Due to their architectural attractiveness and wide- covering feature, membrane structures are increasingly becoming a trend nowadays, and “going big” with these structures is desired. With larger dimensions rises the challenge of ensuring safety and serviceability against wind effects. As a designer of a flexible lightweight structure, one should expect large deformations due to the wind action, resulting in a considerable change in the wind field, and adding air mass to, the relatively very small, structure mass making the structure prone to possible aero-elastic responses (e.g. fluttering).
Furthermore, the consequent low eigen-frequencies of such structures might cause a dynamic excitation by the wind transient flow. Thus, wind action prediction on lightweight membrane structures and their response estimation are necessary and rather complicated tasks.
A valid prediction cannot be reliably accomplished by classical experimental testing. The larger the dimensions of a membrane structure gets, the more questionable wind tunnel experiments becomes; due to the impossibility of achieving a down-scaled model with similar flexibility, and the lack of the representation of membrane zero- bending stiffness due to the rigidity of a wind-tunnel model. In addition, conducting real-scale structure testing is very costly and cannot be frequently repeated in the early design phase. These facts raise the need for virtual wind tunnel technology; i.e. WSI (wind-structure interaction) simulations, which can take the interaction into consideration making it possible for the designer to estimate all expected reaction scenarios of a membrane structure when facing wind.
Virtual Wind Tunnel - Validity and Reliability
To come up with a reliable numerical tool, in addition to acquiring already-tested CFD, CSD, and coupling tools, wind fluctuations should be stochastically modeled with correct distribution of energy (spectrum) fulfilling spatial and temporal coherence that real-wind turbulence has. Due to the stochastic nature of this model, in addition to other sources of uncertainties; e.g. errors caused by discretization or iterative solution procedures, and due to the FSI comparatively high CPU cost, it is essential to identify all different simulations’ parameters, to gain experience in tuning them, and to know what does worth to pay effort for in order to get reliable results in minimum costs. The contribution to this goal, in lights of already- available experimental results or benchmarks as sources of validation, is a central part of the intended work.
Fluctuating Wind vs. Mean Wind Profile (0.00 sec) 
Fluctuating Wind vs. Mean Wind Profile (0.25 sec) 
Fluctuating Wind vs. Mean Wind Profile (0.50 sec) 
Fluctuating Wind vs. Mean Wind Profile (0.75 sec) 
Fluctuating Wind vs. Mean Wind Profile (1.00 sec) 
Fluctuating Wind vs. Mean Wind Profile (1.25 sec) 
Fluctuating Wind vs. Mean Wind Profile (1.50 sec) 
Fluctuating Wind vs. Mean Wind Profile (1.75 sec) 
Fluctuating Wind vs. Mean Wind Profile (2.00 sec) 
Fluctuating Wind vs. Mean Wind Profile (2.25 sec) 
Fluctuating Wind vs. Mean Wind Profile (2.50 sec) 
Fluctuating Wind vs. Mean Wind Profile (2.75 sec) 
Fluctuating Wind vs. Mean Wind Profile (3.00 sec) 
Fluctuating Wind vs. Mean Wind Profile (3.25 sec) 
Fluctuating Wind vs. Mean Wind Profile (3.50 sec)
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Applications
28-m Umbrella structure (engineered by SL-Rasch GmbH) 
28-m Umbrella structure (engineered by SL-Rasch GmbH)
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