Investigation on the abilities of different artificial intelligence methods to predict the aerodynamic coefficients

S Yetkin, S Abuhanieh, S Yigit - Expert Systems with Applications, 2024 - Elsevier
Abstract The Computational Fluid Dynamics (CFD) simulations at different fidelity levels are
a common tool for predicting the aerodynamic performance in many engineering …

Data-driven prediction of critical flutter velocity of long-span suspension bridges using a probabilistic machine learning approach

S Tinmitondé, X He, L Yan, AH Hounye - Computers & Structures, 2023 - Elsevier
Among the consequences of wind-induced excitation on long-span cable-supported
bridges, flutter instability is the most dangerous and can collapse bridge structures. Until …

Coupled numerical simulation of liquid sloshing dampers and wind–structure simulation model

V Vîlceanu, I Kavrakov, G Morgenthal - Journal of Wind Engineering and …, 2023 - Elsevier
Long-span bridges, high-rise buildings, chimneys, or wind turbines are often susceptible to
wind-induced vibrations due to their high flexibility and lightweight. Therefore, these …

Investigation of nonlinear and transitional characteristics of flutter varying with wind angles of attack for some typical sections with different side ratios

B Wu, H Shen, H Liao, Q Wang - Journal of Fluids and Structures, 2023 - Elsevier
This study conducted a comprehensive investigation into the flutter responses of three
closed sections, namely two streamlined box girders and a 5: 1 rectangular cylinder, which …

Stochastic stiffness identification and response estimation of Timoshenko beams via physics-informed Gaussian processes

GR Tondo, S Rau, I Kavrakov, G Morgenthal - Probabilistic Engineering …, 2023 - Elsevier
Abstract Machine learning models trained with structural health monitoring data have
become a powerful tool for system identification. This paper presents a physics-informed …

[HTML][HTML] Data-driven aeroelastic analyses of structures in turbulent wind conditions using enhanced Gaussian Processes with aerodynamic priors

I Kavrakov, G Morgenthal, A McRobie - Journal of Wind Engineering and …, 2024 - Elsevier
Recent advancements in data-driven aeroelasticity have been driven by the wealth of data
available in the wind engineering practice, especially in modeling aerodynamic forces …

Data-driven aerodynamic models for aeroelastic simulations

J Lelkes, DA Horváth, B Lendvai, B Farkas… - Journal of Sound and …, 2023 - Elsevier
Multiple approaches are available for calculating the time-dependent aerodynamic loads of
thin, flexible structures subjected to airflow: analytical, semi-empirical, CFD-based, and …

Numerical investigation of the nonlinear interaction between the sinusoidal motion-induced and gust-induced forces acting on bridge decks

S Tesfaye, I Kavrakov, G Morgenthal - Journal of Fluids and Structures, 2022 - Elsevier
With the increasing spans and complex deck shapes, aerodynamic nonlinearity becomes a
crucial concern in the design of long-span bridges. This paper investigates the nonlinear …

Stochastic Inference of Plate Bending from Heterogeneous Data: Physics-informed Gaussian Processes via Kirchhoff-Love Theory

I Kavrakov, GR Tondo, G Morgenthal - arXiv preprint arXiv:2405.12802, 2024 - arxiv.org
Advancements in machine learning and an abundance of structural monitoring data have
inspired the integration of mechanical models with probabilistic models to identify a …

Investigation on the Abilities of Different Machine Learning Methods to Predict the Aerodynamic Coefficients

S Yetkin, S Abuhanieha, S Yigit - Available at SSRN 4429456 - papers.ssrn.com
Abstract The Computational Fluid Dynamics (CFD) simulations at different fidelity levels are
a common tool for predicting the aerodynamic performance in many engineering …