Infrared image filtering applied to the restoration of the convective heat transfer coefficient distribution in coiled tubes

Abstract

This paper presents and assesses an inverse heat conduction problem (IHCP) solution procedure which was developed to determine the local convective heat transfer coefficient along the circumferential coordinate at the inner wall of a coiled pipe by applying the filtering technique approach to infrared temperature maps acquired on the outer tube’s wall. The data−processing procedure filters out the unwanted noise from the raw temperature data to enable the direct calculation of its Laplacian which is embedded in the formulation of the inverse heat conduction problem. The presented technique is experimentally verified using data that were acquired in the laminar flow regime that is frequently found in coiled−tube heat−exchanger applications. The estimated convective heat transfer coefficient distributions are substantially consistent with the available numerical results in the scientific literature.

Local heat-transfer coefficient estimation in cross-helix corrugated tubes under turbulent regime

“…A suitable method to solve this problem which was found in Gaussian filtering that allows to remove the noise in raw temperature data. Various Researchers [14] evaluated the performance of the Gaussian kernel using this particular method. In a 2-D frequency domain, the transfer function of this sort of filter can be written as follows.…”

Filtered spectral differentiation method for numerical differentiation of periodic functions with application to heat flux estimation

“…Proceeding in this way, it can be readily seen that the estimate Q solves a linear least squares problem with coefficient matrix J and right hand side T(0)− T (Bozzoli et al 2014), where J is the so-called sensitivity matrix and T(0) is the solution to (65)-(67) constrained to Q = 0. Although the cost of computing the matrix J can become expensive, as we need to solve M forward problems, the approach has been shown to be effective in a number of practical applications (Bozzoli et al 2015;Sovari and Malinen 2007). The purpose of this section is to show that the optimization approach described above can be circumvented as far as the thickness tube wall is small and the numerical differentiation problem is solved in a stable way.…”

“…We note in passing that estimate (72) has been derived differently by Bozzoli et al Bozzoli et al 2015, in which second-order derivatives are calculated after the data are preprocessed in order to filter out high-frequency signal components through a Gaussian filter. Our estimation procedure can be summarized as follows:…”

“…Numerical differentiation from noisy data is an important issue in a wide range of areas of science and technology. Applications areas include image processing (Wang et al 2002), inverse heat conduction problems (Bozzoli et al 2015(Bozzoli et al , 2017, parameter identification (Hanke Communicated by Antonio José Silva Neto. and Scherzer 1999), viscoelastic mechanics (Sovari and Malinen 2007) and blood glucose predictions (Mhaskar et al 2013). The main difficulty found in practical applications is that numerical differentiation is an ill-posed problem; hence, small input data errors can produce large errors in the computed derivative.…”

Experimental Study of the Corrugation Profile Effect on the Local Heat Transfer Coefficient

“…Colaço et al [26] used the so-called "reciprocity function approach" for the non-intrusive estimation of internal heat transfer coefficients in ducts. Ngendahayo et al [27] adopted the Tikhonov regularization method for the estimation of surface temperatures from interior measurements, while Bozzoli et al [28] applied the Tikhonov regularization method for estimating the local heat transfer coefficient in coiled pipes. Among these estimation approaches, the one that best fits many unknown variables and input signals, like temperature maps with high resolution, i.e., infrared maps, is the filtering approach [29].…”