PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK
Using computational fluid dynamics (CFD) to solve fluid flow problems can use a lot of computer processing power and simulation time. Artificial neural networks (ANN) can be regarded as universal learners that are capable of learning nonlinear patterns or relationships among many variables. A ver...
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| Κύριος συγγραφέας: | |
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| Μορφή: | Thesis |
| Γλώσσα: | Αγγλικά |
| Έκδοση: |
DEPARTMENT OF MECHANICAL ENGINEERING
2021
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| Θέματα: | |
| Διαθέσιμο Online: | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/663 |
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| _version_ | 1868227051192844288 |
|---|---|
| author | YASIR, MD. ATIF |
| author_browse | YASIR, MD. ATIF |
| author_facet | YASIR, MD. ATIF |
| author_sort | YASIR, MD. ATIF |
| collection | DSpace |
| description | Using computational fluid dynamics (CFD) to solve fluid flow problems can use a lot of
computer processing power and simulation time. Artificial neural networks (ANN) can be
regarded as universal learners that are capable of learning nonlinear patterns or
relationships among many variables. A very well-known benchmark problem for viscous
incompressible fluid flow in the lid-driven cavity problem. People have developed different
numerical procedures to solve it. It is widely regarded as the first problem people usually
try to solve when they come up with a new approach. This research aims to apply fully
connected neural networks to learn and predict fluid flow inside a lid-driven cavity. A
double lid-driven cavity with top and bottom moving walls having some internal square
objects was selected as a training data to train, test and compare several fully connected
neural networks having different parameters to predict fluid flow inside it. The results show
that by training a neural network to recognize fluid velocity patterns around simple square
objects inside the cavity, it is possible to predict fluid velocities around objects having
relatively complex geometries with significant accuracy in a fraction of the time required
by a CFD solver. The results also show the comparison between effects of using different
mesh sizes in CFD and different learning rates in the neural network model. |
| format | Thesis |
| id | oai:localhost:123456789-663 |
| institution | My University |
| language | English |
| publishDate | 2021 |
| publishDateRange | 2021 |
| publishDateSort | 2021 |
| publisher | DEPARTMENT OF MECHANICAL ENGINEERING |
| publisherStr | DEPARTMENT OF MECHANICAL ENGINEERING |
| record_format | dspace |
| spelling | oai:localhost:123456789-6632021-10-07T03:18:18Z PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK YASIR, MD. ATIF CFD, ANN, Double lid-driven cavity, Fluid flow prediction Using computational fluid dynamics (CFD) to solve fluid flow problems can use a lot of computer processing power and simulation time. Artificial neural networks (ANN) can be regarded as universal learners that are capable of learning nonlinear patterns or relationships among many variables. A very well-known benchmark problem for viscous incompressible fluid flow in the lid-driven cavity problem. People have developed different numerical procedures to solve it. It is widely regarded as the first problem people usually try to solve when they come up with a new approach. This research aims to apply fully connected neural networks to learn and predict fluid flow inside a lid-driven cavity. A double lid-driven cavity with top and bottom moving walls having some internal square objects was selected as a training data to train, test and compare several fully connected neural networks having different parameters to predict fluid flow inside it. The results show that by training a neural network to recognize fluid velocity patterns around simple square objects inside the cavity, it is possible to predict fluid velocities around objects having relatively complex geometries with significant accuracy in a fraction of the time required by a CFD solver. The results also show the comparison between effects of using different mesh sizes in CFD and different learning rates in the neural network model. 2021-10-07T03:18:18Z 2021-10-07T03:18:18Z 2021-03 Thesis http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/663 en application/pdf DEPARTMENT OF MECHANICAL ENGINEERING |
| spellingShingle | CFD, ANN, Double lid-driven cavity, Fluid flow prediction YASIR, MD. ATIF PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK |
| title | PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK |
| title_full | PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK |
| title_fullStr | PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK |
| title_full_unstemmed | PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK |
| title_short | PREDICTION OF FLUID FLOW AROUND 2D SQUARE OBJECTS INSIDE A DOUBLE LID DRIVEN CAVITY USING CFD AND ARTIFICIAL NEURAL NETWORK |
| title_sort | prediction of fluid flow around 2d square objects inside a double lid driven cavity using cfd and artificial neural network |
| topic | CFD, ANN, Double lid-driven cavity, Fluid flow prediction |
| url | http://dspace.mist.ac.bd:8080/xmlui/handle/123456789/663 |
| work_keys_str_mv | AT yasirmdatif predictionoffluidflowaround2dsquareobjectsinsideadoubleliddrivencavityusingcfdandartificialneuralnetwork |