RF Linear Accelerators

RF Test Facility (RFTF) has been constructed to support present and future needs in testing, processing and conditioning of various high power RF components of normal conducting and superconducting systems at the SNS. The facility is expected to have additional subsystems that are needed for complete superconducting RF (SRF) testing and processing. A full capacity high voltage converter modulator (HVCM) with 11 MW peak power at 8% duty cycle has been installed for driving one or two klystron RF amplifiers. The waveguides are completed in WR-2100 and WR-1150 for the 402.5 MHz and 805 MHz klystrons being used in the SNS. The 805 MHz system has been used for RF processing the coaxial fundamental power couplers (FPCs) for the SNS superconducting linac (SCL) [1]. The high power RF system can be reconfigured or modified for various tests and conditioning processes along with the neighboring cryo-plant.

/pdf/high-power-rf-test-facility-at-the-sns-1n2016aldc.pdf

High Power RF Test Facility at the SNS

Superconducting Radio-Frequency (SRF) cavities operating in continuous wave (CW) mode and with low beam loading are devices characterized by a high loaded quality factor, in the order of 10^7, and narrow bandwidth, in the order of 10 Hz. The Low Level RF (LLRF) and resonance control systems of SRF cavities become a fundamental component of the entire system operation and in general has very tight stability requirements on the amplitude, phase and resonance frequency of the cavity. Microphonics plays an important role in cavity detuning, which results in more power needed to achieve the desired gradient. Active control approaches to reduce detuning in SRF cavities using piezoelectric actuators have shown promising results. Furthermore, Machine Learning (ML) techniques have also shown important capabilities to improve existing PID controllers. Specifically, Neural Networks (NN) can be used to find optimal PID gains and improve performance of traditional control systems. In this research, we develop new approaches to improve existing LLRF and resonance control systems with ML as a tool to find the optimal gains. We investigate these approaches using the LLRF control system intended for LCLS-II.

Studies in Applying Machine Learning to LLRF and Resonance Control in Superconducting RF Cavities

Particle accelerators are large, complex machines with thousands of coupled and time-varying components. In this work we utilize bounded stabilizing extremum seeking for the automatic minimization of beam loss in the Los Alamos Neutron Science Center proton linear accelerator by tuning six accelerator parameters simultaneously, the phase and amplitude settings of three radio frequency accelerating cavities. In this form of extremum seeking the analytically unknown functions being minimized enter into the system dynamics as arguments of chosen, bounded functions, which is very important for high energy devices such as particle accelerators, which require predictable, analytically known bounds on parameter updates when tuning noisy time-varying and unknown systems.

Extremum Seeking for Minimization of Beam Loss in the LANSCE Linear Accelerator by Tuning RF Cavities.

The LANSCE RM project has restored the proton linac to high power capability after the RF power tube manufacturer could no longer provide devices that consistently met the high average power requirement. Diacrodes® now supply RF power to three of the four DTL tanks. These tetrodes reuse the existing infrastructure including water-cooling systems, coaxial transmission lines, high voltage power supplies and capacitor banks. Each final power amplifier (FPA) system uses a combined pair of LANL-designed cavity amplifiers using the TH628L Diacrode® to produce up to 3.5 MW peak and 420 kW of average power. A new intermediate power amplifier (IPA) was developed using a TH781 tetrode. These amplifiers are the first production of new high power 200 MHz RF sources at accelerators in three decades. Design and prototype testing of the high power stages was completed in 2012, with commercialization following in 2013. Each installation was accomplished during a 4 to 5 month beam outage each year starting in 2014. Simultaneously, a new digital low-level RF control (dLLRF) system was designed and tested, and placed into operation this year, meeting the stringent field control requirements for the linac. The rapid-paced installation project changed over from old to new RF systems while minimizing beam downtime to the user facility schedule.

Design, Fabrication, Installation and Operation of New 201 MHz RF Systems at LANSCE

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

The current LANSCE LLRF system is an analog PI Feedback control system which achieves amplitude and phase error of 1% and 1 degree respectively. The feedback system receives cavity amplitude and phase, crosstalk between the amplitude and phase paths is significant. We propose an In-phase (I) and Quadrature (Q) based feedback control system which easily decouples the crosstalk of the I and Q channels. A PI feedback controller is implemented with an Altera Stratix III FPGA. The control system is modeled with DSP Builder which 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. The NIOS II processor communicates with the host computer via Ethernet, uploads data, computes parameters, and downloads parameters. The network support of the design makes it possible to set and tune the control system parameters on-line and to conduct the calibration of the whole RF system easily. The proposed control system is successfully tested with a LANSCE sided-coupled linear accelerator at 720kw.

FPGA implementation of a prototype PI Feedback control system for the LANSCE accelerator

The RF system for the 805 MHz normal conducting linac of the Spallation Neutron Source (SNS) accelerator was designed, procured and tested at Los Alamos National Laboratory (LANL), and then installed and commissioned at Oak Ridge National Laboratory (ORNL). The RF power for the room temperature coupled cavity linac (CCL) of the SNS accelerator is generated by four, pulsed 5 MW peak-power klystrons operating with a pulse width of 1.25 ms and a 60 Hz repetition frequency. The RF power from each klystron is divided and delivered to the CCL through two separate RF windows. The 5 MW RF system advanced the state of the art for simultaneous peak and average power. This paper summarizes the problems encountered, lessons learned, and results of the high power testing at LANL of the 5 MW klystrons, 5 MW circulators, 5 MW loads, and 2.5 MW windows.

5 MW 805 MHz SNS RF System Experience

The LANL/AES normal-conducting radio-frequency (NCRF) injector has undergone high power testing, confirming field gradients of up to 10 MV/m at the cathode. Most NCRF designs are limited to low-dutyfactor operation to constrain rf power consumption and limit ohmic heat generation. This cavity structure utilizes high density micro-channel cooling to successfully remove heat with the option of dynamic temperature control to actively adjust cavity resonance. This first high power rf test demonstrated stable cw (100% duty cycle) operation using resonant frequency tracking and produced intentional dark current emission from a roughened cathode blank. Resulting end-point x-ray measurements confirm the cathode gradient of 9.8 ± 0.2 MV/m required for acceleration of nC bunches to a beam energy of 2.5 MeV.

First operation of the lanl/aes normal conducting radio frequency photoinjector*

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