Numerical Simulation of Random Wind Loads for Long-Span and Tall Structures

In wind engineering, long-span structures and tall towers are highly susceptible to wind loads due to their prominent geometric characteristics. Accurate simulation of random wind loads is therefore crucial for structural safety assessment. Traditional wind load analysis methods often rely on deterministic models, while modern engineering increasingly adopts numerical simulation techniques to capture the stochastic nature of wind fields. Key aspects of this technology include:

Theoretical Foundation Random wind load simulation is based on atmospheric boundary layer turbulence theory, employing fluctuating wind speed spectra (such as Kaimal spectrum or Von Karman spectrum) to describe the frequency-domain distribution characteristics of wind energy. By combining power spectral density functions with coherence functions, spatial correlation models for multi-point wind fields can be established.

Numerical Implementation Methods Mainstream approaches include the harmonic superposition method and linear filtering methods (AR/MA models). The former converts frequency-domain energy distribution into time-history signals through inverse Fourier transform, typically implemented using FFT algorithms in code. The latter employs autoregressive models to efficiently generate random sequences conforming to target spectra, where MATLAB's arima function or Python's scipy.signal.lfilter can be utilized. Modern hybrid algorithms balance computational efficiency with accuracy requirements.

Engineering Application Value Compared to wind tunnel testing, numerical simulation can rapidly obtain load conditions under different wind directions, supporting parametric studies and sensitivity analysis. For long-span roof structures, it effectively simulates vortex-induced vibration phenomena. For tall tower structures, it accurately reflects the coupling effects between along-wind and across-wind directions.

Key Technical Challenges Special attention must be paid to the accuracy of long-period component simulation, 3D wind field coherence processing, and non-Gaussian characteristic corrections. Current cutting-edge research explores machine learning-based wind field reconstruction techniques to further enhance the physical realism of simulations, potentially implemented through neural network architectures like GANs or VAEs.