دانلود کتاب Inverse Heat Transfer. Fundamentals and Applications

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Cover
Half Title
Series Page
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Preface of the First Edition
Authors
PART I: Introduction and Parameter Estimation
Chapter 1 Basic Concepts
1.1 Inverse Heat Transfer Problem Concept
1.2 Classification of IHTPs
1.3 Difficulties in the Solution of Inverse Heat Transfer Problems
1.4 An Overview of Solution Techniques for Inverse Heat Transfer Problems
1.5 Basic Steps for the Solution of Inverse Heat Transfer Problems
Problems
Note 1: Statistical Concepts
Random Variable
Probability Distribution
Expected Value of X
Expected Value of a Function g(X)
Variance of a Random Variable X
Covariance of Two Random Variables X and Y
Gaussian Distribution
Uniform Distribution
Rayleigh Distribution
Gamma Distribution
Beta Distribution
Chi-Square Distribution
Covariance Matrix
Multivariate Gaussian Distribution
Chapter 2 Parameter Estimation: Minimization of an Objective Function without Prior Information about the Unknown Parameters
2.1 Objective Function
2.2 Technique I: The Levenberg-Marquardt Method
The Direct Problem
The Inverse Problem
The Iterative Procedure for Technique I
The Stopping Criteria for Technique I
The Computational Algorithm for Technique I
2.3 Technique II: The Conjugate Gradient Method for Parameter Estimation
The Direct Problem
The Inverse Problem
The Iterative Procedure for Technique II
The Stopping Criterion for Technique II
The Computational Algorithm for Technique II
2.4 Sensitivity Coefficients
Methods of Determining the Sensitivity Coefficients
Direct Analytic Solution for Determining Sensitivity Coefficients
The Boundary Value Problem Approach for Determining the Sensitivity Coefficients
Finite Difference Approximation for Determining Sensitivity Coefficients
2.5 Design of Optimum Experiments
2.6 The Use of Multiple Sensors
2.7 Statistical Analysis
2.8 Estimation of Thermal Conductivity Components of an Orthotropic Heat Conducting Medium
The Direct Problem
The Inverse Problem
Analysis of the Sensitivity Coefficients and Design of Optimum Experiments
Parameter Estimation and Statistical Analysis
2.9 Technique III: The Conjugate Gradient Method with Adjoint Problem for Parameter Estimation
The Direct Problem
The Inverse Problem
The Sensitivity Problem
The Adjoint Problem
The Gradient Equation
The Iterative Procedure for Technique III
The Stopping Criterion for Technique III
The Computational Algorithm for Technique III
The Use of Multiple Sensors
2.10 Estimation of a Heat Source Term in a Heat Conduction Problem
Problems
Note 1: Search Step-Size for Technique II
Note 2: Search Step-Size for Technique III
Chapter 3 Parameter Estimation: Minimization of an Objective Function with Prior Information about the Unknown Parameters
3.1 Objective Function
Maximum a Posteriori Objective Function with a Uniform Prior
Maximum a Posteriori Objective Function with a Gaussian Prior
Maximum a Posteriori Objective Function with a Truncated Gaussian Prior
3.2 Minimization of the Objective Function
3.3 Identification of the Thermophysical Properties of Semi-Transparent Materials
The Direct Problem
The Inverse Problem
Analysis of the Sensitivity Coefficients and Design of Optimum Experiments
Parameter Estimation and Statistical Analysis
Problems
Chapter 4 Parameter Estimation: Stochastic Simulation with Prior Information about the Unknown Parameters
4.1 Markov Chains
4.2 Technique IV: Markov Chain Monte Carlo (MCMC) Method
Proposal Distribution
Random Walk
Independent Move
4.3 MCMC Estimation of Thermal Conductivity Components of an Orthotropic Heat Conducting Medium
The Direct Problem
The Inverse Problem
Stochastic Simulation
4.4 MCMC Estimation of Thermal Conductivity and Volumetric Heat Capacity of Viscous Liquids with the Line Heat Source Probe
The Direct Problem
The Inverse Problem
Analysis of the Sensitivity Coefficients and Design of Optimum Experiments
Stochastic Simulation
4.5 MCMC Estimation of Thermophysical Parameters of Thin Metal Films Heated by Fast Laser Pulses
The Direct Problem
The Inverse Problem
Analysis of the Sensitivity Coefficients and Design of Optimum Experiments
Stochastic Simulation
4.6 Analysis of Markov Chains
Statistics
Convergence of the Markov Chain
Proposal Distribution
4.7 Reduction of the Computational Time for Solving Inverse Problems with Technique IV
Delayed Acceptance Metropolis-Hastings (DAMH) Algorithm
Approximation Error Model (AEM) Approach
4.8 Approximation Error Model to Account for Convective Effects in the Line Heat Source Probe Method
Problems
Note 1: Metropolis-Hastings Algorithm with Sampling by Blocks of Parameters
PART II: Function Estimation
Chapter 5 Function Estimation: Minimization of an Objective Functional without Prior Information about the Unknown Functions
5.1 Technique V: The Conjugate Gradient Method with Adjoint Problem for Function Estimation
The Direct Problem
The Inverse Problem
The Sensitivity Problem
The Adjoint Problem
The Gradient Equation
The Iterative Procedure for Technique V
The Stopping Criterion for Technique V
The Computational Algorithm for Technique V
5.2 Estimation of the Spacewise and Timewise Variations of the Wall Heat Flux in Laminar Flow
Direct Problem
Inverse Problem
Sensitivity Problem
Adjoint Problem
Gradient Equation
Iterative Procedure
Results
5.3 Simultaneous Estimation of Spatially Dependent Diffusion Coefficient and Source Term in a Diffusion Problem
Direct Problem
Inverse Problem
Sensitivity Problems
Adjoint Problem
Gradient Equations
Iterative Procedure
Results
5.4 Simultaneous Estimation of the Spacewise and Timewise Variations of Mass and Heat Transfer Coefficients in Drying
Direct Problem
Inverse Problem
Sensitivity Problems
Adjoint Problem
Gradient Equations
Iterative Procedure
Results
Problems
Note 1: Hilbert Spaces
Note 2: Conjugate Gradient Method of Function Estimation
Note 3: Additional Measurement for Selecting the Stopping Criterion of the Conjugate Gradient Method
Chapter 6 Function Estimation: Solution within the Bayesian Framework of Statistics with Prior Information about the Unknown Functions
6.1 Prior Distributions
Hierarchical Models
6.2 Estimation of the Kidney Metabolic Heat Generation Rate
Direct Problem
Inverse Problem
Results
6.3 Temperature Estimation of Inflamed Bowel
Direct Problem
Inverse Problem
Results
6.4 Detection of Contact Failures by Using Integral Transformed Measurements
Direct Problem
Inverse Problem
Results
6.5 Accelerated Bayesian Inference for the Estimation of Spatially Varying Heat Flux
Direct Problem
Inverse Problem
Results
Problems
PART III: State Estimation
Chapter 7 State Estimation: Kalman Filter
7.1 State Estimation Problem
7.2 Technique VI: The Kalman Filter
7.3 Estimation of a Transient Boundary Heat Flux That Varies over the Surface
7.4 The Steady-State Kalman Filter
Problems
Chapter 8 State Estimation: Particle Filter
8.1 Technique VII: The Sampling Importance Resampling (SIR) Algorithm
8.2 Technique VII: The Auxiliary Sampling Importance Resampling (ASIR) Algorithm
8.3 Technique VII: The Algorithm of Liu and West
8.4 Estimation of the Fire Front in Regional Scale Wildfire Spread
8.5 A Comparison of Particle Filter Algorithms in Bioheat Transfer
Problems
Appendix: Approximate Bayesian Computation
References
Index