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Hazelwood, Kyle

Publications and source records attributed to Hazelwood, Kyle.

12 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Disentangling Beam Losses in The Fermilab Main Injector Enclosure Using Real-Time Edge AI

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler Ring. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Deciphering the origin of any of the 260 BLM readings is often difficult. The (Accelerator) Real-time Edge AI for Distributed Systems project, or READS, has developed an AI/ML model, and implemented it on fast FPGA hardware, that disentangles mixed beam losses and attributes probabilities to each BLM as to which machine(s) the loss originated from in real-time. The model inferences are then streamed to the Fermilab accelerator controls network (ACNET) where they are available for operators and experts alike to aid in tuning the machines.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Artificial Intelligence for improved facilities operation in the FNAL LINAC

The energy consumption in accelerator structures during beam downtimes is a significant fraction of the overall energy budget. Accurate prediction of downtime duration could inform actions to reduce this energy consumption. The LCAPE project started in 2020 and develops artificial intelligence to improve operations in the FNAL control room by reducing the time to identify the cause of a beam outage, improving the reproducibility of labeling it, predicting their duration and forecasting their occurrence. We present our solution for incorporating information from ~2.5k monitored devices in near-real time to distinguish between dozens of different causes of down time. We discuss the performance of different techniques for modeling the state of health of the facility and we compare unsupervised clustering techniques to distinguish between different causes of down time.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Residual Dose and Environmental Monitoring for the Fermilab Main Injector Tunnel Using the Data Acquisition Logging Engine (Dale)

The Recycler and the Main Injector are part of the Fermilab Accelerator complex used to deliver proton beam to the different experiments. It is very important to control and minimize losses in both machines during operation, to reduce personnel dose from residual activation and to preserve component lifetime. To minimize losses, we need to identify the loss points and adjust the components accordingly. The Data Acquisition Loss Engine (DALE) platform has been developed within the Main Injector department and upgraded throughout the years. DALE is used to survey the entire enclosure for residual dose rates and environmental readings when unrestricted access to the enclosure is possible. Currently DALE has two radiation meters, which are aligned along each machine, so loss points can be identified for both at the same time. DALE attaches to the enclosure carts and is continuously in motion monitoring dose rates and other environmental readings. In this paper we will describe how DALE is used to provide radiation maps of the residual dose rates in the enclosure. We will also compare the loss points with the Beam Loss monitor data.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

The Effect of the Main Injector Ramp on the Recycler

The Recycler and Main Injector are part of the Fermilab Accelerator complex used to deliver a high power proton beam. Both machines share the same enclosure with the Recycler mounted 6 ft above the Main Injector. The Main Injector accelerates beam from 8 GeV to 120 GeV. While the majority of the Recycler has mu metal shielding, the effect of the Main Injector ramp is still significant and can affect both the tunes and the orbit. In this paper, we detail the size of these effects.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Semantic Regression for Disentangling Beam Losses in the Fermilab Main Injector and Recycler

Fermilab’s Main Injector enclosure houses two accelerators: the Main Injector (MI) and the Recycler (RR). In periods of joint operation, when both machines contain high intensity beam, radiative beam losses from MI and RR overlap on the enclosure’s beam loss monitoring (BLM) system, making it difficult to attribute those losses to a single machine. Incorrect diagnoses result in unnecessary downtime that incurs both financial and experimental cost. In this work, we introduce a novel neural approach for automatically disentangling each machine’s contributions to those measured losses. Using a continuous adaptation of the popular UNet architecture in conjunction with a novel data augmentation scheme, our model accurately infers the machine of origin on a per-BLM basis in periods of joint and independent operation. Crucially, by extracting beam loss information at varying receptive fields, the method is capable of learning both local and global machine signatures and producing high quality inferences using only raw BLM loss measurements.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Second Generation Fermilab Main Injector 8 GeV Beamline Collimation Preliminary Design

The current Fermilab Main Injector 8 GeV beamline transverse collimation system was installed in 2006. Since then, proton beam intensities and rates have increased significantly. With the promise of even greater beam intensities and a faster repetition rate when the PIP-II upgrade completes later this decade, the current collimation system will be insufficient. Over the past 18 months, multiple collimation designs have been investigated, some more traditional and others novel. A preliminary design review was conducted and a design chosen. Work is underway to finalize the chosen design, prototype some of its novel components and procure parts for installation Summer 2023.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

The L-CAPE Project at FNAL

The controls system at FNAL records data asynchronously from several thousand Linac devices at their respective cadences, ranging from 15Hz down to once per minute. In case of downtimes, current operations are mostly reactive, investigating the cause of an outage and labeling it after the fact. However, as one of the most upstream systems at the FNAL accelerator complex, the Linac’s foreknowledge of an impending downtime as well as its duration could prompt downstream systems to go into standby, potentially leading to energy savings. The goals of the Linac Condition Anomaly Prediction of Emergence (L-CAPE) project that started in late 2020 are (1) to apply data-analytic methods to improve the information that is available to operators in the control room, and (2) to use machine learning to automate the labeling of outage types as they occur and discover patterns in the data that could lead to the prediction of outages. We present an overview of the challenges in dealing with time-series data from 2000+ devices, our approach to developing an ML-based automated outage labeling system, and the status of augmenting operations by identifying the most likely devices predicting an outage.

43 PARTICLE ACCELERATORS↗
DOE OSTI2022

Improvements to the Recycler/Main Injector to Deliver 850 kW+

The Main Injector is used to deliver a 120 GeV high power proton beam for Neutrino experiments. The design power of 700 kW was reached in early 2017 but further improvements have seen a new sustained peak power of 893 kW. Two of the main improvements include the shortening of the Main Injector ramp length as well optimizing the slip-stacking procedure performed in the Recycler to reduce the amount of uncaptured beam making its way into the Main Injector. These improvements will be discussed in this paper as well future upgrades to reach higher beam powers.

43 PARTICLE ACCELERATORS↗
DOE OSTI2021

Real-Time Edge AI for Distributed Systems (READS): Progress on Beam Loss De-Blending for the Fermilab Main Injector and Recycler

The Fermilab Main Injector enclosure houses two accelerators, the Main Injector and Recycler. During normal operation, high intensity proton beams exist simultaneously in both. The two accelerators share the same beam loss monitors (BLM) and monitoring system. Beam losses in the Main Injector enclosure are monitored for tuning the accelerators and machine protection. Losses are currently attributed to a specific machine based on timing. However, this method alone is insufficient and often inaccurate, resulting in more difficult machine tuning and unnecessary machine downtime. Machine experts can often distinguish the correct source of beam loss. This suggests a machine learning (ML) model may be producible to help de-blend losses between machines. Work is underway as part of the Fermilab Real-time Edge AI for Distributed Systems Project (READS) to develop a ML empowered system that collects streamed BLM data and additional machine readings to infer in real-time, which machine generated beam loss.

43 PARTICLE ACCELERATORS↗
DOE OSTI2020

Design Considerations and Operational Features of the Collimators for the Fermilab Main Injector and Recycler

The Fermilab Main Injector system delivers 700 kW of 120 GeV Proton beam for neutrino experiments. Since 2013 this has been achieved using slip stacking accumulation in the Recycler with up to 12 batches from the Fermilab Booster per Main Injector Ramp Cycle. To control activation from beam loss, collimation systems in the Booster to Recycler transfer line, in the Recycler and in the Main Injector are employed. Residual radiation measurements around the ring with detailed studies at the collimators are required to maintain adequate loss control. We will review design considerations, operational parameters and activation results for more than ten years of operation. Simulations with MARS15 are used to explore the activation rates and the isotopic composition of the resulting activation.

43 PARTICLE ACCELERATORS↗