Course

Module-1

Inverse heat transfer problem concept, Convex and non-convex functions, Fundamentals of nature of solutions of mathematical models, Well and ill-posed problems, Regularization, Conditional probability, Baye’s theorem, conditional independence, Naïve Bayes.

Module-2

Linear and non-linear optimisation problems, Parameter estimation, Gradient descend methods, Levenberg-Marquard method, Conjugate gradient method, Adjoint problem. Review of governing equations of heat transfer and fluid flow problems, inverse problems, examples, Methods of design of experiments
Differential Evolution Techniques’(genetic algorithm based).

Module-3

Deterministic, heuristic, and hybrid methods for Single-Objective optimization and response surface generation, Adjoint methods, Bayesian approaches for the solution of inverse problems, Low-order models and their use for solving inverse boundary problems, Data, Noise, and Model reduction in inverse problems, Applications.

• CO1 : Capability to understand mathematical background of inverse problems.
• CO2 : Capability to understand the nature of mathematical models and methods to solve them
• CO3 : Capability to convert parameter estimation problems into optimization problems and solve them.
• CO4 : Capability to formulate inverse problems in heat transfer and select appropriate method to solve.
• CO5 : Capability of identify research problems in design of systems involving heat transfer.

• Ozisik, M. N. and Orlande, H. R, Inverse heat transfer : fundamentals and applications. CRC press,2021
• Afshin J.Ghajar, Thermal Measurements and Inverse Techniques, CRC press,2011
• Orlande, Helcio RB., Inverse heat transfer : fundamentals and applications. CRCPress,2021
• Beck, James Vere, and Kenneth J. Arnold. Parameter estimation in engineering and science. James Beck, 1977.