M. Tech in Computational Mechanics

Overview

Computational mechanics addresses the study of problems grounded in mechanics through the use of numerical methods. The advancement of computational hardware over the past few decades has radically changed the product design lifecycle. These advancements have enabled simulation based design to become an important part of the design process. The success of computational approaches can be assessed by the increasing adoption of these techniques across various industry verticals and their growing impact on the economy. As per Industry ARC reports, the global FEM market would reach $3.06B by 2025 and as per Business Wire reports, the global CFD market is expected to reach a value of $3.74B by 2026.

About the Program

This 2 year Masters program will be offered by the Department of Mechanical & Aerospace Engineering, Ecole Centrale School of Engineering, Mahindra University. The program emphasizes on three aspects, viz, programming, widely used computational methods such as FEM and CFD, and coupled problems/ Multiphysics. An additional differentiator is the increased number of hours of practical training which would focus on solving industry relevant problems. The emphasis on these areas and form of pedagogy has been placed in accordance with inputs from multiple industry experts from a wide range of industry verticals. Further, students will be trained on advanced concepts and applications through core electives and open electives which will cover areas such as turbulent flows, non-linear FEM and optimization.

The program has been designed to allocate about a third of the required 62 credits for extended industry internship or for conducting original research under the supervision of the faculty at Mahindra University. The academics of the second year of the program are scheduled to provide the student with the opportunity to pursue a full-time internship. This enables students to understand the work culture and expectations of the industry and also provides an in-depth experience in solving industry relevant problems.

Program Outcomes

Graduates of the program are expected to

  • Develop computational models of problems by capturing the relevant physics and phenomena of the problem.

  • Validate designs with relevant experimental protocols and required data analyses.

  • Develop appropriate additional models or programming frameworks to address bespoke process and service conditions.

  • Understand the product and development lifecycle and adapt to relevant development process.

Structure of Curriculum

The curriculum of the proposed program has a balance of courses in Solid and Fluid mechanics, Dynamics and Programming and Data analysis. Students undergo mandatory ‘core’ courses in basic programming, computational methods applied to Solids, Fluids and Dynamics. Further, students have the option of choosing electives which would deepen their knowledge in selected streams. The curriculum is designed to make the graduates future ready by incorporating differentiators such as Multiphysics and Data analysis in the basic requirements. Further, the program is structured in such a way that an extended internship/ research project can be carried out in the second year.

Eligibility Criteria

  • Candidates should have graduated with a full-time degree from any recognized University/Institute with a minimum aggregate of 60% or equivalent grade.

  • B.E./B.Tech in Mechanical, Aerospace, Civil* or Chemical* Engineering with valid GATE Score is Mandatory.

  • Candidates appearing for their final semester exam in the current year are also eligible to apply.

Note: * May have to undergo a bridge course prior to the commencement of the M.Tech Program.

Admission Process

  • Admission for GATE qualified students: Applicants with a valid GATE score & Percentile score 80 & above will undergo an interview for admission.
  • Admission for non-GATE qualified students: Candidates not having valid GATE score or have Percentile score less than 80 will have to appear in a written test conducted by ECSE-MU, followed by an interview for the shortlisted candidates.

Note: Deserving candidates will be awarded stipend as per university policy.

Career Roles

Graduates of the program are expected to function as Design/ Mechanical Engineer, Analysis lead, Research engineer in any of the following verticals

  • Defense and Aerospace

  • Automotive and Electric vehicles

  • Materials, Processing and Manufacturing

  • Energy and Renewables

  • Engineering services and consulting

Curriculum

Proposed Course Curriculum Outline – Semester Wise

Semester I

Course Code Course Name L T P Credit
ME5101 Numerical methods 3 0 0 3
ME5102 Programming with Python 0 0 3 1.5
ME5103 Finite Element Methods and lab 3 0 2 4
ME5104 Computational Fluid Dynamics and programming 3 0 2 4
ME5105 Applied Solid Mechanics* 3 0 0 1.5
ME5106 Applied Fluid Mechanics* 3 0 0 1.5
ME5107 Introduction to Systems Engineering* 3 0 0 1.5
Total Credits 17

Semester II

Course Code Course Name L T P Credit
ME5201 Multiphysics 2 0 1 2.5
ME5202 Computational dynamics and vibrations 3 0 2 4
ME5203 Programming FEM 0 0 2 1
ME5204 CFD Lab 0 0 2 1
ME5205 Communication skills and technical writing 2 0 0 2
ME5206 Experimental methods and statistics* 3 0 0 1.5
ME5xxx Elective I 3 0 0 3
Total Credits 15

Semester III

Course Code Course Name L T P Credit
ME5xxx Elective II* 6 0 0 3
ME5xxx Elective III* 6 0 0 3
ME5301 Thesis/ Internship/ Project** 0 0 28 7
Total Credits 13

Semester IV

Course Code Course Name L T P Credit
ME5401 Thesis/ Internship 0 0 30 15
Total Credits 15

* Fractal for first half of the semester
** Fractal for second half of the semester
Total Credits for the program: 60

Electives
  • ME5001 – Turbulent flows
  • ME5002 – Compressible flows
  • ME5003 – Reacting flows
  • ME5004 – Turbo machinery
  • ME5005 – Special topics in Fluid Mechanics I
  • ME5006 – Special topics in Fluid Mechanics II
  • ME5011 – Nonlinear FEM
  • ME5012 – Materials Modelling
  • ME5013 – Fracture and Fatigue
  • ME5014 – Composite materials
  • ME5021 – Design optimization
  • ME5022 – Machine Learning