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Blokland, Willem

Publications and source records attributed to Blokland, Willem.

At least 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts.

Schram, Malachi [Thomas Jefferson National Acceler↗
DOE OSTI2026

Continual Learning for Particle Accelerators

Particle accelerators operate under dynamically changing conditions, which often lead to data distribution drifts. These drifts pose significant challenges for Machine Learning (ML) models, which typically fail to maintain performance when faced with such non-stationary data. In particle accelerators, the primary sources of these data drifts include changes in accelerator settings and non-measured parameters such as machine degradation and environmental factors. Previous research has proposed conditional models to handle multiple beam configurations effectively; however, it is challenging to train the ML models on all possible configuration settings. Additionally, conditional models alone can not address performance degradation caused by drifts due to non-measured factors. These limitations contribute to a significant gap between ML development and its deployment in real-world operational settings. To bridge this gap, in this paper, we identify some of the key areas within particle accelerators where continual learning can help mitigate drift-induced performance degradation. In addition, we present a practical use case where a conditional Auto-Encoder model coupled with memory-based continual learning has been employed to demonstrate stable performance even when underlying data drifts

Rajput, Kishansingh [Thomas Jefferson National Acc↗
DOE OSTI2024

Process and control variables from the mercury loop at the SNS's mercury target

Set of process and control variables corresponding to the mercury process loop at the Spallation Neutron Source's (SNS) mercury target. The data corresponds to 36 measurement signals (pressure, temperature, flows, valve position, etc.) spanning from 2017/01/01 to 2023/12/31. Each signal is broken intro three-month intervals, resulting in a total of 28 files per signal during the aforementioned period of time.

mercury loop↗
DOE OSTI2024

Errant Beam Prognostics with Machine Leaning at SNS Accelerator

Particle Accelerators are complex machine with many pieces of equipment running in synchronization to deliver required beam. However, faults in particle accelerators reduce the availability of the beam for experiments affecting the overall science output. To avoid these faults, we apply anomaly detection techniques to predict any unusual behavior and perform preemptive actions to improve the total availability. Many researchers have adopted semi-supervised Machine Learning (ML) methods such as auto-encoders and variational auto-encoders for such tasks. However, supervised ML techniques designed for similarity learning such as Siamese Neural Network (SNN) can outperform semi-supervised or unsupervised methods for anomaly prediction. One of the challenges associated with application of ML models to particle accelerators is the variability in observed data over time due to system configuration changes. We employ conditional models such as Conditional Siamese Neural Networks (CSNN), and Conditional-VAE (CVAE) to learn the variability in the data by using beam configuration parameters as conditional input. We apply these models for errant beam prediction at Spallation Neutron Source accelerator under different system configurations and compare their performance. We demonstrate that CSNN outperforms CVAE in our application. This talk will present the data source, collection, analysis, data-preparation, model development, hyper-parameter studies and the results.

Rajput, Kishansingh↗
DOE OSTI2024

A machine learning approach for particle accelerator errant beam prediction using spatial phase deviation

Particle accelerators are extremely complex systems that are expected to operate on high availability. Predicting impending failures only by utilizing data collected from diagnostic equipment already on board can help operators to avoid installing expensive sensors, unscheduled downtime and associated costs. For this purpose we explore the predictive power of Machine Learning algorithms to detect faulty beams prior to the failure. In this study, we propose a Machine Learning approach to model mapping from a pair of sensors located across the accelerator. While the model is trained to represent normal operation, we evaluate the predictive performance on known faulty beam pulses. We also investigate the model performance on unseen data through k-fold cross-validation. Then we recap the analysis with a neural architecture search and hyperparameter optimization study to fine tune our initial model. In conclusion, this paper will also introduce a sustainable framework that can standardize Machine Learning workflow applied to particle accelerators.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

Robust errant beam prognostics with conditional modeling for particle accelerators

Abstract Particle accelerators are complex and comprise thousands of components, with many pieces of equipment running at their peak power. Consequently, they can fault and abort operations for numerous reasons, lowering efficiency and science output. To avoid these faults, we apply anomaly detection techniques to predict unusual behavior and perform preemptive actions to improve the total availability. Supervised machine learning (ML) techniques such as siamese neural network models can outperform the often-used unsupervised or semi-supervised approaches for anomaly detection by leveraging the label information. One of the challenges specific to anomaly detection for particle accelerators is the data’s variability due to accelerator configuration changes within a production run of several months. ML models fail at providing accurate predictions when data changes due to changes in the configuration. To address this challenge, we include the configuration settings into our models and training to improve the results. Beam configurations are used as a conditional input for the model to learn any cross-correlation between the data from different conditions and retain its performance. We employ conditional siamese neural network (CSNN) models and conditional variational auto encoder (CVAE) models to predict errant beam pulses at the spallation neutron source under different system configurations and compare their performance. We demonstrate that CSNNs outperform CVAEs in our application.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Errant Beam Detection Using the AMD Versal ACAP and Vitis AI

The prevalence of ML and AI-powered solutions along with the slowing of Moore's Law has given rise to novel hardware platforms aimed at accelerating ML and AI. While programming these hardware platforms can be difficult, particularly for non-hardware experts, hardware vendors provide high-level tooling in an effort to address this difficulty. The Versal ACAP is an SoC designed by AMD that combines CPU cores, FPGA fabric, and a tiled, vector architecture called an AI engine all on the same socket. In an effort to more easily program this heterogeneous system, AMD has provided the Vitis AI development stack. In this work, we leverage Vitis AI to program a Versal ACAP to perform errant beam detection in the Spallation Neutron Source at Oak Ridge National Laboratory. Our initial work shows that after quantization and compilation of the model for the Versal ACAP, the classification accuracy, as measured by the AUC metric, is over 95% accurate while achieving this accuracy in 46 microseconds on average.

Cabrera, Anthony↗
DOE OSTI2023

Second Target Station Project: LANSCE WNR Target 2 (Blue Room) Experiment 2022

The Second Target Station (STS) at SNS will address emerging scientific challenges by providing a source of intense, cold neutrons to instruments optimized for this source, using rotating, tungsten target blocks. The STS target will receive 1.3 GeV proton beam pulses from the SNS accelerator at a repetition rate of 15 Hz. The facility life is planned for 40 years, and each target assembly life is expected to be approximately 10 years. An accurate strain prediction is then critical for fatigue life assessment of STS target blocks because they will be subject to approximately 108 beam pulses per lifetime. As an R&D activity, the LANSCE WNR Target 2 (blue room) facility was used to test the strain response of prototypical target blocks to the thermal shock of a proton pulse. The blue room was well suited for a pulsed proton beam impact test of subscale STS target blocks; the 800 MeV proton energy is approximately 60% of the 1.3 GeV proton energy expected from the SNS accelerator to the STS. Both the LANSCE Proton Storage Ring (PSR) and SNS are short-pulse proton beam sources with nominal pulse widths of 250 ns and 661 ns, respectively, so the energy deposition in the target occurs in < 1 microsecond pulse duration.

43 PARTICLE ACCELERATORS↗
DOE OSTI2023

Ramp Generator for Electron Scanner in SNS Beam Profile Measurements System

A new fast high voltage Ramp Generator to deflect an electron beam in the Electron Scanner is required at the Spallation Neutron Source (SNS) in the Oak Ridge National Laboratory. The Electron Scanner is used for non-destructively measuring the transverse profiles of the proton beam in the accumulator ring of the SNS [1]. The existing deflector is obsolete and needs to be replaced. A new Ramp Generator is proposed based on fast high voltage MOSFET power switches. Two configurations have been considered. One is bipolar, with two opposite polarity power supplies and one switch module or two single switches, and the other is unipolar, with one power supply and two switch modules. Schematics and performance of both options are described, and results of the prototype testing are presented in the paper.

Peplov, Vladimir↗
DOE OSTI2023

Adrastea: An Efficient FPGA Design Environment for Heterogeneous Scientific Computing and Machine Learning

We present Adrastea, an efficient FPGA design environment for developing scientific machine learning applications. FPGA development is challenging, from deployment, proper toolchain setup, programming methods, interfacing FPGA kernels, and more importantly, the need to explore design space choices to get the best performance and area usage from the FPGA kernel design. Adrastea provides an automated and scalable design flow to parameterize, implement, and optimize complex FPGA kernels and associated interfaces. We show how virtualization of the development environment via virtual machines is leveraged to simplify the setup of the FPGA toolchain while deploying the FPGA boards and while scaling up the automated design space exploration to leverage multiple machines concurrently. Adrastea provides an automated build and test environment of FPGA kernels. By exposing design space hyper-parameters, Adrastea can automatically search the design space in parallel to optimize the FPGA design for a given metric, usually performance or area. Adrastea simplifies the task of interfacing with the FPGA kernels with a simplified interface API. To demonstrate the capabilities of Adrastea, we implement a complex random forest machine learning kernel with 10,000 input features while achieving extremely low computing latency without loss of prediction accuracy, which is required by a scientific edge application at SNS. We also demonstrate Adrastea using an FFT kernel and show that for both applications Adrastea is able to systematically and efficiently evaluate different design options, which reduced the time and effort required to develop the kernel from months of manual work to days of automatic builds.

Young, Aaron↗
DOE OSTI2021

Improvements of pre-emptive identification of particle accelerator failures using binary classifiers and dimensionality reduction

In this paper we look at the properties of the Spallation Neutron Source (SNS) Differential Beam Current Monitor (DCM) data and various methods of data transformation to improve pre-emptive detection of machine trips. In this study, the foundation of the approach is the analysis of new underlying data and understanding various properties with the goal of faster classification, higher precision and higher recall with the aim to reduce false positives as low as required. The result of the research presented in this paper are a binary classifier capable of predicting accelerator failures with millisecond classification time, 96% precision, 58% true positive and 0% false positive rate and optimization techniques enabling real-time implementations.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Strain Measurement in the Spallation Target Using High-Radiation-Tolerant Fiber Sensors

We describe the measurement of fast dynamic strains in the mercury target module of the spallation neutron source using customized high-radiation-tolerant fiber-optic strain sensors. The sensors are made from fluorine-doped single-mode optical fibers that demonstrated high radiation tolerance at 1300 nm. A digital phase demodulation scheme is employed in the signal interrogation system, which enables high bandwidth measurement and has an excellent adaptability to sensor output power fluctuations and sensor gap variations. Fast transient strain waveforms induced by high-energy, high-intensity proton pulses are successfully measured. Here, the sensors have survived radiation doses of 10 9 Gy. Both measurement bandwidth and radiation tolerance of the sensors are an order of magnitude higher than their commercial counterparts.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2018

Minimizing Errant Beam at the Spallation Neutron Source

Over the years SNS has been optimizing the amount of errant beam pulses in the accelerator. The errant beam pulses can damage the superconducting cavities and thus lower the uptime of the accelerator. This talk will present the effort that has been made to lower the amount of errant beam pulses with new diagnostics, analysis of processes that cause errant beam, and the resulting lessons learned.

Peters, Charles C.↗
DOE OSTI2017

Development of a 100 kV pulse generator for driving an electron scanner used in proton beam profile measurements

The Spallation Neutron Source (SNS) utilizes an electron scanner in the accumulator ring for nondestructive transverse profiling of the proton beam. The electron scanner consists of a high voltage pulse generator driving an electron gun, a medium voltage ramp generator, and a CCD camera. A new high voltage pulse generator that provides negative 100 kV pulses with rise times of less than 200 ns, +/-0.5% flattop of greater than 100 ns has been designed, delivered, and undergone extensive testing. The pulse generator has been operationally verified with the existing control system and simulated loads. Full system testing with the actual electron scanner is planned. This paper details the requirements, design, setup, and test results of the high voltage pulse generator.

Morris, B↗