Impedance computation of cryogenic vacuum chambers using boundary element method

Fujita, Kazuhiro

doi:10.1103/physrevaccelbeams.25.064601

Cited by 4 publications

(7 citation statements)

References 29 publications

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“…Accuracy of SF-PINN is assessed in comparison with available analytical solutions [20,21] for a circular vacuum chamber. Numerical results of the boundary element method (BEM) [29] with the same 𝑁 𝑏 boundary nodes are also shown to confirm whether SF-PINN results is physically correct or not.…”

Section: Numerical Resultsmentioning

confidence: 74%

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Impedance modeling of accelerator beams with discontinuous charge density using scattered-field physics-informed neural networks

Fujita

2023

IEICE Electron. Express

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A physics-informed neural network method for solving electrodynamic interaction problems including a relativistic beam of charged particles in particle accelerators has been recently proposed. However, the method still has a limitation on modeling accelerator beams with discontinuous charge density. To remove this limitation, the scattered field formulation is introduced into this method. This approach allows us to model the field of discontinuous beam charge density such as point and ring charges. Its numerical error is shown to be smaller than that of the boundary element method. The presented approach is applied to three different vacuum chambers.

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Section: Numerical Resultsmentioning

confidence: 74%

“…1(b). E (𝑑) on the PEC wall can be analytically determined [20,21] or numerically calculated [29]. Note that the beam charge density is not explicitly involved in the right-hand side of (4).…”

Section: Partial Differential Equation and Boundary Conditionmentioning

confidence: 99%

Impedance modeling of accelerator beams with discontinuous charge density using scattered-field physics-informed neural networks

Fujita

2023

IEICE Electron. Express

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“…where σr is the Gaussian parameter in the radial direction, the direct SC field can be determined analytically [12][13][14] or numerically [7]. In the TF-PINN, the normalized charge density in the scaled coordinates (X, Y) is defined as…”

Section: Numerical Resultsmentioning

confidence: 99%

Comparison of physics-informed neural networks in solving electromagnetic interior scattering problems including a relativistic beam current

Fujita

2024

JASSE

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The strength of electromagnetic interaction between a relativistic beam and its surrounding environment in a particle accelerator can be characterized by the coupling impedance. Recently, the physics-informed neural networks (PINN) have been introduced into the impedance modeling in accelerator physics. Total-field (TF) and scattered-field (SF) formulations are available in calculating the coupling impedance with PINN. In this paper, direct comparison of the two PINNs based on the TF and SF formulations is presented with application to an elliptical vacuum chamber with practical geometry parameters. The numbers of iterations for the training processes and the accuracy of indirect space charge impedance are assessed for the two different PINNs in this comparison.

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“…The electromagnetic field in an infinitely long vacuum chamber can be also expressed in Kirchhoff's boundary integral representation as [30].…”

Section: Kirchhoff's Boundary Integral Representation Of Electromagne...mentioning

confidence: 99%

“…where E l is the longitudinal electric field on the wall surface obtained with the recently proposed boundary element method (BEM) [30]. Note that, as already mentioned in Section 2.3, the nonperturbative model is used to obtain E l in Eq.…”

Section: Data and Equation Scalingmentioning

confidence: 99%

Electromagnetic field computation of multilayer vacuum chambers with physics-informed neural networks

Fujita

2022

Front. Phys.

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The electromagnetic interaction of a charged particle beam with multilayer vacuum chambers is of particular interest in accelerator physics. This paper presents a deep learning-based approach for calculating electromagnetic fields generated by the beam in infinitely long multilayer vacuum chambers with arbitrary cross section. The presented approach is based on physics-informed neural networks and the surface impedance boundary condition of a multilayer structure derived from the transmission line theory. Deep neural networks (DNNs) are utilized to approximate the solution of partial differential equations (PDEs) describing the physics of electromagnetic fields self-generated by a charged particle beam traveling in a particle accelerator. A residual network is constructed from the output of DNNs, the PDEs and boundary conditions are embedded into the loss function and differential operators are calculated using the automatic differentiation. As a result, the presented approach is regarded to be mesh-free. The approach is applied to circular and elliptical vacuum chambers with a three-layer structure. It is verified in comparison with the recently proposed boundary element method. The effects of chamber geometries and multilayer structure on the beam coupling impedance are demonstrated.

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Impedance computation of cryogenic vacuum chambers using boundary element method

Cited by 4 publications

References 29 publications

Impedance modeling of accelerator beams with discontinuous charge density using scattered-field physics-informed neural networks

Impedance modeling of accelerator beams with discontinuous charge density using scattered-field physics-informed neural networks

Comparison of physics-informed neural networks in solving electromagnetic interior scattering problems including a relativistic beam current

Electromagnetic field computation of multilayer vacuum chambers with physics-informed neural networks

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