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Data Driven Turbulence Modeling.

Parmar, Basu.

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Data Driven Turbulence Modeling.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Driven Turbulence Modeling./
作者:	Parmar, Basu.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	176 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
Contained By:	Dissertations Abstracts International85-06B.
標題:	Aerospace engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30811342
ISBN:	9798381164763

Data Driven Turbulence Modeling.
Parmar, Basu.

Data Driven Turbulence Modeling. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 176 p.

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

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

This item must not be sold to any third party vendors.

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.

ISBN: 9798381164763Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Turbulence modeling

Data Driven Turbulence Modeling.
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100 1 $a Parmar, Basu. $0 (orcid)0009-0006-5477-8717 $3 3767065
245 1 0 $a Data Driven Turbulence Modeling.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2023
300 $a 176 p.
500 $a Source: Dissertations Abstracts International, Volume: 85-06, Section: B.
500 $a Advisor: Evans, John.
502 $a Thesis (Ph.D.)--University of Colorado at Boulder, 2023.
506 $a This item must not be sold to any third party vendors.
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.
590 $a School code: 0051.
650 4 $a Aerospace engineering. $3 1002622
650 4 $a Engineering. $3 586835
650 4 $a Fluid mechanics. $3 528155
653 $a Turbulence modeling
653 $a Neural networks
653 $a RANS models
653 $a Turbulence
653 $a Data-driven model
690 $a 0538
690 $a 0800
690 $a 0537
690 $a 0204
710 2 $a University of Colorado at Boulder. $b Aerospace Engineering. $3 1030473
773 0 $t Dissertations Abstracts International $g 85-06B.
790 $a 0051
791 $a Ph.D.
792 $a 2023
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30811342