Academic Staff Profiles

Dr. T. P. Miyanawala

Dept. of Mechanical Engineering,

University of Moratuwa

Moratuwa, Sri Lanka. 10400 

+94112650301 Extension: 4539

tharindum@uom.lk

ORCID: 0000-0002-2484-8896

Teaching and Administrative Work: 

ME2060 - Mechanics of Materials - II

ME2050 - Mechanics of Machines - II

ME4150 - Air Frame Structures and Design

ME4372 - Aerodynamics

ME4090 - Industrial Automation

Research Activities : 

Education and Training : 

Ph. D. in Mechanical Engineering, National University of Singapore - 2019

B. Sc. Engineering (Honours) in Mechanical Engineering, University of Moratuwa - 2014

Career : 

Professional Affiliations

Member - American Physical Society
Member - American Society of Mechanical Engineering

Career History

2019 May to date - Lecturer (On contract), Department of Mechanical Engineering, University of Moratuwa

2015 August to 2019 May - Doctoral Researcher, National University of Singapore

2014 August to 2015 July - Lecturer (Temporary), Department of Mechanical Engineering, University of Moratuwa

Honours and Awards : 

Gold Medal for the Mechanical Engineering Graduand who has obtained the highest overall Grade Point Average of 3.8 or above at the B.Sc. Engineering Honours Degree examinations. (Donated by Family of late Mr. Jayaweera Kuruppu) - Convocation, 2014 - University of Moratuwa

Publications : 

Journal Articles:
1) Miyanawala, T. P. and Jaiman, R. K., 2019. Decomposition of wake dynamics in fluid–structure interaction via low-dimensional models. Journal of Fluid Mechanics867, pp.723-764.
2) Miyanawala, T. P. and Jaiman, R. K., 2018. Self-sustaining turbulent wake characteristics in fluid–structure interaction of a square cylinder. Journal of Fluids and Structures77, pp.80-101.
3) Jaiman, R. K., Guan, M. Z. and Miyanawala, T. P., 2016. Partitioned iterative and dynamic subgrid-scale methods for freely vibrating square-section structures at subcritical Reynolds number. Computers & Fluids133, pp.68-89.
 
Conference papers:
1) Miyanawala, T. P. and Jaiman, R. K., 2018, June. A Novel Deep Learning Method for the Predictions of Current Forces on Bluff Bodies. In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers.
2) Mao, X., Joshi, V., Miyanawala, T. P. and Jaiman, R. K., 2018, June. Data-Driven Computing With Convolutional Neural Networks for Two-Phase Flows: Application to Wave-Structure Interaction. In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers.
3) Guan, M. Z., Narendran, K., Miyanawala, T. P., Ma, P. F. and Jaiman, R. K., 2017, June. Control of flow-induced motion in multi-column offshore platform by near-wake jets. In ASME 2017 36th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers.
4) Miyanawala, T. P., Guan, M. Z. and Jaiman, R. K., 2016, June. Flow-induced vibrations of a square cylinder with combined translational and rotational oscillations. In ASME 2016 35th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers.
 
Conference presentations:
1) Miyanawala, T. P. and Jaiman, R. K., 2018. A Machine Learning Model for Unsteady Wake Dynamics. In APS Division of Fluid Dynamics Meeting AbstractsBulletin of the American Physical Society.
2) Miyanawala, T. P. and Jaiman, R. K., 2017. Convolutional Neural Networks for Wake Flow Predictions. In APS Division of Fluid Dynamics Meeting Abstracts, Bulletin of the American Physical Society.
3) Jaiman, R. K. and Miyanawala T. P., 2018. Using Deep Neural Networks for Data-Driven Inverse Modeling of Turbulent Wake Dynamics. In 13th World Congress on Computational Mechanics Abstracts.
4) Mao X., Joshi V., Miyanawala T. P.  and Jaiman R. K., 2018. Data-driven Computing with Deep Neural Networks for Inverse Modeling of Two-phase Flows. In 13th World Congress on Computational Mechanics Abstracts.
 
Preprints:
1) Miyanawala, T. P. and Jaiman, R. K., 2018. A Low-Dimensional Learning Model via Convolutional Neural Networks for Unsteady Wake-Body Interaction. arXiv preprint arXiv:1807.09591.
2) Miyanawala, T. P. and Jaiman, R. K., 2017. An efficient deep learning technique for the navier-stokes equations: Application to unsteady wake flow dynamics. arXiv preprint arXiv:1710.09099.
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Research Opportunities: 

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