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DOE OSTI2025

FPGA-Based Spill Regulation System for the Muon Delivery Ring at Fermilab

The Muon to Electron Experiment (Mu2e) requires a uniform beam profile from the Muon Delivery Ring to meet their experimental needs. A specialized Spill Regulation System (SRS) has been developed to help achieve consistent spill uniformity. The system is based on a custom-designed carrier board featuring an Arria 10 SoC, capable of executing real-time feedback control. The FPGA processes beam pulses of approximately 200 ns every 1.695 $μ$s, allowing for continuous monitoring of the extracted spill intensity through fast bunch integration. The system directly controls three quadrupole magnets, which work in conjunction with sextupole magnets to achieve third-order resonant extraction. Furthermore, the board interfaces with Fermilab's Accelerator Control Network (ACNET), enabling operators to modify spill regulation settings in real-time via the control network while providing diagnostic waveforms. These waveforms help operators monitor the process and fine-tune the feedback mechanisms. This paper presents an overview of the board's architecture and its initial progress toward regulating beam extraction. This initial version of the regulation system aims to evaluate baseline performance to inform future system improvements.

Berlioz, J. R. [Fermilab]↗

DOE OSTI2025

FPGA-Based Spill Regulation System for the Muon Delivery Ring at Fermilab

The Muon to Electron Experiment (\acrshort{Mu2e}) requires a uniform beam profile from the Muon Delivery Ring to meet their experimental needs. A specialized Spill Regulation System (\acrshort{SRS}) has been developed to help achieve consistent spill uniformity. The system is based on a custom-designed carrier board featuring an Arria 10 SoC, capable of executing real-time feedback control. The FPGA processes beam pulses of approximately 200 ns every 1.695 microseconds, allowing for continuous monitoring of the extracted spill intensity through fast bunch integration. The system directly controls three quadrupole magnets, which work in conjunction with sextupole magnets to achieve third-order resonant extraction. Furthermore, the board interfaces with Fermilab s Accelerator Control Network (ACNET), enabling operators to modify spill regulation settings in real-time via the control network while providing diagnostic waveforms. These waveforms help operators monitor the process and fine-tune the feedback mechanisms. This paper presents an overview of the board's architecture and its initial progress toward regulating beam extraction. This initial version of the regulation system aims to evaluate baseline performance to inform future system improvements.

Berlioz, Jose Rene [Fermilab]↗

DOE OSTI2020

Neural networks for modeling and control of particle accelerators

Charged particle accelerators support a wide variety of scientific, industrial, and medical applications. They range in scale and complexity from systems with just a few components for beam acceleration and manipulation, to large scientific user facilities that span many kilometers and have hundreds-to-thousands of individually-controllable components. Specific operational requirements must be met by adjusting the many controllable variables of the accelerator. Meeting these requirements can be challenging, both in terms of the ability to achieve specific beam quality metrics in a reliable fashion and in terms of the time needed to set up and maintain the optimal operating conditions. One avenue toward addressing this challenge is to incorporate techniques from the fields of machine learning (ML) and artificial intelligence (AI) into the way particle accelerators are modeled and controlled. While many promising approaches within AI/ML could be used for particle accelerators, this dissertation focuses on approaches based on neural networks. Neural networks are particularly well-suited to modeling, control, and diagnostic analysis of nonlinear systems, as well as systems with large parameter spaces. They are also very appealing for their ability to process high-dimensional data types, such as images and time series (both of which are ubiquitous in particle accelerators). In this work, key studies that demonstrated the potential utility of modern neural network-based approaches to modeling and control of particle accelerators are presented. The context for this work is important: at the start of this work in 2012, there was little interest in AI/ML in the particle accelerator community, and many of the advances in neural networks and deep learning that enabled its present success had not yet been made at that time. As such, this work was both an exploration of possible application areas and a generator of proof-of-concept demonstrations in these areas.

43 PARTICLE ACCELERATORS↗

DOE OSTI2025

eLog Analysis for Accelerators: Status and Future Outlook

This work demonstrates electronic logbook (eLog) systems leveraging modern AI-driven information retrieval capabilities at the accelerator facilities of Fermilab, Jefferson Lab, Lawrence Berkeley National Laboratory (LBNL), SLAC National Accelerator Laboratory. We evaluate contemporary tools and methodologies for information retrieval with Retrieval Augmented Generation (RAGs), focusing on operational insights and integration with existing accelerator control systems. The study addresses challenges and proposes solutions for state-of-the-art eLog analysis through practical implementations, demonstrating applications and limitations. We present a framework for enhancing accelerator facility operations through improved information accessibility and knowledge management, which could potentially lead to more efficient operations.

Hellert, Thorsten [LBNL, ALS]↗

DOE OSTI2025

eLog analysis for accelerators: status and future outlook

Sulc, A. [LBL, Berkeley]↗

DOE OSTI2025

Operation at Reduced Atmospheric Pressure and Concept of Reliability Redundancy for Optimized Design of Insulation Systems

Electrified transportation is calling for insulation design criteria that is adequate to provide elevated levels of power density, power dynamics and reliability. Increasing voltage levels are expected to cause accelerated intrinsic and extrinsic aging effects which will not be easily predictable at the design stage due to a lack of suitable modeling. Designing reliable insulation systems would require finding solutions able to control accelerated aging due to an unpredictable increase of intrinsic stresses and the onset of extrinsic stresses as partial discharges. This paper proposes the concept of reliability redundancy for the insulation design of aerospace electrical asset components, which is also validated at lower-than-standard atmospheric pressure. The principle is that extrinsic-aging-free design might be achieved upon determining the aging stress or abnormal service stresses distribution and being sure that aging will not generate conditions that can incept extrinsic aging (partial discharges) during operation life. However, such information is never, in practice, fully available to insulation system designers. Hence, especially in critical applications such as electrified aircraft, aerospace, and combat ships a further level of reliability should be added to a partial-discharge-free design, which can consist of the use of corona-resistant materials and/or of life models able to consider the accelerated aging effect of partial discharges (or any other type of extrinsic-accelerated aging factor). Innovative life modeling considering both extrinsic and intrinsic aging stresses, insulating material testing to estimate model parameters, and a metric for quantifying the extent of corona (or partial discharge) resistance can lead to establishing feasibility and limit conditions for optimized or fully reliability-redundant design. It is shown in the paper that if an extrinsic-aging-free design is not feasible, and it is therefore replaced by a redundant design, a further level of reliability redundancy can be provided by effective condition monitoring plans.

Montanari, Gian Carlo↗

DOE OSTI2023

Artificial Intelligence in Nuclear Physics

Artificial Intelligence (AI) and Machine Learning (ML) are rapidly developing fields providing data-driven algorithms to predict, classify, and make decisions based on data. Nuclear Physics Research is data-driven and AI/ML techniques have been implemented for experiment and accelerator control, in theoretical applications, and in data processing and analysis. These algorithms open possibilities for automation, thereby augmenting human capabilities. Additionally, Open Science is enabled by simultaneous analyses of multiple data sources, leading to scientific knowledge. This talk will summarize current applications of AI/ML in nuclear physics, as well as accelerator applications, and will cover upcoming initiatives and research in AI/ML.

Jeske, Torri↗

DOE OSTI2024

Power Module Precursors and Prognostics

This project focuses on finding and utilizing electrical properties (precursors) of power electronic devices and modules whose changes have strong correlations with different modes and levels of degradations in order to realize online and lightweight state of health evaluation and fault diagnosis. Results from this project will contribute to higher safety, reliability, and power density of power electronic components. The approaches mainly include reviewing academic literature and industry practices for state of the art precursor identification and extraction, accelerated life cycles (ALC) of various power modules to create different degradations in-house, and developing techniques for gate-driver-integratable precursor detection circuit. In FY24, the results includes two sets of definitively detected aging precursors (on-state resistance and gate threshold voltage) for two sets of power modules respectively, controlled accelerated lifetime cycling of multiple silicon carbide (SiC) metal-oxide-semiconductor field-effect transistor (MOSFET) modules, and the development and validation of a gate voltage detection system.

ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2026

Flutter

The following repositories are a set of libraries that are needed for applications that are being developed for users of the Accelerator control system. The programming language used is Dart and for the user interface use the Flutter framework. URL for code repositories: - https://github.com/fermi-ad/flutter-controls-core - https://github.com/fermi-ad/flutter-controls-plotting - https://github.com/fermi-ad/flutter-controls-auth - https://github.com/fermi-ad/flutter-gql-acsys - https://github.com/fermi-ad/flutter-gql-faas - https://github.com/fermi-ad/dart-gql-acsys - https://github.com/fermi-ad/dart-explicit-imports - https://github.com/fermi-ad/dart-gql-faas - https://github.com/fermi-ad/design-system

Neswold, Rich [Fermi National Accelerator Laborato↗

DOE OSTI2022

Nonresonant particle acceleration in strong turbulence: Comparison to kinetic and MHD simulations

Collisionless, magnetized turbulence offers a promising framework for the generation of nonthermal high-energy particles in various astrophysical sites. Yet, the detailed mechanism that governs particle acceleration has remained subject to debate. By means of 2D and 3D particle-in-cell, as well as 3D (incompressible) magnetohydrodynamic (MHD) simulations, we test here a recent model of nonresonant particle acceleration in strongly magnetized turbulence, which ascribes the energization of particles to their continuous interaction with the random velocity flow of the turbulence, in the spirit of the original Fermi model. To do so, we compare, for a large number of particles that were tracked in the simulations, the predicted and the observed histories of particles momenta. The predicted history is that derived from the model, after extracting from the simulations, at each point along the particle trajectory, the three force terms that control acceleration: the acceleration of the field line velocity projected along the field line direction, its shear projected along the same direction, and its transverse compressive part. Overall, we find a clear correlation between the model predictions and the numerical experiments, indicating that this nonresonant model can successfully account for the bulk of particle energization through Fermi-type processes in strongly magnetized turbulence. Additionally we also observe that the parallel shear contribution tends to dominate the physics of energization in the particle-in-cell simulations, while in the magnetohydrodynamic incompressible simulation, both the parallel shear and the transverse compressive term provide about equal contributions.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2023

GUI Control System for the Mu2e Electrostatic Septum High Voltage at Fermilab

The Mu2e Experiment has stringent beam structure requirements; namely, its proton bunches with a time structure of 1.7 $\mu$s in the Fermilab Delivery Ring. This beam structure will be delivered using the Fermilab 8-GeV Booster, the 8-GeV Recycler Ring, and the Delivery Ring. The 1.7-$\mu$s period of the Delivery Ring will generate the required beam structure by means of a third order resonant extraction system operating on a single circulating bunch. The electrostatic septum (ESS) for this system is particularly challenging, requiring mechanical precision in a ultra high vacuum of 1 x 10$^-8$ Torr to generate 100 kV across 15 mm. This paper describes a graphical user interface that has been developed to automate the conditioning and commissioning process for the electrostatic septa. It is based on an interface to the Fermilab ACNET system using the ACSys Python Data Pool Manager (DPM) Client produced and maintained by Fermilab Accelerator Controls. Network interfacing between data pool managers made by the application and ACNET devices introduce an inherent (approximately 1 s) latency in throughput of the readouts. This delay is utilized to process and graph incoming data events of devices crucial to conditioning of a electrostatic septum (ESS). 'Ramping' and 'Monitoring' modes adjust settings of the power supply based on internal logic to efficaciously increase and maintain the high voltage (HV) in the ESS, easing the voltage setting on incidence of sparking or other possibly damaging events. A timestamped log file is produced as the application runs.

43 PARTICLE ACCELERATORS↗

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 OSTI2024

Third-integer Resonant Extraction Regulation System for Mu2e

A third-integer resonant slow extraction system is being developed for Fermilab's Delivery Ring to deliver protons to the upcoming Mu2e experiment. The timescale of the extraction (or spill) duration is 43 milliseconds, which is extremely short and unprecedented. Additionally, the experiment's strict and challenging requirements on the quality of the spill at this time scale has led to the development of a new Spill Regulation System (SRS) design. The SRS primarily consists of three components - slow regulation, fast regulation, and harmonic content suppressor. Contributions to the first two components of the SRS, i.e., Slow Regulation and Fast Regulation subsystems, will be presented in which new adaptive learning algorithm schemes for the slow regulation of the spill -- validated using particle tracking simulations -- shall be described. In addition to these novel methods for the enhancement of the spill regulation system, results of employing Machine Learning in enhancing the performance of the resonant extraction are also presented. At the forefront of applying ML techniques to solve non-linear accelerator control problems, this work includes optimizing the PID gains as well as the replacement of the traditional PID controller using Recurrent Neural Networks and Gated Recurrent Unit (GRU) ML models to achieve efficiencies greater than a PID controller. Cutting-edge on-going Reinforcement Learning efforts, including an actor-critic family of learning algorithms, to regulate the spill rate will be reviewed, as well as present analytical calculations pertaining the transit time of particles in a third-integer resonant extraction. Detailed numerical investigations and validations of such calculations, the model of which could be exported and reliably used in future analytical modeling of any resonant extraction, are discussed.

43 PARTICLE ACCELERATORS↗

DOE OSTI2022

KIPT ADS Target Cooling Analyses by MCNP/Fluent

This report presents multi-physics analyses of the target cooling system of the accelerator driven system (ADS) of the Kharkiv Institute of Physics and Technology (KIPT) using MCNP and Fluent computer programs. MCNP has been used to transport electrons, gammas, and neutrons, and to calculate the energy deposition in the target using a mesh-tally. The mesh-tally data have been imported in Fluent by a C subroutine that has been compiled and linked to Fluent as a user defined function. The KIPT ADS is in operation and Fluent used the CAD model from the manufacturing process of the target. The Fluent results for the reference case match very well the literature results obtained by STAR-CCM+ during the design phase. Other cases that differ from the reference one have been analyzed; in these cases, it is assumed a malfunction of the electron accelerator control or the primary water coolant pump. The target cooling system operates normally for all the analyzed cases except when the inlet water mass flow rate is decreased. If the water coolant mass flow decreases to 25%, the water maximum temperature exceeds the boiling point. However, the transient analysis showed that the target cooling system can operate for 180 seconds when the inlet water mass flow rate is decreased down by 50%.

42 ENGINEERING↗

DOE OSTI2024

Beam Synchronous for the Rest of Us!

Fermilab’s Tevatron Clock (TCLK) infrastructure has been an integral part of the accelerator control network since the 1980’s. This 10MHz Manchester encoded protocol has enabled flexible, real-time event distribution for thousands of devices connected to the timing network with a high degree of reliability. Forthcoming upgrades to the Fermilab complex (PIP-II, LBNF, ACORN) necessitate higher levels of precision to maintain inter-bunch timing for Instrumentation and Control purposes. This presents as an opportunity to refine the event distribution protocol for tighter synchronization between machines, experiments, and eventually far-site operations. This paper outlines a method by which beam-synchronous events may be distributed through asynchronous serial protocols via integration with local LLRF and global PPS reference signals. This method is ideal for synchrotron machines with aggressive frequency sweeps (such as Fermilab's 38~53MHz Booster) and allows for precision timing to be maintained across machines without specialized hardware.

43 PARTICLE ACCELERATORS↗

DOE OSTI2021

Statistical Multiobjective Optimization of Thiospinel CoNi 2 S 4 Nanocrystal Synthesis via Design of Experiments

Thiospinels, such as CoNi 2 S 4 , are showing promise for numerous applications, including as catalysts for the hydrogen evolution reaction, hydrodesulfurization, and oxygen evolution and reduction reactions; however, CoNi 2 S 4 has not been synthesized as small, colloidal nanocrystals with high surface-area-to-volume ratios. Traditional optimization methods to control nanocrystal attributes such as size typically rely upon one variable at a time (OVAT) methods that are not only time and labor intensive but also lack the ability to identify higher-order interactions between experimental variables that affect target outcomes. Herein, we demonstrate that a statistical design of experiments (DoE) approach can optimize the synthesis of CoNi 2 S 4 nanocrystals, allowing for control over the responses of nanocrystal size, size distribution, and isolated yield. After implementing a 2 5–2 fractional factorial design, the statistical screening of five different experimental variables identified temperature, Co:Ni precursor ratio, Co:thiol ratio, and their higher-order interactions as the most critical factors in influencing the aforementioned responses. Second-order design with a Doehlert matrix yielded polynomial functions used to predict the reaction parameters needed to individually optimize all three responses. A multiobjective optimization, allowing for the simultaneous optimization of size, size distribution, and isolated yield, predicted the synthetic conditions needed to achieve a minimum nanocrystal size of 6.1 nm, a minimum polydispersity (σ/$\bar{d}$) of 10%, and a maximum isolated yield of 99%, with a desirability of 96%. The resulting model was experimentally verified by performing reactions under the specified conditions. Furthermore, our work illustrates the advantage of multivariate experimental design as a powerful tool for accelerating control and optimization in nanocrystal syntheses.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2020

Computational modeling and neutron imaging to understand interface shape and solute segregation during the vertical gradient freeze growth of BaBrCl:Eu

In this work we apply continuum models to analyze phase change, heat transfer, fluid flow, solute transport, and segregation in order to understand prior neutron imaging observations of the vertical gradient freeze growth of Eu-doped BaBrCl. The models provide a rigorous framework in which to understand the mechanisms that are responsible for the complicated evolution of interface shape and dopant distribution in the growth experiment. We explain how a transition in the solid/liquid interface shape from concave to convex is driven by changes in radial heat transfer caused by furnace design. We also provide a mechanistic explanation of how dynamic growth conditions and changes of the flow structure in the melt result in complicated segregation patterns in this system. A growth pause caused by controller lock-up is shown to result in a band of solute depletion in accordance with classical theory. However, changing flow patterns during growth result in a non-monotonic axial distribution of solute that cannot be explained by simple application of classical segregation models. We assert that the approach presented here, namely the use of rigorous models in conjunction advanced diagnostics, such as neutron imaging, provides an exciting path forward for process optimization and control, accelerating the incremental advances that have, in the past, typically relied on empiricism, experience, and intuition.

36 MATERIALS SCIENCE↗

DOE OSTI2022

Dependence of adhesion on degradation mechanisms of ethylene co-vinyl acetate encapsulants over the lifetime of photovoltaic modules

The long-term reliability of encapsulation has a critical impact on the overall cost and effectiveness of photovoltaic modules. An often overlooked but important element of this reliability concerns adhesive degradation of the encapsulant, which results in delamination. This work critically examines the degradation mechanisms – deacetylation, β-scission, and hydrolytic depolymerization – primarily responsible for adhesion loss in ethylene co-vinyl acetate (EVA) encapsulants to provide evidence and clarity regarding the contribution of these mechanisms to the adhesive degradation over the lifetime of a module. EVA samples obtained from mini-modules exposed in two sets of controlled, accelerated conditions – with and without ultraviolet (UV) radiation – are characterized to find specific evidence for the proposed degradation mechanisms. The mini-modules were exposed up to 10,000 h, which represents up to 15–20 years in the field. Results affirm that initially, deacetylation dominates the degradation before scission takes over, followed by hydrolytic depolymerization. Additional analysis highlights the climacteric interdependency between deacetylation and hydrolytic depolymerization as it relates to eventual delamination, where deacetylation products catalyze interfacial hydrolysis reactions. Furthermore, effects occurring at a given interface are isolated, explicitly providing evidence for how these mechanisms are nonuniform throughout the thickness of the EVA. Results demonstrate that the EVA that interfaces with the glass as opposed to the cell experiences increased levels of degradation, explaining why this initially stronger interface becomes the preferential interface for delamination after longer periods of exposure. Additionally, elevated temperatures alone are shown to induce limited migration of sodium ions across the glass/EVA interface.

36 MATERIALS SCIENCE↗