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SPHKernels

Julia implementation of some common SPH kernels

https://github.com/ludwigboess/sphkernels.jl

Science Score: 36.0%

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eeg uncertainty-quantification dynamical-systems finite-volume stochastic-processes systems-biology nonlinear-programming computational-biology parameter-estimation physics
Last synced: 10 months ago · JSON representation

Repository

Julia implementation of some common SPH kernels

Basic Info
  • Host: GitHub
  • Owner: LudwigBoess
  • License: mit
  • Language: Julia
  • Default Branch: master
  • Homepage:
  • Size: 2.45 MB
Statistics
  • Stars: 10
  • Watchers: 1
  • Forks: 0
  • Open Issues: 1
  • Releases: 15
Created almost 6 years ago · Last pushed about 1 year ago
Metadata Files
Readme License Zenodo

README.md

| Documentation | Build Status | Licence | Citation | |:-----------------------------------------------------------------:|:-----------------------------------------------------------------------------------------------:| :-----------------------------------------------------------------------------------------------:|:-----------:| | Run CI on master codecov.io | The MIT License | DOI |

SPHKernels.jl

This package supplies a number of kernels frequently used in Smoothed-Particle Hydrodynamics (SPH), as well as functions to evaluate their values and derivatives in 2D and 3D.

The implementation closely follows the one in Gadget2, see Springel (2005) for details.

These kernels include the B-splines (Cubic and Quintic) suggested in Monaghan & Lattanzio (1985), the Wendland functions (WendlandC2, WendlandC4 and WendlandC6 from Wendland (2009)) as suggested in Dehnen & Aly (2012) and WendlandC8 as suggested by Kummer et. al. (2019).

:warning: The version numbering of this package is unfortunately not really reflective of the state. I made an error on the original setup of the repository, so I had to start out with version 1.0. View this more as v0.2, instead of v2.0!: Please sanity-check everything before you use it in production!

Evaluating Kernels

To evaluate a 3D kernel you need to use the function

julia kernel_value(k::AbstractSPHKernel, u::Real, h_inv::Real)

where AbstractSPHKernel is the supertype for an implemented SPH kernel, u = \frac{x}{h} is the distance to the kernel origin in measures of the smoothing length and h_inv is the inverse of the smoothing length.

If you want your code to look a little more fancy you can also use the alternative functions 𝒲:

julia 𝒲( kernel::AbstractSPHKernel, u::Real, h_inv::Real) = kernel_value(kernel, u, h_inv)

As an example: ```julia using SPHKernels

Wendland C6 kernel with double precision in 3D

k = WendlandC6(Float64, 3)

distance between the particle and the origin of the kernel

r = 0.5 h = 1.0 hinv = 1.0 / h u = r * hinv

kernel value at position r

val = 𝒲(k, u, h_inv) ```

Evaluating Derivatives

Similar to Evaluating Kernels you can evluate a kernel derivative with

julia kernel_deriv(k::AbstractSPHKernel, u::Real, h_inv::Real)

or in the fancy way:

julia d𝒲(kernel::AbstractSPHKernel, u::Real, h_inv::Real) = kernel_deriv(kernel, u, h_inv)

Bias Correction

You can correct for the kernel bias of the Wendland kernels as described in Dehnen & Aly (2012), Eq. 18 + 19 with the functions:

julia bias_correction(kernel::AbstractSPHKernel, density::Real, m::Real, h_inv::Real, n_neighbours::Integer)

or again in the fancy way

julia δρ(kernel::AbstractSPHKernel, density::Real, m::Real, h_inv::Real, n_neighbours::Integer) = bias_correction(kernel, density, m, h_inv, n_neighbours)

This will return a new value for the density:

```julia using SPHKernels density = 1.0 kernel = WendlandC6(3)

correct density

density = bias_correction(kernel, density, 1.0, 0.5, 295) ```

Owner

  • Name: Ludwig
  • Login: LudwigBoess
  • Kind: user
  • Location: Munich

I'm a PhD student at the University Observatory Munich working on cosmological MHD simulations.

GitHub Events

Total
  • Delete event: 1
  • Issue comment event: 2
  • Push event: 2
  • Pull request event: 3
  • Create event: 2
Last Year
  • Delete event: 1
  • Issue comment event: 2
  • Push event: 2
  • Pull request event: 3
  • Create event: 2

Committers

Last synced: over 3 years ago

All Time
  • Total Commits: 119
  • Total Committers: 7
  • Avg Commits per committer: 17.0
  • Development Distribution Score (DDS): 0.378
Top Committers
Name Email Commits
LudwigBoess l****s@u****e 74
Ludwig Böss l****s@o****e 23
Ludwig Boess d****g@c****e 11
Ludwig 3****s@u****m 6
github-actions[bot] 4****]@u****m 2
Julia Package Butler 2
CompatHelper Julia c****y@j****g 1
Committer Domains (Top 20 + Academic)
julialang.org: 1 c2paplogin.cos.lrz.de: 1 outlook.de: 1 usm.lmu.de: 1

Issues and Pull Requests

Last synced: 11 months ago

All Time
  • Total issues: 10
  • Total pull requests: 28
  • Average time to close issues: 5 months
  • Average time to close pull requests: 2 days
  • Total issue authors: 4
  • Total pull request authors: 3
  • Average comments per issue: 6.2
  • Average comments per pull request: 0.93
  • Merged pull requests: 26
  • Bot issues: 0
  • Bot pull requests: 8
Past Year
  • Issues: 0
  • Pull requests: 6
  • Average time to close issues: N/A
  • Average time to close pull requests: about 19 hours
  • Issue authors: 0
  • Pull request authors: 3
  • Average comments per issue: 0
  • Average comments per pull request: 0.67
  • Merged pull requests: 5
  • Bot issues: 0
  • Bot pull requests: 3
View more stats
Top Authors
Issue Authors
  • AhmedSalih3d (4)
  • LudwigBoess (3)
  • PharmCat (2)
  • JuliaTagBot (1)
Pull Request Authors
  • LudwigBoess (23)
  • github-actions[bot] (8)
  • dependabot[bot] (2)
Top Labels
Issue Labels
enhancement (3)
Pull Request Labels
dependencies (2) github_actions (2)

Packages

  • Total packages: 1
  • Total downloads:
    • julia 30 total
  • Total dependent packages: 1
  • Total dependent repositories: 0
  • Total versions: 16
juliahub.com: SPHKernels

Julia implementation of some common SPH kernels

  • Versions: 16
  • Dependent Packages: 1
  • Dependent Repositories: 0
  • Downloads: 30 Total
Rankings
Dependent repos count: 9.9%
Dependent packages count: 23.0%
Average: 34.8%
Stargazers count: 52.9%
Forks count: 53.5%
Last synced: 11 months ago

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