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Stirling Refrigerating Machine Modeling Using Schmidt and Finite Physical Dimensions Thermodynamic Models: A
Cătălina Dobre1, Lavinia Grosu2, Alexandru Dobrovicescu1
1Department of Engineering Thermodynamics, Engines, Thermal and Refrigeration Equipment, University Politehnica of Bucharest, Splaiul Independenței 313, 060042 Bucharest, Romania.
Two thermodynamic models accurately predict Stirling refrigeration machine performance by accounting for heat exchanger and regenerator irreversibilities. The finite physical dimensions thermodynamics (FPDT) model was validated against experimental data.
Area of Science:
- Thermodynamics
- Refrigeration Engineering
- Energy Systems
Background:
- Stirling refrigeration machines are vital for cooling applications.
- Accurate thermodynamic modeling is crucial for optimizing performance.
- Irreversibilities in heat exchangers and regeneration significantly impact efficiency.
Purpose of the Study:
- To demonstrate the efficacy of two simplified thermodynamic models in predicting refrigerating machine behavior.
- To analyze irreversibilities in a beta-type Stirling refrigeration machine.
- To validate a proposed finite physical dimensions thermodynamics (FPDT) model.
Main Methods:
- Application of the finite physical dimensions thermodynamics (FPDT) method.
- 0-D modeling using the Schmidt model with imperfect regeneration.
- Inclusion of irreversibility from imperfect regeneration and finite temperature differences.
- Exergy balance analysis of the Stirling refrigerator.
Main Results:
- The regenerator exhibits greater irreversibility than the heat exchangers.
- Exchanged energies are expressed using practical engineering parameters.
- The FPDT model shows good agreement with experimental results.
- Validation of the FPDT model for functional and constructive parameters.
Conclusions:
- Simplified thermodynamic models can effectively indicate refrigerating machine behavior.
- The FPDT model provides a validated approach for analyzing Stirling refrigeration machines.
- Understanding and quantifying irreversibilities is key to improving refrigeration efficiency.
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