Optimal design of compact heat exchangers by an artificial neural network method
(2003) ASME Summer Heat Transfer Conference (HT2003), 2003 In Proceedings of the ASME Summer Heat Transfer Conference p.655664 Abstract
 The artificial neural network (ANN) methods are introduced (mainly for calculation of thermal and hydraulic coefficients) into a computeraided design code of compact heat exchangers (CCHE). CCHE integrates the optimization, database, and process drawing into a software package. In the code, a strategy is developed for the optimization of compact heat exchangers (CHEs), which is a problem with changeable objective functions and constraints. However, the applicability and/or accuracy of all these methods are limited by the availability of reliable data sets of the heat transfer coefficients (j or Nu) and friction factors (f) for different finned geometries. In fact, due to expenses and difficulties in experiments, only a limited number of... (More)
 The artificial neural network (ANN) methods are introduced (mainly for calculation of thermal and hydraulic coefficients) into a computeraided design code of compact heat exchangers (CCHE). CCHE integrates the optimization, database, and process drawing into a software package. In the code, a strategy is developed for the optimization of compact heat exchangers (CHEs), which is a problem with changeable objective functions and constraints. However, the applicability and/or accuracy of all these methods are limited by the availability of reliable data sets of the heat transfer coefficients (j or Nu) and friction factors (f) for different finned geometries. In fact, due to expenses and difficulties in experiments, only a limited number of experiments has been carried out for some kinds of heat transfer surfaces. The information, therefore, is usually given by means of correlations. It is well known, however, that the errors in the predicted results by means of correlations are much larger than the measurement errors, being mainly due to the data reduction represented by them. This implies doubts on the optimal solutions. Fortunately, a welltrained network is capable of correlating the data with errors of the same order as the uncertainty of the measurements. This is the main reason for the present introduction of the ANN method to correlate the discrete experimental data sets into continuous formulas. In this study, the ANN method is used to formulate the complex relationship between the thermal and hydraulic coefficients and the other parameters, including the geometry and process data. A specific case on the optimal analysis of a platefin heat exchanger (PFH) is presented to show how the trained ANNs can be used for optimal design of heat exchangers. In addition, a case is presented to illustrate how an inverse heat transfer problem is solved by the optimization methodology developed in the present code. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/593472
 author
 Jia, Rongguang ^{LU} and Sundén, Bengt ^{LU}
 organization
 publishing date
 2003
 type
 Chapter in Book/Report/Conference proceeding
 publication status
 published
 subject
 keywords
 Compact heat exchangers (CHE), Platefin heat exchangers (PFH)
 in
 Proceedings of the ASME Summer Heat Transfer Conference
 pages
 655  664
 publisher
 American Society Of Mechanical Engineers (ASME)
 conference name
 ASME Summer Heat Transfer Conference (HT2003), 2003
 external identifiers

 Scopus:1842729386
 ISBN
 0791836932
 language
 English
 LU publication?
 yes
 id
 5132b1fda7194f41bd7f3b3775eb94b9 (old id 593472)
 date added to LUP
 20071130 10:34:22
 date last changed
 20161013 04:37:40
@misc{5132b1fda7194f41bd7f3b3775eb94b9, abstract = {The artificial neural network (ANN) methods are introduced (mainly for calculation of thermal and hydraulic coefficients) into a computeraided design code of compact heat exchangers (CCHE). CCHE integrates the optimization, database, and process drawing into a software package. In the code, a strategy is developed for the optimization of compact heat exchangers (CHEs), which is a problem with changeable objective functions and constraints. However, the applicability and/or accuracy of all these methods are limited by the availability of reliable data sets of the heat transfer coefficients (j or Nu) and friction factors (f) for different finned geometries. In fact, due to expenses and difficulties in experiments, only a limited number of experiments has been carried out for some kinds of heat transfer surfaces. The information, therefore, is usually given by means of correlations. It is well known, however, that the errors in the predicted results by means of correlations are much larger than the measurement errors, being mainly due to the data reduction represented by them. This implies doubts on the optimal solutions. Fortunately, a welltrained network is capable of correlating the data with errors of the same order as the uncertainty of the measurements. This is the main reason for the present introduction of the ANN method to correlate the discrete experimental data sets into continuous formulas. In this study, the ANN method is used to formulate the complex relationship between the thermal and hydraulic coefficients and the other parameters, including the geometry and process data. A specific case on the optimal analysis of a platefin heat exchanger (PFH) is presented to show how the trained ANNs can be used for optimal design of heat exchangers. In addition, a case is presented to illustrate how an inverse heat transfer problem is solved by the optimization methodology developed in the present code.}, author = {Jia, Rongguang and Sundén, Bengt}, isbn = {0791836932}, keyword = {Compact heat exchangers (CHE),Platefin heat exchangers (PFH)}, language = {eng}, pages = {655664}, publisher = {ARRAY(0x98fa520)}, series = {Proceedings of the ASME Summer Heat Transfer Conference}, title = {Optimal design of compact heat exchangers by an artificial neural network method}, year = {2003}, }