Journal of Applied physics,Vol.33 : 1962年に発表されたレオロジー関連の論文

農林水産省経営局長賞 尿石症により尿道閉塞した雄子牛に対するバルーンカテーテルを用いた膀胱-皮膚フィステル装着による治療法の考案 (平成26年度家畜診療等技術全国研究集会・入賞論文)

Transport phenomena in plasmas can be promoted by external and by internal forces. In a spatially homogeneous plasma under the influence of external forces a drifting of the electrons can occur. This motion induced by external forces is referred to as mobility. Since the electrons have mass and electric charge, their motion implies transport of mass and conduction of electricity when acted upon by an external electric field. On the other hand, in a spatially inhomogeneous plasma, collisions cause the electrons to drift from the high-pressure to the low-pressure regions. The existence of pressure gradients is associated with the existence of either density gradients or temperature gradients, or both. This motion of the electrons, induced by internal pressure gradients, is called diffusion. Since the electrons also have kinetic energy associated with their random thermal motion, their drift implies the transport of thermal energy and therefore heat conduction. When the plasma is spatially inhomogeneous and is also acted upon by external forces, then the particle flux is due to both diffusion and mobility.

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Transport Processes in Plasmas

Guiding of relativistically intense laser pulses with peak power of 0.85 PW over 15 diffraction lengths was demonstrated by increasing the focusing strength of a capillary discharge waveguide using laser inverse bremsstrahlung heating. This allowed for the production of electron beams with quasimonoenergetic peaks up to 7.8 GeV, double the energy that was previously demonstrated. Charge was 5 pC at 7.8 GeV and up to 62 pC in 6 GeV peaks, and typical beam divergence was 0.2 mrad.

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Petawatt laser guiding and electron beam acceleration to 8 GeV in laser-heated capillary discharge waveguides

High-fidelity physics simulations are powerful tools in the design and optimization of charged particle accelerators. However, the computational burden of these simulations often limits their use in practice for design optimization and experiment planning. It also precludes their use as online models tied directly to accelerator operation. We introduce an approach based on machine learning to create nonlinear, fast-executing surrogate models that are informed by a sparse sampling of the physics simulation. The models are 10^6 to 10^7 times more efficient to execute.We also demonstrate that these models can be reliably used with multi-objective optimization to obtain orders-of-magnitude speedup in initial design studies and experiment planning. For example, we required 132 times fewer simulation evaluations to obtain an equivalent solution for our main test case, and initial studies suggest that between 330 to 550 times fewer simulation evaluations are needed when using an iterative retraining process. Our approach enables new ways for high-fidelity particle accelerator simulations to be used, at comparatively little computational cost.

Machine Learning for Orders of Magnitude Speedup in Multi-Objective Optimization of Particle Accelerator Systems.

The Proton Improvement Plan-II (PIP-II) encompasses a set of upgrades and improvements to the Fermilab accelerator complex aimed at supporting a world-leading neutrino program over the next several decades. PIP-II is an integral part of the strategic plan for U.S. High Energy Physics as described in the Particle Physics Project Prioritization Panel (P5) report of May 2014 [1] and formalized through the Mission Need Statement approved in November 2015. As an immediate goal, PIP-II is focused on upgrades to the Fermilab accelerator complex capable of providing proton beam power in excess of 1 MW on target at the initiation of the Long Baseline Neutrino Facility/Deep Underground Neutrino Experiment (LBNF/DUNE) program [2], currently anticipated for the mid- 2020s. PIP-II is a part of a longer-term goal of establishing a high-intensity proton facility that is unique within the world, ultimately leading to multi-MW capabilities at Fermilab....

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The PIP-II Conceptual Design Report

Machine learning (ML) is a subfield of artificial intelligence. The term applies broadly to a collection of computational algorithms and techniques that train systems from raw data rather than a priori models. ML techniques are now technologically mature enough to be applied to particle accelerators, and we expect that ML will become an increasingly valuable tool to meet new demands for beam energy, brightness, and stability. The intent of this white paper is to provide a high-level introduction to problems in accelerator science and operation where incorporating ML-based approaches may provide significant benefit. We review ML techniques currently being investigated at particle accelerator facilities, and we place specific emphasis on active research efforts and promising exploratory results. We also identify new applications and discuss their feasibility, along with the required data and infrastructure strategies. We conclude with a set of guidelines and recommendations for laboratory managers and administrators, emphasizing the logistical and technological requirements for successfully adopting this technology. This white paper also serves as a summary of the discussion from a recent workshop held at SLAC on ML for particle accelerators.

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arXiv : Opportunities in Machine Learning for Particle Accelerators

The Proton Improvement Plan II (PIP-II) at Fermilab is a program of upgrades to the injection complex At its core is the design and construction of a CW-compatible, pulsed H- superconducting RF linac To validate the concept of the front-end of such machine, a test accelerator known as PIP-II Injector Test is under construction It includes a 10mA DC, 30 keV H- ion source, a 2 m-long Low Energy Beam Transport (LEBT), a 21 MeV CW RFQ, followed by a Medium Energy Beam Transport (MEBT) that feeds the first of 2 cryomodules increasing the beam energy to about 25 MeV, and a High Energy Beam Transport section (HEBT) that takes the beam to a dump The ion source, LEBT, RFQ, and initial version of the MEBT have been built, installed, and commissioned This report presents the overall status of the Injector Test warm front end, including results of the beam commissioning through the installed components, and progress with SRF cryomodules and other systems

PIP-II Injector Test: Challenges and Status

As part of the PIP-II Injector Experiment (PXIE) accel-erator, a four-vane radio frequency quadrupole (RFQ) accelerates a 30-keV, 1-mA to 10-mA H' ion beam to 2.1 MeV. It is designed to operate at a frequency of 162.5 MHz with arbitrary duty factor, including continuous wave (CW) mode. The resonant frequency is controlled solely by a water-cooling system. We present an initial neural network model of the RFQ frequency response to changes in the cooling system and RF power conditions during pulsed operation. A neural network model will be used in a model predictive control scheme to regulate the resonant frequency of the RFQ.

Neural Network Modeling of the PXIE RFQ Cooling System and Resonant Frequency Response

When an un-gated thermionic cathode is operated in a radio-frequency gun, some fraction of the emitted electrons will return to the cathode due to the change in sign of the electric field in the gun. This back-bombardment current causes heating of the cathode, and this reduces the ability of the cathode heater to control the bunch charge. In this paper, we investigate the fundamental factors in single-frequency back-bombardment for a short-gap electron gun. Simulations revealed that the back-bombardment power depends strongly on the operating frequency and the bunch charge. Additionally, the use of a two-frequency TM010/TM020 electron gun to mitigate this effect was investigated which revealed that the effectiveness of this technique depends strongly upon single-frequency back-bombardment power but with the optimal reduction in back-bombardment power (62% of the baseline) occurring in the low-frequency limit.

Jonathan Edelen

Papers

The PIP-II Conceptual Design Report

arXiv : Opportunities in Machine Learning for Particle Accelerators

PIP-II Injector Test: Challenges and Status

Neural Network Modeling of the PXIE RFQ Cooling System and Resonant Frequency Response

Electron Back-Bombardment and Mitigation in a Short Gap, Thermionic Cathode RF Gun