Merge branch 'master' of https://github.com/ypaul21/OpenSAFT.jl

README.md

@@ -6,6 +6,10 @@
 
 Welcome to Clapeyron! This module provides both a large library of equations of state and a framework for one to easily implement their own equations of state.
 
+We have recently presented at the JuliaCon 2021 conference! Feel free to take a look at our talk:
+
+[![Clapeyron.jl: An Extensible Implementation of Equations of State | Paul Yew et al | JuliaCon2021](https://img.youtube.com/vi/Re5qI-9zyIM/0.jpg)](https://www.youtube.com/watch?v=Re5qI-9zyIM "Clapeyron.jl: An Extensible Implementation of Equations of State | Paul Yew et al | JuliaCon2021")
+
 SAFT equations of state currently available:
 
 | EoS           | Seg./Mono.?        | Chain?             | Assoc.?            | Parameters?        |
@@ -109,9 +113,7 @@ Note that at its current stage, Clapeyron is still in the very early stages of d
 
 # Installing Clapeyron
 
-Clapeyron is not yet in the JuliaHub (but it will be soon!).
-
-To load Clapeyron, launch Julia with
+To install Clapeyron, launch Julia with
 
 ```julia
 > julia
@@ -124,7 +126,7 @@ Pkg> add Clapeyron
 ```
 Or to add the development version:
 ```julia
-Pkg> add https://github.com/ypaul21/Clapeyron.jl
+Pkg> add https://github.com/ypaul21/Clapeyron.jl#development
 ```
 Exit Pkg mode by hitting backspace.
 
@@ -134,8 +136,7 @@ Now you may begin using functions from the Clapeyron library by entering the com
 using Clapeyron
 ```
 
-To remove the package (for when we finally get it on JuliaHub and you want to use the official version),
-hit the ```]``` key to enter Pkg mode, then type
+To remove the package, hit the ```]``` key to enter Pkg mode, then type
 
 ```julia
 Pkg> rm Clapeyron

docs/src/user_guide/basic_usage.md

@@ -59,6 +59,21 @@ Multi-parameter equations:
 
 We also support the SPUNG method (`SPUNG`)
 
+### Specifying an ideal term
+
+By default, Clapeyron uses what we refer to as the `BasicIdeal` model to account for the ideal contribution. For properties which only have derivatives with respect to volume or composition (_e.g._ pressure, isothermal compressibility, critical points, saturation points), or monoatomic species (_e.g._ noble gases), this is perfectly fine. However, for any other properties or species, the results obtained will most likely be quite poor. This is because this model does not account for the rotational and vibrational modes of the species. To amend this, we provide two additional ideal models to be used instead (more soon to come):
+
+- Walker and Haslam's ideal correlation (`WalkerIdeal`)
+- Reid's polynomial correlation (`ReidIdeal`)
+
+These can be specified for any of the cubic or SAFT-type equations of state using:
+
+```julia
+model4 = PCSAFT(["carbon dioxide"]; idealmodel = WalkerIdeal)
+```
+
+Everything else will work as normal.
+
 ### Available properties
 
 Once we have our model object, we will be able to call the respective thermodynamic methods to obtain the properties that we are looking for. For example, to find the isobaric heat capacity of a 0.5 mol methanol and 0.5 mol ethanol mixture using PC-SAFT at a pressure of 10 bar and a temperature of 300 K, we just call the `isobaric_heat_capacity(model, p, T, z)` function with the desired model and conditions as parameters.

docs/src/user_guide/custom_dtb.md

@@ -37,11 +37,11 @@ Note that it is extremely important that the cell A2 has the word 'Like', 'Unlik
 
 ## Using your own parameters
 
-If you have CSV files formatted as above with your own parameters, and you want to implement these into one of the existing equations of state in Clapeyron, all that is needed is to provide the path to those files in the definition of your model:
+If you have CSV files formatted as above with your own parameters, and you want to implement these into one of the existing equations of state in Clapeyron, all that is needed is to provide the path to those files in the definition of your model (note that ideal term related parameters are specified separately):
 
 ```julia
-model1 = PR(["your_species_1","your_species_2"];userlocations=["path/to/your/database/"])
-model2 = PCSAFT(["your_species_1","your_species_2"];userlocations=["dtb_like","dtb_unlike","dtb_assoc"])
+model1 = PR(["your_species_1","your_species_2"];userlocations=["path/to/your/database/"], ideal_userlocations=["path/to/your/ideal_database"])
+model2 = PCSAFT(["your_species_1","your_species_2"];userlocations=["dtb_like","dtb_unlike","dtb_assoc"],ideal_userlocations=["dtb_ideal"])
 ```
 
 The rest works exactly as it normally would! We recommend reading the background documentation for the various models to ensure the units of the parameters you provide are correct.

src/methods/methods.jl

@@ -1,4 +1,4 @@
-molecular_weight(model::EoSModel,z = @SVector [1.]) = 0.001*mapreduce(+,*,mw(model),z)
+molecular_weight(model::EoSModel,z = @SVector [1.]) = 0.001*mapreduce(*,+,mw(model),z)
 mw(model::EoSModel) = paramvals(model.params.Mw)
 
 include("initial_guess.jl")