scispace - formally typeset
Journal ArticleDOI

Improved Heat Transfer Correlation in the Transition Region for a Circular Tube with Three Inlet Configurations Using Artificial Neural Networks

TLDR
In this article, local forced and mixed heat transfer coefficients were measured by Ghajar and Tam in a horizontal circular straight tube fitted with reentrant, square-edged, and bellmouth inlets under uniform wall heat flux boundary condition.
Abstract
Local forced and mixed heat transfer coefficients were measured by Ghajar and Tam [5] in a horizontal circular straight tube fitted with reentrant, square-edged, and bell-mouth inlets under uniform wall heat flux boundary condition. For the experiments, the Reynolds, Prandtl, and Grashof numbers varied from about 280 to 49000, 4 to 158, and 1000 to 2.5 × 105, respectively. The heat transfer transition regions were established by observing the change in the heat transfer behavior. The data in the transition region were correlated by using the traditional least squares method. The correlation predicted the transitional data with an average absolute deviation of about 8%. However, about 30% of the data in the transition region were predicted with 10–20% deviation, and about 3% with deviations greater than 20%. This is due to the abrupt change in the heat transfer characteristic and its intermittent behavior in this region. Since the value of the heat transfer coefficient has a direct impact on the size of th...

read more

Citations
More filters
Journal ArticleDOI

Applications of artificial neural networks for thermal analysis of heat exchangers – A review

TL;DR: In this paper, the authors reviewed the applications of ANN for thermal analysis of heat exchangers and highlighted the limitations of ANN in this field and its further research needs in the field.
Journal ArticleDOI

Applications of ANNs in flow and heat transfer problems in nuclear engineering: A review work

TL;DR: Recent work on the applications of ANNs for predicting the flow regime, pressure drop, void fraction, critical heat flux, onset of nucleate boiling, heat transfer coefficient and boiling curve has been reviewed.
Journal ArticleDOI

Transitional Heat Transfer in Plain Horizontal Tubes

TL;DR: In this paper, the heat transfer behavior in the transition region for plain horizontal tubes under a uniform wall heat flux boundary condition is discussed in detail, in particular, the influence of inlet configuration and free convection superimposed on the forced convection (or mixed convection) at the start and end of the transition regions and the magnitude of heat transfer are addressed.
Journal ArticleDOI

Artificial neural networks: applications in chemical engineering

TL;DR: A comprehensive review of various ANN applications within the field of chemical engineering (CE) deals with the significant aspects of ANN (architecture, methods of developing and training, and modeling strategies) in correlation with various types of applications.
Journal ArticleDOI

Correlating heat transfer and friction in helically-finned tubes using artificial neural networks

TL;DR: An artificial neural network (ANN) approach was used to correlate experimentally determined Colburn j-factors and Fanning friction factors for flow of liquid water in straight tubes with internal helical fins as discussed by the authors.
References
More filters
Book ChapterDOI

Learning internal representations by error propagation

TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book

Learning internal representations by error propagation

TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Journal ArticleDOI

Approximation capabilities of multilayer feedforward networks

TL;DR: It is shown that standard multilayer feedforward networks with as few as a single hidden layer and arbitrary bounded and nonconstant activation function are universal approximators with respect to L p (μ) performance criteria, for arbitrary finite input environment measures μ.
Journal ArticleDOI

Evolving artificial neural networks

TL;DR: It is shown, through a considerably large literature review, that combinations between ANNs and EAs can lead to significantly better intelligent systems than relying on ANNs or EAs alone.
Related Papers (5)