StochasticDiffEq.jl
Clapeyron.jl
| StochasticDiffEq.jl | Clapeyron.jl | |
|---|---|---|
| 1 | 2 | |
| 235 | 182 | |
| 0.9% | 1.7% | |
| 7.8 | 9.8 | |
| 10 days ago | 4 days ago | |
| Julia | Julia | |
| GNU General Public License v3.0 or later | MIT License |
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StochasticDiffEq.jl
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Writing unit tests in scientific computing
For stochastic processes you have to work a little bit more. However maybe the StochasticDiffEq.jl package can give some guiding there https://github.com/SciML/StochasticDiffEq.jl/tree/master/test
Clapeyron.jl
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How are Equations of State used to calculate thermophysical properties ?
There are other formulations of this, the V,T,Nᵢ is basically the Clapeyron.jl and FeoS formulation. other software that does this, teqp, uses a Vi,T instead, and uses Φ = A/V instead for their base function. (their paper says that it will replace REFPROP, and the author is one of the creators of REFPROP, so his words have weight in that regard).
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What Is a Supercritical Fluid?
i'm working on calculating thermodynamic properties from equations of state [1], and yeah, looking for criteria on how to distinguish from a low pressure, a near critical pressure and a supercritical pressure is not trivial, specially when you don't know the critical point of a fluid (the case for all molecular EoS). one property i've relying on is that the second virial coefficient minimum density is always lower than the saturated gas density.. until a threshold near criticality. a lot of assumptions about a thermodynamic model fail at near-critical points. Understanding and exploiting those properties helps on building more accurate property solvers.
[0] https://github.com/ypaul21/Clapeyron.jl
What are some alternatives?
SciMLTutorials.jl - Tutorials for doing scientific machine learning (SciML) and high-performance differential equation solving with open source software.
julia - The Julia Programming Language
DiffEqBase.jl - The lightweight Base library for shared types and functionality for defining differential equation and scientific machine learning (SciML) problems
DifferentialEquations.jl - Multi-language suite for high-performance solvers of differential equations and scientific machine learning (SciML) components. Ordinary differential equations (ODEs), stochastic differential equations (SDEs), delay differential equations (DDEs), differential-algebraic equations (DAEs), and more in Julia.
SciMLSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, adjoint methods, and more for ODEs, SDEs, DDEs, DAEs, etc.
JuMP.jl - Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)
OrdinaryDiffEq.jl - High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
feos - FeOs - A Framework for Equations of State and Classical Density Functional Theory
DiffEqSensitivity.jl - A component of the DiffEq ecosystem for enabling sensitivity analysis for scientific machine learning (SciML). Optimize-then-discretize, discretize-then-optimize, and more for ODEs, SDEs, DDEs, DAEs, etc. [Moved to: https://github.com/SciML/SciMLSensitivity.jl]
teqp - A highly efficient, flexible, and accurate implementation of thermodynamic EOS powered by automatic differentiation
DiffEqOperators.jl - Linear operators for discretizations of differential equations and scientific machine learning (SciML)