• Korea-Australia Rheology Journal
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• v.17 no.3
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• pp.131-140
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• 2005

In this work we show the results of our most recent Direct Numerical Simulations (DNS) of turbulent viscoelastic channel flow using spectral spatial approximations and a stabilizing artificial diffusion in the viscoelastic constitutive model. The Finite-Elasticity Non-Linear Elastic Dumbbell model with the Peterlin approximation (FENE-P) is used to represent the effect of polymer molecules in solution, The corresponding rheological parameters are chosen so that to get closer to the conditions corresponding to maximum drag reduction: A high extensibility parameter (60) and a moderate solvent viscosity ratio (0.8) are used with two different friction Weissenberg numbers (50 and 100). We then first find that the corresponding achieved drag reduction, in the range of friction Reynolds numbers used in this work (180-590), is insensitive to the Reynolds number (in accordance to previous work). The obtained drag reduction is at the level of $49\%\;and\;63\%$, for the friction Weissenberg numbers 50 and 100, respectively. The largest value is substantially higher than any of our previous simulations, performed at more moderate levels of viscoelasticity (i.e. higher viscosity ratio and smaller extensibility parameter values). Therefore, the maximum extensional viscosity exhibited by the modeled system and the friction Weissenberg number can still be considered as the dominant factors determining the levels of drag reduction. These can reach high values, even for of dilute polymer solution (the system modeled by the FENE-P model), provided the flow viscoelasticity is high, corresponding to a high polymer molecular weight (which translates to a high extensibility parameter) and a high friction Weissenberg number. Based on that and the changes observed in the turbulent structure and in the most prevalent statistics, as presented in this work, we can still rationalize for an increasing extensional resistance-based drag reduction mechanism as the most prevalent mechanism for drag reduction, the same one evidenced in our previous work: As the polymer elasticity increases, so does the resistance offered to extensional deformation. That, in turn, changes the structure of the most energy-containing turbulent eddies (they become wider, more well correlated, and weaker in intensity) so that they become less efficient in transferring momentum, thus leading to drag reduction. Such a continuum, rheology-based, mechanism has first been proposed in the early 70s independently by Metzner and Lamley and is to be contrasted against any molecularly based explanations.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2004.03a
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• pp.216-227
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• 2004

A new cavitating model by using bubble size distribution based on bubbles-mass has been proposed. Both liquid and vapor phases are treated with Eulerian framework as a mixture containing minute cavitating bubbles. In addition vapor phase consists of various sizes of vapor bubbles, which are distributed to classes based on their mass. The bubble number-density for each class was solved by considering the change of the bubble-mass due to phase change as well as generation of new bubbles due to heterogeneous nucleation. In this method, the bubble-mass is treated as an independent variable, and the other dependent variables are solved in spatial coordinates and bubble-mass coordinate. Firstly, we employed this method to calculate bubble nucleation and growth in stationary super-heated liquid nitrogen, and bubble collapse in stationary sub-cooled one. In the case of bubble growth in super-heated liquid, bubble number-density of the smallest class based on its mass is increased due to the nucleation. These new bubbles grow with time, and the bubbles shift to larger class. Therefore void fraction of each class is increased due to the growth in the whole class. On the other hand, in the case of bubble collapse in sub-cooled liquid, the existing bubbles are contracted, and then they shift to smaller class. It finally becomes extinct at the smallest one. Secondly, the present method is applied to a cavitating flow around NACA00l5 foil. Liquid nitrogen and liquid oxygen are employed as working fluids. Cavitation number, $\sigma$, is fixed at 0.15, inlet velocities are changed at 5, 10, 20 and 50m/s. Inlet temperatures are 90K in case of liquid nitrogen, and 90K and 1l0K in case of liquid oxygen. 110K of oxygen is corresponding to the 90K of nitrogen because of the same relative temperature to the critical one, $T_{r}$=$T/T_c^{+}$. Cavitating flow around the NACA0015 foils was properly analyzed by using bubble size distribution. Finally, the method is applied to a cavitating flow in an inducer of the LE-7A hydrogen turbo-pump. This inducer has 3 spiral foils. However, for simplicity, 2D calculation was carried out in an unrolled channel at 0.9R cross-section. The channel moves against the fluid at a peripheral velocity corresponding to the inducer revolutions. Total inlet pressure, $Pt_{in}$, is set at l00KPa, because cavitation is not generated at a design point, $Pt_{in}$=260KPa. The bubbles occur upstream of the foils and collapse between them. Cavitating flow in the inducer was successfully predicted by using the bubble size distribution.

• Korean Journal of Remote Sensing
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• v.37 no.5_3
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• pp.1405-1423
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• 2021

Precipitation is one of the main factors that affect water and energy cycles, and its estimation plays a very important role in securing water resources and timely responding to water disasters. Satellite-based quantitative precipitation estimation (QPE) has the advantage of covering large areas at high spatiotemporal resolution. In this study, machine learning-based rainfall intensity models were developed using Himawari-8 Advanced Himawari Imager (AHI) water vapor channel (6.7 ㎛), infrared channel (10.8 ㎛), and weather radar Column Max (CMAX) composite data based on random forest (RF). The target variables were weather radar reflectivity (dBZ) and rainfall intensity (mm/hr) converted by the Z-R relationship. The results showed that the model which learned CMAX reflectivity produced the Critical Success Index (CSI) of 0.34 and the Mean-Absolute-Error (MAE) of 4.82 mm/hr. When compared to the GeoKompsat-2 and Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks (PERSIANN)-Cloud Classification System (CCS) rainfall intensity products, the accuracies improved by 21.73% and 10.81% for CSI, and 31.33% and 23.49% for MAE, respectively. The spatial distribution of the estimated rainfall intensity was much more similar to the radar data than the existing products.