Aeroelastic analyses of bridges using a Pseudo-3D vortex method and velocity-based synthetic turbulence generation

Kavrakov, Igor; Morgenthal, Guido

doi:10.1016/j.engstruct.2018.08.093

Cited by 14 publications

(9 citation statements)

References 54 publications

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“…The coupled model is constructed of structural and fluid partial models, both defined in R2. Considering an R2 instead of R3 domain is itself an assumption in terms of the structural behaviour (three-dimensionality of the energy transfer between modes), fluid behaviour (spatial coherence of the free-stream turbulence, three-dimensionality of the turbulent energy cascade and non-uniform mean wind profile along the bridge span) and FSI (strip assumption) [12,39,40]. Nevertheless, defining both partial models in R2 is compliant with (iv) from definition 2.3 and is sufficient for the purpose of this study.…”

Section: Categorical Framework For Aerodynamic Modellingmentioning

confidence: 99%

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A categorical perspective towards aerodynamic models for aeroelastic analyses of bridge decks

et al. 2019

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Reliable modelling in structural engineering is crucial for the serviceability and safety of structures. A huge variety of aerodynamic models for aeroelastic analyses of bridges poses natural questions on their complexity and thus, quality. Moreover, a direct comparison of aerodynamic models is typically either not possible or senseless, as the models can be based on very different physical assumptions. Therefore, to address the question of principal comparability and complexity of models, a more abstract approach, accounting for the effect of basic physical assumptions, is necessary. This paper presents an application of a recently introduced category theory-based modelling approach to a diverse set of models from bridge aerodynamics. Initially, the categorical approach is extended to allow an adequate description of aerodynamic models. Complexity of the selected aerodynamic models is evaluated, based on which model comparability is established. Finally, the utility of the approach for model comparison and characterization is demonstrated on an illustrative example from bridge aeroelasticity. The outcome of this study is intended to serve as an alternative framework for model comparison and impact future model assessment studies of mathematical models for engineering applications.

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Section: Categorical Framework For Aerodynamic Modellingmentioning

confidence: 99%

“…[36,37]) and modelling of free-stream turbulence (cf. [38,39]). We note that the use of the vortex particle method is to merely illustrate the overall concept of a CFD model within the categorical framework.…”

Section: Aerodynamic Modelsmentioning

confidence: 99%

A categorical perspective towards aerodynamic models for aeroelastic analyses of bridge decks

et al. 2019

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“…For this purpose, the rigid section of the bridge is considered to be suspended on springs which represent its structural dynamic properties. Extensive validation studies on benchmark cases and the real bridge sections have also been performed [28,[38][39][40][41][42][43]. For the sake of brevity, the details of the aforementioned method have not been presented here.…”

Section: Prediction Frame For Cfd Simulationsmentioning

confidence: 99%

Prediction of aeroelastic response of bridge decks using artificial neural networks

Abbas

Kavrakov

Morgenthal

et al. 2020

Computers & Structures

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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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“…e.g. Ge and Xiang (2008), Morgenthal and McRobie (2002), Morgenthal et al (2014, Kavrakov andMorgenthal (2018a)).…”

Section: Introductionmentioning

confidence: 99%

Comparison Metrics for Time-Histories: Application to Bridge Aerodynamics

2020

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Wind effects can be critical for the design of lifelines such as long-span bridges. The existence of a significant number of aerodynamic force models, used to assess the performance of bridges, poses an important question regarding their comparison and validation. This study utilizes a unified set of metrics for a quantitative comparison of time-histories in bridge aerodynamics with a host of characteristics. Accordingly, nine comparison metrics are included to quantify the discrepancies in local and global signal features such as phase, time-varying frequency and magnitude content, probability density, nonstationarity and nonlinearity. Among these, seven metrics available in the literature are introduced after recasting them for time-histories associated with bridge aerodynamics. Two additional metrics are established to overcome the shortcomings of the existing metrics. The performance of the comparison metrics is first assessed using generic signals with prescribed signal features. Subsequently, the metrics are applied to a practical example from bridge aerodynamics to quantify the discrepancies in the aerodynamic forces and response based on numerical and semi-analytical aerodynamic models. In this context, it is demonstrated how a discussion based on the set of comparison metrics presented here can aid a model evaluation by offering deeper insight. The outcome of the study is intended to provide a framework for quantitative comparison and validation of aerodynamic models based on the underlying physics of fluid-structure interaction. Immediate further applications are expected for the comparison of time-histories that are simulated by data-driven approaches.

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Aeroelastic analyses of bridges using a Pseudo-3D vortex method and velocity-based synthetic turbulence generation

Cited by 14 publications

References 54 publications

A categorical perspective towards aerodynamic models for aeroelastic analyses of bridge decks

A categorical perspective towards aerodynamic models for aeroelastic analyses of bridge decks

Prediction of aeroelastic response of bridge decks using artificial neural networks

Comparison Metrics for Time-Histories: Application to Bridge Aerodynamics

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