshore oil and gas platforms. This effort resulted in significant changes to how these oil fields were operated and allowed the industry to continue profitable operation of these platforms. This example has some remarkable parallels to the U.S. commercial nuclear industry as described in the next section. This report provides an analysis and planning framework for such a nuclear plant operating model transformation based on the transferable learnings from the North Sea transformation. This framework is termed Integrated Operations for Nuclear or ION. This report describes the key principles and methods of Integrated Operations and how they are being applied in a collaboration between the DOE Light Water Reactor Sustainability Program and Xcel Energy in an initiative to transform its operating model for performance improvement and long-term sustainability. It describes a method to bring the operating costs of a nuclear fleet in line with market-based pricing, transforming work functions to reduce cost with technology innovations. This initiative will continue over the next several years in the detailed development of transformative concepts for nuclear plants, which will be published as follow-up to this initial report on Integrated Operations for Nuclear.
Project' managers continually seek an ever-greater optimization of time between remote handling operations and those carried out manually; therefore, new technological solutions must be deployed. Robotics offers a great opportunity in this new field of technology to carry out, for example, samplings or remediation in hostile, cluttered surroundings. Teams in charge of dismantling at the CEA have therefore first defined robotizable functions. These functions have been assembled from existing technological blocks to arrive at robots which are operating today [RICAIII, patent: FR 2925702]. Lessons learned, particularly from experience with the RICA robot, have enabled the operating technical specifications to be fine-tuned. A new study phase has been launched applying the same principle of adapting existing, proven means. The growing role of robotics today is unquestioned. Led by research and the academic world; robots such as those equipped with wheels, tracks, feet or even helicopter rotors, are today accessible to the general public, particularly via broadening of the 'open source' concept. Added to these we need tools able to manage large component deconstruction systems, like MAESTRO. Industrialization of such high-potential technological solutions has been aided by: - Easy use, - Increasing reliability, - Flexibility of 'open source' solutions, - Widening skill networks, and therefore greater technical support - Lower costs. Decontamination and dismantling (D and D) projects must be able to meet a number of special demands, increasing the number of unit designs, their costs and delivery times. The complexity of dismantling works sites mean that each is a special case to be dealt with almost independently. Such a way of approaching these projects is not on the same wavelength as industry, with tool and method standardization. The answer to the challenge of operations in difficult environments is an eco-system of functions, performed by a set of inter-connected robots. The first step towards the construction of such robot teams is devoted to functions where strength is not necessary: investigating and clean-up in hostile environments. With this in mind, the CEA Marcoule teams have been given the objective of merging the strengthening commercial robotic world with the needs of D and D, and thus to improve the transversal use of the systems. (authors)
Grid operating security studies are typically employed to establish operating boundaries, ensuring secure and stable operation for a range of operation under NERC guidelines. However, if these boundaries are severely violated, existing system security margins will be largely unknown, as would be a secure incremental dispatch path to higher security margins while continuing to serve load. As an alternative to the use of complex optimizations over dynamic conditions, this work employs the use of machine learning to identify a sequence of secure state transitions which place the grid in a higher degree of operating security with greater static and dynamic stability margins. Several reinforcement learning solution methods were developed using deep learning neural networks, including Deep Q-learning, Mu-Zero, and the continuous algorithms Proximal Reinforcement Learning, and Advantage Actor Critic Learning. The work is demonstrated on a power grid with three control dimensions but can be scaled in size and dimensionality, which is the subject of ongoing research.
The EGS (Enhanced Geothermal Systems) Collab project is performing stimulation experiments in highly monitored and well-characterized intermediate-scale (~10-20 m) field test beds. The characterization and monitoring system includes an array of electrodes grouted in place within six monitoring wells, with 16 electrodes per well. The electrodes enable characterization of the 3D low-frequency electrical properties of the host rock, and changes in those properties during stimulation and tracer testing using both static and time-lapse Electrical Resistivity Tomography. In this talk we present: 1) ERT array design and installation, 2) baseline (pre-stimulation) imaging results, 3) time-lapse data collected during stimulation operations, and 4) real-time imaging during clean water injections intended to illuminate flow paths as a decrease in bulk electrical conductivity. We will also discuss aspects and lessons learned for conducting ERT imaging operations in the presence of high fluid pressure gradients during stimulation and flow operations. Results to date reveal a highly heterogeneous rock fabric, ranging over four orders of magnitude in bulk electrical conductivity, and high polarization capacity. Baseline imaging results are consistent with borehole logs and core studies. Time-lapse imaging results to identify major flow paths during tracer studies are forthcoming and will be discussed during this presentation. Ultimately the ERT imaging data sets provide useful information for validating and verifying model predictions in preparation for research at DOE’s Frontier Observatory for Research in Geothermal Energy (FORGE).