Energies, Vol. 18, Pages 4814: Modified Analytical Model of the Stirling Cycle: Impact of Loss Mechanisms on Stirling Engine Efficiency

Energies, Vol. 18, Pages 4814: Modified Analytical Model of the Stirling Cycle: Impact of Loss Mechanisms on Stirling Engine Efficiency

Energies doi: 10.3390/en18184814

Authors:
Guan Wang
Jarosław Goszczak
Wissam Bou Nader
Damian Batory
Grzegorz Mitukiewicz

Stirling engines are widely applied due to their high thermal efficiency and ability to operate with diverse heat sources. An accurate thermodynamic model is essential for optimising engine design parameters and evaluating the thermal performance of Stirling engines. Despite the fact that the Schmidt model and the ideal adiabatic model quite commonly approximate the performance, they frequently neglect critical loss mechanisms such as regenerator inefficiency, flow resistance, and mechanical friction. In order to address the limitations identified, this study proposes a numerical performance analysis of the GENOA 03 α-type Stirling engine under real operating conditions. The analysis is conducted using an extended second-order Simple Model and Finite Speed approach. The model incorporates significant irreversibilities, including the effectiveness of the regenerator, pressure losses, the shuttle effect, and mechanical friction. A novel aspect of this study is the experimental determination of mechanical friction losses under no-load conditions at various rotational speeds, which are then integrated into the numerical model. The findings of the study indicate that regenerator imperfection and friction losses are significant factors affecting the performance of Stirling engines, and contribute approximately 23% and 14% of the total engine inefficiency, respectively. The model also identifies 1.25 MPa as the minimum operational pressure threshold. The proposed approach integrates experimental data with modified analytical modelling providing more accurate performance predictions for Stirling engines.

More From Author

Energies, Vol. 18, Pages 4815: Increasing Economic Benefits in Renewable Energy Communities with Solar PV and Battery Storage Technologies: Insights from New Member Integration

Energies, Vol. 18, Pages 4816: Correction: Liu et al. Quarter-Hourly Power Load Forecasting Based on a Hybrid CNN-BiLSTM-Attention Model with CEEMDAN, K-Means, and VMD. Energies 2025, 18, 2675

Leave a Reply

Your email address will not be published. Required fields are marked *