An Immersed Boundary-Lattice Boltzmann Prediction for Particle Hydrodynamic Focusing in Annular Microchannels

We numerically study the dynamics of particle crystals in annular microchannels by the immersed-boundary（IB）lattice Boltzmann（LB） coupled model, analyze the fluid-particle interactions during the migration of particles,and reveal the underlying mechanism of a particle focusing on the presence of fluid flows. The results show that the Reynolds and Dean numbers are key factors influencing the hydrodynamics of particles. The particles migrate onto their equilibrium tracks by adjusting the Reynolds and Dean numbers. Elliptical tracks of particles during hydrodynamic focusing can be predicted by the IB-LB model. Both the small Dean number and the small particle can lead to a small size of the focusing track. This work would possibly facilitate the utilization of annular microchannel flows to obtain microfluidic flowing crystals for advanced applications in biomedicine and materials synthesis.

The prediction of external flow field and hydrodynamic force with limited data using deep neural network

Predicting the external flow field with limited data or limited measurements has attracted long-time interests of researchers in many industrial applications.Physics informed neural network(PINN)provides a seamless framework for combining the measured data with the deep neural network,making the neural network capable of executing certain physical constraints.Unlike the data-driven model to learn the end-to-end mapping between the sensor data and high-dimensional flow field,PINN need no prior high-dimensional field as the training dataset and can construct the mapping from sensor data to high dimensional flow field directly.However,the extrapolation of the flow field in the temporal direction is limited due to the lack of training data.Therefore,we apply the long short-term memory(LSTM)network and physics-informed neural network(PINN)to predict the flow field and hydrodynamic force in the future temporal domain with limited data measured in the spatial domain.The physical constraints(conservation laws of fluid flow,e.g.,Navier-Stokes equations)are embedded into the loss function to enforce the trained neural network to capture some latent physical relation between the output fluid parameters and input tempo-spatial parameters.The sparsely measured points in this work are obtained from computational fluid dynamics(CFD)solver based on the local radial basis function(RBF)method.Different numbers of spatial measured points(4–35)downstream the cylinder are trained with/without the prior knowledge of Reynolds number to validate the availability and accuracy of the proposed approach.More practical applications of flow field prediction can compute the drag and lift force along with the cylinder,while different geometry shapes are taken into account.By comparing the flow field reconstruction and force prediction with CFD results,the proposed approach produces a comparable level of accuracy while significantly fewer data in the spatial domain is needed.The numerical results demonstrate that the proposed approach with a specific deep neural network configuration is of great potential for emerging cases where the measured data are often limited.

Prediction and test of cavity＇s hydrodynamic self-noise induced by turbulent boundary layer

The self-noise in cavity is tested in the circling tank, prediction method of cavity＇s self-noise induced by turbulent boundary layer is established. The window＇s vibration is using the simply supported boundary condition, the sound wave in the cavity is expanded using the rigid wall boundary condition, the modal coupling vibration equation between them is established using the radiation boundary condition. The turbulent boundary layer pulsating pressure is random, the self-noise power spectrum in the cavity is solved. Test of self-noise and turbulent pressure is carried out in the circling tank when the flow velocity is 5 m/s and 8 m/s, the result verifies that the theoretical method can predict the real cavity＇s hydrodynamic noise approximately, the trends are similar, this provides one analytical method for sonar dome＇s material selection and noise control.

Prediction and validation of tonal noise from underwater propellers

Prediction and validation of low-frequency line spectrum noise from ship propeller under non-cavitating condition is presented.The flow field is analyzed with potential-based panel method,which requires the hydrodynamic forces to be integrated over the actual blade surface,rather than over the mean-chord surface.Then the pressure data is used as the input for Ffowcs Williams-Hawkings formulation to predict the far field acoustics.At the same time,propeller unsteady force is measured in hull-behind condition in China Large Cavitation Channel（CLCC）.Line spectrum noise of the 1st blade passage frequency（BPF） of a five-bladed propeller operating in a non-uniform flow field is got according to the calculated and measured unsteady forces,in which good agreement is obtained,and the 1st BPF noise difference is within 3.0 dB.The investigation reveals that prediction precision of the propeller＇s 1st BPF unsteady force with panel method have reached engineering practical degree,providing significant parameters for prediction of propeller line spectrum noise.

Detailed parametric design methodology for hydrodynamics of liquid-solid circulating fluidized bed using design of experiments

A design-of-experiments methodology is used to develop a statistical model for the prediction of the hydrodynamics of a liquid–solid circulating fluidized bed. To illustrate the multilevel factorial design approach, a step by step methodology is taken to study the effects of the interactions among the independent factors considered on the performance variables. A multilevel full factorial design with three levels of the two factors and five levels of the third factor has been studied. Various statistical models such as the linear, two-factor interaction, quadratic, and cubic models are tested. The model has been developed to predict responses, viz., average solids holdup and solids circulation rate. The validity of the developed regression model is verified using the analysis of variance. Furthermore, the model developed was compared with an experimental dataset to assess its adequacy and reliability. This detailed statistical design methodology for non-linear systems considered here provides a very important tool for design and optimization in a cost-effective approach