Façade geometrical details can substantially influence the near-façade airflow patterns and pressures. This is especially the case for building balconies as their presence can lead to multiple ...separation and recirculation areas near the façades and hence large changes in surface pressure distribution. Computational fluid dynamics (CFD) has been widely used to investigate the impact of building balconies, mainly based on the steady Reynolds-averaged Navier-Stokes (RANS) approach. The objective of the present study is to evaluate the performance of steady RANS and large-eddy simulations (LES) in predicting the near-façade airflow patterns and mean surface pressure coefficients (Cp) for a building with balconies for three wind directions θ = 0°, 90°, 180°, where 0° is perpendicular to the façade under study. The evaluation is based on validation with wind-tunnel measurements of Cp. The results show that both RANS and LES can accurately predict Cp on the windward façade for θ = 0° with average absolute deviations of 0.113 and 0.091 from the measured data, respectively. For the other two wind directions, LES is clearly superior. For θ = 90°, the average absolute deviations for RANS and LES are 0.302 and 0.096, while these are 0.161 and 0.038 for θ = 180°. Large differences are found in the computed flow fields on the balcony spaces. Because RANS systematically underestimates the absolute values of both Cp and mean wind speed on the balconies, it is suggested that building design based on RANS might result in excessive ventilation and in too high wind nuisance level.
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•Performance of RANS and LES is evaluated for a high-rise building with balconies.•RANS and LES accurately predict mean static pressure for wind direction 0°.•LES performs better than RANS for wind directions 90° and 180°.•Prediction of wind speed ratios on balcony spaces is systematically investigated.•Design based on RANS will result in too high wind nuisance level.
The decomposition of experimental data into dynamic modes using a data-based algorithm is applied to Schlieren snapshots of a helium jet and to time-resolved PIV-measurements of an unforced and ...harmonically forced jet. The algorithm relies on the reconstruction of a low-dimensional inter-snapshot map from the available flow field data. The spectral decomposition of this map results in an eigenvalue and eigenvector representation (referred to as dynamic modes) of the underlying fluid behavior contained in the processed flow fields. This dynamic mode decomposition allows the breakdown of a fluid process into dynamically revelant and coherent structures and thus aids in the characterization and quantification of physical mechanisms in fluid flow.
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A fluid dynamics approach to modelling of fusion welding in titanium alloys is proposed. The model considers the temporal and spatial evolution of liquid metal/gas interface to ...capture the transient physical effects during the heat source–material interaction of a fusion welding process. Melting and vaporisation have been considered through simulation of all interfacial phenomena such as surface tension, Marangoni force and recoil pressure. The evolution of the metallic (solid and liquid) and gaseous phases which are induced by the process enables the formation of the keyhole, keyhole dynamics, and the fully developed weld pool geometry. This enables the likelihood of fluid flow-induced porosity to be predicted. These features are all a function of process parameters and formulated as time-dependent phenomena. The proposed modelling framework can be utilised as a simulation tool to further develop understanding of defect formation such as weld-induced porosity for a particular fusion welding application. The modelling results are qualitatively compared with available experimental information.
•Taking the place of the time-consuming CFD simulation and the expensive wind-tunnel experiments in flow field prediction.•The method is based on the generative adversarial networks with the deep ...learning framework.•The method is validated on the supercritical airfoils for efficiently and accurately evaluating the flow field.
The efficient and accurate access to the aerodynamic performance is important for the design and optimization of supercritical airfoils. The aerodynamic performance is usually obtained by using computational fluid dynamics (CFD) methods or wind-tunnel experiments. But the computations of CFD are very time intensive and expensive, and the prior knowledge in wind-tunnel experiments plays a decisive role in engineering. Though many surrogate methods were proposed to alleviate the costs of these traditional approaches, most of them can only calculate the low-dimensional aerodynamic performance, and is not able to provide the accurate prediction of transonic flow fields for supercritical airfoils. Since the flow fields are equipped with its own discipline as a physical system in fluid dynamics, it is therefore possible to learn this discipline via data-driven machine learning approaches. Deep learning is witness to expansive growth into diverse applications due to its immense ability to extract essential features from complicated physical systems. Generative adversarial networks (GANs) as a recent popular method in deep leaning are capable of efficiently capturing the distribution of training data. In this work, we proposed a surrogate model, ffsGAN, which leverage the property of GANs combined with convolution neural networks (CNNs) to directly establish a one-to-one mapping from a parameterized supercritical airfoil to its corresponding transonic flow field profile over the parametric space. Compared with the most existing surrogate models, the ffsGAN is superior in efficiently and accurately predicting the high-dimensional flow field rather than the low-dimensional aerodynamic characteristics. The ffsGAN method is first trained using 500 airfoils that sampled based on RAE2822. The flow fields are then predicted for unseen airfoils to evaluate the generalization of the model in terms of prediction accuracy. An investigation of the effects of various hyper-parameters in the network architectures and loss functions is performed. The experimental results show that ffsGAN is a promising tool for rapid evaluation of detailed aerodynamic performance. The elaborate flow field predicted by ffsGAN is possible to be considered in airfoil design to further improve the design and optimization quality in the future.
Tsunami waves induced by landslides are a threat to human activities and safety along coastal areas. In this paper, we characterize experimentally the waves generated by the gravity-driven collapse ...of a dry granular column into water. Three nonlinear wave regimes are identified depending on the Froude number Fr based on the ratio of the velocity of the advancing granular front and the velocity of linear gravity waves in shallow water: transient bores for large Fr , solitary waves for intermediate values of Fr , and non-linear transition waves at small Fr. The wave amplitude relative to the water depth increases with Fr in the three regimes but with different non-linear scalings, and the relative wavelength is an increasing or decreasing function of Fr. Two of these wave regimes are rationalized by considering that the advancing granular front acts as a vertical piston pushing the water, while the last one is found to be a transition from shallow to deep water conditions. The present modeling contributes to a better understanding of the rich hydrodynamics of the generated waves, with coastal risk assessment as practical applications.
The absorption of light or radiation drives turbulent convection inside stars, supernovae, frozen lakes, and Earth’s mantle. In these contexts, the goal of laboratory and numerical studies is to ...determine the relation between the internal temperature gradients and the heat flux transported by the turbulent flow. This is the constitutive law of turbulent convection, to be input into large-scale models of such natural flows. However, in contrast with the radiative heating of natural flows, laboratory experiments have focused on convection driven by heating and cooling plates; the heat transport is then severely restricted by boundary layers near the plates, which prevents the realization of the mixing length scaling law used in evolution models of geophysical and astrophysical flows. There is therefore an important discrepancy between the scaling laws measured in laboratory experiments and those used, e.g., in stellar evolution models. Here we provide experimental and numerical evidence that radiatively driven convection spontaneously achieves the mixing length scaling regime, also known as the “ultimate” regime of thermal convection. This constitutes a clear observation of this regime of turbulent convection. Our study therefore bridges the gap between models of natural flows and laboratory experiments. It opens an experimental avenue for a priori determinations of the constitutive laws to be implemented into models of geophysical and astrophysical flows, as opposed to empirical fits of these constitutive laws to the scarce observational data.
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