svFSIplus

Introduction

svFSIplus is a C++ implementation of the Fortran svFSI multi-physics finite element solver designed for computational modeling of the cardiovascular system. It represents the First Stage in the development of a C++ multi-physics finite element solver and is essentially a direct line-by-line translation of the svFSI Fortran code. This was done to maintain a simple mapping between the code of the two versions and to facilitate debugging by comparing intermediate data (i.e. element stiffness matrices) and simulation results.

The Second Stage of the solver development will be an entirely new implementation encapsulating portions of the First Stage code into C++ classes forming a clean and simple abstraction that can be enhanced by any developer.

Getting started

Please see the documentation for getting started and implementation details. To run examples, have a look at our testing guide.

CMake Options

Three options are available in CMake:

• buildPy - Build Python Interface

• buildDocs - If ON the documentation is included. After running make, it can be built using

make docs

• sparseSolverType Use Sparse Matrix Solvers.

Sparse Solver Options

The discrete linear system of equations can be solver with various methods. These has a significant impact on the solver performance and total solution time. The following sparse solver options are available:

• Skyline Solver. This is a serial skyline matrix solver.
• SuperLU_MT Solver. This is a sparse solver that performs factorization in parallel on a shared memory machine. ==To use SuperLU_MT you need to download and install its library before building svOneDSolver.==
• CSparse Solver. This is a serial sparse solver. The source codes are included with the svOneDSolver source code, so it doesn't require the installation of external libraries.

Skyline Solver

Solutions are typically one order of magniture slower with this solver then other sparse solver options.

SuperLU_MT Solver

SuperLU_MT is a parallel direct linear solver for shared memory architectures. The source code for this library is available at this page. The build functionality in svOneDSolver requires the user to specify the local installation folder for the library using the variable SUPERLU_DIR. Let's assume you have already downloaded the library, unzipped its content to, say, "path/to/folder/SuperLU_MT_3.0" and built the library through the make utility. From inside the OneDBin folder (see above) you only have to type the following cmake command:

cmake -DsparseSolverType="superlu" -DSUPERLU_DIR="path/to/folder/SuperLU_MT_3.0" ../svOneDSolver/

NOTE: svOneDSolver assumes that your SuperLU_MT library has been built using the PTHREAD library. Please follow the instruction below on how to built SuperLU_MT to make sure this is the case.

==How to build the SuperLU_MT library so it works with svOneDSolver==

Please refer to the documentation in the README file distributed with the SuperLU_MT library.

Here we assume:

• You are in the "path/to/folder/SuperLU_MT_3.0" folder
• You are using an Ubuntu operating system or equivalent.

For a different operating system and if you want to link SuperLU_MT to a different multithreading library, please refer to the SuperLU_MT installation documentation.

1. Delete your file make.inc.
2. Copy the file MAKE_INC/make.pthread one folder up and rename it as make.inc.
3. Install the BLAS library on your system and edit the BLASLIB entry in make.inc so that it points to the correct location of your system BLAS library:

BLASLIB = /YOUR-BLAS-PATH/libblas.a

4. Build the code by typing "make blaslib" and then "make".

CSparse

This is the default solver and does not require any external library to be installed.

CSparse is a coincise sparse linear algebra package. The source codes are publicly available at this link. For convenience, these source code have been included in the svOneDSolver source code.