Numerical simulation of bubble characteristics in bubble columns with different liquid viscosities and surface tensions using a CFD-PBM coupled model

Comparative Analysis of Bubbles Behavior in Different Liquids by Laser-Induced Plasma Micromachining Single-Crystal Silicon

Laser-induced plasma micromachining (LIPMM) can be used to fabricate high-quality microstructures of hard and brittle materials. The liquid medium of the LIPMM process plays a key role in inducing the plasma and cooling the materials, but the liquid medium is overheated which induces lots of bubbles to defocus the laser beam and reduce machining stability. In this paper, a comparative investigation on bubble behavior and its effect on the surface integrity of microchannels in three types of liquids and at different depths during LIPMM has been presented. Firstly, the formation mechanism of microbubbles was described. Secondly, a series of experiments were conducted to study the number and maximum diameter of the attached bubbles and the buoyancy movement of floating bubbles in the LIPMM of single-crystal silicon under deionized water, absolute ethyl alcohol, and 5.6 mol/L phosphoric acid solution with a liquid layer depth of 1–5 mm. It was revealed that the number and maximum diameter of attached bubbles in deionized water were the highest due to its high tension. Different from the continuous rising of bubbles at the tail of the microchannels in the other two liquids, microbubbles in 5.6 mol/L phosphoric acid solution with high viscosity rose intermittently, which formed a large area of bubble barrier to seriously affect the laser focus, resulting in a discontinuous microchannel with an unablated segment of 26.31 μm. When the depth of the liquid layer was 4 mm, absolute ethyl alcohol showed the advantages in narrow width (27.15 μm), large depth (16.5 μm), and uniform depth profile of the microchannel by LIPMM. This was because microbubbles in the anhydrous ethanol quickly and explosively spread towards the edge of the laser processing zone to reduce the bubble interference. This research contributes to a better understanding of the behavior and influence of bubbles in different liquid media and depths in LIPMM of single-crystal silicon.

Investigation on performance of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) in a combination of CFD modeling for prediction of fluid flow

Herein, a reactor of bubble column type with non-equilibrium thermal condition between air and water is mechanistically modeled and simulated by the CFD technique. Moreover, the combination of the adaptive network (AN) trainer with the fuzzy inference system (FIS) as the artificial intelligence method calling ANFIS has already shown potential in the optimization of CFD approach. Although the artificial intelligence method of particle swarm optimization (PSO) algorithm based fuzzy inference system (PSOFIS) has a good background for optimizing the other fields of research, there are not any investigations on the cooperation of this method with the CFD. The PSOFIS can reduce all the difficulties and simplify the investigation by elimination of the additional CFD simulations. In fact, after achieving the best intelligence, all the predictions can be done by the PSOFIS instead of the massive computational efforts needed for CFD modeling. The first aim of this study is to develop the PSOFIS for use in the CFD approach application. The second one is to make a comparison between the PSOFIS and ANFIS for the accurate prediction of the CFD results. In the present study, the CFD data are learned by the PSOFIS for prediction of the water velocity inside the bubble column. The values of input numbers, swarm sizes, and inertia weights are investigated for the best intelligence. Once the best intelligence is achieved, there is no need to mesh refinement in the CFD domain. The mesh density can be increased, and the newer predictions can be done in an easier way by the PSOFIS with much less computational efforts. For a strong verification, the results of the PSOFIS in the prediction of the liquid velocity are compared with those of the ANFIS. It was shown that for the same fuzzy set parameters, the PSOFIS predictions are closer to the CFD in comparison with the ANFIS. The regression number (R) of the PSOFIS (0.98) was a little more than that of the ANFIS (0.97). The PSOFIS showed a powerful potential in mesh density increment from 9477 to 774,468 and accurate predictions for the new nodes independent of the CFD modeling.