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An Efficient Computational Model for Solidification of Liquids in Large Partially Filled Tanks

Terala, Shashank
http://rave.ohiolink.edu/etdc/view?acc_num=osu1681998210481129

Abstract Details

2023, Doctor of Philosophy, Ohio State University, Mechanical Engineering.
A 32.5% water-urea mixture, commercially known as AdBlue®, is stored onboard diesel vehicles as a liquid within storage tanks and is used for exhaust aftertreatment. In cold weather conditions, the mixture may freeze and expand over the span of several hours or days, resulting in the damage of the enclosing tank. However, computational modelling of the solidification/melting process in tanks of such “large” size and over such “long” durations is a challenging task, partly due to the simultaneous presence of all three phases (solid, liquid and gas). Furthermore, as natural convection plays an important role during the freezing process, it cannot be ignored. Capturing the dynamics of natural convection requires the use of extremely small time-step sizes, in relation to the overall freezing time scales, which significantly affects the computational speed of these simulations. This fact is demonstrated in the preliminary assessment phase of this study, where the in-built models of the commercial CFD solver ANSYS FluentTM are utilized to study the freezing process in a simple, small, partially filled 2D tank. Results show that though the models are able to provide great physical details of the solidification process, they result in impractically long simulation run times (~year). This led to the main objective of this work: the development, validation, and demonstration of an efficient 3D computational model that can be used to model the solidification process in large, partially-filled tanks containing either water or Adblue®. The first part of this work developed a new “reduced” model that accounts for the heat transfer due to natural convection during solidification/melting but, ignores the movement of the gas-(solid/liquid) interface due to expansion of ice. This new reduced natural convection model bypasses solving for flow and reduces the energy equation to a pure conduction equation by modeling convective heat fluxes using an equivalent conductive heat flux via an artificial thermal conductivity. Temperature vs. time data collected by the Ford Motor Company during freezing experiments of a partially-filled tank using combinations of three different fill levels (25%, 50% and 80%) and two working liquids (water and AdBlue®) was used for validation of the models. The validation studies showed good agreement with measured temperature data, while also providing significant improvement in simulation run times: reduction from ~year to a few days. To test the capabilities of the reduced model when applied to a general natural convection problem, a study involving heat transfer in a differentially heated cavity was also undertaken. Results for three Rayleigh numbers comparing predictions to those from high-fidelity calculations show good agreement. The second part of the work involved accounting for the expansion of ice and its coupling to thermal transport and phase change. As flow is not calculated as part of the reduced natural convection model, conventional methods for tracking phase boundaries are incompatible with this model. Therefore, a new diffusion-based model is instead proposed. As this model introduces a new volume (mass) conservation equation outside the suite of equations normally solved by Fluent, a parallel, unstructured conjugate gradient squared solver with Jacobi pre-conditioning, written within Fluent’s User-Defined Function (UDF) framework, is developed from ground up. The measured temperature data previously used for validation of the reduced model is used once again for validation of the model, but now with the inclusion of ice expansion. It is found that the implementation of the formation of the ice dome primarily improves agreement with experimental data at locations closer to the surface of the solid/liquid. The ice dome itself is also seen to clearly rise above the initial liquid surface. As a test of the ability of the models to handle more complex geometries, a simulation of the freezing process within a production DEF (Diesel Exhaust Fluid) tank used by the Ford Motor Company was also conducted successfully. Key contributions of the work covered in this thesis include the development of two new computational models. The first model is a model to account for the heat transfer due to natural convection during freezing of water in large tanks but without solving for flow. The second is a model to account for the expansion of ice and its effects during the freezing process. The models were integrated into ANSYS FluentTM using UDFs making them completely general-purpose and ready for commercial use.
Sandip Mazumder (Advisor)
Seung Hyun Kim (Committee Member)
Datta Gaitonde (Committee Member)
Marcello Canova (Committee Member)
Fluid Dynamics; Mechanical Engineering
Solidification, CFD, CHT, Numerical methods, Ice expansion, Natural convection

Recommended Citations

Citations

  • Terala, S. (2023). An Efficient Computational Model for Solidification of Liquids in Large Partially Filled Tanks [Doctoral dissertation, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1681998210481129

    APA Style (7th edition)

  • Terala, Shashank. An Efficient Computational Model for Solidification of Liquids in Large Partially Filled Tanks. 2023. Ohio State University, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1681998210481129.

    MLA Style (8th edition)

  • Terala, Shashank. "An Efficient Computational Model for Solidification of Liquids in Large Partially Filled Tanks." Doctoral dissertation, Ohio State University, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=osu1681998210481129

    Chicago Manual of Style (17th edition)

osu1681998210481129
177
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