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Population Balance and Computational Fluid Dynamics Modeling of Swirl Flow Microbubble Generator
Alam H.S.a,b, Soetikno P.a, Soelaiman T.A.F.a, Sugiarto A.T.b
a Bandung Institute of Technology, Faculty of Mechanical and Aerospace Engineering, Bandung, Indonesia
b Indonesian Institute of Sciences, Technical Implementation Unit for Instrumentation Development, Bandung, Indonesia
[vc_row][vc_column][vc_row_inner][vc_column_inner][vc_separator css=”.vc_custom_1624529070653{padding-top: 30px !important;padding-bottom: 30px !important;}”][/vc_column_inner][/vc_row_inner][vc_row_inner layout=”boxed”][vc_column_inner width=”3/4″ css=”.vc_custom_1624695412187{border-right-width: 1px !important;border-right-color: #dddddd !important;border-right-style: solid !important;border-radius: 1px !important;}”][vc_empty_space][megatron_heading title=”Abstract” size=”size-sm” text_align=”text-left”][vc_column_text]© 2020 IEEE.Microbubbles technology has become an issue that has attracted the attention of researchers in the last decade, because it has a wide application in various fields, one of which is for water treatment. However, the design of microbubble generators involving multi-phase studies remains a challenge in producing efficient designs. In this study, the design involved multi-phase modeling based on the Computational Fluid Dynamics (CFD) coupled with the Population Balance Method (PBM) was used to predict the size distribution of microbubbles. Two coalescence models (Luo model and Turbulent model) and two breakage models (Luo model and Lehr model) were investigated to determine their compatibility with test results using Particle Image Velocimetry (PIV). Based on the CFD-PBM simulation, the bubble size distribution was predicted successfully. The combination of the Turbulent coalescence model and the Lehr breakage model has the best compatibility with experimental results (PIV) so that it can be used as a reference in designing a microbubble generator. The possibility of the existence of an ultrafine bubble shortly after microbubble generation needs to be studied further, especially its effect on the bubble stability and level of dissolved gas in water.[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Author keywords” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Indexed keywords” size=”size-sm” text_align=”text-left”][vc_column_text]computational fluid dynamics,microbubble,particle image velocimetry,population balance method[/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”Funding details” size=”size-sm” text_align=”text-left”][vc_column_text][/vc_column_text][vc_empty_space][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][vc_empty_space][megatron_heading title=”DOI” size=”size-sm” text_align=”text-left”][vc_column_text]https://doi.org/10.1109/ICSEEA50711.2020.9306166[/vc_column_text][/vc_column_inner][vc_column_inner width=”1/4″][vc_column_text]Widget Plumx[/vc_column_text][/vc_column_inner][/vc_row_inner][/vc_column][/vc_row][vc_row][vc_column][vc_separator css=”.vc_custom_1624528584150{padding-top: 25px !important;padding-bottom: 25px !important;}”][/vc_column][/vc_row]