An Inverse Problem Method for Overall Heat Transfer Coefficient Estimation in a Partially Filled Rotating Cylinder

Abstract

The objective of this article is to study the estimation of an overall heat transfer coefficient in a partially filled rotating cylinder. Herein is an inverse analysis for estimating the overall heat transfer coefficient in an arbitrary cross-section of the aforementioned system from the temperatures measured on the shell. The material employs the finite-volume method to solve the direct problem. The hybrid effective algorithm applied here contains the local optimization algorithm to estimate the unknown parameter by minimizing the objective function. The data measured here are simulated by adding random errors to the exact solution. An investigation is made of the impact of the measurement errors on the accuracy of the inverse analysis. Two-optimization algorithms in determining the overall heat transfer coefficient are used. It is determined that the Conjugate Gradient Method is better than the Levenberg-Marquardt Method because the former produces greater accuracy for the same measurement errors. The resulting observation indicates that good agreement exists between the exact value and estimated result for both algorithms.

Numerical Prediction and Analysis of Surface Transfer Coefficients on Moist Object During Heat and Mass Transfer Application

“…They found that the convective heat transfer coefficient varied from 1.31 to 12.7 W/m 2 -• C for different crops with an experimental error up to 20%. A method was introduced for estimating the overall heat transfer coefficient in the partially filled rotating cylinder by Farhaneih and Heydari [6]. Temperature reading on the surface of the rotating cylinder was taken and the overall heat transfer coefficient was estimated using inverse heat transfer techniques.…”

Surface transfer coefficients estimation for heat conduction problem using the Bayesian framework

“…Instead of measuring directly, an alternative method is investigated by solving an inverse two-dimensional non-linear heat conduction problem. Several methods have been proposed in the literature to solve such inverse problem as presented in [13] with primary examples for the estimation of heat transfer coefficients in [14,15]. Here, the Bayesian framework statistics is selected for estimating the model parameters [16,17].…”