國立虎尾科技大學 |

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

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	An Efficient Computational Model for Solidification of Liquids in Large Partially Filled Tanks./
作者:	Terala, Shashank.
面頁冊數:	1 online resource (257 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-03, Section: A.
Contained By:	Dissertations Abstracts International85-03A.
標題:	Sustainability. -
電子資源:	click for full text (PQDT)
ISBN:	9798380370301

An Efficient Computational Model for Solidification of Liquids in Large Partially Filled Tanks.
Terala, Shashank.

An Efficient Computational Model for Solidification of Liquids in Large Partially Filled Tanks. - 1 online resource (257 pages)

Source: Dissertations Abstracts International, Volume: 85-03, Section: A.

Thesis (Ph.D.)--The Ohio State University, 2023.

Includes bibliographical references

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 over the span of several hours or days. Since its freezing is accompanied by expansion, it often results in damage of the enclosing tank. In the interest of avoiding such outcomes, the freezing process within these tanks is of interest. Computational modeling can shed light on the exact freezing process and provide insight into mitigation strategies. However, computational modelling of the solidification/melting process in tanks of such "large" size and over such "long" durations is a challenging task. This is 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 by the first study in this thesis, where the limitations of the capabilities of commercial CFD codes are highlighted when used to solve solidification/melting problems. 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®.This objective is achieved using two newly proposed models: the first is a reduced natural convection model for heat transfer during solidification/melting, and the second is a diffusion-based volume transport model used to account for the expansion of ice during the freezing process.In the preliminary assessment phase of this study, the in-built models of the commercial CFD solver ANSYS FluentTM are utilized to study the freezing process in a simple, partially filled 2D tank, which is a quarter of the size of tanks used in the application at hand. 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) due to the requirement of very small time-step sizes. Subsequently, the two new models mentioned earlier were developed.The first phase 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. The idea is borrowed from turbulence modeling wherein turbulent transport is modeled using an eddy diffusivity. The physical laws governing natural convection are used to obtain an appropriate expression for this artificial thermal conductivity. Additionally, the latent heat release due to solidification is represented in this model using a volumetric source term. The reduced model generates an unknown material constant that is ultimately calibrated by matching temperature vs. time data collected during freezing experiments of a partially-filled tank. This experimental data was collected at the Ford Motor Company. In the experimental study, additional experiments were carried out using combinations of three different fill levels (25%, 50% and 80%) and two working liquids (water and AdBlue®) to provide data for validation of the models. Validation studies were then performed and 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 phase 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, such as the volume-of-fluid (VOF), are incompatible with this model and cannot be used to track the expansion of ice and the resulting movement of the gas-(solid/liquid) interface. Therefore, a new model, which is a diffusion-based form of the VOF method and solves a new conservation equation derived from volume (mass) conservation, is instead proposed. As this model introduces a new governing equation outside the suite of equations normally solved by ANSYS FluentTM, 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 and rise 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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798380370301Subjects--Topical Terms:

793436
Sustainability.
Subjects--Index Terms:

SolidificationIndex Terms--Genre/Form:

554714
Electronic books.

An Efficient Computational Model for Solidification of Liquids in Large Partially Filled Tanks.
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520 $a 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 over the span of several hours or days. Since its freezing is accompanied by expansion, it often results in damage of the enclosing tank. In the interest of avoiding such outcomes, the freezing process within these tanks is of interest. Computational modeling can shed light on the exact freezing process and provide insight into mitigation strategies. However, computational modelling of the solidification/melting process in tanks of such "large" size and over such "long" durations is a challenging task. This is 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 by the first study in this thesis, where the limitations of the capabilities of commercial CFD codes are highlighted when used to solve solidification/melting problems. 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®.This objective is achieved using two newly proposed models: the first is a reduced natural convection model for heat transfer during solidification/melting, and the second is a diffusion-based volume transport model used to account for the expansion of ice during the freezing process.In the preliminary assessment phase of this study, the in-built models of the commercial CFD solver ANSYS FluentTM are utilized to study the freezing process in a simple, partially filled 2D tank, which is a quarter of the size of tanks used in the application at hand. 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) due to the requirement of very small time-step sizes. Subsequently, the two new models mentioned earlier were developed.The first phase 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. The idea is borrowed from turbulence modeling wherein turbulent transport is modeled using an eddy diffusivity. The physical laws governing natural convection are used to obtain an appropriate expression for this artificial thermal conductivity. Additionally, the latent heat release due to solidification is represented in this model using a volumetric source term. The reduced model generates an unknown material constant that is ultimately calibrated by matching temperature vs. time data collected during freezing experiments of a partially-filled tank. This experimental data was collected at the Ford Motor Company. In the experimental study, additional experiments were carried out using combinations of three different fill levels (25%, 50% and 80%) and two working liquids (water and AdBlue®) to provide data for validation of the models. Validation studies were then performed and 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 phase 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, such as the volume-of-fluid (VOF), are incompatible with this model and cannot be used to track the expansion of ice and the resulting movement of the gas-(solid/liquid) interface. Therefore, a new model, which is a diffusion-based form of the VOF method and solves a new conservation equation derived from volume (mass) conservation, is instead proposed. As this model introduces a new governing equation outside the suite of equations normally solved by ANSYS FluentTM, 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 and rise 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.
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