Abstract
This dataset contains the results of turbulence field simulations using computational fluid dynamics (CFD) to determine the maximum turbulent dissipation rate (TDR) and droplet size distribution (DSD). The results were compared to DSD experimental values obtained by Aman et al., 2015 using an autoclave. The results presented here were obtained using a finite volume method based ANSYS Fluent 16.2 and 17.2. The data includes normalized turbulent kinetic energy vs radius at the midplane of the domain, simulated and experimental values of the Sauter mean diameter, contours of turbulent dissipation rate (TDR) and turbulent kinetic energy (TKE). The dataset contains all the values presented in the tables and graphs of the publication: Booth, C. P., Leggoe, J. W., & Aman, Z. M. (2018). The use of computational fluid dynamics to predict the turbulent dissipation rate and droplet size in a stirred autoclave. Chemical Engineering Science. DOI:10.1016/j.ces.2018.11.017
Purpose
Determine if computational fluid dynamics was capable of predicting the droplet sizes that form in a high-pressure autoclave and investigate using a turbulence Reynolds number to scale across different experimental systems.
Keywords
Droplet Size, Simulation, Reynolds Averaged Navier Stokes, Scaling, turbulence dissipation rate (TDR), turbulent kinetic energy (TKE), Kolmogorov lengths, autoclave, ANSYS Fluent, droplet size distribution (DSD), computational fluid dynamics (CFD)
UDI
R4.x267.000:0141
Date
February 2019
Point of Contact
Name
Zachary M. Aman
Organization
The University of Western Australia / School of Mechanical and Chemical Engineering
Name
Jeremy Leggoe
Organization
The University of Western Australia
Funding Source
RFP-4
DOI
10.7266/n7-s3m1-x947
Rights Information
This work is licensed under a
Creative Commons Public Domain Dedication 1.0 License.
Scholar Commons Citation
Zach Aman. 2019. Dataset for: The use of computational fluid dynamics to predict the turbulent dissipation rate and droplet size in a stirred autoclave. Distributed by: Gulf of Mexico Research Initiative Information and Data Cooperative (GRIIDC), Harte Research Institute, Texas A&M University–Corpus Christi. doi:10.7266/n7-s3m1-x947
Comments
Extent
Dataset contains the results of turbulence field simulations using computational fluid dynamics, no field sampling involved.
Supplemental Information
The x- and y- coordinates, turbulence dissipation rate (TDR, m^2/s^3), turbulent kinetic energy (TKE, m^2/s^2), normalized TKE (unitless; TKE/v_tip^2, where v_tip is the velocity at the tip of the turbine, v_tip = 0.1309 m/s), normalized radius at the midplane (unitless; Radius/r_b, where r_b is radius of the turbine used in the validation study, r_b= 12.5 mm), Reynolds number (unitless), Sauter mean diameter (μm), Kolmogorov lengths (μm), minimum observed droplet size (μm), mesh size (mm), mean absolute difference of the normalized turbulence kinetic energy. The dataset also includes the normalized averaged turbulent kinetic energy versus radius at the midplane of the domain from Dong et al., 1994. Correction note: In excel tab “Figure 3” the radius and TKE variable for Dong et al., 1994 should be Radius= [1.2075, 2.0114, 2.8146, 3.6121] on TKE = [0.0924, 0.0501, 0.0232, 0.0079].|||||Aman, Z. M., Paris, C. B., May, E. F., Johns, M. L., & Lindo-Atichati, D. (2015). High-pressure visual experimental studies of oil-in-water dispersion droplet size. Chemical Engineering Science, 127, 392–400. doi:10.1016/j.ces.2015.01.058 Dong, L., Johansen, S. T., & Engh, T. A. (1994). Flow induced by an impeller in an unbaffled tank—I. Experimental. Chemical Engineering Science, 49(4), 549–560. doi:10.1016/0009-2509(94)80055-3