OpenFOAM
The OpenFOAM® (Open Field Operation and Manipulation) CFD Toolbox is a free, open source CFD software package produced by OpenCFD Ltd. It has a large user base across most areas of engineering and science, from both commercial and academic organisations. OpenFOAM has an extensive range of features to solve anything from complex fluid flows involving chemical reactions, turbulence and heat transfer, to solid dynamics and electromagnetics. It includes tools for meshing, notably snappyHexMesh, a parallelised mesher for complex CAD geometries, and for pre- and post-processing. Almost everything (including meshing, and pre- and post-processing) runs in parallel as standard, enabling users to take full advantage of computer hardware at their disposal. By being open, OpenFOAM offers users complete freedom to customise and extend its existing functionality, either by themselves or through support from OpenCFD. It follows a highly modular code design in which collections of functionality (e.g. numerical methods, meshing, physical models, …) are each compiled into their own shared library. Executable applications are then created that are simply linked to the library functionality. OpenFOAM includes over 80 solver applications that simulate specific problems in engineering mechanics and over 170 utility applications that perform pre- and post-processing tasks, e.g. meshing, data visualisation, etc.
Keywords for this software
References in zbMATH (referenced in 372 articles )
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Sorted by year (- Abbasi, S.; Pirker, S.; Lichtenegger, T.: Application of recurrence CFD (rCFD) to species transport in turbulent vortex shedding (2020)
- Aguerre, Horacio J.; Venier, César M.; Pairetti, César I.; Márquez Damián, Santiago; Nigro, Norberto M.: A SIMPLE-based algorithm with enhanced velocity corrections: the COMPLEX method (2020)
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- Cao, Yong; Tamura, Tetsuro; Kawai, Hidenori: Spanwise resolution requirements for the simulation of high-Reynolds-number flows past a square cylinder (2020)
- D’Alessandro, Valerio; Falone, Matteo; Ricci, Renato: Direct computation of aeroacoustic fields in laminar flows: solver development and assessment of wall temperature effects on radiated sound around bluff bodies (2020)
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- Fadhila, H.; Medina, H.; Aleksandrova, S.; Benjamin, S.: A new non-linear RANS model with enhanced near-wall treatment of turbulence anisotropy (2020)
- Kohlstädt, S.; Vynnycky, M.; Jäckel, J.: Towards the modelling of fluid-structure interactive lost core deformation in high-pressure die casting (2020)
- Lappa, Marcello; Boaro, Alessio: Rayleigh-Bénard convection in viscoelastic liquid bridges (2020)
- Liao, Yixiang; Upadhyay, Kartik; Schlegel, Fabian: Eulerian-Eulerian two-fluid model for laminar bubbly pipe flows: validation of the baseline model (2020)
- Meldi, Marcello; Mariotti, A.; Salvetti, M. V.; Sagaut, P.: Numerical investigation of skewed spatially evolving mixing layers (2020)
- Mohanamuraly, P.; Hascoët, L.; Müller, J.-D.: Seeding and adjoining zero-halo partitioned parallel scientific codes (2020)
- Nishikawa, Hiroaki; White, Jeffery A.: An efficient cell-centered finite-volume method with face-averaged nodal-gradients for triangular grids (2020)
- Pan, Shaowu; Duraisamy, Karthik: Physics-informed probabilistic learning of linear embeddings of nonlinear dynamics with guaranteed stability (2020)
- Rajamuni, Methma M.; Thompson, Mark C.; Hourigan, Kerry: Efficient FSI solvers for multiple-degrees-of-freedom flow-induced vibration of a rigid body (2020)
- Ramos, Douglas Jhon; Daniel, Gregory Bregion: Evaluation of bearing’s cavitation effects on the rotor dynamic behavior (2020)
- Rutkowski, Mariusz; Gryglas, Wojciech; Szumbarski, Jacek; Leonardi, Christopher; Łaniewski-Wołłk, Łukasz: Open-loop optimal control of a flapping wing using an adjoint lattice Boltzmann method (2020)
- Sellountos, Euripides J.: A single domain velocity-vorticity fast multipole boundary domain element method for three dimensional incompressible fluid flow problems. II (2020)
- Sun, Luning; Gao, Han; Pan, Shaowu; Wang, Jian-Xun: Surrogate modeling for fluid flows based on physics-constrained deep learning without simulation data (2020)
- Tolle, Tobias; Bothe, Dieter; Marić, Tomislav: SAAMPLE: a segregated accuracy-driven algorithm for multiphase pressure-linked equations (2020)