This book introduces the fundamental concepts of inverse heat transfer solutions and their applications for solving problems in convective, conductive, radiative, and multi-physics problems. Inverse Heat Transfer: Fundamentals and Applications, Second Edition includes techniques within the Bayesian framework of statistics for the solution of inverse problems. By modernizing the classic work of the late Professor M. Necati Özisik and adding new examples and problems, this new edition provides a powerful tool for instructors, researchers, and graduate students studying thermal-fluid systems and heat transfer.
- Introduces the fundamental concepts of inverse heat transfer
- Presents in systematic fashion the basic steps of powerful inverse solution techniques
- Develops inverse techniques of parameter estimation, function estimation, and state estimation
- Applies these inverse techniques to the solution of practical inverse heat transfer problems
- Shows inverse techniques for conduction, convection, radiation, and multi-physics phenomena
M. Necati Özisik (1923–2008) retired in 1998 as Professor Emeritus of North Carolina State University’s Mechanical and Aerospace Engineering Department.
Helcio R. B. Orlande is a Professor of Mechanical Engineering at the Federal University of Rio de Janeiro (UFRJ), where he was the Department Head from 2006 to 2007.
Part I: Introduction and Parameter Estimation
1. Basic Concepts
2. Parameter Estimation: Minimization of an Objective Function without Prior Information about the Unknown Parameters
3. Parameter Estimation: Minimization of an Objective Function with Prior Information about the Unknown Parameters
4. Parameter Estimation: Stochastic Simulation with Prior Information about the Unknown Parameters
Part II: Function Estimation
5. Function Estimation: Minimization of an Objective Functional without Prior Information about the Unknown Functions
6. Function Estimation: Solution within the Bayesian Framework of Statistics
with Prior Information about the Unknown Functions
Part III: State Estimation
7. State Estimation: Kalman Filter
8. State Estimation: Particle Filter