Coupled Mesoscale Weather Prediction and Computational Fluid Dynamics Modeling for Maximum Wind Flow Analysis: A Case Study for Istanbul Airport

Modeling turbulent air flow accurately is quite difficult, particularly when topographic factors and changing boundary conditions are present. The objective of this research is to combine data from high-resolution Computational Fluid Dynamics (CFD) simulations with a low-resolution meteorological model in order to describe air flow with high accuracy and resolution. To accomplish this, the FLUENT CFD solver was integrated with the widely used Weather Research and Forecasting (WRF) model.

Time-dependent boundary conditions for mesoscale atmospheric conditions are provided by the WRF model, and high-resolution Navier-Stokes simulations are carried out using FLUENT. To ensure ground surface compatibility and resolve inconsistencies caused by variations in mesh-grid structures and resolutions between the models, modified boundary conditions and an unstructured grid framework were applied. Data from the WRF were integrated into FLUENT via User-Defined Functions (UDFs). This approach enhanced the accuracy of turbulent atmospheric flow solutions and improved adaptability to time-dependent variables.

As it known, one of the challenges in the meteorological modeling is representing maximum values, which are expected to observe frequently during climate change. In here, the analyses were conducted for Istanbul Airport, one of the busiest airports in the world, focusing on the dates with the maximum wind speed values. The combined model outputs were compared with observations from the meteorological station in the region, followed by a comprehensive analysis of the wind flow fields across the area. The results demonstrated that low-resolution meteorological models can be successfully integrated into high-resolution CFD simulations, leading to significant improvements in the spatial and temporal resolution of turbulent flow analyses. Moreover, this approach offers broad applicability in areas such as renewable energy and weather forecasting. The study presents a new methodology for atmospheric flow simulations by improving data compatibility and solution accuracy between models.