A modified lattice Bhatnagar-Gross-Krook model for convection heat transfer in porous media

The lattice Bhatnagar-Gross-Krook (LBGK) model has become the most popular one in the lattice Boltzmann method for simulating the convection heat transfer in porous media. However, the LBGK model generally suffers from numerical instability at low fluid viscosities and effective thermal diffusivities. In this paper, a modified LBGK model is developed for incompressible thermal flows in porous media at the representative elementary volume scale, in which the shear rate and temperature gradient are incorporated into the equilibrium distribution functions. With two additional parameters, the relaxation times in the collision process can be fixed at a proper value invariable to the viscosity and the effective thermal diffusivity. In addition, by constructing a modified equilibrium distribution function and a source term in the evolution equation of temperature field, the present model can recover the macroscopic equations correctly through the Chapman-Enskog analysis, which is another key point different from previous LBGK models. Several benchmark problems are simulated to validate the present model with the proposed local computing scheme for the shear rate and temperature gradient, and the numerical results agree well with analytical solutions and/or those well-documented data in previous studies. It is also shown that the present model and the computational schemes for the gradient operators have a second-order accuracy in space, and better numerical stability of the present modified LBGK model than previous LBGK models is demonstrated.Comment: 38pages,50figure

Volume-averaged macroscopic equation for fluid flow in moving porous media

Darcy's law and the Brinkman equation are two main models used for creeping fluid flows inside moving permeable particles. For these two models, the time derivative and the nonlinear convective terms of fluid velocity are neglected in the momentum equation. In this paper, a new momentum equation including these two terms are rigorously derived from the pore-scale microscopic equations by the volume-averaging method, which can reduces to Darcy's law and the Brinkman equation under creeping flow conditions. Using the lattice Boltzmann equation method, the macroscopic equations are solved for the problem of a porous circular cylinder moving along the centerline of a channel. Galilean invariance of the equations are investigated both with the intrinsic phase averaged velocity and the phase averaged velocity. The results demonstrate that the commonly used phase averaged velocity cannot serve as the superficial velocity, while the intrinsic phase averaged velocity should be chosen for porous particulate systems

Joint statistics between temperature and its dissipation rate components in a round jet

J. Mi, R. A. Antonia, and F. Anselme

Deep Learning Neural Networks Trained with MODIS Satellite-Derived Predictors for Long-Term Global Solar Radiation Prediction

Solar energy predictive models designed to emulate the long-term (e.g., monthly) global solar radiation (GSR) trained with satellite-derived predictors can be employed as decision tenets in the exploration, installation and management of solar energy production systems in remote and inaccessible solar-powered sites. In spite of a plethora of models designed for GSR prediction, deep learning, representing a state-of-the-art intelligent tool, remains an attractive approach for renewable energy exploration, monitoring and forecasting. In this paper, algorithms based on deep belief networks and deep neural networks are designed to predict long-term GSR. Deep learning algorithms trained with publicly-accessible Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data are tested in Australia’s solar cities to predict the monthly GSR: single hidden layer and ensemble models. The monthly-scale MODIS-derived predictors (2003–2018) are adopted, with 15 diverse feature selection approaches including a Gaussian Emulation Machine for sensitivity analysis used to select optimal MODIS-predictor variables to simulate GSR against ground-truth values. Several statistical score metrics are adopted to comprehensively verify surface GSR simulations to ascertain the practicality of deep belief and deep neural networks. In the testing phase, deep learning models generate significantly lower absolute percentage bias (≤3%) and high Kling–Gupta efficiency (≥97.5%) values compared to the single hidden layer and ensemble model. This study ascertains that the optimal MODIS input variables employed in GSR prediction for solar energy applications can be relatively different for diverse sites, advocating a need for feature selection prior to the modelling of GSR. The proposed deep learning approach can be adopted to identify solar energy potential proactively in locations where it is impossible to install an environmental monitoring data acquisition instrument. Hence, MODIS and other related satellite-derived predictors can be incorporated for solar energy prediction as a strategy for long-term renewable energy exploration

Dependence of a plane turbulent jet on its nozzle contraction profile

The present study investigates experimentally the effect of the nozzle contraction profile on the downstream development of a plane turbulent jet. The variation of the contraction profile was made by using various orifice plates with different radii. It is found that the decay and spread rates of the jet’s mean velocity increase as the radius decreases. A decrease in the radius also results in a higher formation rate of the primary vortices. Moreover, the turbulence intensity is found to depend on the contraction profile

Experimental investigations of the effect of Reynolds number on a plane jet

[Abstract]: The effect of Reynolds number, Re = Uo,cH/μ , where Uo,c is the nozzle exit centreline velocity, H is the slot-opening width and n is the kinematic viscosity of air) on the velocity field of a turbulent plane jet from a radially contoured nozzle of aspect ratio 60 is investigated. Measurements are conducted using a single wire anemometer over an axial distance of 160h. The Reynolds number is varied between 1,500 and 16,500. Results show that the Re affects various flow properties such as the velocity decay rate, half-width and turbulence intensity. The significant dependence on Re of the mean flow field persists till Re = 16,500, while the Re effect on the turbulent properties becomes weaker above Re = 10,000. The present investigation also suggests that an increase in Re leads to a higher rate of mixing in the near field but a lower rate in the far field

Approach towards self-preservation of turbulent cylinder and screen wakes

Antonia, R.A. ; Mi, J

Evolution of centreline temperature skewness in a circular cylinder wake

Mi, J. ; Antonia, R.A