Swirling-strength based large eddy simulation of turbulent flow around single square cylinder at low Reynolds numbers

In view of the fact that large scale vortices play the substantial role of momentum transport in turbulent flows, large eddy simulation （LES） is considered as a better simulation model. However, the sub-grid scale （SGS） models reported so far have not ascertained under what flow conditions the LES can lapse into the direct nu-merical simulation. To overcome this discrepancy, this paper develops a swirling strength based the SGS model to properly model the turbulence intermittency, with the primary characteristics that when the local swirling strength is zero, the local sub-grid viscosity will be vanished. In this paper, the model is used to investigate the flow characteris-tics of zero-incident incompressible turbulent flows around a single square cylinder （SC） at a low Reynolds number range Re ∈ [103, 104]. The flow characteristics investigated include the Reynolds number dependence of lift and drag coefficients, the distributions of time-spanwise averaged variables such as the sub-grid viscosity and the logarithm of Kolmogorov micro-scale to the base of 10 at Re=2 500 and 104, the contours of spanwise and streamwise vorticity components at t = 170. It is revealed that the peak value of sub-grid viscosity ratio and its root mean square （RMS） values grow with the Reynolds number. The dissipation rate of turbulent kinetic energy is larger near the SC solid walls. The instantaneous factor of swirling strength intermittency （FSI） exhibits some laminated structure involved with vortex shedding.

Computational fluid dynamic(CFD)modelling of transient flow in the intermittent gas lift

A computational fluid dynamics model(CFD)is developed for intermittent gas lift techniques.The simulation is conducted for a test section of 18 m vertical tube with 0.076 m in diameter using air as injection gas and oil as a formation fluid.The results obtained from the CFD model are validated with the experiment results from the literature.The current study shows that computational modeling is a proven simulation program for predicting intermittent gas lift characteristics and the transient flow parameters that are changing with time and position in the coordinate system.The model can predict the slug velocity behavior for different injection pressure.The slug velocity profile shows three regions;the first region is the rapid acceleration at the initial time of injection,the second region shows the nearly constant velocity until the slug reaches the surface and the third region is again the rapid acceleration when the liquid starts to produce.Also,the results obtained from this model show that as the gas injection pressure increases,the liquid slug velocity increase,and the region of the constant velocity decrease.The effect of the injection time on the liquid production rate has been studied for two different gas injection pressures of 40 psig and 50 psig.The developed model shows that more than 50%of the liquid production is coming from after flow period.

Application of machine learning algorithms to predict tubing pressure in intermittent gas lift wells

Tubing pressure at gas injection depth in intermittent wells is one of the most critical parameters for production engineers to evaluate the performance of the system.However,monitoring of the tubing pressure is not usually carried out in real time.It has been realized that the generally used correlations are not effective enough due to complexity of the intermittent process which involve many parameters and assumptions to develop such equations.The focus of this study is to utilize machine learning(ML)algorithms to develop a model that can accurately predict tubing pressure in artificial intermittent gas lift wells.intelligent algorithms built on the field data provide a solution that is easy to use and universally applicable to the complex problems.Various non-linear regression ML methods are employed in this study,namely,Decision Tree-regression(DT),Random Forest-regression(RF)and K Nearest Neighbors-regression(KNN).All the tubing pressures obtained from ML models were compared with the actual values to ensure the effectiveness of the work.The developed models show that it can predict the pressure with more than 99.9%accuracy.This is an interesting result,as such outcome accuracy has not been reported usually in the open literature.