MATLAB Code Implementation for Simulating Pulsating Wind Time History

Pulsating wind time history is commonly used in structural wind engineering to simulate the action process of random wind loads. MATLAB provides a convenient platform for implementing simulations of this process. Pulsating wind time history generation is typically based on random process theory, utilizing power spectral density functions (such as Davenport spectrum, Kaimal spectrum, etc.) combined with random phase angles to produce wind speed time series with specific statistical characteristics. The core computational steps include: Power Spectral Density Determination: Selecting appropriate wind speed spectrum models and calculating spectral values at different frequencies. In MATLAB, this can be implemented using analytical functions that define the spectral shape parameters. Random Phase Generation: Generating random phase angles uniformly distributed between 0 and 2π. MATLAB's rand function can be used to create these random phases, ensuring proper statistical distribution. Inverse Fourier Transform: Converting frequency domain signals to time domain wind speed sequences through inverse Fourier transform. MATLAB's ifft function efficiently handles this transformation, with proper scaling considerations for accurate amplitude representation. Time History Modification: Potential adjustments to the mean value, variance, or filtering processing of the time history to better match actual wind field characteristics. MATLAB's filter design tools and signal processing functions can implement these refinements. In MATLAB, the built-in FFT (Fast Fourier Transformation) functions can efficiently implement this process. To enhance simulation accuracy, additional considerations may include wind speed spatial correlations, non-stationary characteristics, and other complex factors through advanced statistical modeling techniques. This methodology is not only applicable to structural wind vibration analysis but can also be extended to simulations of other random vibration problems, making it a versatile tool in engineering analysis and research applications.