Dr. Tikendra Nath Verma has completed B.E. in Mechanical Engineering, M.Tech. in Thermal Engineering and PhD in Thermal Engineering (CFD). He is currently working as Assistant Professor in the department of Mechanical Engineering, National Institute of Technology Manipur. His areas of interest include Heat Transfer, CFD, I.C. Engines and Renewable Energy etc
Mr. Th.S.Singh has completed B.E. in Mechanical Engineering, M.E. in Thermal Engineering. He is presently working as Lecturer in the department of Mechanical Engineering, National Institute of Technology Manipur. His areas of interest include Alternative fuels, I.C Engines, Renewable energy systems, Heat transfer and Fluid flow etc.
Mr. Dheerendra Vikram Singh is currently working as Professor in the department of Mechanical Engineering, CMR Engineering College, Hyderabad. His areas of interest include AbsorptionRefrigeration System, Exergy Analysis, Artificial neural network, I.C. engine, Renewable energy system, Heat transfer and fluid flow.
Fluid flow and heat transfer plays a very important role in nature. They are coupled and rarely an engineer solves a problem of either pure fluid flow or pure heat transfer. The various applications of fluid flow and heat transfer are: biomedical engineering (blood flows through arteries and veins), all methods of power production (thermal, nuclear, hydraulic, wind, solar), HVAC, metrology (weather prediction), processing of solid and liquid wastes, marine engineering etc. Main advantages of Computational fluid dynamics are low cost, high speed, complete information, ability to simulate real condition and ability to simulate ideal condition. The disadvantages of numerical predictions are the discretization error, truncation error and round off error and difficulty in simulating complex boundary conditions. Computational Fluid Dynamics or simply CFD is the method of utilization of numerical solutions to predict the fluid flow and hear transfer of the test subject. Since, conventional experimental methods are quite expensive as they are based on the similarity laws of some dimensionless numbers and the method is relatively slower, CFD provides the room for a faster, qualitative and quantitative prediction of fluid flow on the subject under test using mathematical modeling (partial differential equations), numerical methods (discretization and solution techniques), software tools etc.