@article {StarStabileBelloniRozzaDegroote2019,
	title = {A novel iterative penalty method to enforce boundary conditions in Finite Volume POD-Galerkin reduced order models for fluid dynamics problems},
	journal = {Communications in Computational Physics},
	volume = {30},
	number = {1},
	year = {2021},
	pages = {34{\textendash}66},
	publisher = {Global Science Press},
	abstract = {A Finite-Volume based POD-Galerkin reduced order model is developed for fluid dynamic problems where the (time-dependent) boundary conditions are controlled using two different boundary control strategies: the control function method, whose aim is to obtain homogeneous basis functions for the reduced basis space and the penalty method where the boundary conditions are enforced in the reduced order model using a penalty factor. The penalty method is improved by using an iterative solver for the determination of the penalty factor rather than tuning the factor with a sensitivity analysis or numerical experimentation. The boundary control methods are compared and tested for two cases: the classical lid driven cavity benchmark problem and a Y-junction flow case with two inlet channels and one outlet channel. The results show that the boundaries of the reduced order model can be controlled with the boundary control methods and the same order of accuracy is achieved for the velocity and pressure fields. Finally, the speedup ratio between the reduced order models and the full order model is of the order 1000 for the lid driven cavity case and of the order 100 for the Y-junction test case.},
	doi = {https://doi.org/10.4208/cicp.OA-2020-0059},
	author = {Kelbij Star and Giovanni Stabile and Francesco Belloni and Gianluigi Rozza and Joris Degroote}
}
@article {2021,
	title = {A POD-Galerkin reduced order model of a turbulent convective buoyant flow of sodium over a backward-facing step},
	journal = {Applied Mathematical Modelling},
	volume = {89},
	year = {2021},
	pages = {486-503},
	abstract = {<p>A Finite-Volume based POD-Galerkin reduced order modeling strategy for steady-state Reynolds averaged Navier{\textendash}Stokes (RANS) simulation is extended for low-Prandtl number flow. The reduced order model is based on a full order model for which the effects of buoyancy on the flow and heat transfer are characterized by varying the Richardson number. The Reynolds stresses are computed with a linear eddy viscosity model. A single gradient diffusion hypothesis, together with a local correlation for the evaluation of the turbulent Prandtl number, is used to model the turbulent heat fluxes. The contribution of the eddy viscosity and turbulent thermal diffusivity fields are considered in the reduced order model with an interpolation based data-driven method. The reduced order model is tested for buoyancy-aided turbulent liquid sodium flow over a vertical backward-facing step with a uniform heat flux applied on the wall downstream of the step. The wall heat flux is incorporated with a Neumann boundary condition in both the full order model and the reduced order model. The velocity and temperature profiles predicted with the reduced order model for the same and new Richardson numbers inside the range of parameter values are in good agreement with the RANS simulations. Also, the local Stanton number and skin friction distribution at the heated wall are qualitatively well captured. Finally, the reduced order simulations, performed on a single core, are about 105 times faster than the RANS simulations that are performed on eight cores.</p>},
	doi = {10.1016/j.apm.2020.07.029},
	author = {Kelbij Star and Giovanni Stabile and Gianluigi Rozza and Joris Degroote}
}
@article {StarSanderseStabileRozzaDegroote2020,
	title = {Reduced order models for the incompressible Navier-Stokes equations on collocated grids using a {\textquoteleft}discretize-then-project{\textquoteright} approach},
	journal = {International Journal for Numerical Methods in Fluids},
	volume = {93},
	number = {8},
	year = {2021},
	pages = {2694{\textendash}2722},
	publisher = {Wiley},
	doi = {10.1002/fld.4994},
	url = {https://doi.org/10.1002/fld.4994},
	author = {Kelbij Star and Benjamin Sanderse and Giovanni Stabile and Gianluigi Rozza and Joris Degroote}
}
@conference {StarStabileGeorgakaBelloniRozzaDegroote2019,
	title = {POD-Galerkin Reduced Order Model of the Boussinesq Approximation for Buoyancy-Driven Enclosed Flows},
	booktitle = {International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering, M and C 2019},
	year = {2019},
	isbn = {9780894487699},
	author = {Kelbij Star and Giovanni Stabile and Sokratia Georgaka and Francesco Belloni and Gianluigi Rozza and Joris Degroote}
}
