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This is an Intern Poster submission regarding my research in the digital engineering team. My task was to create a software adapter to communicate between a data acquisition system (LabVIEW) and a data warehouse (Deep Lynx) as described in the poster.
During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.
During the operation of CEBAF, one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. Identifying an unstable cavity out of the hundreds of cavities installed at CEBAF is difficult and time-consuming. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detect fast transient instabilities. A fast data acquisition system for the legacy SRF cavities is being developed which samples and reports at 5 kHz to allow for detection of transients. A prototype chassis has been installed and tested in CEBAF. An autoencoder based machine learning model is being developed to identify anomalous SRF cavity behavior. The model is presently being trained on the slow (1 Hz) data that is currently available, and a separate model will be developed and trained using the fast (5 kHz) DAQ data once it becomes available. This paper will discuss the present status of the new fast data acquisition system and results of testing the prototype chassis. This paper will also detail the initial performance metrics of the autoencoder model.
During the operation of the Continuous Electron Beam Accelerator Facility (CEBAF), one or more unstable superconducting radio-frequency (SRF) cavities often cause beam loss trips while the unstable cavities themselves do not necessarily trip off. The present RF controls for the legacy cavities report at only 1 Hz, which is too slow to detectfast transient instabilities during these trip events. These challenges make the identification of an unstable cavity out of the hundreds installed at CEBAF a difficult and time-consuming task. To tackle these issues, a fast data acquisition system (DAQ) for the legacy SRF cavities has been developed, which records the sample at 5 kHz. An unsupervised learning framework has been developed to identify anomalous SRF cavity behavior. We will discuss the present status of the DAQ system and our framework, along with recent successes in detecting anomalous cavity behavior. Overall, our method offers a practical solution for identifying unstable SRF cavities, contributing to increased beam availability and machine reliability.
Electrical power converters are critical to a wide range of applications ranging from renewable integration to transportation electrification, and can be a key factor determining the size, weight, and efficiency of energy conversion systems. Magnetic components are typically the largest and least efficient components in power electronics. While there have been major strides in the modeling and analysis of power semiconductor devices and circuit simulations, the necessary advances in the design of power magnetics have lagged. In this project, we have transformed the modeling and design of power magnetics with machine learning enabled methods and catalyze simultaneous disruptive improvements for ML-based power electronics design tools. A fully automated open-source machine learning based magnetics modeling platform – the MagNet project - with innovations in full stack have been developed to greatly accelerate the design process and provide new insights to magnetic material and geometry design. The ARPA-E funded MagNet platform contains three major building blocks: 1) a ML-Integrated Data Acquisition System (MIDAS): a highly automated data acquisition testbed which is capable of measuring a large number of magnetic cores with a wide range of electrical circuit excitations; 2) a ML-integrated Core Loss Model (MICLM): a machine-learning trained modeling method for modeling the core loss and saturation effects of magnetic materials for arbitrary excitation waveforms; 3) ML-guided Magnetics SPICE Simulation Tool (PMSPICE): a fully integrated CAD tool which can simulate the magnetics in SPICE. It can help the designers to quickly model the linear and non-linear characteristics of magnetic components and evaluate their behavior in SPICE simulations. The developed MagNet system has fully demonstrated the proposed performance target and has been open sourced to the entire power electronics community to advance the modeling and design of power magnetics from many different angles.
Contains datasets from experimental measurements that were used to validate Re Vision's Persistence PTO's efficiency and performance. These measurements were obtained using a dynamometer test bench setup. The data includes open-circuit voltage and loss measurements to validate machine characteristics, efficiency mapping tests to determine the generator's performance mapping, and efficiency mapping tests to determine the converter's efficiency over the feasible operating range. This data was collected between June 2023 and September 2023. The data was collected at the National Renewable Energy Laboratory's Flatirons Campus, Colorado, United States. The data was collected using NREL's 5-kW dynamometer test bench, equipped with a torque sensor and various voltage and current sensors fed to a dedicated data acquisition system. Units for the data are included in the data file headers for each data series. A text editor or spreadsheet software such as Excel is required to view the *.csv data. The data are also provided in *.mat files. To view data plots, a Matlab script with *.mat files are provided.
Since its inception in 1984, the Colorado State University Industrial Assessment Center has performed industrial assessments at more than 720 manufacturing facilities in Colorado, Montana, Nebraska, Nevada, New Mexico, North Dakota, South Dakota, Utah, and Wyoming. From 2017 to 2019 there was a funding gap and the IAC shutdown. In 2020 through DOE extension funding the University relaunched the IAC as an extension center in order to provide assessments to underserved areas. Under this award, the CSU Industrial Assessment Center (IAC) was rebuilt with the help of student employees and the director. The CSU team experienced difficulty as the program was in the process of being restarted right as the 2020 pandemic hit. Nonetheless, the CSU IAC was instrumental in providing energy assessments to manufacturers in Colorado and Wyoming during the period of performance of 09/2019 – 12/2022. The Department of Energy's Industrial Assessment Centers (IACs) provide a valuable service to small and medium-sized manufacturers seeking to optimize their operations. These university-based centers offer no-cost, on-site assessments conducted by engineering faculty and students, analyzing energy consumption, production processes, and waste streams. They utilize advanced data acquisition systems to record operational data, and then use the data to create assessment recommendations. These recommendations form the foundation of the comprehensive energy report. The comprehensive report delivers actionable recommendations for enhancing energy efficiency, reducing waste, and reducing greenhouse gas emissions and improving productivity, often identifying significant cost savings. Furthermore, IACs facilitate access to implementation grants, enabling the businesses to readily adopt these improvements. This program not only strengthens individual businesses but also contributes to national goals of training the next generation of energy experts, as well as increasing industrial competitiveness and reduced environmental impact.
Early fault detection and fault prognosis are crucial to ensure efficient and safe operations of complex engineering systems such as the Spallation Neutron Source (SNS) and its power electronics (high voltage converter modulators). Following an advanced experimental facility setup that mimics SNS operating conditions, the authors successfully conducted 21 early fault detection experiments, where fault precursors are introduced in the system to a degree enough to cause degradation in the waveform signals, but not enough to reach a real fault. Nine different machine learning techniques based on ensemble trees, convolutional neural networks, support vector machines, and hierarchical voting ensembles are proposed to detect the fault precursors. Although all 9 models have shown a perfect and identical performance during the training and testing phase, the performance of most models has decreased in the next test phase once they got exposed to realworld data from the 21 experiments. The hierarchical voting ensemble, which features multiple layers of diverse models, maintains a distinguished performance in early detection of the fault precursors with 95% success rate (20/21 tests), followed by adaboost and extremely randomized trees with 52% and 48% success rates, respectively. The support vector machine models were the worst with only 24% success rate (5/21 tests). The study concluded that a successful implementation of machine learning in the SNS or particle accelerator power systems would require a major upgrade in the controller and the data acquisition system to facilitate streaming and handling big data for the machine learning models. In addition, this study shows that the best performing models were diverse and based on the ensemble concept to reduce the bias and hyperparameter sensitivity of individual models.
Accelerator magnet test facilities frequently need to measure different magnets on differently equipped test stands and with different instrumentation. Designing a modular and highly reusable system that combines flexibility built-in at the architectural level as well as on the component level addresses this need. Specification of the backbone of the system, with the interfaces and dataflow for software components and core hardware modules, serves as a basis for building such a system. The design process and implementation of an extensible magnetic measurement data acquisition and control system are described, including techniques for maximizing the reuse of software. The discussion is supported by showing the application of this methodology to constructing two dissimilar systems for rotating coil measurements, both based on the same architecture and sharing core hardware modules and many software components. The first system is for production testing 10 m long cryo-assemblies containing two MQXFA quadrupole magnets for the high-luminosity upgrade of the Large Hadron Collider and the second for testing IQC conventional quadrupole magnets in support of the accelerator system at Fermilab.
Imaging systems, including radio frequency, microwave and millimeter-wave arrangements, and related methods are described. According to one aspect, an imaging system includes an antenna array, a position capture system configured to generate position information indicative of locations of one of the antenna array and the target at the first and second moments in time, and wherein the one of the antenna array and the target move between the first and second moments in time, a transceiver configured to control the antenna array to emit electromagnetic energy towards the target and to generate an output that is indicative of the received electromagnetic energy, a data acquisition system configured to generate radar data, processing circuitry configured to process the position information and the radar data to generate image data regarding the target, and an interface configured to use the image data to generate visual images regarding the target.
Imaging systems, including radio frequency, microwave and millimeter-wave arrangements, and related methods are described. According to one aspect, an imaging system includes an antenna array, a position capture system configured to generate position information indicative of locations of one of the antenna array and the target at the first and second moments in time, and wherein the one of the antenna array and the target move between the first and second moments in time, a transceiver configured to control the antenna array to emit electromagnetic energy towards the target and to generate an output that is indicative of the received electromagnetic energy, a data acquisition system configured to generate radar data, processing circuitry configured to process the position information and the radar data to generate image data regarding the target, and an interface configured to use the image data to generate visual images regarding the target.
The Mu2e experiment at Fermilab imposes stringent requirements on the elimination of out-of-time beam in its pulsed proton beam, a requirement known as “extinction”. Utilizing a new μTCA-based FPGA data acquisition system, we recorded live particle data from scattered particles incident on an array of quartz Cherenkov radiators and photomultiplier tubes to measure the extinction in the inter-pulse gaps in the pulsed proton beam. Minuscule errors in the derived signal period can make a measurement of the extinction impossible, so after taking a Fourier transform, further optimizations on the period were done based on the assumption that the signal period is stable over the full time of the beam spill while it is being resonantly extracted. After these optimizations, the beam extinction was shown to be on the level of 10^3.
A two-terminator RF adapter for background noise measurement in a test environment comprises a system test port comprising a system test port termination and a system test port connector to connect to a system under test; and a data acquisition port, comprising a data acquisition port termination and a data acquisition port connector to connect to a data acquisition system.
Herein, the Mu2e field mapping system is designed to produce high accuracy field maps of the detector solenoid used in the experiment. The data acquisition system is mobile and uses a self-propelled mapper with rotating arms equipped with 3D Hall probes. The measurements require not only accurate magnetic field readouts, but also accurate locations of all Hall probes while measuring data. The latter is accomplished by using the laser tracker to measure positions of several retroreflectors on the field mapper during data acquisition. The measurement process requires scanning the whole space inside the large solenoid and takes many hours to complete, which necessitates its full automation. The automation software includes control of the mapper, readout of Hall and NMR probes and control of a laser tracker, including prediction of retroreflector positions and execution of quality control checks. The software architecture and data acquisition hardware of the field mapping system are described, with special attention given to control of the laser tracker and its integration with the rest of the system.
We present an unsupervised learning framework for detecting anomalous superconducting radio-frequency (SRF) cavity behavior at the Continuous Electron Beam Accelerator Facility (CEBAF), emphasizing its initial performance and effectiveness. Key to the system’s success was the development of data acquisition systems (DAQs) that capture fast-sampled, information-rich signals, essential for detecting transient effects. The approach involves creating daily cavity-specific models using principal component analysis to handle variations in rf signal behavior and mitigate performance degradation from data drift. This unsupervised method eliminates the need for expensive labeling by continuously updating models with recent data. Deployed and operational for 3 months before a scheduled shutdown, the system successfully identified several issues with DAQ signals, confirming its effectiveness. Despite access to only a fraction of CEBAF’s SRF cavity signals, the framework efficiently detected several instances requiring intervention, demonstrating a significant improvement over traditional, labor-intensive methods of manual plot inspection. Published by the American Physical Society 2025
We present a comprehensive end-to-end pipeline to classify triggers versus background events in this paper. This pipeline makes online decisions to select signal data and enables the intelligent trigger system for efficient data collection in the Data Acquisition System (DAQ) of the upcoming sPHENIX and future EIC (Electron-Ion Collider) experiments. Starting from the coordinates of pixel hits that are lightened by passing particles in the detector, the pipeline applies three-stage of event processing (hits clustering, track reconstruction, and trigger detection) and labels all processed events with the binary tag of trigger versus background events. The pipeline consists of deterministic algorithms such as clustering pixels to reduce event size, tracking reconstruction to predict candidate edges, and advanced graph neural network-based models for recognizing the entire jet pattern. In particular, we apply the message-passing graph neural network to predict links between hits and reconstruct tracks and a hierarchical pooling algorithm (DiffPool) to make the graph-level trigger detection. We obtain an impressive performance (≥70% accuracy) for trigger detection with only 3200 neuron weights in the end-to-end pipeline. We deploy the end-to-end pipeline into a field-programmable gate array (FPGA) and accelerate the three stages with speedup factors of 1152, 280, and 21, respectively.
In a multi-channel radiation detector readout system, waveform sampling, digitization, and raw data transmission to the data acquisition system constitute a conventional processing chain. The deposited energy on the sensor is estimated by extracting peak amplitudes, area under pulse envelopes from the raw data, and starting times of signals or time of arrivals. However, such quantities can be estimated using machine learning algorithms on the front-end Application-Specific Integrated Circuits (ASICs), often termed as “edge computing”. Edge computation offers enormous benefits, especially when the analytical forms are not fully known or the registered waveform suffers from noise and imperfections of practical implementations. In this work, we aim to predict peak amplitude from a single waveform snippet whose rising and falling edges containing only 3 to 4 samples. We thoroughly studied two well-accepted neural network algorithms, Multi-Layer Perceptron (MLP) and Convolutional Neural Network (CNN) by varying their model sizes. Further, to better fit front-end electronics, neural network model reduction techniques, such as network pruning methods and variable-bit quantization approaches, were also studied. By combining pruning and quantization, our best performing model has the size of 1.5 KB, reduced from 16.6 KB of its full model counterpart. It can reach mean absolute error of 0.034 comparing to that of a naive baseline of 0.135. Such parameter-efficient and predictive neural network models established feasibility and practicality of their deployment on front-end ASICs.
An important component of the United States Nuclear Fuel Safety Transient Testing Program, the Fuel Motion Monitoring System (FMMS) at Idaho National Laboratory's Transient Reactor Test Facility (TREAT) is fast-neutron hodoscope capable of imaging the location, movement, and relocation of nuclear fuel experiments under simulated transient accident conditions. The FMMS was refurbished in parallel with the TREAT restart program starting in 2014, restoring 96-channels of fast-neutron detection. Since returning to operation in 2017 the FMMS has supported many fuel safety experiments supporting accident tolerant fuel development, light-water reactor safety, space thermal nuclear propulsion fuel development, and advanced reactor research and development. In Phase 2 of the FMMS restoration. work is now under way to expand the FMMS' field-of-view by adding an additional 96 channels of fast neutron detectors to the system's hodoscope, along with an expanded data acquisition system and associated transient timing electronics. An overview of the FMSS system and its fast-neutron detectors will be presented along with examples of current FMMS imaging performance and associated information.