A deep-learning approach for 3D realization of mean wake flow of marine hydrokinetic turbine arrays
Not Available
Zhang, Zexia↗
Engineering topics
Khosronejad, Ali
Publications and source records attributed to Khosronejad, Ali.
Uncertainty quantification of bank vegetation impacts on the flood flow field in the American River, California, using large‐eddy simulations
Bank vegetation plays a key role in both hydrodynamics and morphodynamics of natural rivers; however, these effects are often unaccounted for in the computational flow dynamics of natural waterways. Recent studies using the large‐eddy simulation (LES), however, have attempted to gain insights into the impacts of bank vegetation on the mean flow field of the natural rivers using a vegetation model, which applies a sink term to the momentum equations of motion. This approach accounts for the effects of the vegetation and provides a practical approach to account for the complex patches of bank vegetation in large‐scale rivers. To implement the vegetation model, a drag coefficient reflecting the overall resistance of vegetal structures to the flow is needed, but due to the lack of calibrated data and range of size, density and type of vegetation, this parameter can be a significant source of uncertainty in the model results. Here, in this study, we use uncertainty quantification (UQ) to investigate the hydrodynamics and bed shear results when a bank vegetation is incorporated in an LES model. To this end, we used the polynomial chaos expansion and Monte Carlo sampling techniques to determine the uncertainties associated with the drag coefficient in the vegetation model and from uncertainties in the bed roughness and inflow discharge. The UQ analysis provided spatially varying confidence levels for the spanwise and vertical distribution of velocity magnitude and for the bed shear stress distributions. In addition, Sobol indices were computed to indicate the relative influence that each parameter had on the overall uncertainty. In general, it was found that uncertainty in flow discharge was the dominant source of uncertainty; however, the drag coefficient in the vegetation model and the bed roughness parameter also made significant contribution to the uncertainty near the banks and bed, respectively.
54 ENVIRONMENTAL SCIENCES↗
Lagrangian dynamics of particle transport in oral and nasal breathing
Here, we present a large-eddy simulation (LES) of saliva particle transport during normal human breathing through the nose and mouth. The flow of the air–saliva mixture is modeled using an Eulerian LES that is coupled with a Lagrangian particle tracking module to obtain trajectories of saliva particles in a room with stagnant air conditions. The coupled Eulerian–Lagrangian simulation yields novel insights into the intricate dynamics of Lagrangian coherent structures (LCS) and fundamental material lines that emerge from the saliva particles' trajectories during several breathing cycles. Further, we systematically compare the quantitative LCS diagnostics of mouth breathing with those of mouth and nose normal breathing. Analyzing the simulation results of human breathing from the mouth and nose, we show that, soon after the first breathing cycle, saliva particles form a series of roll-up vortex rings that propagate forward. The forward propagation of these vortex rings leads to the formation of an asymmetrical primary forefront vortex. The individual vortex rings continuously propagate forward, merging with the forefront vortex, and ascending along the limb of the leading vortex.
42 ENGINEERING↗
Virtual Flow Solver - Geophysics: A 3D Incompressible Navier-Stokes Solver
Virtual Flow Solver - Geophysics (VFS-Geophysics) is a three-dimensional (3D) incompressible Navier-Stokes solver based on the Curvilinear Immersed Boundary (CURVIB) method. The CURVIB is a sharp interface type of immersed boundary (IB) method that enables the simulation of fluid flows in the presence of geometrically complex moving bodies. The CURVIB method can be applied to wind/MHK turbine simulations and energy applications. VFS-Geophysics is the result of many years of research work by several graduate students, post-docs, and research associates that have been involved in the Computational Hydrodynamics and Biofluids Laboratory directed by Professor Fotis Sotiropoulos. The preparation of the present manual has been supported by the U.S. Department of Energy (DE-EE 0005482).
16 TIDAL AND WAVE POWER↗
Toward prediction of turbulent atmospheric flows over propagating oceanic waves via machine-learning augmented large-eddy simulation
Wind-wave interactions have important effects on the energy harvesting of offshore wind farms. High-fidelity large-eddy simulation (LES) is a powerful approach for investigating wind-wave interactions in turbulent oceanic environments. Due to the large scale of the flow domain and the high grid resolution required to resolve multi-scale flow motions, however, brute-force LES of wind-wave interactions is computationally very expensive. We propose augmenting brute-force LES via machine-learning data-driven modeling (ML-LES) to dramatically reduce the computational time required to obtain converged turbulence statistics when the brute-force approach is employed. Namely, we employ a convolutional neural network (CNN) autoencoder trained and validated with LES data sets to develop a highly efficient ML-LES approach for computing turbulence statistics from just a few snapshots of instantaneous LES flow fields. Further, our results demonstrate the accuracy and efficiency of ML-LES in predicting the mean velocity, velocity fluctuations, and turbulence kinetic energy in highly stretched computational grid systems required to carry out simulations in real-life oceanic environments.
42 ENGINEERING↗