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Fortran fpm Documentation License: MPL 2.0 CI codecov

There are multiple open source equations of state libraries, like:

Here we are presenting yet another (still in development) one, that tackles the same problem just, in another way. Mostly exploiting the readability and extensibility of Modern Fortran for scientists to have an easy way to implement new thermodynamic models without dealing with lower-level languages but still getting decent performance. And also this framework provides the possibility of using analytically obtained derivatives, so both options are easily available.

This is an experimental work in progress, and we recommend the before mentioned libraries if you are intending to use some of this in real work. Big part of the code comes from a refactoring process of older codes so not all parts are easily readable, yet.

We focus mainly on that the addition of a new thermodynamic model as easily as possible. Also providing our models too!

Documentation

The latest API documentation for the main branch can be found:

The Fortran API documentation can also be generated by processing the source files with FORD. On the other hand, the Python API documentation can be generated by processing the source files with Sphinx.

Developers

This library is currently maintained by the research group of Prof. Martín Cismondi-Duarte at IPQA (UNC-CONICET)

Available models

  • CubicEoS
    • SoaveRedlichKwong
    • PengRobinson76
    • PengRobinson78
  • ExcessGibbs models
    • NRTL
    • UNIFAC VLE

Available properties

  • Bulk Properties
    • Volume(n, P, T)
    • Pressure(n, V, T)
  • Residual Properties
    • H^R(n, V, T)
    • S^R(n, V, T)
    • G^R(n, V, T)
    • Cv^R(n, V, T)
    • Cp^R(n, V, T)
  • Phase-Equilibria
    • FlashPT, FlashVT
    • Saturation points (bubble, dew and liquid-liquid)
    • Phase Envelope PT (isopleths)

A little taste of yaeos

A lot of users get the bad picture of Fortran being old and archaic since most of the codes they've seen are written in ancient F77.

use yaeos, only: PengRobinson76, ArModel

integer, parameter :: n=2   ! Number of components
real(8) :: V, T, P, dPdN(n) ! variables to calculate
class(ArModel), allocatable :: model ! Model

real(pr) :: z(n), tc(n), pc(n), w(n), kij(n, n), lij(n, n)

z = [0.3, 0.7]
tc = [190., 310.]
pc = [14., 30.]
w = [0.001, 0.03]
kij = reshape([0., 0.1, 0.1, 0.], [n,n])
lij = kij / 2 

model =  PengRobinson76(tc, pc, w, kij, lij)

V = 1
T = 150

call model%pressure(z, V, T, P)
print *, P

! Obtain derivatives adding them as optional arguments! 
call model%pressure(model, z, V, T, P, dPdN=dPdN)
print *, dPdN

Examples of code with simple applications showing the capabilities of yaeos can be found at example/tutorials. Each example can be run with:

 fpm run --example <example name here>

Not providing any examples will show all the possible examples that can be run.

How to install/run it

Dependencies

yaeos needs to have both lapack and nlopt libraries on your system.

Debian/Ubuntu-like

sudo apt install libnlopt-dev libblas-dev liblapack-dev

Installing yaeos

yaeos is intended to use as a fpm package. fpm is the Fortran Package Manager, which automates the compilation and running process of Fortran libraries/programs.

You can either:

  • Generate a new project that uses yaeos as a dependency with:
fpm new my_project

In the fpm.toml file add:

[dependencies]
yaeos = {git="https://github.com/ipqa-research/yaeos"}
  • Clone this repository and just modify the executables in the app directory
git clone https://github.com/ipqa-research/yaeos
cd yaeos
fpm run

Developing with vscode

If your intention is either to develop for yaeos or to explore in more detail the library with debugging. We provide some predefined defuaults to work with vscode. You can add them to the cloned repository by running:

git clone https://github.com/ipqa-research/vscode-fortran .vscode

From the project main directory

Available examples

In this repository we provide a series of examples of the different things that can be calculated with yaeos. The source codes for the examples can be seen at the example/tutorials directory.

All the examples can be run with

fpm run --example <example_name_here>

Including new models with Automatic Differentiation.

Hyperdual Numbers autodiff

We are using the hyperdual module developed by Philipp Rehner and Gernot Bauer

The automatic differentiation API isn't fully optimized yet so performance is much slower than it should be.

A complete implementation of the PR76 Equation of State can me found in example/adiff/adiff_pr76.f90. Or in the documentation pages.

Tapenade-based autodiff

It is also possible to differentiate with tapenade. Examples can be seen in the documentation pages or in The tools directory