Merge branch 'joss-submission' of https://github.com/mberto79/XCALibr…

…e.jl into joss-submission

joss-paper/paper.bib

@@ -172,4 +172,24 @@ @article{montoison-orban-2023
  pages = {5187},
  year = {2023},
  doi = {10.21105/joss.05187}
+}
+
+@article{Ellis1,
+author = {Ellis, Christopher and Xia, Hao},
+year = {2023},
+month = {10},
+title = {Data-driven turbulence anisotropy in film and effusion cooling flows},
+volume = {35},
+journal = {Physics of Fluids},
+doi = {10.1063/5.0166685}
+}
+
+@article{Ellis2,
+author = {Ellis, Christopher and Xia, Hao},
+year = {2024},
+month = {09},
+title = {LES informed data-driven models for RANS simulations of single-hole cooling flows},
+volume = {235},
+journal = {International Journal of Heat and Mass Transfer},
+doi = {10.1016/j.ijheatmasstransfer.2024.126150}
 }

joss-paper/paper.md

@@ -54,7 +54,7 @@ Understanding the behaviour of fluid flow, such as air over a wing, oil lubricat
 
 # Statement of need
 
-Given the importance of fluid flow simulation in engineering applications, it is not surprising that there is a wealth of CFD solvers available, both open-source and commercially available. Well established open-source codes include: OpenFOAM [@OpenFOAM], SU2 [@SU2], CODE_SATURN [@CODE_SATURN], Gerris [@Gerris], etc. It is a testament to the open-source philosophy, and their developers, that some of these codes offer almost feature parity with commercial codes. However, the more feature-rich open-source codes have large codebases and, for performance reasons, have been implemented in statically compiled languages which makes it difficult to adapt and incorporate recent trends in scientific computing, for example, GPU computing and interfacing with machine learning frameworks, which is also the case for commercial codes (to a larger extent due to their closed source nature where interfaces to code internals can be quite rigid – although thanks to access to more resources commercial codes have been steadily ported to work on GPUs). As a result, the research community has been actively developing new CFD codes, which is evident within the Julia ecosystem. 
+Given the importance of fluid flow simulation in engineering applications, it is not surprising that there is a wealth of CFD solvers available, both open-source and commercially available. Well established open-source codes include: OpenFOAM [@OpenFOAM], SU2 [@SU2], CODE_SATURN [@CODE_SATURN], Gerris [@Gerris], etc. It is a testament to the open-source philosophy, and their developers, that some of these codes offer almost feature parity with commercial codes. However, established open-source and commercial codes have large codebases and, for performance reasons, have been implemented in statically compiled languages which makes it difficult to adapt and incorporate recent trends in scientific computing, for example, GPU computing and interfacing with machine learning frameworks to support the development of new models [@Ellis1; @Ellis2]. As a result, the research community has been actively developing new CFD codes, which is evident within the Julia ecosystem. 
 
 The Julia programming language offers a fresh approach to scientific computing, with the benefits of dynamism whilst retaining the performance of statically typed languages thanks to its just-in-time compilation approach (using LLVM compiler technology). Thus, Julia makes it easy to prototype and test new ideas whilst producing machine code that is performant. This simplicity-performance dualism has resulted in a remarkable growth in its ecosystem offering for scientific computing, which includes state-of-the-art packages for solving differential equations e.g. `DifferentialEquations.jl` [@rackauckas2017differentialequations] , building machine learning models such as `Flux.jl` [@innes:2018], `Knet.jl` [@KNET] and `Lux.jl` [@pal2023lux], optimisation frameworks e.g. `JUMP.jl` [@Lubin2023] , automatic differentiation, such as `Enzyme.jl` [@NEURIPS2020_9332c513] , etc. Likewise, excellent CFD packages have also been developed, most notoriously: `Oceananigans.jl` [@OceananigansJOSS], which provides tools for ocean modelling, `Trixi.jl` [@schlottkelakemper2021purely] which provides high-order for solvers using the Discontinuous Garlekin method, and `Waterlilly.jl` [@WeymouthFont2024] which implements the immerse boundary method on structured grids using a staggered finite volume method. In this context, `XCALibre.jl` aims to complement and extend the Julia ecosystem by providing a cell-centred and unstructured finite volume general-purpose CFD framework for the simulation of both incompressible and weakly compressible flows. The package is intended primarily for researchers and students, as well as engineers, who are interested in CFD applications using the built-in solvers or those who seek a user-friendly framework for developing new CFD solvers or methodologies.