Master viscous flow analysis using Navier-Stokes equations in this comprehensive 12-week MIT course.
Master viscous flow analysis using Navier-Stokes equations in this comprehensive 12-week MIT course.
Dive deep into the world of viscous fluid dynamics with MIT's Advanced Fluid Mechanics course. This program focuses on the application of Navier-Stokes equations to various flow scenarios, including pipe, channel, and free surface flows. You'll explore crucial concepts such as dynamical similarity, dimensional analysis, and Stokes flows. The course covers advanced topics like similarity solutions, transient responses, lubrication analysis, and surface tension effects. Through a combination of lecture videos, demonstrations, and extensive problem sets, you'll develop the skills to analyze and model complex fluid physics phenomena. This course is ideal for engineers and scientists in mechanical, chemical, and process industries seeking to optimize flow processes and fluid handling problems. It's the second part of a three-course sequence, designed to provide a comprehensive understanding of incompressible fluid mechanics at a graduate level.
4.9
(9 ratings)
Instructors:
English
English
What you'll learn
Apply Navier-Stokes equations to viscous-dominated flows
Analyze pipe flows, channel flows, and free surface flows
Utilize dynamical similarity and dimensional analysis in fluid mechanics
Solve problems involving Stokes flows and similarity solutions
Develop transient response models for fluid systems
Perform lubrication analysis for thin films and free surfaces
Skills you'll gain
This course includes:
Live video
Lecture concept checks, Practice problems, Extensive problem sets
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Limited Access access
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There are 5 modules in this course
This advanced course delves into the application of Navier-Stokes equations for viscous flows, providing a comprehensive understanding of complex fluid dynamics. The curriculum covers a wide range of topics, starting with the fundamentals of Navier-Stokes equations and their application to various flow scenarios such as pipe flows, channel flows, and free surface flows. Students will explore the concept of dynamical similarity and learn to apply dimensional analysis to complex problems. The course progresses to more advanced topics, including Stokes flows, similarity solutions, and transient responses. A significant portion is dedicated to lubrication analysis for thin fluid films and slender geometries, as well as the effects of surface tension. Throughout the course, students will engage with lecture and demo videos, concept checks, practice problems, and extensive problem sets, allowing for a deep, practical understanding of the material. This course is designed to equip students with the ability to analyze and develop physically-based, approximate models for complex fluid physics phenomena, skills that are directly applicable in industrial and academic research settings.
The Navier-Stokes equation and viscous flow
Module 1
Pipe flows, channel flows and free surface flows
Module 2
Dynamical Similarity and dimensional analysis
Module 3
More Complex Viscous Flows; Stokes Flows, Similarity Solutions and Transient Responses
Module 4
Lubrication Analysis for Thin fluid films and slender geometries
Module 5
Fee Structure
Instructors
1 Course
Expert in Complex Fluid Dynamics and Soft Matter Mechanics
Bavand Keshavarz is an Assistant Professor in the Thomas Lord Department of Mechanical Engineering and Materials Science at Duke University. After completing his B.S. from Sharif University of Technology and M.S. from the University of British Columbia, he earned his Ph.D. from MIT in 2017. His doctoral research focused on the nonlinear dynamics of complex fluids in fragmentation and fracture, developing new experimental tools for precise rheological measurements. His excellence in teaching was recognized through the Wunsch Foundation Silent Hoist and Crane Award in 2013 and 2017 for outstanding contributions as a teaching assistant in fluid mechanics courses at MIT.
1 Course
Pioneer in Learning Engineering and Digital Manufacturing Education
Dr. John Liu is a distinguished educator and researcher at MIT, where he leads the Learning Engineering and Practice (LEAP) Group as Principal Investigator. After earning his B.S. in Applied Physics from Caltech and both S.M. and Ph.D. in Mechanical Engineering from MIT, he has established himself as an innovator in engineering education and digital learning. His work spans multiple disciplines, including mixed reality, haptic experiences, and workforce development solutions, particularly focusing on addressing the manufacturing skills gap. As former Director of the Principles of Manufacturing MicroMasters program, he has helped transform manufacturing education through digital technology, reaching over 200,000 learners globally. His research interests encompass educational technology, MOOC development, and curriculum design, with particular emphasis on open-ended assessments for scalable education settings. His excellence in education has been recognized through awards including Best Paper at the American Society Engineering Education in 2020. Currently, he leads education and workforce development efforts for MIT's Manufacturing@MIT initiative while continuing to innovate in digital learning approaches.
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4.9 course rating
9 ratings
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