Predicting the phenomenon of aerodynamic instability is essential for the analysis and design of long-span cable-supported bridges. This paper reviews the history and development of aerodynamic analysis techniques and state-of-the-art numerical and experimental methods for flutter stability analysis. A discussion of the flutter phenomenon is followed by a literature review. This study provides a perspective on self-excited aerodynamic force models, identification of aerodynamic derivatives and methods for determining aerodynamic instability of long-span bridges in two dimensions and three dimensions. Computational fluid dynamics (CFD) techniques for modelling the flow past bluff bodies are also covered. Different model combination techniques are presented, utilising analytical, numerical and experimental approaches to predict the flutter limit. The effect of different parameters on the flutter stability limit is also highlighted. Furthermore, an overview of the complementary relationship between wind tunnel testing and CFD is provided. Finally, this paper also describes the engineering solutions adopted as countermeasures to prevent flutter instabilities.
The assessment of wind-induced vibrations is considered vital for the design of long-span bridges. The aim of this research is to develop a methodological framework for robust and efficient prediction strategies for complex aerodynamic phenomena using hybrid models that employ numerical analyses as well as meta-models. Here, an approach to predict motion-induced aerodynamic forces is developed using artificial neural network (ANN). The ANN is implemented in the classical formulation and trained with a comprehensive dataset which is obtained from computational fluid dynamics forced vibration simulations. The input to the ANN is the response time histories of a bridge section, whereas the output is the motion-induced forces. The developed ANN has been tested for training and test data of different cross section geometries which provide promising predictions. The prediction is also performed for an ambient response input with multiple frequencies. Moreover, the trained ANN for aerodynamic forcing is coupled with the structural model to perform fully-coupled fluid-structure interaction analysis to determine the aeroelastic instability limit. The sensitivity of the ANN parameters to the model prediction quality and the efficiency has also been highlighted. The proposed methodology has wide application in the analysis and design of long-span bridges.
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