Development of a Heat Transfer Correlation for the Transitional Flow in a Horizontal Tube Using Support Vector Machines

2008 ◽  
Author(s):  
L. M. Tam ◽  
A. J. Ghajar ◽  
H. K. Tam ◽  
S. C. Tam
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Support Vector Machines ◽  
Least Squares Method ◽  
Horizontal Tube ◽  
Good Method ◽  
Transitional Flow ◽  
Support Vector ◽  
Flux Boundary Condition ◽  
Vector Machines

In this paper the Support Vector Machines (SVM) method is used to correlate the transitional forced and mixed convection experimental data of Ghajar and Tam (1994) that were obtained along a stainless steel horizontal circular tube fitted with re-entrant, square-edged, and bell-mouth inlets under uniform wall heat flux boundary condition. The SVM method has been chosen to further improve the accuracy of the correlations that were developed by Ghajar and his co-workers using the traditional least-squares method (Ghajar and Tam, 1994) and more recently the artificial neural networks (ANN) method (Ghajar et al., 2004). Using the ANN method improved the accuracy of their correlation. However, there are drawbacks associated with ANN method. One of the major problems with the ANN method is that it does not provide a unique correlation due to different coefficient matrices. The SVM method used in this study eliminated the drawbacks associated with the ANN method and provided a unique correlation with comparable accuracy as the ANN method. For the experimental data used, majority of the data points were predicted within 5% deviation. Comparisons were made regarding the accuracy of the developed correlation and its characteristic using SVM and ANN methods. The results showed that SVM is a good method to correlate complex heat transfer data.

scholarly journals Towards Rational Biosurfactant Design—Predicting Solubilization in Rhamnolipid Solutions

Molecules ◽  
2021 ◽  
Vol 26 (3) ◽  
pp. 534
Author(s):  
Ilona E. Kłosowska-Chomiczewska ◽  
Adrianna Kotewicz-Siudowska ◽  
Wojciech Artichowicz ◽  
Adam Macierzanka ◽  
Agnieszka Głowacz-Różyńska ◽  
...  
Keyword(s):  
Experimental Data ◽  
Support Vector Machines ◽  
Evolutionary Algorithm ◽  
Environmental Conditions ◽  
Molecular Volume ◽  
Support Vector ◽  
Micellar Solubilization ◽  
Vector Machines ◽  
Predictive Relationships ◽  
Kernel Support Vector Machines

The efficiency of micellar solubilization is dictated inter alia by the properties of the solubilizate, the type of surfactant, and environmental conditions of the process. We, therefore, hypothesized that using the descriptors of the aforementioned features we can predict the solubilization efficiency, expressed as molar solubilization ratio (MSR). In other words, we aimed at creating a model to find the optimal surfactant and environmental conditions in order to solubilize the substance of interest (oil, drug, etc.). We focused specifically on the solubilization in biosurfactant solutions. We collected data from literature covering the last 38 years and supplemented them with our experimental data for different biosurfactant preparations. Evolutionary algorithm (EA) and kernel support vector machines (KSVM) were used to create predictive relationships. The descriptors of biosurfactant (logPBS, measure of purity), solubilizate (logPsol, molecular volume), and descriptors of conditions of the measurement (T and pH) were used for modelling. We have shown that the MSR can be successfully predicted using EAs, with a mean R2val of 0.773 ± 0.052. The parameters influencing the solubilization efficiency were ranked upon their significance. This represents the first attempt in literature to predict the MSR with the MSR calculator delivered as a result of our research.

scholarly journals Identification of a Surface Marine Vessel Using LS-SVM

2013 ◽  
Vol 2013 ◽  
pp. 1-11 ◽  
Author(s):  
David Moreno-Salinas ◽  
Dictino Chaos ◽  
Jesús Manuel de la Cruz ◽  
Joaquín Aranda
Keyword(s):  
Experimental Data ◽  
Support Vector Machines ◽  
System Identification ◽  
Mathematical Models ◽  
Nonlinear System Identification ◽  
Experimental Tests ◽  
Support Vector ◽  
Experimental Systems ◽  
Research Areas ◽  
Vector Machines

The availability of adequate system models to reproduce, as faithfully as possible, the actual behaviour of the experimental systems is of key importance. In marine systems, the changing environmental conditions and the complexity of the infrastructure needed to carry out experimental tests call for mathematical models for accurate simulations. There exist a wide number of techniques to define mathematical models from experimental data. Support Vector Machines (SVMs) have shown a great performance in pattern recognition and classification research areas having an inherent potential ability for linear and nonlinear system identification. In this paper, this ability is demonstrated through the identification of the Nomoto second-order ship model with real experimental data obtained from a zig-zag manoeuvre made by a scale ship. The mathematical model of the ship is identified using Least Squares Support Vector Machines (LS-SVMs) for regression by analysing the rudder angle, surge and sway speed, and yaw rate. The coefficients of the Nomoto model are obtained with a linear kernel function. The model obtained is validated through experimental tests that illustrate the potential of SVM for system identification.

Development of a Flow Regime Map for a Horizontal Pipe With the Multi-Classification Support Vector Machines

2008 ◽  
Author(s):  
L. M. Tam ◽  
A. J. Ghajar ◽  
H. K. Tam ◽  
S. C. Tam
Keyword(s):  
Heat Transfer ◽  
Experimental Data ◽  
Mixed Convection ◽  
Flow Regime ◽  
Single Phase ◽  
Flow Regimes ◽  
Support Vector ◽  
Flow Maps ◽  
Vector Machines ◽  
Flow Regime Maps

For horizontal circular pipes under uniform wall heat flux boundary condition and three different inlet configurations (re-entrant, square-edged, bell-mouth), Ghajar and Tam (1995) developed flow regime maps for the determination of the boundary between single-phase forced and mixed convection using experimental data of Ghajar and Tam (1994). Based on the ratio of the local peripheral heat transfer coefficient at the top and the bottom, the heat transfer data was classified as either forced or mixed convection among the different flow regimes. The forced-mixed convection boundary was then obtained by empirical correlations. From the flow maps, heat transfer correlations for different flow regimes were recommended. Recently Trafalis et al. (2005) used the Multiclass Support Vector Machines (SVM) method to classify vertical and horizontal two-phase flow regimes in 4 pipes with good accuracy. In this study, the SVM method was applied to the single-phase experimental data of Ghajar and Tam (1994) and new flow regime maps were developed. Five flow regimes (forced turbulent, forced transition, mixed transition, forced laminar, mixed laminar) were identified in the flow maps using Reynolds and Rayleigh numbers as the identifying parameters. The flow regimes on the boundaries of the new maps were represented by the SVM decision functions. The results show that the new flow regime maps for the three types of inlets can classify the forced and mixed convection experimental data in different flow regimes with good accuracy.

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