Description
Taylor & Francis Inverse Heat Transfer Fundamentals and Applications 2021 Edition by M. Necat Ozisik, Helcio R.B. Orlande
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 Oezisik 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.Featuresintroduces The Fundamental Concepts Of Inverse Heat Transferpresents In Systematic Fashion The Basic Steps Of Powerful Inverse Solution Techniquesdevelops Inverse Techniques Of Parameter Estimation, Function Estimation, And State Estimationapplies These Inverse Techniques To The Solution Of Practical Inverse Heat Transfer Problemsshows Inverse Techniques For Conduction, Convection, Radiation, And Multi-Physics Phenomenam. Necati Oezisik (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. Table Of Contents : - Part I: Introduction And Parameter Estimation1. Basic Concepts2. Parameter Estimation: Minimization Of An Objective Function Without Prior Information About The Unknown Parameters3. Parameter Estimation: Minimization Of An Objective Function With Prior Information About The Unknown Parameters4. Parameter Estimation: Stochastic Simulation With Prior Information About The Unknown Parameterspart Ii: Function Estimation5. Function Estimation: Minimization Of An Objective Functional Without Prior Information About The Unknown Functions6. Function Estimation: Solution Within The Bayesian Framework Of Statisticswith Prior Information About The Unknown Functionspart Iii: State Estimation7. State Estimation: Kalman Filter8. State Estimation: Particle Filter