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BLonD code development meeting
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BLonD code development meeting
Review of BLonD manuscript
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In 2 weeks' time: finish your own part of the manuscript
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In 4 weeks' time: review entire manuscript and leave your comments to others
Ivan: Faster induced-voltage calculation
- Application: FCC with large circumference, 10000s of bunches
- 10 B macro particles needed if scaling directly
- Transverse feedback studies done in 2018 with PyHEADTAIL
- Present BLonD implementations
- Freq domain: circular convolution -> periodic result, any impedance model; uniform sampling needed
- Time domain: linear convolution with zero padding, any impedance model; uniform sampling needed
- Resonator impedance: matrix multiplication -> non-uniform sampling needed, but N^2 complexity
- Ideally, want to have non-uniform implementation, but with Nlog(N) complexity
- Compressed convolution by ABP
- Drawback: double number of slices needed
- For multi-turn wake, empty bucket sampling needed
- How does it work?
- Induced voltage only needed on the points where the line density is not zero
- Introducing a MASKING of the data -> can cut the wakefield data
- Calculate convolution on compressed data
- For the relevant bins, the results are the same
- For other locations, data is not correct => CANNOT use it for beam with LOSSES
- Performance test:
- Full convolution with 5 bucket bunch spacing vs. full conv with 15 bucket bunch spacing vs compressed convolution: compressed convolution performs best of all
- The smaller the spacing, the less the improvement
- Remarks on wake calculation
- N. Mounet: exact analytical wake calculation can be approximated by cubic Hermitian interpolation
- example: resistive wall impedance is not correctly represented by standard FFT, while analytical approximation is correct
- Drawback: analytical expression of the impedance needed
- For cases where the impedance is known, but wake cannot be calculated
- N. Mounet: exact analytical wake calculation can be approximated by cubic Hermitian interpolation
- Implementation in BLonD: Ivan will make a first implementation
- Already existing SparseSlicing objects
- Issue reported by Markus in C++ implementation
- Already existing SparseSlicing objects
- Mihaly: how about using non-uniform FFT?
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