國立虎尾科技大學 |

Data Driven Turbulence Modeling.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	Data Driven Turbulence Modeling./
作者:	Parmar, Basu.
面頁冊數:	1 online resource (176 pages)
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Fluid mechanics. -
電子資源:	click for full text (PQDT)
ISBN:	9798381164763

Data Driven Turbulence Modeling.
Parmar, Basu.

Data Driven Turbulence Modeling. - 1 online resource (176 pages)

Source: Dissertations Abstracts International, Volume: 85-06, Section: B.

Thesis (Ph.D.)--University of Colorado at Boulder, 2023.

Includes bibliographical references

In this dissertation we present contributions to the field of data-driven turbulence modeling. The research work centers on the development of data-driven RANS models, which address the predictive limitations of traditional RANS models.Firstly, we introduce the Generalized Non-Linear Eddy Viscosity (GNLEV) model, which shows superior accuracy when compared to both traditional and data-driven linear and non-linear eddy viscosity models. By incorporating a Reynolds stress dependence on additional flow variables we reduced the model form error associated with linear and non-linear eddy viscosity models.Secondly, we present the N-frame model, which incorporates information about orientation with respect to geometry in its formulation to enhance accuracy compared to existing data-driven models. The inputs and outputs are represented in a coordinate system constructed from vorticity and the gradient of the distance to the wall. Using this approach, allowed us to construct a data-driven model with significantly less inputs and outputs compared to the GNLEV model. The N-frame model is implemented using two approaches for turbulent scale consistency: the corrective approach, which utilizes inputs from an inexact RANS simulation, and the iterative approach, which uses inputs from an exact DNS simulation. Numerical results show that the N-frame iterative model outperforms the N-frame corrective model in terms of accuracy. The results also demonstrate that incorporating geometry information into the data-driven model yields accurate predictions.Lastly, we construct an iterative model for CFD design space exploration, utilizing coordinate system and scales specific to the problem in consideration. The tailored approach to the specific design space enables accurate predictions with fewer inputs and transport equations compared to the N-frame model. In summary, the proposed data-driven RANS models offer advancements in accuracy and provide significant contributions to the field of data-driven turbulence modeling.

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

Mode of access: World Wide Web

ISBN: 9798381164763Subjects--Topical Terms:

555551
Fluid mechanics.
Subjects--Index Terms:

Turbulence modelingIndex Terms--Genre/Form:

554714
Electronic books.

Data Driven Turbulence Modeling.
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504 $a Includes bibliographical references
520 $a In this dissertation we present contributions to the field of data-driven turbulence modeling. The research work centers on the development of data-driven RANS models, which address the predictive limitations of traditional RANS models.Firstly, we introduce the Generalized Non-Linear Eddy Viscosity (GNLEV) model, which shows superior accuracy when compared to both traditional and data-driven linear and non-linear eddy viscosity models. By incorporating a Reynolds stress dependence on additional flow variables we reduced the model form error associated with linear and non-linear eddy viscosity models.Secondly, we present the N-frame model, which incorporates information about orientation with respect to geometry in its formulation to enhance accuracy compared to existing data-driven models. The inputs and outputs are represented in a coordinate system constructed from vorticity and the gradient of the distance to the wall. Using this approach, allowed us to construct a data-driven model with significantly less inputs and outputs compared to the GNLEV model. The N-frame model is implemented using two approaches for turbulent scale consistency: the corrective approach, which utilizes inputs from an inexact RANS simulation, and the iterative approach, which uses inputs from an exact DNS simulation. Numerical results show that the N-frame iterative model outperforms the N-frame corrective model in terms of accuracy. The results also demonstrate that incorporating geometry information into the data-driven model yields accurate predictions.Lastly, we construct an iterative model for CFD design space exploration, utilizing coordinate system and scales specific to the problem in consideration. The tailored approach to the specific design space enables accurate predictions with fewer inputs and transport equations compared to the N-frame model. In summary, the proposed data-driven RANS models offer advancements in accuracy and provide significant contributions to the field of data-driven turbulence modeling.
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