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Thermodynamic Equations of State, Fortran library with both automatic and anallytical derivation capabilities

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<br>

</div>
There are multiple open source equations of state libraries, like:

- [Clapeyron](https://github.com/ClapeyronThermo/Clapeyron.jl) `julia`
- [FeOs](https://github.com/feos-org/feos) `rust` with `Python` bindings
- [teqp](https://github.com/usnistgov/teqp) `C++` with `Python` bindings
- [thermo](https://github.com/CalebBell/thermo) `python`
- [thermopack](https://github.com/thermotools/thermopack) `Fortran` with `Python` bindings
- [CoolProp](https://github.com/CoolProp/CoolProp) `C++` with `Python` bindings

Here we are presenting yet another 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 and derivatives obtained with automatic differentiation.

The main focus of `yaeos` is giving robusts algorithms for calculations of
diagrams and phase-equilibria properties, with a good framework for the user 
to add new models.

## Documentation
The latest API documentation for the `main` branch can be found:
   - [Fortran API documentation](https://ipqa-research.github.io/yaeos/)
   - [Python API documentation](https://ipqa-research.github.io/yaeos/page/python-api/index.html)

The Fortran API documentation can also be generated by processing the source
files with [FORD](https://github.com/Fortran-FOSS-Programmers/ford). On the
other hand, the Python API documentation can be generated by processing the
source files with [Sphinx](https://github.com/sphinx-doc/sphinx).

## Available models

### Residual Helmholtz models
- Multiparametric Family
   - GERG2008
- CubicEoS Family
   - SoaveRedlichKwong
   - PengRobinson76
   - PengRobinson78
   - RKPR
   - PSRK
   - MixingRules:
      - QMR
      - QMRTD
      - MHV1
      - HV
- SAFT Family
   - PC-SAFT

###  ExcessGibbs models
- NRTL
- NRTL-HV
- UNIQUAC
- UNIFAC VLE
- UNIFAC Dortmund

## 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)
   - PT Phase Envelope (isopleths)
   - Px Phase Envelope (isotherms)
   - Tx Phase Envelope (isobars)

## Developers
This library is currently maintained by the research group of Prof. Martín
Cismondi-Duarte at [IPQA (UNC-CONICET)](https://ipqa.unc.edu.ar/en/)

## Versioning
We use [Semantic Versioning](https://semver.org/) to define our version numbers.

# 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`.

```fortran
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](example/tutorials). Each example can be run
with:

```bash
 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 `lapack` in your system.

### Debian/Ubuntu-like
```bash
sudo apt install libblas-dev liblapack-dev
```

## Installing `yaeos`
`yaeos` is intended to use as a [`fpm`](fpm.fortran-lang.org) 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:

> ```bash
> fpm new my_project
> ```
> 
> In the `fpm.toml` file add:
> 
> ```toml
> [dependencies]
> yaeos = {git="https://github.com/ipqa-research/yaeos"}
> ```

- Clone this repository and just modify the executables in the `app` directory

> ```bash
> 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:

```bash
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](example/tutorials) directory.

All the examples can be run with

```bash
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](https://github.com/prehner) 
and [Gernot Bauer](https://github.com/g-bauer)

A complete implementation of the PR76 Equation of State can me found in
`example/adiff/adiff_pr76.f90`. Or in the documentation pages. Which can
serve as an example of how to implement models.

## Tapenade-based autodiff
It is also possible to differentiate with `tapenade`. Examples can be seen
in the documentation pages or in [The tools directory](tools/tapenade_diff/)

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