An advanced wall treatment for a 1D model of packed bed thermal energy storage systems

•A new 1D numerical model of a packed bed Thermal Energy Storage system is developed including different wall treatment options (two dimensional, equivalent, adiabatic).•Various numerical schemes have been discussed, implemented and compared between each other to figure out the optimal trade-off between results accuracy and computational time.•The model has been extensively validated with analytical and experimental data.•The accuracy of the proposed wall treatment options has been evaluated by comparing the model results with conventional CFD simulations.•The developed code appears well suited to be coupled with other (0D) codes describing the dynamic behavior of entire plants thanks to its reduced computational time. Packed bed thermal energy storages are used to store the thermal energy conveyed by a heat transfer fluid within a porous media. The container wall plays an important role in the thermodynamic behaviour of these systems, because of its thermal inertia and the heat losses towards the ambient. Within the many numerical models existing in the literature for these energy storage systems, the wall is either solved, resulting accurate but expensive in computational resources, or modelled, resulting light and fast but often limited to particular configurations. In the present work a quasi-1D model, called athens, is developed using an advanced wall treatment with the aim of being faster than the most complex models and, at the same time, more accurate than the simplest. The wall behaviour is then coupled to the fluid and the packed bed equations, using equivalent properties computed from the solution of the conduction equation in the radial direction only. This new approach is validated and compared with a more common two-dimensional solution of the wall. The results show a notable improvement in the computational time and only a small decrease in accuracy, to the point that particular cases had to be specially prepared in order to highlight the differences between the models.