From b416b59d1c98640cf8b90869d6b5ae44440c2352 Mon Sep 17 00:00:00 2001 From: kharold23 <52462115+kharold23@users.noreply.github.com> Date: Sat, 27 Apr 2024 11:36:51 -0700 Subject: [PATCH] sv0D tests --- tests/cases/fluid/pipe_RCR_genBC/README.md | 88 +++++++ .../cases/fluid/pipe_RCR_genBC/genBC/Makefile | 79 +++++++ .../fluid/pipe_RCR_genBC/genBC/Makefile.in | 165 +++++++++++++ .../fluid/pipe_RCR_genBC/genBC/src/GenBC.f | 220 ++++++++++++++++++ .../fluid/pipe_RCR_genBC/genBC/src/Modules.f | 17 ++ .../fluid/pipe_RCR_genBC/genBC/src/USER.f | 127 ++++++++++ .../fluid/pipe_RCR_genBC/lumen_inlet.flw | 201 ++++++++++++++++ .../mesh-complete/mesh-complete.exterior.vtp | 3 + .../mesh-complete/mesh-complete.mesh.vtu | 3 + .../mesh-surfaces/lumen_inlet.vtp | 3 + .../mesh-surfaces/lumen_outlet.vtp | 3 + .../mesh-surfaces/lumen_wall.vtp | 3 + .../mesh-complete/walls_combined.vtp | 3 + .../fluid/pipe_RCR_genBC/result_020_cpp.vtu | 3 + tests/cases/fluid/pipe_RCR_genBC/svFSI.xml | 101 ++++++++ .../pipe_RCR_genBC/validate_analytical.ipynb | 170 ++++++++++++++ tests/cases/fluid/pipe_RCR_sv0D/README.md | 124 ++++++++++ .../fluid/pipe_RCR_sv0D/result_020_cpp.vtu | 3 + tests/cases/fluid/pipe_RCR_sv0D/svFSI.xml | 2 +- .../pipe_RCR_sv0D/validate_analytical.ipynb | 170 ++++++++++++++ .../LV_NeoHookean_passive_sv0D/README.md | 110 +++++++++ .../LV_NeoHookean_passive_sv0D/svFSI.xml | 2 +- .../LV_NeoHookean_passive_sv0D/README.md | 110 +++++++++ .../mesh/mesh-complete.exterior.vtp | 3 + .../mesh/mesh-complete.mesh.vtu | 3 + .../mesh/mesh-surfaces/endo.vtp | 3 + .../mesh/mesh-surfaces/epi.vtp | 3 + .../mesh/mesh-surfaces/top.vtp | 3 + .../result_003_cpp.vtu | 3 + .../LV_NeoHookean_passive_sv0D/svFSI.xml | 102 ++++++++ .../svZeroD_interface.dat | 3 + .../svzerod_3Dcoupling.json | 46 ++++ 32 files changed, 1877 insertions(+), 2 deletions(-) create mode 100755 tests/cases/fluid/pipe_RCR_genBC/README.md create mode 100755 tests/cases/fluid/pipe_RCR_genBC/genBC/Makefile create mode 100755 tests/cases/fluid/pipe_RCR_genBC/genBC/Makefile.in create mode 100755 tests/cases/fluid/pipe_RCR_genBC/genBC/src/GenBC.f create mode 100755 tests/cases/fluid/pipe_RCR_genBC/genBC/src/Modules.f create mode 100755 tests/cases/fluid/pipe_RCR_genBC/genBC/src/USER.f create mode 100644 tests/cases/fluid/pipe_RCR_genBC/lumen_inlet.flw create mode 100644 tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-complete.exterior.vtp create mode 100644 tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-complete.mesh.vtu create mode 100644 tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_inlet.vtp create mode 100644 tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_outlet.vtp create mode 100644 tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_wall.vtp create mode 100644 tests/cases/fluid/pipe_RCR_genBC/mesh-complete/walls_combined.vtp create mode 100644 tests/cases/fluid/pipe_RCR_genBC/result_020_cpp.vtu create mode 100644 tests/cases/fluid/pipe_RCR_genBC/svFSI.xml create mode 100644 tests/cases/fluid/pipe_RCR_genBC/validate_analytical.ipynb create mode 100755 tests/cases/fluid/pipe_RCR_sv0D/README.md create mode 100644 tests/cases/fluid/pipe_RCR_sv0D/result_020_cpp.vtu create mode 100644 tests/cases/fluid/pipe_RCR_sv0D/validate_analytical.ipynb create mode 100644 tests/cases/struct/LV_NeoHookean_passive_sv0D/README.md create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/README.md create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-complete.exterior.vtp create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-complete.mesh.vtu create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/endo.vtp create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/epi.vtp create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/top.vtp create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/result_003_cpp.vtu create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svFSI.xml create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svZeroD_interface.dat create mode 100644 tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svzerod_3Dcoupling.json diff --git a/tests/cases/fluid/pipe_RCR_genBC/README.md b/tests/cases/fluid/pipe_RCR_genBC/README.md new file mode 100755 index 00000000..0368cc1f --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/README.md @@ -0,0 +1,88 @@ + +# **Problem Description** + +Solve the same problem as in [fluid/pipe_RCR_3d](../pipe_RCR_3D). Instead of using the RCR within the `svFSIplus` solver, this example demonstrates how to set up RCR boundary condition in a more generalized framework using sv0DSolver. + +## Introduction + +Both sv0DSolver and genBC (see [pipe_RCR_genBC](../pipe_RCR_genBC)) provide a framework to programmatically define custom inflow and outflow boundary conditions for a CFD simulation. The framework allows users to create an arbitrary lumped parameter network (LPN, or 0D model) layout suitable for their application. Some common examples include a lumped parameter heart model that models contraction of the heart chambers to use as an inlet boundary condition, sophisticated models of the downstream circulation for various areas of the body such as the legs and upper body, or a closed-loop formulation where all outflow of the SimVascular model returns back to the inflow after passing through the veins, heart, and pulmonary arteries. + +**Essentially, sv0D and genBC are two different implementations of the same functionality, and sv0D is the preferred choice.** genBC is a legacy code developed for [svSolver](https://github.com/SimVascular/svSolver) and requires direct input of a system of equations. sv0DSolver has pre-defined blocks with associated equations and is more user-friendly. Still, `svFSIplus` provides backward compatibility for genBC so that svSolver users can migrate to the new solver easily. + +## Configuration of genBC + +There are excellent tutorials online that show the users how to set up genBC step-by-step. +SimVascular website: https://simvascular.github.io/docsGenBC.html +Youtube tutorial: https://www.youtube.com/watch?v=znfV0XLV79s&ab_channel=SimVascular + +**We strongly encourage users to go through these tutorials first to become familiar with the concept and the workflow.** + +The input file [svFSI_genBC.inp](./svFSI_genBC.inp) follows the master input file as a template. Some specific input options are discussed below: + +``` + + genBC/genBC.exe + +``` + +This tells the solver that the 0d models will be calculated through genBC. Options to couple 0D codes with svFSI are `N`: none; `I`: implicit; `SI`: semi-implicit; `E`: explicit. + +``` + + Dir + Unsteady + lumen_inlet.flw + true + true + + + + Neu + Coupled + +``` + +In this example, we use the LPN for just the outlet RCR boundary condition and use a file to specify the inlet flow conditions. + +### Matching faces between 3D and 0D + +In genBC, user has to provide face tags in the USER.f file and make sure that these tags match the ones in solver.inp file for svSolver. In essence, when using genBC, it is the user's responsibility to match both the bcs provided in the svPresolver and svSolver files, and also in the USER.f file. + +### Implementation of 0D model + +The 0D model or LPN is defined in USER.f. Here are the boundary conditions implemented in genBC: + +```fortran +! BC + Rp = 121D0 + C = 1.5D-4 + Rd = 1212D0 + + f(1) = (1D0/C) * (Q(1) - x(1)/Rd) + offset(1) = Q(1)*Rp + +! Assign the additional parameters to be printed + Xprint(1)=t + Xprint(2)= Q(1) + Xprint(3) = offset(1) + +``` + +Here, we specify the RCR boundary used at the outlet, with the corresponding formulation defined in `f(1)`. + +In genBC, `Q(:)` and `P(:)` are defined as flow rates of Neumann faces and pressure of Dirichlet faces, respectively. It is the user's responsibility to carefully match the component `Q(i)` to the corresponding face, which can be error-prone when the number of faces are large. On the other hand, in cplBC, `Q(i)` simply represents the flow rate on the ith face. + + +### Outputs from 0D model + +genBC writes out the results in `AllData` in the current folder, and it includes all the unknowns and the user-specified outputs. In this case, the order will be + +``` +outlet_pressure time outlet_flux offset(1) +``` + +Here, the last three are user-defined outputs. + +### Initial conditions + +In genBC, the initial conditions are specified in USER.f through variable `tZeroX`. Hence, user needs to recompile genBC every time it changes. diff --git a/tests/cases/fluid/pipe_RCR_genBC/genBC/Makefile b/tests/cases/fluid/pipe_RCR_genBC/genBC/Makefile new file mode 100755 index 00000000..384f82a2 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/genBC/Makefile @@ -0,0 +1,79 @@ +# +# Copyright (c) Stanford University, The Regents of the University of +# California, and others. +# +# All Rights Reserved. +# +# See Copyright-SimVascular.txt for additional details. +# +# Permission is hereby granted, free of charge, to any person obtaining +# a copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject +# to the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS +# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED +# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A +# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER +# OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +#-------------------------------------------------------------------- +# +# This is the Makefile to build GenBC. +# +#-------------------------------------------------------------------- + +# HERE COMES THE DEFINITION + +include Makefile.in + +INCLUDES = -I./include + +MYFUN = Modules.f \ + USER.f \ + GenBC.f + +GenBC_EXE = genBC.exe + +DSYM_DIR = +ifeq ($(debug),1) + ifeq ($(OS),Darwin) + DSYM_DIR = $(GenBC_EXE).dSYM + endif +endif +############################################################################# +# AND HERE ARE THE RULES + +SRC = $(patsubst %,$(SRC_DIR)/%,$(MYFUN)) +OBJ = $(patsubst %.f,$(OBJ_DIR)/%.o,$(MYFUN)) + +#.PHONY: $(TEST) +#$(TEST): $(TEST:.exe=.f) $(GenBC_EXE) +# $(FORTRAN) $< $(FFLAGS) $(INCLUDES) -o $@ + +.PHONY: $(GenBC_EXE) +$(GenBC_EXE): $(OBJ) + $(FORTRAN) $(OBJ) $(FFLAGS) -o $@ + +$(OBJ): | $(OBJ_DIR) + +$(OBJ_DIR): + mkdir -p $@ + +$(OBJ_DIR)/%.o : $(SRC_DIR)/%.f + $(FORTRAN) $(FFLAGS) $(INCLUDES) -c $< -o $@ + +clean: + rm -r -f $(OBJ_DIR) $(GenBC_EXE) $(TEST) $(DSYM_DIR) diff --git a/tests/cases/fluid/pipe_RCR_genBC/genBC/Makefile.in b/tests/cases/fluid/pipe_RCR_genBC/genBC/Makefile.in new file mode 100755 index 00000000..7a434ad0 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/genBC/Makefile.in @@ -0,0 +1,165 @@ +# +# Copyright (c) Stanford University, The Regents of the University of +# California, and others. +# +# All Rights Reserved. +# +# See Copyright-SimVascular.txt for additional details. +# +# Permission is hereby granted, free of charge, to any person obtaining +# a copy of this software and associated documentation files (the +# "Software"), to deal in the Software without restriction, including +# without limitation the rights to use, copy, modify, merge, publish, +# distribute, sublicense, and/or sell copies of the Software, and to +# permit persons to whom the Software is furnished to do so, subject +# to the following conditions: +# +# The above copyright notice and this permission notice shall be included +# in all copies or substantial portions of the Software. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS +# IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED +# TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A +# PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER +# OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, +# EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +# PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR +# PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +# LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING +# NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS +# SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. +# +#-------------------------------------------------------------------- +# +# This is the definitions for building process. +# +#-------------------------------------------------------------------- + + +.SUFFIXES: .f .o + +OS:= $(shell uname) + +AR = ar rv +RANLIB = ar -ts + +CFLAGS = -O3 -DAdd_ +CXXFLAGS = -O3 -std=c++11 +FFLAGS = -O3 -cpp +FCFLAGS = -lstdc++ -cpp +#FCFLAGS = -lc++ -cpp ## This may work if lstdc++ is not found +OBJ_DIR = obj +SRC_DIR = src + +MAKE_GUI = 0 + +debug = 0 +ifeq ($(debug),1) + CFLAGS = -O0 -DAdd_ + CXXFLAGS = -O0 -std=c++11 + FFLAGS = -O0 -cpp +endif + +seq = 1 +ifeq ($(seq),1) + CC = gcc + CXX = g++ + FORTRAN = gfortran + CFLAGS += -DSEQ +else + CC = mpicc + CXX = mpicxx + FORTRAN = mpif90 +endif + +# ---------------------------------------------------------------- +# Normally you would not need to change any line beyond this point +# ---------------------------------------------------------------- + +# Here I am finding the compiler group +ifeq ($(seq),1) + F_COMP = $(FORTRAN) +else + F_COMP = $(firstword $(shell $(FORTRAN) -show)) +endif +ifeq ($(F_COMP),gfortran) + COMP_GRP = gnu +endif +ifeq ($(F_COMP),gcc) + COMP_GRP = gnu +endif +ifeq ($(F_COMP),g77) + COMP_GRP = gnu +endif +ifeq ($(F_COMP),f95) + COMP_GRP = gnu +endif +ifeq ($(F_COMP),ifort) + COMP_GRP = intel +endif +ifeq ($(F_COMP),pgf90) + COMP_GRP = pgi +endif +ifeq ($(F_COMP),pgf77) + COMP_GRP = pgi +endif + +ifeq ($(OS),Darwin) + COMP_GRP = gnu +endif + +# If profiling is requested +prof = 0 +ifeq ($(prof),1) + FFLAGS += -pg -g +endif + +# If debuging is requested +ifeq ($(debug),1) + ifeq ($(COMP_GRP),gnu) + FFLAGS += -g -Wall -Wconversion -Wline-truncation -pedantic -fimplicit-none -fbacktrace -fbounds-check -p -fcheck=all #-ffpe-trap=invalid,zero,overflow,underflow + CXXFLAGS += -g -Wall -pedantic -fbounds-check + CFLAGS += -g -Wall -pedantic -fbounds-check + endif + ifeq ($(COMP_GRP),intel) + FFLAGS += -g -traceback -check all -check bounds -check uninit -ftrapuv -debug all -fpe0 + CFLAGS += -g -traceback -check-uninit -fpe0 + CXXFLAGS += -g -traceback -check-uninit -fpe0 + endif + ifeq ($(COMP_GRP),pgi) + FFLAGS += # You need to add debuging flag for pgi compiler here + endif + OBJ_DIR = dbg +endif + +# If openMP parallelization is requested +mp = 0 +ifeq ($(mp),1) + ifeq ($(COMP_GRP),gnu) + FFLAGS += -fopenmp + endif + ifeq ($(COMP_GRP),intel) + FFLAGS += -openmp + endif + ifeq ($(COMP_GRP),pgi) + FFLAGS += -mp + endif +endif + +# To make directories a bit cleaner with intel compiler +ifeq ($(COMP_GRP),intel) + FFLAGS += -module $(OBJ_DIR) +endif +ifeq ($(COMP_GRP),gnu) + FFLAGS += -J $(OBJ_DIR) + CFLAGS += -J $(OBJ_DIR) + CXXFLAGS += -J $(OBJ_DIR) +endif + +#LAPACK Library +ifeq ($(COMP_GRP),gnu) + LAPACK_INC = + LAPACK_LIB = + LAPACK_LFLAGS = -llapack +endif + diff --git a/tests/cases/fluid/pipe_RCR_genBC/genBC/src/GenBC.f b/tests/cases/fluid/pipe_RCR_genBC/genBC/src/GenBC.f new file mode 100755 index 00000000..e827ff0f --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/genBC/src/GenBC.f @@ -0,0 +1,220 @@ +c Created by Mahdi Esmaily Moghadam 05-25-2011 +c Please report any problem to mesmaily@ucsd.edu, memt63@yahoo.com + +c Here data are received from Phasta and it setup the data required for +c integration of ODE's inside FINDX + PROGRAM GenBC + USE COM + USE, INTRINSIC :: IEEE_ARITHMETIC + IMPLICIT NONE + + INTEGER i, n, nTimeStep + REAL(KIND=8) t, dt, tFinal, pMin, temp + CHARACTER(LEN=2048) string + CHARACTER(LEN=32) sTmp + CHARACTER flag + + REAL(KIND=8), ALLOCATABLE :: Qi(:), Qf(:), Pi(:), Pf(:), X(:), + 2 Xo(:), f(:,:) + +c flag = I : Initializing +c flag = T : Iteration loop +c flag = L : Last iteration +c flag = D : Derivative +c +c Here is the period of time that you should integrate for +c each of these flags: +c +c Flags I D&T&L +c ^ ^ +c 3D code time step: N.............................................N+1 +c 0D code time step: 1......................................nTimeStep+1 +c Flowrates: Qi............................ ................Qf +c Time, t: tInitial..............................tInitial+tFinal + + + CALL INITIALIZE(nTimeStep) + +c******************************************************************** +c Block for reading the data from phasta + OPEN (1, FILE='GenBC.int', STATUS='OLD', FORM='UNFORMATTED'); + READ (1) flag + READ (1) tFinal + READ (1) i + IF (i .NE. nDirichletSrfs) THEN + PRINT *, 'Error: Number of Dirichlet Surfaces from Phasta is:', + 2 i + PRINT *, 'While nDirichletSrfs is equal to:', nDirichletSrfs + PRINT * + PRINT *, 'Number of Dirichlet surfaces defined in solver.inp', + 2 ' should match with nDirichletSrfs defined in USER.f' + STOP + END IF + READ (1) i + IF (i .NE. nNeumannSrfs) THEN + PRINT *, 'Error: Number of Neumann Surfaces from Phasta is:', + 2 i + PRINT *, 'While nNeumannSrfs is equal to:', nNeumannSrfs + PRINT * + PRINT *, 'Number of Neumann surfaces defined in solver.inp', + 2 ' should match with nNeumannSrfs defined in USER.f' + STOP + END IF + IF (nDirichletSrfs .GT. 0) THEN + IF (qCorr .OR. pCorr) THEN + PRINT *, 'You should only use P/Q correction when all', + 2 ' the surfaces are Neumann surfaces' + STOP + END IF + END IF + + ALLOCATE (Pi(nDirichletSrfs), Pf(nDirichletSrfs), + 2 PDirichlet(nDirichletSrfs,4)) + ALLOCATE (Qi(nNeumannSrfs), Qf(nNeumannSrfs), + 2 QNeumann(nNeumannSrfs,4)) + + DO i=1, nDirichletSrfs + READ(1) Pi(i) + READ(1) Pf(i) + END DO + Pi = Pi/pConv + Pf = Pf/pConv + + DO i=1,nNeumannSrfs + READ (1) Qi(i) + READ (1) Qf(i) + END DO + CLOSE(1) + Qi = Qi/qConv + Qf = Qf/qConv + +c******************************************************************** +c Block for initializing the unknowns + + IF (flag .EQ. 'I') THEN + nTimeStep = 0 + ELSE + dt = tFinal/REAL(nTimeStep,8) + END IF + ALLOCATE (X(nUnknowns), Xo(nUnknowns), f(nUnknowns,4), + 2 offset(nUnknowns), Xprint(nXprint)) + Xprint = 0D0 + + OPEN (1, FILE='InitialData', STATUS='OLD', FORM='UNFORMATTED') + READ (1) t + DO i=1,nUnknowns + READ (1) Xo(i) + END DO + CLOSE (1) + +c******************************************************************** +c Setting up the system of equations + offset = 0D0 + DO n=1, nTimeStep + DO i=1, 4 + temp = (REAL(n-1,8) + REAL(i-1,8)/3D0)/REAL(nTimeStep,8) + + QNeumann(:,i) = Qi + (Qf-Qi)*temp + PDirichlet(:,i) = Pi + (Pf-Pi)*temp + + IF (qCorr) THEN + temp = SUM(QNeumann(:,i))/REAL(nNeumannSrfs,8) + QNeumann(:,i) = QNeumann(:,i) - temp + END IF + END DO + + X = Xo + CALL FINDF (t, X, f(:,1), QNeumann(:,1), + 2 PDirichlet(:,1)) + X = Xo + dt*f(:,1)/3D0 + + CALL FINDF (t+dt/3D0, X, f(:,2), QNeumann(:,2), + 2 PDirichlet(:,2)) + X = Xo - dt*f(:,1)/3D0 + dt*f(:,2) + + CALL FINDF (t+dt*2D0/3D0, X, f(:,3), QNeumann(:,3), + 2 PDirichlet(:,3)) + X = Xo + dt*f(:,1) - dt*f(:,2) + dt*f(:,3) + + CALL FINDF (t+dt, X, f(:,4), QNeumann(:,4), + 2 PDirichlet(:,4)) + + f(:,1) = (f(:,1) + 3D0*f(:,2) + 3D0*f(:,3) + f(:,4))/8D0 + Xo = Xo + dt*f(:,1) + t = t + dt + END DO + +c******************************************************************** +c Time to write the results + X = Xo + IF (pCorr .AND. flag.NE.'D') THEN + pMin = X(srfToXPtr(1)) + DO i=2, nNeumannSrfs + IF (X(srfToXPtr(i)) .LT. pMin) THEN + pMin = X(srfToXPtr(i)) + END IF + END DO + ELSE + pMin = 0D0 + END IF + +c Writing nDirichlet flowrates here + OPEN (1, FILE='GenBC.int', STATUS='OLD', FORM='UNFORMATTED') + DO i=1, nDirichletSrfs + IF(IEEE_IS_NAN(X(srfToXdPtr(i)))) THEN + PRINT*, 'Error! NAN encountered..' + STOP + END IF + WRITE (1) X(srfToXdPtr(i))*qConv + END DO + +c Writing nNeumannSrfs pressures here + DO i=1, nNeumannSrfs + IF(IEEE_IS_NAN(X(srfToXPtr(i)))) THEN + PRINT*, 'Error! NAN encountered..' + STOP + END IF + WRITE (1) (X(srfToXPtr(i)) - pMin + 2 + offset(srfToXPtr(i)))*pConv + END DO + CLOSE(1) + + IF (flag .EQ. 'L') THEN + OPEN(1, FILE='InitialData', STATUS='OLD', FORM='UNFORMATTED'); + WRITE (1) t + DO i=1,nUnknowns + WRITE (1) X(i) + END DO + CLOSE(1) + +c PRINT *,'Before AllData' + + OPEN(1, FILE='AllData', STATUS='UNKNOWN', ACCESS='APPEND'); + string = '' + DO i=1, nUnknowns + WRITE (sTmp,"(ES14.6E2)") X(i) + string = TRIM(string)//sTmp + END DO + DO i=1, nXprint + WRITE (sTmp,"(ES14.6E2)") Xprint(i) + string = TRIM(string)//sTmp + END DO + WRITE (1,"(A)") TRIM(string) + CLOSE(1) + END IF + + DEALLOCATE (Pi) + DEALLOCATE (Pf) + DEALLOCATE (PDirichlet) + DEALLOCATE (Qi) + DEALLOCATE (Qf) + DEALLOCATE (QNeumann) + DEALLOCATE (X) + DEALLOCATE (Xo) + DEALLOCATE (f) + DEALLOCATE (srfToXdPtr) + DEALLOCATE (srfToXPtr) + DEALLOCATE (offset) + DEALLOCATE (Xprint) + + END PROGRAM GenBC diff --git a/tests/cases/fluid/pipe_RCR_genBC/genBC/src/Modules.f b/tests/cases/fluid/pipe_RCR_genBC/genBC/src/Modules.f new file mode 100755 index 00000000..6e472f3b --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/genBC/src/Modules.f @@ -0,0 +1,17 @@ +c Created by Mahdi Esmaily Moghadam 05-25-2011 +c Please report any problem to mesmaily@ucsd.edu, memt63@yahoo.com + + MODULE COM + + LOGICAL pCorr, qCorr + + INTEGER nUnknowns, nDirichletSrfs, nNeumannSrfs, nXprint + + REAL(KIND=8) pConv, qConv + + INTEGER, ALLOCATABLE :: srfToXPtr(:), srfToXdPtr(:) + + REAL(KIND=8), ALLOCATABLE :: QNeumann(:,:), PDirichlet(:,:), + 2 offset(:), Xprint(:) + + END MODULE COM diff --git a/tests/cases/fluid/pipe_RCR_genBC/genBC/src/USER.f b/tests/cases/fluid/pipe_RCR_genBC/genBC/src/USER.f new file mode 100755 index 00000000..91b4015a --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/genBC/src/USER.f @@ -0,0 +1,127 @@ +c MUSC 2 Immediate postop +c Ethan Kung keo@ucsd.edu + +c Created by Mahdi Esmaily Moghadam 12-01-2010 +c Please report any problem to mesmaily@ucsd.edu, memt63@yahoo.com + +c This subroutine initializes the parameters, you need to read the +c comments and specify them carefully +c-------------------------------------------------------------------- +c This is an example for RCR boundary condition with parameters +c Rd, R, and C which are distal, and proximal resistance and capacitor. + + SUBROUTINE INITIALIZE(nTimeStep) + USE COM + IMPLICIT NONE + INTENT(OUT) nTimeStep + + LOGICAl ierr + INTEGER i, nTimeStep + REAL(KIND=8), ALLOCATABLE :: tZeroX(:) +c +c******************************************************************** +c For instance if pressure in 3D solver is in cgs and here mmHg +c pConv=1334=1.334D3, also if flowrate in 3D solver is mm^3/s and +c here is mL/s qConv=1000=1D3. In the case both solver are using same +c unites you can set these two conversion coefficients to 1D0 + pConv = 1D0 + qConv = 1D0 + +c Only when all the surfaces of you model are coupled with NeumannSrfs +c you may set this to .TRUE. + pCorr = .FALSE. + qCorr = .FALSE. + +c******************************************************************** +c Block for your inputs + +c These two value should match to that of defined in solver.inp + nDirichletSrfs = 0 + nNeumannSrfs = 1 +c Number of unknowns that you need inside your lumped parameter network + nUnknowns = 1 +c Number of time step between N and N+alpha, for higher values this code +c would be more accurate and more costly + nTimeStep = 1000 + +c Number of parameters to be printed in AllData file (the first +c nUnknowns columns are the "X" values) + nXprint = 3 + +c-------------------------------------------------------------------- +c You don't need to change this part ! + ALLOCATE (tZeroX(nUnknowns), srfToXdPtr(nDirichletSrfs)) ! + ALLOCATE (srfToXPtr(nNeumannSrfs)) ! + tZeroX = 0D0 ! +c-------------------------------------------------------------------- + +c Value of your unknown at time equal to zero (This is going to be used +c ONLY when you are initiating simulation) + +C INITIALIZE INLET FLOW AND RCR CAPACITOR PRESSURE + tZeroX(1) = 0D0 + +c-------------------------------------------------------------------- +c You don't need to change this part ! + INQUIRE (FILE='InitialData', EXIST=ierr) ! + IF (.NOT.ierr) THEN ! + PRINT *, 'Initializing unknowns in LPM' ! + OPEN (1, FILE='InitialData',STATUS='NEW',FORM='UNFORMATTED')! + WRITE (1) 0D0 ! + DO i=1, nUnknowns ! + WRITE (1) tZeroX(i) ! + END DO ! + CLOSE(1) ! + END IF ! +c-------------------------------------------------------------------- + +c Surface to X pointer: this defines which Unknown corresponds to which +c suface in "List of Neumann Surfaces" inside solver.inp +c For example, if you have "List of Neumann Surfaces= 2 8 4 ...." +c and you set "srfToXPtr = (/5,3,9,.../)" +C this means X(5) corresponds to surface 2, X(3) <-> surface 8, +c and X(9) <-> surface 4 +c srfToXPtr = (/7/) +c This is exactly the same for srfToXdPtr only srfToXdPtr is for Dirichlet +c surfaces +c srfToXdPtr = (/1/) + srfToXPtr = (/1/) + + END SUBROUTINE INITIALIZE + +c#################################################################### +c Here you should find the f_i=dx_i/dt, based on the following parameters: +c Current x_i: x(i) +c Current time: t +c Flowrates of Neumann faces: Q(i) +c Pressure of Dirichlet faces: P(i) + + SUBROUTINE FINDF(t, x, f, Q, P) + USE COM + IMPLICIT NONE + INTENT(IN) t, Q + INTENT(OUT) f + + REAL(KIND=8) t, x(nUnknowns), f(nUnknowns), Q(nNeumannSrfs), + 2 P(nDirichletSrfs), pi + +c RCR parameters + REAL(8) Rp, C, Rd + + pi = ATAN(1D0)*4D0 + + Rp = 121D0 + C = 1.5D-4 + Rd = 1212D0 + + f(1) = (1D0/C) * (Q(1) - x(1)/Rd) + offset(1) = Q(1)*Rp + + Xprint(1)=t + Xprint(2)= Q(1) + Xprint(3) = offset(1) + + RETURN + END SUBROUTINE FINDF + + diff --git a/tests/cases/fluid/pipe_RCR_genBC/lumen_inlet.flw b/tests/cases/fluid/pipe_RCR_genBC/lumen_inlet.flw new file mode 100644 index 00000000..56c6afee --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/lumen_inlet.flw @@ -0,0 +1,201 @@ +200 16 +0.005 -0.03100373 +0.01 -0.1239843 +0.015 -0.27885 +0.02 -0.4954479 +0.025 -0.7735644 +0.03 -1.112925 +0.035 -1.513194 +0.04 -1.973978 +0.045 -2.494821 +0.05 -3.07521 +0.055 -3.714571 +0.06 -4.412274 +0.065 -5.167629 +0.07 -5.979893 +0.075 -6.848262 +0.08 -7.771881 +0.085 -8.749836 +0.09 -9.781165 +0.095 -10.86485 +0.1 -11.99982 +0.105 -13.18495 +0.11 -14.41908 +0.115 -15.70098 +0.12 -17.0294 +0.125 -18.40302 +0.13 -19.82049 +0.135 -21.2804 +0.14 -22.78132 +0.145 -24.32177 +0.15 -25.90022 +0.155 -27.51511 +0.16 -29.16486 +0.165 -30.84784 +0.17 -32.56238 +0.175 -34.30679 +0.18 -36.07935 +0.185 -37.87832 +0.19 -39.70191 +0.195 -41.54832 +0.2 -43.41574 +0.205 -45.30232 +0.21 -47.20621 +0.215 -49.12551 +0.22 -51.05834 +0.225 -53.00279 +0.23 -54.95693 +0.235 -56.91885 +0.24 -58.88661 +0.245 -60.85826 +0.25 -62.83185 +0.255 -64.80545 +0.26 -66.7771 +0.265 -68.74485 +0.27 -70.70677 +0.275 -72.66092 +0.28 -74.60537 +0.285 -76.5382 +0.29 -78.4575 +0.295 -80.36138 +0.3 -82.24796 +0.305 -84.11538 +0.31 -85.9618 +0.315 -87.78539 +0.32 -89.58435 +0.325 -91.35692 +0.33 -93.10133 +0.335 -94.81587 +0.34 -96.49884 +0.345 -98.14859 +0.35 -99.76349 +0.355 -101.3419 +0.36 -102.8824 +0.365 -104.3833 +0.37 -105.8432 +0.375 -107.2607 +0.38 -108.6343 +0.385 -109.9627 +0.39 -111.2446 +0.395 -112.4788 +0.4 -113.6639 +0.405 -114.7989 +0.41 -115.8825 +0.415 -116.9139 +0.42 -117.8918 +0.425 -118.8154 +0.43 -119.6838 +0.435 -120.4961 +0.44 -121.2514 +0.445 -121.9491 +0.45 -122.5885 +0.455 -123.1689 +0.46 -123.6897 +0.465 -124.1505 +0.47 -124.5508 +0.475 -124.8901 +0.48 -125.1683 +0.485 -125.3849 +0.49 -125.5397 +0.495 -125.6327 +0.5 -125.6637 +0.505 -125.6327 +0.51 -125.5397 +0.515 -125.3849 +0.52 -125.1683 +0.525 -124.8901 +0.53 -124.5508 +0.535 -124.1505 +0.54 -123.6897 +0.545 -123.1689 +0.55 -122.5885 +0.555 -121.9491 +0.56 -121.2514 +0.565 -120.4961 +0.57 -119.6838 +0.575 -118.8154 +0.58 -117.8918 +0.585 -116.9139 +0.59 -115.8825 +0.595 -114.7989 +0.6 -113.6639 +0.605 -112.4788 +0.61 -111.2446 +0.615 -109.9627 +0.62 -108.6343 +0.625 -107.2607 +0.63 -105.8432 +0.635 -104.3833 +0.64 -102.8824 +0.645 -101.3419 +0.65 -99.76349 +0.655 -98.14859 +0.66 -96.49884 +0.665 -94.81587 +0.67 -93.10133 +0.675 -91.35692 +0.68 -89.58435 +0.685 -87.78539 +0.69 -85.9618 +0.695 -84.11538 +0.7 -82.24796 +0.705 -80.36138 +0.71 -78.4575 +0.715 -76.5382 +0.72 -74.60537 +0.725 -72.66092 +0.73 -70.70677 +0.735 -68.74485 +0.74 -66.7771 +0.745 -64.80545 +0.75 -62.83185 +0.755 -60.85826 +0.76 -58.88661 +0.765 -56.91885 +0.77 -54.95693 +0.775 -53.00279 +0.78 -51.05834 +0.785 -49.12551 +0.79 -47.20621 +0.795 -45.30232 +0.8 -43.41574 +0.805 -41.54832 +0.81 -39.70191 +0.815 -37.87832 +0.82 -36.07935 +0.825 -34.30679 +0.83 -32.56238 +0.835 -30.84784 +0.84 -29.16486 +0.845 -27.51511 +0.85 -25.90022 +0.855 -24.32177 +0.86 -22.78132 +0.865 -21.2804 +0.87 -19.82049 +0.875 -18.40302 +0.88 -17.0294 +0.885 -15.70098 +0.89 -14.41908 +0.895 -13.18495 +0.9 -11.99982 +0.905 -10.86485 +0.91 -9.781165 +0.915 -8.749836 +0.92 -7.771881 +0.925 -6.848262 +0.93 -5.979893 +0.935 -5.167629 +0.94 -4.412274 +0.945 -3.714571 +0.95 -3.07521 +0.955 -2.494821 +0.96 -1.973978 +0.965 -1.513194 +0.97 -1.112925 +0.975 -0.7735644 +0.98 -0.4954479 +0.985 -0.27885 +0.99 -0.1239843 +0.995 -0.03100373 +1 7.088226e-13 diff --git a/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-complete.exterior.vtp b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-complete.exterior.vtp new file mode 100644 index 00000000..d1e55ad2 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-complete.exterior.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:62af67ef55bf425674003e698384f9a2cae6a38dd9b9159e07b3e124e61dba02 +size 36271 diff --git a/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-complete.mesh.vtu b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-complete.mesh.vtu new file mode 100644 index 00000000..400f595b --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-complete.mesh.vtu @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a455921750f4dc008ff4eeea3dc549454292257948b50a5d6f9ecdce5a7115f2 +size 157916 diff --git a/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_inlet.vtp b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_inlet.vtp new file mode 100644 index 00000000..15cd8c42 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_inlet.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ce627bf216bdbb7aad81ba23192fec152c455e6134df7fe1c743b8f8e5dd29b6 +size 4048 diff --git a/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_outlet.vtp b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_outlet.vtp new file mode 100644 index 00000000..66d2972d --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_outlet.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6ac77d6055555998de8a4f4df48278a4305c226ab1907c18e9ae4b621f4942c2 +size 4118 diff --git a/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_wall.vtp b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_wall.vtp new file mode 100644 index 00000000..d96cec6b --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/mesh-surfaces/lumen_wall.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6cd13eaebf02b92053476f7b6f8dc51e62d524552b56ed72a280bd3fd0c1b375 +size 34477 diff --git a/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/walls_combined.vtp b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/walls_combined.vtp new file mode 100644 index 00000000..d96cec6b --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/mesh-complete/walls_combined.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6cd13eaebf02b92053476f7b6f8dc51e62d524552b56ed72a280bd3fd0c1b375 +size 34477 diff --git a/tests/cases/fluid/pipe_RCR_genBC/result_020_cpp.vtu b/tests/cases/fluid/pipe_RCR_genBC/result_020_cpp.vtu new file mode 100644 index 00000000..67268245 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/result_020_cpp.vtu @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9f3623d7f248fa92e236fb8aede538594e3e21657a4aeb4f6afda77a97487360 +size 362767 diff --git a/tests/cases/fluid/pipe_RCR_genBC/svFSI.xml b/tests/cases/fluid/pipe_RCR_genBC/svFSI.xml new file mode 100644 index 00000000..41bb4955 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/svFSI.xml @@ -0,0 +1,101 @@ + + + + + + false + 3 + 1600 + 0.005 + 0.50 + STOP_SIM + + 1 + result + 10 + 1 + + 200 + 0 + + 1 + 0 + 0 + + + + + + mesh-complete/mesh-complete.mesh.vtu + + + mesh-complete/mesh-surfaces/lumen_inlet.vtp + + + + mesh-complete/mesh-surfaces/lumen_outlet.vtp + + + + mesh-complete/mesh-surfaces/lumen_wall.vtp + + + + + + 1 + 3 + 10 + 1e-3 + 0.2 + + 1.06 + + 0.04 + + + + true + true + true + true + + + + 10 + 3 + 500 + 1e-3 + 1e-3 + 1e-3 + 50 + + + + genBC/genBC.exe + + + + Dir + Unsteady + lumen_inlet.flw + true + true + + + + Neu + Coupled + + + + Dir + Steady + 0.0 + + + + + + + diff --git a/tests/cases/fluid/pipe_RCR_genBC/validate_analytical.ipynb b/tests/cases/fluid/pipe_RCR_genBC/validate_analytical.ipynb new file mode 100644 index 00000000..d37a4437 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_genBC/validate_analytical.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "gen_plus = np.loadtxt(\"AllData\") # Output file from coupling with genBC\n", + "gen_t = gen_plus[:, 1]\n", + "gen_press = (gen_plus[:, 0] + gen_plus[:, 3])\n", + "gen_flow = gen_plus[:, 2]\n", + "\n", + "sv0 = np.loadtxt(\"svZeroD_data\", skiprows=1) # Output file from coupling with sv0DSolver\n", + "sv_t = sv0[:, 0]\n", + "sv_press = sv0[:, 2]\n", + "sv_flow = sv0[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def integrate_rcr(tsteps, Q):\n", + " dt = tsteps[1]-tsteps[0]\n", + " Rp = 121.0 \n", + " Rd = 1212.0\n", + " C = 1.5e-4\n", + " Pd = 0.0\n", + " R_vessel = 0.19 # R = 8*0.04*30/(pi*2^4)\n", + "\n", + " num_tsteps_int = len(Q)\n", + " Pc_int = np.zeros(num_tsteps_int)\n", + " tsteps_int = np.zeros_like(Pc_int)\n", + " P_in_int = np.zeros_like(Pc_int)\n", + " for i in range(num_tsteps_int-1):\n", + " Pc_int[i+1] = (Pc_int[i] + (dt/C)*Q[(i+1)%len(Q)] + (dt/C/Rd)*Pd)/(1.0 + (dt/C/Rd))\n", + " tsteps_int[i+1] = dt*(i+1)\n", + " P_in_int[i+1] = Pc_int[i+1] + Rp*Q[(i+1)%len(Q)]\n", + " \n", + " return tsteps_int, P_in_int, Pc_int\n", + "\n", + "# Compare 3D with semi-analytical solution ------------------------\n", + "itime_sv, ipress_sv, ipc_sv = integrate_rcr(sv_t, sv_flow)\n", + "\n", + "itime_gen, ipress_gen, ipc_gen = integrate_rcr(gen_t, gen_flow)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2,1)\n", + "plt.subplots_adjust(hspace=0.5)\n", + "ax[0].plot(itime_gen, ipress_gen/1333.3,)\n", + "ax[0].plot(gen_t, gen_press/1333.3, '--r')\n", + "ax[0].legend(['Analytical Solution', 'genBC'])\n", + "ax[0].set_title(\"Outlet Pressure (genBC)\")\n", + "ax[0].set_ylabel(\"mmHg\")\n", + "ax[0].set_xlabel(\"seconds\")\n", + "ax[0].set_xlim((0,5))\n", + "\n", + "ax[1].plot(itime_sv, ipress_sv/1333.3,)\n", + "ax[1].plot(sv_t, sv_press/1333.3, '--y')\n", + "ax[1].legend(['Analytical Solution', 'sv0DSolver'])\n", + "ax[1].set_title(\"Outlet Pressure (sv0DSolver)\")\n", + "ax[1].set_ylabel(\"mmHg\")\n", + "ax[1].set_xlabel(\"seconds\")\n", + "ax[1].set_xlim((0,5))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semi-analytical vs svFSI+ genBC (in mmHg)\n", + "\tRoot mean squared error: 0.27432904617767667\n", + "\tMax absolute error: 0.46586876411313555\n", + "\tMin absolute error: 0.0006266755930705394\n", + "\tMedian absolute error: 0.29760993853203666\n", + "\n", + "Semi-analytical vs svFSI+ sv0DSolver (in mmHg)\n", + "\tRoot mean squared error: 0.2743154647001451\n", + "\tMax absolute error: 0.4659451038911528\n", + "\tMin absolute error: 0.0005631083721610342\n", + "\tMedian absolute error: 0.2976412189303046\n" + ] + } + ], + "source": [ + "error = np.abs((gen_press - ipress_gen))/1333.3\n", + "mean_error = np.mean(np.sqrt(error**2))\n", + "max_error = np.max(error)\n", + "min_error = np.min(error)\n", + "med_error = np.median(error)\n", + "\n", + "print(\"\\nSemi-analytical vs svFSI+ genBC (in mmHg)\")\n", + "print(\"\\tRoot mean squared error:\", mean_error)\n", + "print(\"\\tMax absolute error:\", max_error)\n", + "print(\"\\tMin absolute error:\", min_error)\n", + "print(\"\\tMedian absolute error:\", med_error)\n", + "\n", + "error = np.abs((sv_press - ipress_sv))/1333.3\n", + "mean_error = np.mean(np.sqrt(error**2))\n", + "max_error = np.max(error)\n", + "min_error = np.min(error)\n", + "med_error = np.median(error)\n", + "\n", + "print(\"\\nSemi-analytical vs svFSI+ sv0DSolver (in mmHg)\")\n", + "print(\"\\tRoot mean squared error:\", mean_error)\n", + "print(\"\\tMax absolute error:\", max_error)\n", + "print(\"\\tMin absolute error:\", min_error)\n", + "print(\"\\tMedian absolute error:\", med_error)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/cases/fluid/pipe_RCR_sv0D/README.md b/tests/cases/fluid/pipe_RCR_sv0D/README.md new file mode 100755 index 00000000..360acfc7 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_sv0D/README.md @@ -0,0 +1,124 @@ + +# **Problem Description** + +Solve the same problem as in [fluid/pipe_RCR_3d](../pipe_RCR_3D). Instead of using the RCR within the `svFSIplus` solver, this example demonstrates how to set up RCR boundary condition in a more generalized framework using sv0DSolver. + +## Introduction + +Both sv0DSolver and genBC (see [pipe_RCR_genBC](../pipe_RCR_genBC)) provide a framework to programmatically define custom inflow and outflow boundary conditions for a CFD simulation. The framework allows users to create an arbitrary lumped parameter network (LPN, or 0D model) layout suitable for their application. Some common examples include a lumped parameter heart model that models contraction of the heart chambers to use as an inlet boundary condition, sophisticated models of the downstream circulation for various areas of the body such as the legs and upper body, or a closed-loop formulation where all outflow of the SimVascular model returns back to the inflow after passing through the veins, heart, and pulmonary arteries. + +**Essentially, sv0D and genBC are two different implementations of the same functionality, and sv0D is the preferred choice.** genBC is a legacy code developed for [svSolver](https://github.com/SimVascular/svSolver) and requires direct input of a system of equations. sv0DSolver has pre-defined blocks with associated equations and is more user-friendly. Still, `svFSIplus` provides backward compatibility for genBC so that svSolver users can migrate to the new solver easily. + + +## Configuration of sv0DSolver + +The following files require user's attention: [svFSI.xml](./svFSI.xml), [svzerod_3Dcoupling.json](./svzerod_3Dcoupling.json) and [svZeroD_interface.dat](./svZeroD_interface.dat). + +### svFSI.xml + +The input file [svFSI_genBC.xml](./svFSI.xml) follows the master input file as a template. Some specific input options are discussed below: + +``` + + +``` + +This tells the solver that the 0d models will be calculated through sv0DSolver. Options to couple 0D codes with svFSI are `N`: none; `I`: implicit; `SI`: semi-implicit; `E`: explicit. + +``` + + Dir + Unsteady + lumen_inlet.flw + true + true + + + + Neu + Coupled + +``` + +In this example, we use the LPN for just the outlet RCR boundary condition and use a file to specify the inlet flow conditions. + +### svzerod_3Dcoupling.json + +This is the configuration file for sv0DSolver and contains the elements of the 0D model being coupled to the 3D simulation. + +For more information on the available parameters and elements, documentation is available here: [svZeroDSolver](https://github.com/SimVascular/svZeroDSolver) + +**The following are necessary in "simulation_parameters" for a coupled simulation:** +"coupled_simulation": true, +"steady_initial": false + +The external coupling block is what connects the 3D element to the 0D model. sv0D allows you to create a name for this element and specify its type (in this case, we are interested in **flow** out of the pipe). It is connected at the **inlet** of the block with the name **RCR**. Values of **time** (t) are set to the beginning and end of a cardiac cycle (0.0 to 1.0 s) and the corresponding **flow values** (Q) are set to 1.0, as this flow will be received from the 3D simulation. + +The RCR boundary condition block sets up the RCR element with the desired resistance and pressure values. + +``` +{ + "simulation_parameters": { + "coupled_simulation": true, + "number_of_time_pts": 100, + "output_all_cycles": true, + "steady_initial": false + }, + "boundary_conditions": [ + { + "bc_name": "RCR", + "bc_type": "RCR", + "bc_values": { + "Rp": 121.0, + "Rd": 1212.0, + "C": 1.5e-4, + "Pd": 0.0 + } + } + ], + "external_solver_coupling_blocks": [ + { + "name": "RCR_coupling", + "type": "FLOW", + "location": "inlet", + "connected_block": "RCR", + "periodic": false, + "values": { + "t": [0.0, 1.0], + "Q": [1.0, 1.0] + } + } + ], + "junctions": [], + "vessels": [] +} +``` + +### svZeroD_interface.dat + +This file sets up the interface between svFSIplus and sv0DSolver. It requires the path of the dynamic library for svZeroDSolver and the input file (svzerod_3Dcoupling.json) discussed above. + +This file also matches the external coupling blocks in the 0D model to the coupled surfaces in svFSIplus: +The first element in each line should be the name of the block from the json file and the second element should be the index of the coupled surface in svFSIplus. In this case, there is only one coupled surface with index 0. + +``` +svZeroD external coupling block names to surface IDs (where surface IDs are from *.svpre file): +RCR_coupling 0 +``` + +The next lines initialize the pressure and flow of these coupled surfaces in the 0D model: +0 indicates that the values will not be initialized, and 1 indicates that they will be initialized to the value provided afterwards. + +``` +Initialize external coupling block flows: +0 + +External coupling block initial flows (one number is provided, it is applied to all coupling blocks): +0.0 + +Initialize external coupling block pressures: +1 + +External coupling block initial pressures (one number is provided, it is applied to all coupling blocks): +0.0 +``` \ No newline at end of file diff --git a/tests/cases/fluid/pipe_RCR_sv0D/result_020_cpp.vtu b/tests/cases/fluid/pipe_RCR_sv0D/result_020_cpp.vtu new file mode 100644 index 00000000..0015d11a --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_sv0D/result_020_cpp.vtu @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:438ec3a28dd1ef8ec1caf1df2d8b5a2b1be932bfb86bfc3426133a4e9d00408b +size 362759 diff --git a/tests/cases/fluid/pipe_RCR_sv0D/svFSI.xml b/tests/cases/fluid/pipe_RCR_sv0D/svFSI.xml index fb035095..4c3fd6da 100644 --- a/tests/cases/fluid/pipe_RCR_sv0D/svFSI.xml +++ b/tests/cases/fluid/pipe_RCR_sv0D/svFSI.xml @@ -5,7 +5,7 @@ false 3 - 1600 + 2 0.005 0.50 STOP_SIM diff --git a/tests/cases/fluid/pipe_RCR_sv0D/validate_analytical.ipynb b/tests/cases/fluid/pipe_RCR_sv0D/validate_analytical.ipynb new file mode 100644 index 00000000..d37a4437 --- /dev/null +++ b/tests/cases/fluid/pipe_RCR_sv0D/validate_analytical.ipynb @@ -0,0 +1,170 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "gen_plus = np.loadtxt(\"AllData\") # Output file from coupling with genBC\n", + "gen_t = gen_plus[:, 1]\n", + "gen_press = (gen_plus[:, 0] + gen_plus[:, 3])\n", + "gen_flow = gen_plus[:, 2]\n", + "\n", + "sv0 = np.loadtxt(\"svZeroD_data\", skiprows=1) # Output file from coupling with sv0DSolver\n", + "sv_t = sv0[:, 0]\n", + "sv_press = sv0[:, 2]\n", + "sv_flow = sv0[:, 1]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "def integrate_rcr(tsteps, Q):\n", + " dt = tsteps[1]-tsteps[0]\n", + " Rp = 121.0 \n", + " Rd = 1212.0\n", + " C = 1.5e-4\n", + " Pd = 0.0\n", + " R_vessel = 0.19 # R = 8*0.04*30/(pi*2^4)\n", + "\n", + " num_tsteps_int = len(Q)\n", + " Pc_int = np.zeros(num_tsteps_int)\n", + " tsteps_int = np.zeros_like(Pc_int)\n", + " P_in_int = np.zeros_like(Pc_int)\n", + " for i in range(num_tsteps_int-1):\n", + " Pc_int[i+1] = (Pc_int[i] + (dt/C)*Q[(i+1)%len(Q)] + (dt/C/Rd)*Pd)/(1.0 + (dt/C/Rd))\n", + " tsteps_int[i+1] = dt*(i+1)\n", + " P_in_int[i+1] = Pc_int[i+1] + Rp*Q[(i+1)%len(Q)]\n", + " \n", + " return tsteps_int, P_in_int, Pc_int\n", + "\n", + "# Compare 3D with semi-analytical solution ------------------------\n", + "itime_sv, ipress_sv, ipc_sv = integrate_rcr(sv_t, sv_flow)\n", + "\n", + "itime_gen, ipress_gen, ipc_gen = integrate_rcr(gen_t, gen_flow)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": "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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(2,1)\n", + "plt.subplots_adjust(hspace=0.5)\n", + "ax[0].plot(itime_gen, ipress_gen/1333.3,)\n", + "ax[0].plot(gen_t, gen_press/1333.3, '--r')\n", + "ax[0].legend(['Analytical Solution', 'genBC'])\n", + "ax[0].set_title(\"Outlet Pressure (genBC)\")\n", + "ax[0].set_ylabel(\"mmHg\")\n", + "ax[0].set_xlabel(\"seconds\")\n", + "ax[0].set_xlim((0,5))\n", + "\n", + "ax[1].plot(itime_sv, ipress_sv/1333.3,)\n", + "ax[1].plot(sv_t, sv_press/1333.3, '--y')\n", + "ax[1].legend(['Analytical Solution', 'sv0DSolver'])\n", + "ax[1].set_title(\"Outlet Pressure (sv0DSolver)\")\n", + "ax[1].set_ylabel(\"mmHg\")\n", + "ax[1].set_xlabel(\"seconds\")\n", + "ax[1].set_xlim((0,5))\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "Semi-analytical vs svFSI+ genBC (in mmHg)\n", + "\tRoot mean squared error: 0.27432904617767667\n", + "\tMax absolute error: 0.46586876411313555\n", + "\tMin absolute error: 0.0006266755930705394\n", + "\tMedian absolute error: 0.29760993853203666\n", + "\n", + "Semi-analytical vs svFSI+ sv0DSolver (in mmHg)\n", + "\tRoot mean squared error: 0.2743154647001451\n", + "\tMax absolute error: 0.4659451038911528\n", + "\tMin absolute error: 0.0005631083721610342\n", + "\tMedian absolute error: 0.2976412189303046\n" + ] + } + ], + "source": [ + "error = np.abs((gen_press - ipress_gen))/1333.3\n", + "mean_error = np.mean(np.sqrt(error**2))\n", + "max_error = np.max(error)\n", + "min_error = np.min(error)\n", + "med_error = np.median(error)\n", + "\n", + "print(\"\\nSemi-analytical vs svFSI+ genBC (in mmHg)\")\n", + "print(\"\\tRoot mean squared error:\", mean_error)\n", + "print(\"\\tMax absolute error:\", max_error)\n", + "print(\"\\tMin absolute error:\", min_error)\n", + "print(\"\\tMedian absolute error:\", med_error)\n", + "\n", + "error = np.abs((sv_press - ipress_sv))/1333.3\n", + "mean_error = np.mean(np.sqrt(error**2))\n", + "max_error = np.max(error)\n", + "min_error = np.min(error)\n", + "med_error = np.median(error)\n", + "\n", + "print(\"\\nSemi-analytical vs svFSI+ sv0DSolver (in mmHg)\")\n", + "print(\"\\tRoot mean squared error:\", mean_error)\n", + "print(\"\\tMax absolute error:\", max_error)\n", + "print(\"\\tMin absolute error:\", min_error)\n", + "print(\"\\tMedian absolute error:\", med_error)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.18" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/tests/cases/struct/LV_NeoHookean_passive_sv0D/README.md b/tests/cases/struct/LV_NeoHookean_passive_sv0D/README.md new file mode 100644 index 00000000..22768a5d --- /dev/null +++ b/tests/cases/struct/LV_NeoHookean_passive_sv0D/README.md @@ -0,0 +1,110 @@ +This test case simulates an idealized left ventricle (LV) with a NeoHookean material model +coupled to a lumped-parameter network (LPN), implemented in sv0DSolver. The LPN consists of a large pressure source and large resistor, which together produce an approximately constant flowrate into +the LV. This inflates the LV at an approximately constant rate of change of volume. + +## Configuration of sv0DSolver + +The following files require user's attention: [svFSI.xml](./svFSI.xml), [svzerod_3Dcoupling.json](./svzerod_3Dcoupling.json) and [svZeroD_interface.dat](./svZeroD_interface.dat). + +### svFSI.xml + +The input file [svFSI_genBC.xml](./svFSI.xml) follows the master input file as a template, but with coupling to sv0DSolver specified in the options: + +``` + + +``` + +This tells the solver that the 0d models will be calculated through sv0DSolver. Options to couple 0D codes with svFSI are `N`: none; `I`: implicit; `SI`: semi-implicit; `E`: explicit. + +``` + + Dirichlet + 0.0 + + + + Neu + Coupled + true + +``` + +In this example, we use the LPN for the flow into the endocardial surface. + +### svzerod_3Dcoupling.json + +This is the configuration file for sv0DSolver and contains the elements of the 0D model being coupled to the 3D simulation. + +For more information on the available parameters and elements, documentation is available here: [svZeroDSolver](https://github.com/SimVascular/svZeroDSolver) + +**The following are necessary in "simulation_parameters" for a coupled simulation:** +"coupled_simulation": true, +"steady_initial": false + +The boundary condition "P_SOURCE" defines the pressure source, starting with a ramp from 0.0 to 1.0E7 and then a plateau. + +The large resistor is defined as a blood vessel block that receives pressure from "P_SOURCE" and has resistance 1.0E5. + +The external coupling block "LV_IN" specifies the connection between the 0D model and the coupled surface from svFSIplus. Here, we indicate that it will receive values of flow (type: FLOW) from the outlet (location: outlet) of the resistor block (connected_block: branch0_seg0). + +``` +{ + "simulation_parameters": { + "coupled_simulation": true, + "number_of_time_pts": 100, + "output_all_cycles": true, + "steady_initial": false + }, + "boundary_conditions": [ + { + "bc_name": "P_SOURCE", + "bc_type": "PRESSURE", + "bc_values": { + "P": [0.0, 1.0E7, 1.0E7], + "t": [0.0, 0.1, 1.0] + } + } + ], + "external_solver_coupling_blocks": [ + { + "name": "LV_IN", + "type": "FLOW", + "location": "outlet", + "connected_block": "branch0_seg0", + "periodic": false, + "values": { + "Q": [1.0, 1.0], + "t": [0.0, 1.0] + } + } + ], + "junctions": [], + "vessels": [ + { + "boundary_conditions": { + "inlet": "P_SOURCE" + }, + "vessel_id": 0, + "vessel_length": 10.0, + "vessel_name": "branch0_seg0", + "zero_d_element_type": "BloodVessel", + "zero_d_element_values": { + "R_poiseuille": 1.0E5 + } + } + ] +} +``` + +### svZeroD_interface.dat + +This file sets up the interface between svFSIplus and sv0DSolver. It requires the path of the dynamic library for svZeroDSolver and the input file (svzerod_3Dcoupling.json) discussed above. + +This file also matches the external coupling blocks in the 0D model to the coupled surfaces in svFSIplus: +The first element in each line should be the name of the block from the json file and the second element should be the index of the coupled surface in svFSIplus. In this case, there is only one coupled surface with index 0. + +``` +svZeroD external coupling block names to surface IDs (where surface IDs are from *.svpre file): +LV_IN 0 +``` diff --git a/tests/cases/struct/LV_NeoHookean_passive_sv0D/svFSI.xml b/tests/cases/struct/LV_NeoHookean_passive_sv0D/svFSI.xml index 94698c55..bcc2da2d 100644 --- a/tests/cases/struct/LV_NeoHookean_passive_sv0D/svFSI.xml +++ b/tests/cases/struct/LV_NeoHookean_passive_sv0D/svFSI.xml @@ -4,7 +4,7 @@ 0 3 - 20 + 3 1e-2 0.50 STOP_SIM diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/README.md b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/README.md new file mode 100644 index 00000000..22768a5d --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/README.md @@ -0,0 +1,110 @@ +This test case simulates an idealized left ventricle (LV) with a NeoHookean material model +coupled to a lumped-parameter network (LPN), implemented in sv0DSolver. The LPN consists of a large pressure source and large resistor, which together produce an approximately constant flowrate into +the LV. This inflates the LV at an approximately constant rate of change of volume. + +## Configuration of sv0DSolver + +The following files require user's attention: [svFSI.xml](./svFSI.xml), [svzerod_3Dcoupling.json](./svzerod_3Dcoupling.json) and [svZeroD_interface.dat](./svZeroD_interface.dat). + +### svFSI.xml + +The input file [svFSI_genBC.xml](./svFSI.xml) follows the master input file as a template, but with coupling to sv0DSolver specified in the options: + +``` + + +``` + +This tells the solver that the 0d models will be calculated through sv0DSolver. Options to couple 0D codes with svFSI are `N`: none; `I`: implicit; `SI`: semi-implicit; `E`: explicit. + +``` + + Dirichlet + 0.0 + + + + Neu + Coupled + true + +``` + +In this example, we use the LPN for the flow into the endocardial surface. + +### svzerod_3Dcoupling.json + +This is the configuration file for sv0DSolver and contains the elements of the 0D model being coupled to the 3D simulation. + +For more information on the available parameters and elements, documentation is available here: [svZeroDSolver](https://github.com/SimVascular/svZeroDSolver) + +**The following are necessary in "simulation_parameters" for a coupled simulation:** +"coupled_simulation": true, +"steady_initial": false + +The boundary condition "P_SOURCE" defines the pressure source, starting with a ramp from 0.0 to 1.0E7 and then a plateau. + +The large resistor is defined as a blood vessel block that receives pressure from "P_SOURCE" and has resistance 1.0E5. + +The external coupling block "LV_IN" specifies the connection between the 0D model and the coupled surface from svFSIplus. Here, we indicate that it will receive values of flow (type: FLOW) from the outlet (location: outlet) of the resistor block (connected_block: branch0_seg0). + +``` +{ + "simulation_parameters": { + "coupled_simulation": true, + "number_of_time_pts": 100, + "output_all_cycles": true, + "steady_initial": false + }, + "boundary_conditions": [ + { + "bc_name": "P_SOURCE", + "bc_type": "PRESSURE", + "bc_values": { + "P": [0.0, 1.0E7, 1.0E7], + "t": [0.0, 0.1, 1.0] + } + } + ], + "external_solver_coupling_blocks": [ + { + "name": "LV_IN", + "type": "FLOW", + "location": "outlet", + "connected_block": "branch0_seg0", + "periodic": false, + "values": { + "Q": [1.0, 1.0], + "t": [0.0, 1.0] + } + } + ], + "junctions": [], + "vessels": [ + { + "boundary_conditions": { + "inlet": "P_SOURCE" + }, + "vessel_id": 0, + "vessel_length": 10.0, + "vessel_name": "branch0_seg0", + "zero_d_element_type": "BloodVessel", + "zero_d_element_values": { + "R_poiseuille": 1.0E5 + } + } + ] +} +``` + +### svZeroD_interface.dat + +This file sets up the interface between svFSIplus and sv0DSolver. It requires the path of the dynamic library for svZeroDSolver and the input file (svzerod_3Dcoupling.json) discussed above. + +This file also matches the external coupling blocks in the 0D model to the coupled surfaces in svFSIplus: +The first element in each line should be the name of the block from the json file and the second element should be the index of the coupled surface in svFSIplus. In this case, there is only one coupled surface with index 0. + +``` +svZeroD external coupling block names to surface IDs (where surface IDs are from *.svpre file): +LV_IN 0 +``` diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-complete.exterior.vtp b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-complete.exterior.vtp new file mode 100644 index 00000000..37ab77b3 --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-complete.exterior.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:516ca5f67f51c432410c65a1f98d73baca653b0378664607a20eba3fd5e94db4 +size 19832 diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-complete.mesh.vtu b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-complete.mesh.vtu new file mode 100644 index 00000000..cf145a1d --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-complete.mesh.vtu @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9b7c72acb6c365fae7c33091f8d932c95863d95ffee7fed934a2f832aeba976d +size 27192 diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/endo.vtp b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/endo.vtp new file mode 100644 index 00000000..e284a4c6 --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/endo.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:4028808552b03a2854ce33bf5016243c6b994e62a61928625691b72938f1480d +size 10038 diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/epi.vtp b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/epi.vtp new file mode 100644 index 00000000..66272c04 --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/epi.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:f125c7001fc4a5ad90313fcff8ef24749ccd054a6b045521dfc3d06169160a09 +size 11982 diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/top.vtp b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/top.vtp new file mode 100644 index 00000000..2d90c222 --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/mesh/mesh-surfaces/top.vtp @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2050d191642b719f651de6587571a7021eff67801eddd97c0fec397f3e9b62cd +size 3940 diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/result_003_cpp.vtu b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/result_003_cpp.vtu new file mode 100644 index 00000000..658f68ea --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/result_003_cpp.vtu @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:6e8bb8523255238af803adcc3e69b30cf1f8d2d9c97d05d3a13b19c97435f35b +size 248341 diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svFSI.xml b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svFSI.xml new file mode 100644 index 00000000..74c631af --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svFSI.xml @@ -0,0 +1,102 @@ + + + + + 0 + 3 + 3 + 1e-2 + 0.50 + STOP_SIM + + 1 + result + + 1 + 1 + + 10 + 0 + + 1 + 1 + 1 + + + + + + + mesh/mesh-complete.mesh.vtu + + + mesh/mesh-surfaces/endo.vtp + + + + mesh/mesh-surfaces/epi.vtp + + + + mesh/mesh-surfaces/top.vtp + + + 100.0 + + + + + + + true + 3 + 10 + 1e-9 + + 1.0 + 1.0e5 + 0.483333 + + + ST91 + + 0.0 + + + + + true + true + true + true + true + true + true + true + true + + + + FSILS + 1e-9 + 1000 + 50 + + + + + + + Dirichlet + 0.0 + + + + Neu + Coupled + true + + + + + \ No newline at end of file diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svZeroD_interface.dat b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svZeroD_interface.dat new file mode 100644 index 00000000..04eb09ec --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svZeroD_interface.dat @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:49e710d7b657c4d2feb1309509c455aeee7b6042c89c26aa69f157d06b3cc88a +size 627 diff --git a/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svzerod_3Dcoupling.json b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svzerod_3Dcoupling.json new file mode 100644 index 00000000..3f16dc8d --- /dev/null +++ b/tests/cases/ustruct/LV_NeoHookean_passive_sv0D/svzerod_3Dcoupling.json @@ -0,0 +1,46 @@ +{ + "simulation_parameters": { + "coupled_simulation": true, + "number_of_time_pts": 100, + "output_all_cycles": true, + "steady_initial": false + }, + "boundary_conditions": [ + { + "bc_name": "P_SOURCE", + "bc_type": "PRESSURE", + "bc_values": { + "P": [0.0, 1.0E7, 1.0E7], + "t": [0.0, 0.1, 1.0] + } + } + ], + "external_solver_coupling_blocks": [ + { + "name": "LV_IN", + "type": "FLOW", + "location": "outlet", + "connected_block": "branch0_seg0", + "periodic": false, + "values": { + "Q": [1.0, 1.0], + "t": [0.0, 1.0] + } + } + ], + "junctions": [], + "vessels": [ + { + "boundary_conditions": { + "inlet": "P_SOURCE" + }, + "vessel_id": 0, + "vessel_length": 10.0, + "vessel_name": "branch0_seg0", + "zero_d_element_type": "BloodVessel", + "zero_d_element_values": { + "R_poiseuille": 1.0E5 + } + } + ] +}