“…The wind tunnel tests can apparently be guided or even be replaced by numerical modelling. A series of successful numerical section studies have been performed with detailed validation to measurements [8][9][10][11]. A full review of these is found in [12].…”
In this paper a novel fluid-structure interaction approach for simulating flutter phenomenon is presented. The method is capable of modelling the structural motion and the fluid flow coupling in a fully three-dimensional manner. The key step of the proposed FSI procedure is a hybrid scaling of the physical fields; certain properties of the CFD simulation are scaled, while those of the mechanical system are kept original. This kind of scaling provides a significant speedup, since the number of the costly CFD time steps can be remarkably reduced. The acceptable computational time makes it possible to consider complex engineering problems such as buffeting, vortex shedding or flutter of a bridge deck or a wing of an airplane.
“…The wind tunnel tests can apparently be guided or even be replaced by numerical modelling. A series of successful numerical section studies have been performed with detailed validation to measurements [8][9][10][11]. A full review of these is found in [12].…”
In this paper a novel fluid-structure interaction approach for simulating flutter phenomenon is presented. The method is capable of modelling the structural motion and the fluid flow coupling in a fully three-dimensional manner. The key step of the proposed FSI procedure is a hybrid scaling of the physical fields; certain properties of the CFD simulation are scaled, while those of the mechanical system are kept original. This kind of scaling provides a significant speedup, since the number of the costly CFD time steps can be remarkably reduced. The acceptable computational time makes it possible to consider complex engineering problems such as buffeting, vortex shedding or flutter of a bridge deck or a wing of an airplane.
“…Compared to the travel time under UE state in the pre-maintenance period, the maintenance activities add up to 22,610 h of traffic delays. In the bridge maintenance practice, two empirical strategies, i.e., flow-first scheduling strategy (FFSS) [38] and worst-first scheduling strategy (WFSS) [39] are two commonly adopted strategies. FFSS determines the time sequence of bridge maintenance activities by traffic flow from highest to lowest.…”
An optimal maintenance scheduling strategy for bridge networks can generate an efficient allocation of resources with budget limits and mitigate the perturbations caused by maintenance activities to the traffic flows. This research formulates the optimal maintenance scheduling problem as a bi-level programming model. The upper-level model is a multi-objective nonlinear programming model, which minimizes the total traffic delays during the maintenance period and maximizes the number of bridges to be maintained subject to the budget limit and the number of crews. In the lower-level, the users’ route choice following the upper-level decision is simulated using a modified user equilibrium model. Then, the proposed bi-level model is transformed into an equivalent single-level model that is solved by the simulated annealing algorithm. Finally, the model and algorithm are tested using a highway bridge network. The results show that the proposed method has an advantage in saving maintenance costs, reducing traffic delays, minimizing makespan compared with two empirical maintenance strategies. The sensitivity analysis reveals that traffic demand, number of crews, availability of budget, and decision maker’s preference all have significant effects on the optimal maintenance scheduling scheme for bridges including time sequence and job sequence.
“…To better satisfy the requirement of the mesh size of the numerical method, a 1/10-scale model was used in the computation. The unsteady Reynolds-averaged Navier-Stokes (URANS) equation is used in most numerical studies involving these conditions to describe the time-averaged flow; the results obtained by this equation are also valid for determining the pressure field, velocity field, and aerodynamic forces (Haque et al, 2016;Liu et al, 2018). According to a previous study conducted by Morden et al (2015), the shear stress transport (SST) k − ω approach can reduce the computational expense with a slight reduction in accuracy when predicting forces and surface pressures.…”
Section: The Computation Methods and The Computational Domainmentioning
To reduce the impact of a mountain ridge on the crosswind flow field of a specific section of the Lanzhou-Xinjiang high-speed railway, a portion of the mountain ridge was removed to increase the distance from the windbreak. To verify the effects of this flow optimization measure, the wind speed and wind angle were tested in this mountain ridge region. In this paper, under the actual conditions of this specific case study, the average and transient wind characteristics were analyzed based on the test results. Then, based on the actual terrain model, using the computational fluid dynamics (CFD) method with two inlet boundary conditions, namely, constant wind and exponential wind, the results obtained from the two boundary conditions were validated. Furthermore, the visualized flow structures and wind speed distributions along the railway under both boundary conditions were compared. Finally, along the railway, the impacts of different terrain types on the flow field of the railway were compared.
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