Wind-structure interaction data-trained predictive neural-network model for wind-induced buckling of ground-supported, unstiffened, open-top, cylindrical steel-tank
Jul 2026· Engineering computations· pp. 1-34· 0 citations· 48 references
Abstract
To aid the design of empty, open-top, unstiffened, ground-supported, steel cylindrical-tanks against wind-induced buckling, this study proposes a fast and innovative artificial neural network (ANN) to predict buckling load-multiplier, assessing the protective effects of geometric aspect ratios of tank and fill level, based on stability analysis.
A multiphysics system coupling has been utilized to perform finite element methodology-based one-way wind-structure interaction analysis by joining computational fluid dynamics and structural mechanics (eigenvalue buckling) solvers. The accuracy of the numerical model is ensured through experimental and theoretical validations. Basic wind speed (Vb), tank diameter (D), filling height to tank height (HF/H), tank height to diameter (H/D) and tank radius to wall thickness (r/t) ratios have been varied as inputs for studying the wind-induced buckling through buckling load multiplier (λ).
Four different stability conditions, namely safe stability (λ>2), low stability (1<λ ≤ 2), critical stability (λ≈1) and instability (λ<1) are observed based on load-multiplier values. An economically safe buckling capacity is observed for H/D ratios of 0.5 and ≥ 0.75 up to 1.0 in 75% filled tanks with diameters of 15m and 20m with r/t ratios of 1,000 and 750, respectively. An empty tank with H/D ≤ 0.25 is completely safe against wind-induced buckling when r/t ratio 750 and 1,000 are ensured, respectively for tank diameter ≤ 15m and 20m.
ANN has been trained efficiently with ≥ 60% data from the multiphysics analyses, which showcased 97.03% accuracy for assessing the buckling load multiplier of unstiffened, open-top, steel tank against wind-induced buckling. The developed ANN model can predict the required fluid level inside the unstiffened tank to maintain its stability against wind-induced buckling, based on the velocity of an impending storm and the tank’s geometrical features.
Aerodynamic shape optimization of low-rise buildings requires repeated evaluation of wind-induced forces over many geometric parameters and wind directions, making direct wind tunnel testing or high-fidelity computational fluid dynamics impractical for broad design-space exploration. This study develops a data-efficien...
Kun Wang, Yao-Wei Fan, Chao Tan et al.· The Physics of Fluids· 0 citations
Buried horizontal cylindrical tanks are susceptible to stress instabilities, such as shell buckling and weld fatigue, under nonuniform ground settlement. Classical Terzaghi-based earth pressure theories simplify key parameters into static constants, rendering them inadequate for capturing the dynamic soil-tank inte...
Quanen Li, Yu Zhang, Sheng-Jie Di et al.· Journal of computing in civi...· 0 citations
Thin-walled truncated conical shells are widely used in aerospace, marine, offshore, and lightweight infrastructure systems due to their high strength-to-weight ratio and geometric efficiency. Their buckling resistance under axial compression, however, is highly sensitive to geometric imperfections, manufacturing toler...
Devasmit Dutta, Budhaditya De, Rohan Majumder et al.· 0 citations
Purpose. To develop a hybrid methodology that integrates Artificial Neural Networks (ANN) with Finite Element Method (FEM) simulations for the rapid and accurate prediction of slope stability.
Methodology. A dataset of 1,000 FEM simulations was generated by systematically varying seven key input parameters: slope geo...
F. Benayoun, M. Feligha, S. Bekkouche et al.· Naukovyi Visnyk Natsionalnoh...· 0 citations
We use cookies to run the site and, with your consent, for analytics and to show ads.
See our Cookie Policy.