Performance analysis of stirling engine using computational intelligence techniques (ANN & Fuzzy Mamdani Model) and hybrid algorithms (ANN-PSO & ANFIS)

Stirling engine is considered as one of the most promising alternatives to conventional combustion units due to its versatility and potential to achieve relatively high efficiency. The output power and torque are the main performance indicators that depend on many variables. Many studies have pointed out that the relationship between the performance indicators of the Stirling engine and its input variables was nonlinear. This study analyses the prediction performance of power and torque in a Stirling engine system using soft computing techniques-artificial neural network (ANN) and Fuzzy Mamdani Model (FMM) and hybrid algorithms-adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained with particle swarm optimization (ANN-PSO). The performance of these approaches has been discussed using a dataset from a test conducted on an existing Stirling engine. The performance indicators of the different models considering the power and the torque were predicted and analysed. A parametric analysis has been performed for the ANN-PSO model to identify the best model configuration considering the number of neurons in hidden layers, the number of swarm size and acceleration factors. A detailed description of the process leading to the identification of the best networks architecture for the power and torque model has been provided. The comparison of the four approaches indicates that FMM exhibits the highest performance prediction considering the power while the ANN-PSO and ANFIS model exhibit the highest performance considering the torque. This study demonstrates the suitability of soft computing techniques and hybrid algorithms for the prediction of Stirling engine characteristics and its potential to optimize time and experimental cost.

Automated summary

The Stirling engine is considered as a promising alternative to traditional combustion devices, and this study analyzes the prediction performance of power and torque using soft computing techniques and hybrid algorithms. The results show that the Fuzzy Mamdani Model (FMM) performs best in predicting power, while the adaptive neuro-fuzzy inference system (ANFIS) and artificial neural network trained with particle swarm optimization (ANN-PSO) models perform best in predicting torque.