Computational fluid dynamics (CFD) technique is the most commonly used numerical approach to simulate fluid flow behaviour. Owing to its computationally, cost-intensive nature CFD models may not be easily and quickly deployable. In this regard, this study aims to present a support vector machine (SVM)-based metamodelling approach that can be easily trained and quickly deployed for carrying out large-scale studies.
Radial basis function and ε^*-insensitive loss function are used as kernel function and loss function, respectively. To prevent overfitting of the model, five-fold cross-validation root mean squared error is used while training the SVM metamodel. Rather than blindly using any SVM tuning parameters, a particle swarm optimisation (PSO) is used to fine-tune them. The developed SVM metamodel is tested using various error metrics on disjoint test data.
Using the SVM metamodel, a parametric study is conducted to understand the effect of various factors influencing the behaviour of the turbulent fluid flow in the pipe bend with CFD simulation data set. Based on the parametric study carried out, it is seen that the diametric position has the most effect on dimensionless axial velocity, whereas Reynolds number has the least effect.
This paper provides an effective PSO-tuned SVM metamodelling approach, which may be used as a significant cost-saving approach to quickly and accurately estimate fluid flow characteristics that, in general, require the use of expensive CFD models.
