Assessment of damping coefficients ranges in design of a free piston Stirling engine: Simulation and experiment

Zare, Shahryar; Tavakolpour-Saleh, Alireza; Shourangiz-Haghighi, Alireza; Binazadeh, Tahereh

doi:10.1016/j.energy.2019.07.069

Cited by 28 publications

(6 citation statements)

References 35 publications

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“…According to the defined fitness function, it is obvious that the coefficients c 1 to c 4 are functions of unknown parameters of nondominant poles, ie, C T 1 ; C T 2 ; C T 3 ; and C T 4 considering ω as a known variable (see Equation 19) while h 1 to h 4 are functions of the unknown design variables, ie, T H ,D d ,and L ac if the rest of the parameters presented in Equations (14) to (17) are known. Accordingly, the unknown variables C T 1 ; C T 2 ; C T 3 ; C T 4 ; T H ; D d ; and L ac should be obtained through the search algorithm such that the presented cost function defined by Equation (24) is minimized, meaning that the pressure …”

Section: Genetic Algorithmmentioning

confidence: 99%

“…[5][6][7][8][9] So far, two types of thermo-acoustic converters, namely, standing-wave and traveling wave converters, have been proposed, each of which can be designed as an engine or refrigerator. 14,15 The TASEs are in fact the extended version of standing-wave thermo-acoustic engines. Indeed, the TASEs are a subset of the Stirling engines.…”

Section: Introductionmentioning

confidence: 99%

“…12,13 Other categories of dynamic Stirling engines are known as Fluidyne and free piston Stirling engines, which are not within the scope of this article. 14,15 The TASEs are in fact the extended version of standing-wave thermo-acoustic engines. In standingwave thermo-acoustic engines, self-sustained oscillating pressure waves are first generated using the applied heat and then are converted into electricity through reversed acoustical speakers.…”

Section: Introductionmentioning

confidence: 99%

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Design of a traveling wave thermo‐acoustic engine based on genetic algorithm

Zare

Tavakolpour-Saleh

2019

Int J Energy Res

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Summary This paper presents a novel design scheme for traveling wave thermo‐acoustic Stirling engines (TASEs) based on a target frequency using genetic algorithm (GA). First, the effects of engine design parameters on the system performance are studied via root locus method. Accordingly, it is found that the resonator length, the inertance diameter, and the hot gas temperature are the most effective parameters in the engine design procedure. Next, the relation between the closed‐loop poles positions of the thermo‐acoustic system and the effective design variables are described parametrically. Consequently, in order to estimate the optimum values of the effective design parameters as well as the unknown positions of the nondominant poles, an appropriate fitness function based on the desired engine frequency is proposed. Subsequently, a GA is used to achieve the optimal values of design parameters so as to minimize the considered cost function. Finally, the validity of the proposed design technique is verified by comparing the obtained results with the available data of an experimental case study.

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Section: Genetic Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Design of a traveling wave thermo‐acoustic engine based on genetic algorithm

Zare

Tavakolpour-Saleh

2019

Int J Energy Res

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“…One possible solution is using the Stirling engine, which has attracted much attention in recent years due to its many capabilities. 1,2 The efficiency of a Stirling engine is close to a Carnot efficiency compared with other thermal engines 3 ; therefore, making the engine ideal for use in power generation systems. Due to the advantage of high efficiency, the Stirling engines have always been under an intense spotlight amongst researchers.…”

Section: Introductionmentioning

confidence: 99%

Design optimization of a heat‐to‐cool Stirling cycle using artificial neural network

Katooli

Moghadam

Mehrpooya

2022

Intl J of Energy Research

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This study focuses on applying artificial neural network (ANN) to investigate different parameters and operating conditions that affect the cooling flux and efficiency of a heat-to-cool gamma-type Stirling machine. ANN model is developed to predict output parameters of the Stirling machine based on sample data generated by a MATLAB code-named Nlog code. The code was previously developed and validated by the authors. To reach the addressed goal, three input parameters, including Stirling refrigerator frequency (f), displacer stroke (d), and phase angle (φ) were selected. The output parameters are coefficient of performance (COP) and cooling power of the Stirling refrigerator, and efficiency (η) and output power (P e ) of the Stirling engine. The ANNs proved to have the most effective performance with 10 neurons and the ANN structure as 4-10-1 since it could perfectly map the input parameters to the output parameters. The presented ANNs were compared with four different design cases of a Stirling refrigerator against the experimental data. The ANN results had good agreement with experimental data with an average error of less than 1% for the cooling power and an average error of less than 8% for the COP.Besides, the ANN model and Nlog code error for the efficiency and output power of the Stirling engine were 1.33% and 0.21%, respectively.

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“…Control of mechanical systems has been drawing great attention recently due to their extensive range of applications such as robotic manipulators which are employed in the industrial, medical, and aerospace fields [1][2][3][4][5]. Mechanical systems need high precision control in order to achieve their required performance.…”

Section: Introductionmentioning

confidence: 99%