Review of Charged Particle Dynamics. Beam Optics and Focusing Systems Without Space Charge. Linear Beam Optics with Space Charge. Self-Consistent Theory of Beams. Emittance Variation. Beam Physics Research from 1993 to 2007. Appendices. List of Frequently Used Symbols. Bibliography. Index.

Theory and Design of Charged Particle Beams

Experimental studies of the collisions of heavy nuclei at relativistic energies have established the properties of the quark–gluon plasma (QGP), a state of hot, dense nuclear matter in which quarks and gluons are not bound into hadrons1–4. In this state, matter behaves as a nearly inviscid fluid5 that efficiently translates initial spatial anisotropies into correlated momentum anisotropies among the particles produced, creating a common velocity field pattern known as collective flow. In recent years, comparable momentum anisotropies have been measured in small-system proton–proton (p+p) and proton–nucleus (p+A) collisions, despite expectations that the volume and lifetime of the medium produced would be too small to form a QGP. Here we report on the observation of elliptic and triangular flow patterns of charged particles produced in proton–gold (p+Au), deuteron–gold (d+Au) and helium–gold (3He+Au) collisions at a nucleon–nucleon centre-of-mass energy $\sqrt {s_{{\mathrm{NN}}}} = 200$ GeV. The unique combination of three distinct initial geometries and two flow patterns provides unprecedented model discrimination. Hydrodynamical models, which include the formation of a short-lived QGP droplet, provide the best simultaneous description of these measurements.

/pdf/creation-of-quark-gluon-plasma-droplets-with-three-distinct-i9ykw2l8oa.pdf

Creation of quark–gluon plasma droplets with three distinct geometries

We present the first measurement of elliptic ($v_2$) and triangular ($v_3$) flow in high-multiplicity $^{3}$He$+$Au collisions at $\sqrt{s_{_{NN}}}=200$ GeV. Two-particle correlations, where the particles have a large separation in pseudorapidity, are compared in $^{3}$He$+$Au and in $p$$+$$p$ collisions and indicate that collective effects dominate the second and third Fourier components for the correlations observed in the $^{3}$He$+$Au system. The collective behavior is quantified in terms of elliptic $v_2$ and triangular $v_3$ anisotropy coefficients measured with respect to their corresponding event planes. The $v_2$ values are comparable to those previously measured in $d$$+$Au collisions at the same nucleon-nucleon center-of-mass energy. Comparison with various theoretical predictions are made, including to models where the hot spots created by the impact of the three $^{3}$He nucleons on the Au nucleus expand hydrodynamically to generate the triangular flow. The agreement of these models with data may indicate the formation of low-viscosity quark-gluon plasma even in these small collision systems.

Measurements of Elliptic and Triangular Flow in High-Multiplicity He 3 +Au Collisions at sNN =200GeV

RF Linear Accelerators

Chemical short-range orders and the induced structural transition in high-entropy alloys

In this work, an implementation of a recently developed model-independent adaptive control scheme, for tuning uncertain and time varying systems, is demonstrated on the Los Alamos linear particle accelerator. The main benefits of the algorithm are its simplicity, ability to handle an arbitrary number of components without increased complexity, and the approach is extremely robust to measurement noise, a property which is both analytically proven and demonstrated in the experiments performed. We report on the application of this algorithm for simultaneous tuning of two buncher radio frequency (RF) cavities, in order to maximize beam acceptance into the accelerating electromagnetic field cavities of the machine, with the tuning based only on a noisy measurement of the surviving beam current downstream from the two bunching cavities. The algorithm automatically responds to arbitrary phase shift of the cavity phases, automatically re-tuning the cavity settings and maximizing beam acceptance. Because it is model independent it can be utilized for continuous adaptation to time-variation of a large system, such as due to thermal drift, or damage to components, in which the remaining, functional components would be automatically re-tuned to compensate for the failing ones. We start by discussing the general model-independent adaptive scheme and how it may be digitally applied to a large class of multi-parameter uncertain systems, and then present our experimental results.

In-hardware demonstration of model-independent adaptive tuning of noisy systems with arbitrary phase drift

In contrast to a normal conducting RF cavity, a superconducting RF cavity is very susceptible to shifts in its resonance frequency. The main sources of the shift are Lorentz force detuning and microphonics. In a spallation neutron source, to compensate for the frequency shift, a feedforward control is to be applied. In this paper, as an initiative step, a frequency shift observer is proposed which is simple enough to be implemented with a digital signal processor in real time. Simulation results of the proposed frequency shift observer show reliable performance and acceptable computation time for the real time implementation.

/pdf/frequency-shift-observer-for-an-sns-superconducting-rf-4pug6tq04i.pdf

Frequency shift observer for an SNS superconducting RF cavity

As part of the modernization of the Los Alamos Neutron Science Center (LANSCE), a digital low level RF (LLRF) system was designed. The LLRF control system was implemented in a Field Programmable Gate Array (FPGA) using embedded Experimental Physics and Industrial Control System (EPICS) Input Output Controller (IOC) under the Real-Time Executive for Multiprocessor Systems (RTEMS). Proportional-Integral (PI) feedback controller, static beam feedforward controller, and iterative learning controller are implemented on the FPGA. The closed loop system performance was tested with a 10mA peak current proton beam. INTRODUCTION The modernization of the LANSCE 201 MHZ RF systems including the LLRF control systems, RF amplifier systems, water-cooling systems and networking is under way[1,2]. Analog low level RF control and electronics have been replaced with FPGA based control systems. The legacy LANSCE LLRF system was an analog PI Feedback control system which provided amplitude and phase error of < ±0.12% and ±0.1°, respectively. However, it does not support network control or data acquisition of LLRF parameters. The new design of the LLRF system using an FPGA with an embedded softcore processor and network support. Also, the FPGA-based design gives the ability for algorithm and processor modification and upgrade. In this note, an overview of the new LLRF system and the control system performance with a baseline proton beam of LANSCE are addressed. LLRF SYSTEM OVERVIEW The LLRF control system is illustrated in Figure 1. It consists of the clock system, analog front end, down converting mixers, FPGA (Stratix III) based control system, the RF power amplifier chain, and a cavity. The clock system generates the FPGA clock and LO signal from the 10MHz reference signal. The mixers are located in the LINAC tunnel, which down-converts the 201.25 MHz cavity and reference RF signal to the 25.15625 MHz intermediate-frequency (IF) signals. These IF signals are filtered by diplexer filters to eliminate the higher order harmonics and fed to 16-bit ADCs (LTC2208) where four times oversampling scheme is used. The ADC output of the reference IF signal is conditioned and is used for IF modulation of the base band I/Q control signals. The I and Q stream outputs of the ADC of the cavity IF signal are processed to remove the DC offsets and then they are filtered by 150-tap linear-phase low pass FIR filter, yielding baseband cavity field I and Q signals. Meanwhile, the relative phase of the cavity field with respect to the reference is calculated using CORDICs (Coordinate Rotation Digital Computers) from I and Q data of the reference IF signal and I and Q data of the cavity IF signal. The PI control algorithm is applied to the cavity field I and Q signals to obtain the control actions that will be digitally modulated onto control IF signal. A 16-bit DAC (AD9726) transforms the digital control IF signal back to analog IF signal and the analog IF signal is up-converted to the 201.25 MHz control RF. The analog control RF signal is amplified through the RF amplifier chain to the cavity. The LLRF control system parameters are loaded to memory mapped-registers located in the FPGA through the EPICS IOC over the network. For the register reads and writes, and waveform uploads, a CPU softcore, NIOS II processor, is embedded in FPGA and the EPICS IOC on the operating system Real-Time Executive for Multiprocessor Systems (RTEMS). Figure 1: Schematic block diagram of the digital field control module (FCM). ___________________________________________ # skwon@lanl.gov Proceedings of IPAC2016, Busan, Korea WEPOR044 06 Beam Instrumentation, Controls, Feedback and Operational Aspects T27 Low Level RF ISBN 978-3-95450-147-2 2771 C op yr ig ht © 20 16 C C -B Y3. 0 an d by th e re sp ec tiv e au th or s FEEDBACK CONTROL PERFORMANCE Because of the high power requirement of the LANSCE accelerator, two diacrodes are installed and their outputs are combined through a hybrid to drive a DTL tank[2]. The performance of the feedback control system is shown figure 2. The PI feedback is applied. The amplitude and phase stabilities are ±0.05% and ±0.05°, respectively. Note that with higher PI gains, better performance is obtained. However, because there is a loop delay of a few μsec, in order to choose the gains it is necessary to consider the output performance and robustness, input usage and noise sensitivity. In order to confirm the performance of the feedback control, the amplitude and phase are measured with external monitors based on ADL5511 RF amplitude detector and AD8302 Phase Detector. The obtained signals are shown in figure 2 and the amplitude and phase stabilities on the flattop are ±0.04% and ±0.02°, respectively. Figure 2: Amplitude and Phase Stabilities of the Feedback System. (a) FCM measured amplitude, (b) FCM measured phase, (c) ADL5511 measured amplitude, (d) AD8302 phase detector measured phase. BEAM LOADING COMPENSATION BY FEEDFORWARD CONTROLS Two types of beam loading compensation feedforward controllers are implemented. The first beam feedforward controller is the static beam feedforward controller (SBFFC), where the detected beam current is read-back to the LLRF system and a proper amplification and rotation of the detected beam current generates the feedforward I and Q control signals. Figure 3 shows SBFFC performance when 10mA production beam having 20usec ramp is operating at 1 Hz rate on LANSCE DTL Tank 2. It is observed that the stabilities on the flattop are ±0.20% and ±0.07°, respectively. It is noted that around the middle of the beam ramp (10usec), the beam loading effect on the amplitude is not suppressed well (0.58%) though it is improved a lot compared with the case when only feedback controller is applied (Figure 4). This may be improved by a new scheme where the SBFFC enable time is adjusted optimally. Figure 3: Amplitude and Phase Stabilities of the Static Beam Feedforward Control. (a) ADL5511 RF amplitude detector, and (b) AD8302 Phase Detector. (c) Schematic of SBFFC. Green: Beam Current, 10mA. Figure 4: Amplitude stability Comparison. Feedback Only System has 2.3% transient stability. Green: Beam Current, 10mA. 0 100 200 300 400 500 600 700 800 90

Fpga Implementation of a Control System for the LANSCE Accelerator

The current LANSCE LLRF system is an analog PI Feedback control system which achieves the amplitude and phase error within 1% and 1 degree. The feedback system receives the cavity amplitude and phase and the crosstalk between the amplitude and the phase paths is significant. We propose an In-phase (I) and Quadrature (Q) based feedback control system which easily decouples the crosstalk of I and Q channels. A gain scheduled PI feedback controller with optimally generated set point trajectory reduces the transient peak of the feedback controller and hence reduce the fatigue of the RF amplifier chain. An additional feature of the controller is the Neural network based self-tuning PI feedback, where the neural network tunes the feedback gains to minimize the errors in the least squares sense. The proposed control system is implemented with Altera Stratix II FPGA. The control system is modeled with DSP Builder and automatically generates HDL. Altera SOPC Builder is used for the hardware integration of the DSP Builder model, memories, peripherals, and 32 bit NIOS II embedded processor. NIOS II processor equipped with real time operating system communicates with the host computer via Ethernet, uploads data, computes parameters, and downloads parameters. The proposed control system is tested with the low power test-stand for the robustness of the algorithm.

Gain scheduled neural network tuned pi feedback control system for the lansce accelerator

This paper addresses the system identification and the decoupling PI controller design for a normal conducting RF cavity. Based on the open-loop measurement data of an SNS DTL cavity, the open-loop system's bandwidths and loop time delays are estimated by using batched least square. With the identified system, a PI controller is designed in such a way that it suppresses the time varying klystron droop and decouples the In-phase and Quadrature of the cavity field. The Levenberg-Marquardt algorithm is applied for nonlinear least squares to obtain the optimal PI controller parameters. The tuned PI controller gains are downloaded to the low-level RF system by using channel access. The experiment of the closed-loop system is performed and the performance is investigated. The proposed tuning method is running automatically in real time interface between a host computer with controller hardware through ActiveX Channel Access.

Decoupling PI controller design for a normal conducting RF cavity using a recursive LEVENBERG-MARQUARDT algorithm

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

M. Prokop

Author Tools

Chat about Author