Abstract. This paper presents a 3D numerical analysis of flow field patterns in a dam break laboratory scale by applying the numerical code based on Finite Volume Method (FVM), OpenFOAM. In the numerical model the turbulence is treated with RANS methodology and the VOF (Volume of Fluid) method is used to capture the free surface of the water. The numerical results of the code are assessed against experimental data. Water depth and pressure measures are used to validate the numerical model. The results demonstrate that the 3D numerical code satisfactorily reproduce the temporal variation of these variables. For citation: Sánchez-Cordero E., Gómez M., Bladé E. Three-dimensional numerical analysis of a dam-break using OpenFOAM. Trudy ISP RAN/Proc.
Citar como: Sánchez-Cordero, E., Boix, J., Gómez, M., Bladé, E. 2018. 3D numerical analysis of a dam -break using VOF method and LES turbulence model. Ingeniería del agua, 22(3), 167-176. https://doi.org/10.4995/Ia.2018.9374
RESUMENEl presente trabajo muestra un análisis numérico 3D del comportamiento del flujo de agua en una rotura de presa a escala de laboratorio. La simulación se realizó utilizando el software de dinámica de fluidos computacional (CFD) basado en el método de volúmenes finitos (FVM) -OpenFOAM. En el modelo numérico la turbulencia es tratada con la metodología LES (Large Eddy Simulation) y el método VOF (Volume of Fluid) es usado para la captura de la superficie libre del agua. Los resultados numéricos obtenidos se comparan con datos experimentales publicados haciendo uso de las variables de calado y presión. Los resultados muestran que la configuración del código numérico 3D es capaz de reproducir satisfactoriamente la variación temporal de las variables en estudio, con tendencias correctas y una alta correlación con los valores experimentales.Palabras clave | rotura de presa; 3D; VOF; LES; OpenFOAM.
ABSTRACT
In this paper, three-dimensional numerical analysis of dam-break flow pattern in a laboratory-scale is reported. The simulation was performed using the open source computational fluid dynamics (CFD) solver based on finite volume method (FVM) -OpenFOAM. Turbulence is treated using large eddy simulation (LES) approach. The free surface is tracked using the Volume of Fluid method (VOF
The behavior of many physical systems is described by means of differential equations. These equations are usually derived from balance principles and certain modelling assumptions. For realistic situations, the solution of the associated initial boundary value problems requires the use of some discretization technique, such as finite differences or finite volumes. This research tackles the numerical solution of a 1D differential equation to predict water surface profiles in a river, as well as to estimate the so-called roughness parameter. A very important concern when solving this differential equation is the ability of the numerical model to capture different flow regimes, given that hydraulic jumps are likely to be observed. To approximate the solution, Physics-Informed Neural Networks (PINN) are used. Benchmark cases with different bed profile shapes, which induce different flows types (supercritical, subcritical, and mixed) are tested first. Then a real mountain river morphology, the so-called Step-pool, is studied. PINN models were implemented in Tensor Flow using two neural networks. Different numbers of layers and neurons per hidden layer, as well as different activation functions (AF), were tried. The best performing model for each AF (according to the loss function) was compared with the solution of a standard finite difference discretization of the steady-state 1D model (HEC-RAS model). PINN models show good predictability of water surface profiles for slowly varying flow cases. For a rapid varying flow, the location and length of the hydraulic jump is captured, but it is not identical to the HEC-RAS model. The predictability of the tumbling flow in the Step-pool was good. In addition, the solution of the estimation of the roughness parameter (which is an inverse problem) using PINN shows the potential of this methodology to calibrate this parameter with limited cross-sectional data. PINN has shown potential for its application in open channel studies with complex bed profiles and different flow types, having in mind, however, that emphasis must be given to architecture selection.
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