In this paper, we propose a workable Geometrical-Wind Tunnel-Computational (GWTC) approach to model the wind effect on single tree. Firstly, L-System models based on tree species-specific growth and branching process is developed to process the laser-scanned point cloud model and reconstruct biologically and visually representative fractal tree for wind tree modelling. Subsequently, a scaled down fractal tree is generated with 3D printing and subjected to tunnel testing with load cell and PIV measurement data under the wind speed of 10 and 15 m/s. Lastly, CFD Reynolds-Average Navier Stokes (RANS) simulation with Full Closure Model and Large Eddy Simulation (LES) using appropriate momentum sink and turbulence source terms for the volumetric tree is carried out. Yellow flame (Peltophorum pterocarpum) tree is tested; and reasonable agreement of drag force prediction and velocity profiles is obtained when comparing the CFD simulation results with wind tunnel data. The RANS modelled drag force results exhibit 20% of over-prediction; while the normalized velocity profiles display good match of velocity decay at the tree leeward sides. On the other hands, LES simulation produce better results with only 3% discrepancy with experimental results. Preliminary experimental result comparison between yellow flame and Khaya senegalensis (to be subsequently referred as Khaya in this paper) is also carried out. Due to actual random wind direction, current methodology still has limitation for validation with urban on site measurement. Nonetheless, this GWTC approach is the first step in establishing modelling tool applicability to examine the effect of forest structure and composition on wind loads. In future, sensitivity analysis with CFD parametric study associated with other tree species (e.g. Hopea odorata) for model improvement will be carried.
License type:
http://creativecommons.org/licenses/by-nc-nd/4.0/
Funding Info:
The authors gratefully acknowledge the financial support of the research grant under Virtual Singapore
Programme (NRF2017VSG-AT3DCM001-029) from the National Research Foundation of Singapore.