On the apex seal analysis of limaçon positive displacement machines

Phung, Truong H.; Sultan, Ibrahim A.; Kahandawa, Gayan

doi:10.1016/j.mechmachtheory.2018.05.003

Cited by 8 publications

(3 citation statements)

References 10 publications

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“…In fact, the use of the limaçon technology in fluid machinery can be traced back to the 1800s, but none of those early designs received enough attention from either industrial or academic communities owing to the limitation of manufacturing methods and the lack of mathematical understanding. Over the last two decades, the work of Sultan [15][16][17][18] offered good insights into the limaçon technology, and some studies on the application of the limaçon technology in the gas expander have been reported recently [19][20][21]. However, the investigation of this technology in the compressor is still rarely reported in the available literature.…”

Section: Geometric Characteristics Of the Limaçon Compressormentioning

confidence: 99%

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On the Design of a Class of Rotary Compressors Using Bayesian Optimization

Phung

Sultan

2021

Machines

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The optimization process of compressors is usually regarded as a ‘black-box’ problem, in which the mathematical form underlying the relationship between design parameters and the design objective is impractical and costly to be obtained. To solve the ‘black-box’ problem, Bayesian optimization has been proven as an accurate and efficient method. However, the application of such a method in the design of compressors is rarely discussed, particularly no work has been reported in terms of the positive displacement type compressor. Therefore, this paper aims to introduce the Bayesian optimization to the design of positive displacement compressors through the optimization process of the novel limaçon compressor. In this paper, a two-stage optimization process is presented, in which the first stage optimizes the geometric parameters as per design requirements and the second stage focuses on revealing an optimum setting of port geometries that improves machine performance. A numerical illustration is offered to prove the validity of the presented approach.

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Section: Geometric Characteristics Of the Limaçon Compressormentioning

confidence: 99%

“…To reduce the effect of the apex leakage on machine performance, a sealing scheme can be applied to the machine apices. The mathematical model of apex seal and the seal dynamic behavior is detailed in the work of Phung and Sultan [21].…”

Section: Side and Apex Leakagementioning

confidence: 99%

On the Design of a Class of Rotary Compressors Using Bayesian Optimization

Phung

Sultan

2021

Machines

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“…Later, Sultan [32] incorporated a cam-operated inlet control valve into his optimum limaçon expander design and reported an improved isentropic efficiency. Phung et al investigated the possibility of realizing gas-expander operation using L2C configuration and provided sophisticated mathematical models of expander rotor profile and apex seal [33,34]. Recently, Phung and Sultan have furthered this work by investigating the possibility of rotor profile modifications to simplify the manufacturing process and reduce cost [35].…”

Section: Introductionmentioning

confidence: 99%

An Optimized Artificial Neural Network Model of a Limaçon-to-Circular Gas Expander with an Inlet Valve

Hossain,

Sultan,

Phung

et al. 2024

Thermo

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In this work, an artificial neural network (ANN)-based model is proposed to describe the input–output relationships in a Limaçon-To-Circular (L2C) gas expander with an inlet valve. The L2C gas expander is a type of energy converter that has great potential to be used in organic Rankine cycle (ORC)-based small-scale power plants. The proposed model predicts the different performance indices of a limaçon gas expander for different input pressures, rotor velocities, and valve cutoff angles. A network model is constructed and optimized for different model parameters to achieve the best prediction performance compared to the classic mathematical model of the system. An overall normalized mean square error of 0.0014, coefficient of determination (R2) of 0.98, and mean average error of 0.0114 are reported. This implies that the surrogate model can effectively mimic the actual model with high precision. The model performance is also compared to a linear interpolation (LI) method. It is found that the proposed ANN model predictions are about 96.53% accurate for a given error threshold, compared to about 91.46% accuracy of the LI method. Thus the proposed model can effectively predict different output parameters of a limaçon gas expander such as energy, filling factor, isentropic efficiency, and mass flow for different operating conditions. Of note, the model is only trained by a set of input and target values; thus, the performance of the model is not affected by the internal complex mathematical models of the overall valved-expander system. This neural network-based approach is highly suitable for optimization, as the alternative iterative analysis of the complex analytical model is time-consuming and requires higher computational resources. A similar modeling approach with some modifications could also be utilized to design controllers for these types of systems that are difficult to model mathematically.

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Geometric design of the limaçon machine

Sultan

Phung

2019

Positive Displacement Machines

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On the apex seal analysis of limaçon positive displacement machines

Cited by 8 publications

References 10 publications

On the Design of a Class of Rotary Compressors Using Bayesian Optimization

On the Design of a Class of Rotary Compressors Using Bayesian Optimization

An Optimized Artificial Neural Network Model of a Limaçon-to-Circular Gas Expander with an Inlet Valve

Geometric design of the limaçon machine

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