Numerical modeling and optimization of beta-type Stirling engine

Ahmed, Fawad; Huang, Hulin; Khan, Aqib Mashood

doi:10.1016/j.applthermaleng.2018.12.003

Cited by 49 publications

(30 citation statements)

References 19 publications

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“…The derived equations are summarized in Table . For detailed derivation of the governing equation refer to previous studies . Once the engine type and configuration are specified, 22 variables and a system of 16 ordinary differential equations (ODEs) (mentioned in Table .)…”

Section: Thermodynamic Modelsmentioning

confidence: 99%

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A comprehensive review on modeling and performance optimization of Stirling engine

et al. 2020

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Summary Modeling with optimization has become a ubiquitous practice in the field of Stirling engine. A plethora of studies in the literature is dedicated in developing a feasible optimized model that can precisely predict the performance of Stirling engine. Hence, the purpose of this article is to compile and expansively review the thermodynamic models and optimization efforts made in pursuit of performance enhancement of the Stirling engine. An extensive range of models available in the literature is painstakingly discussed. Likewise, a wide variety of available optimization techniques spanning from conventional experimental and univariate methods to more complex multiobjective optimization approach are critically reviewed. A comparative analysis of the models is carried out based on the accuracy of their predictability of the performance of Stirling engines. Results obtained from the models are validated through the experimental data of the GPU‐3 Stirling engine prototype. Several optimization techniques are investigated based on the effective and efficient optimization of operating and geometric parameters of the Stirling engine. The review concluded that the Comprehensive Polytropic Model of Stirling engine (CPMS) demonstrated better accuracy in comparison with other models. In addition, the multiobjective particle swarm optimization (MOPSO) technique was found to be effective and computationally efficient.

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Section: Thermodynamic Modelsmentioning

confidence: 99%

“…In consequence of these changes, the governing equations of the adiabatic analysis were altered to incorporate the above‐mentioned losses, these modified equations are summarized in Table . For detailed derivation of the equations refer to previous works …”

Section: Thermodynamic Modelsmentioning

confidence: 99%

A comprehensive review on modeling and performance optimization of Stirling engine

et al. 2020

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“…Li et al proposed another polytropic analysis called Polytropic Stirling Model with Losses (PSML) with all losses coupled in the model. There were several studies reported on beta‐type Stirling engines in literature with various drive mechanisms including rhombic‐driven, crank‐driven and lever‐driven engine . However, little research was conducted on engine performance with different drive mechanisms for the same engine type.…”

Section: Introductionmentioning

confidence: 99%

Thermodynamic analysis of rhombic‐driven and crank‐driven beta‐type Stirling engines

Alfarawi

2020

Int J Energy Res

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This work aims to compare beta-type Stirling engine performance [ground power unit]) driven by rhombic and crank mechanisms. A modified non-ideal adiabatic model accounting for different frictional and thermal losses was adopted in this study. After validating the current model with engine experimental data, different scenarios of operating conditions including heater temperature, cooler temperature, charge pressure and engine speed were investigated. The results revealed that rhombic drive mechanism generates 32% more power and provides 20% more efficiency than crank mechanism at normal operating conditions. However, at low hot end temperature (300 C) and high charge pressure (50 bar) crank drive mechanism tends to slightly generate power more than rhombic drive mechanism at lower engine speeds. At low hot end temperature (300 C) and charge pressure (10 bar) both mechanisms cannot deliver any positive power. Higher power loss is recognized in crank drive mechanism at higher speeds due to increased pumping and gas spring hysteresis losses. This study highlights a wide analysis opportunity for designers and researchers of GPU-3 Stirling engine for further optimization.

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“…Combined methodologies using the three parameters previously mention such as the optimization of geometric and operating parameters of a beta Stirling engine [40], the development of an experimental free-piston engine used in a micro combined heat and power system [41], and the implementation of a new control method related to a variable-speed operation of a dish Stirling system [42].…”

Section: Introductionmentioning

confidence: 99%

Performance Analysis of a Stirling Engine Hybrid Power System

et al. 2020

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The Bolivian government’s concerns that are related to reducing the consumption of diesel fuel, which is imported, subsidized, and provided to isolated electric plants in rural communities, have led to the implementation of hybrid power systems. Therefore, this article presents the performance analysis in terms of energy efficiency, economic feasibility, and environmental sustainability of a photovoltaic (PV)/Stirling battery system. The analysis includes the dynamic start-up and cooling phases of the system, and then compares its performance with a hybrid photovoltaic (PV)/diesel/battery system, whose configuration is usually more common. Both systems were initially optimized in size using the well-known energy optimization software tool, HOMER. An estimated demand for a hypothetical case study of electrification for a rural village of 102 households, called “Tacuaral de Mattos”, was also considered. However, since the characteristics of the proposed systems required a detailed analysis of its dynamics, a dynamic model that complemented the HOMER analysis was developed using MATLAB Simulink TM 8.9. The results showed that the PV/Stirling battery system represented a higher performance option to implement in the electrification project, due to its good environmental sustainability (69% savings in CO2 emissions), economic criterion (11% savings in annualized total cost), and energy efficiency (5% savings in fuel energy conversion).

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Numerical modeling and optimization of beta-type Stirling engine

Cited by 49 publications

References 19 publications

A comprehensive review on modeling and performance optimization of Stirling engine

A comprehensive review on modeling and performance optimization of Stirling engine

Thermodynamic analysis of rhombic‐driven and crank‐driven beta‐type Stirling engines

Performance Analysis of a Stirling Engine Hybrid Power System

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