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Data Driven Surrogate Modeling of Tw...

Ganti, Himakar.

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Data Driven Surrogate Modeling of Two-Phase Flows.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Data Driven Surrogate Modeling of Two-Phase Flows./
作者:	Ganti, Himakar.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2023,
面頁冊數:	130 p.
附註:	Source: Dissertations Abstracts International, Volume: 85-05, Section: B.
Contained By:	Dissertations Abstracts International85-05B.
標題:	Aerospace engineering. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=30866464
ISBN:	9798380824194

Data Driven Surrogate Modeling of Two-Phase Flows.
Ganti, Himakar.

Data Driven Surrogate Modeling of Two-Phase Flows. - Ann Arbor : ProQuest Dissertations & Theses, 2023 - 130 p.

Source: Dissertations Abstracts International, Volume: 85-05, Section: B.

Thesis (Ph.D.)--University of Cincinnati, 2023.

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

The ready availability of computational resources has enabled scientists and researchers to generate huge volumes of data for multiphase flows. For routine design calculations, detailed numerical simulations of multiphase flows with disparate time and length scales and turbulence resolution (DNS/ LES) are resource intensive and computationally expensive. A data-driven surrogate model built with available simulation and experimental data can be used instead of intensive numerical calculations and in the absence of an available mathematical models. Such an attempt is made in this thesis, where data-driven surrogate models are built and developed with applications to two-phase flows.This study is the first of its kind to have developed data-driven surrogate modelling frameworks and algorithms with applications to spatiotemporally varying, statistically stationary and steady state multiphase flows. This work discusses the frameworks necessary for identifying and conducting the surrogate modeling process and with quantified errors. GPs were selected as the algorithm of choice as they can be used with the relatively fewer number of numerical simulations data for training purposes. The GP algorithm was modified to work with multiple independent variables of a multiphase flow configuration. When multiple ML algorithms are available and applicable for the same multiphase flow configuration, it becomes necessary to know how each of the algorithm will perform in terms of efficiency, accuracy and speedup. In this thesis, performance comparison of GP and NN machine algorithms was conducted for accuracy and speedup for a 2D Rayleigh-Taylor instability configuration. Prediction results were reported for accuracy, speedup and efficiency. This thesis addresses three major issues for building, developing and comparing various learning algorithms for application to multiphase flows, by identifying a set of guidelines to -1. build and develop data-driven surrogate models based on Gaussian processes for spatiotemporally varying and statistically stationary multiphase flows.2. modify Gaussian processes to enhance prediction capability of multiphase flows with more than one independent variable.3. performance comparison of Gaussian Process and Neural Network based Machine Learning frameworks for efficiency, accuracy and speedup with extensions to other possible machine learning algorithms.It is hoped that this research contributed in establishing guidelines and approaches for building computationally inexpensive data-driven surrogate models for multiphase flows that can replace numerical simulations for initial and routine design for power generation devices.

ISBN: 9798380824194Subjects--Topical Terms:

1002622
Aerospace engineering.
Subjects--Index Terms:

Multiphase flows

Data Driven Surrogate Modeling of Two-Phase Flows.
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506 $a This item must not be sold to any third party vendors.
520 $a The ready availability of computational resources has enabled scientists and researchers to generate huge volumes of data for multiphase flows. For routine design calculations, detailed numerical simulations of multiphase flows with disparate time and length scales and turbulence resolution (DNS/ LES) are resource intensive and computationally expensive. A data-driven surrogate model built with available simulation and experimental data can be used instead of intensive numerical calculations and in the absence of an available mathematical models. Such an attempt is made in this thesis, where data-driven surrogate models are built and developed with applications to two-phase flows.This study is the first of its kind to have developed data-driven surrogate modelling frameworks and algorithms with applications to spatiotemporally varying, statistically stationary and steady state multiphase flows. This work discusses the frameworks necessary for identifying and conducting the surrogate modeling process and with quantified errors. GPs were selected as the algorithm of choice as they can be used with the relatively fewer number of numerical simulations data for training purposes. The GP algorithm was modified to work with multiple independent variables of a multiphase flow configuration. When multiple ML algorithms are available and applicable for the same multiphase flow configuration, it becomes necessary to know how each of the algorithm will perform in terms of efficiency, accuracy and speedup. In this thesis, performance comparison of GP and NN machine algorithms was conducted for accuracy and speedup for a 2D Rayleigh-Taylor instability configuration. Prediction results were reported for accuracy, speedup and efficiency. This thesis addresses three major issues for building, developing and comparing various learning algorithms for application to multiphase flows, by identifying a set of guidelines to -1. build and develop data-driven surrogate models based on Gaussian processes for spatiotemporally varying and statistically stationary multiphase flows.2. modify Gaussian processes to enhance prediction capability of multiphase flows with more than one independent variable.3. performance comparison of Gaussian Process and Neural Network based Machine Learning frameworks for efficiency, accuracy and speedup with extensions to other possible machine learning algorithms.It is hoped that this research contributed in establishing guidelines and approaches for building computationally inexpensive data-driven surrogate models for multiphase flows that can replace numerical simulations for initial and routine design for power generation devices.
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653 $a Machine learning algorithms
653 $a Power generation devices
653 $a Surrogate modeling process
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710 2 $a University of Cincinnati. $b Engineering and Applied Science: Aerospace Engineering. $3 3277533
773 0 $t Dissertations Abstracts International $g 85-05B.
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791 $a Ph.D.
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