Search results

Because of the appreciable application of heat recovery systems for the increment of overall efficiency of micro gas turbines, promising evaluation and optimization are crucial. This paper aims to propose a multi-factor theoretical methodology for analysis, optimization and comparison of potential plate-fin recuperators incorporated into micro gas turbines. Energetic, exergetic, economic and environmental factors are covered.

To demonstrate applicability and reliability of the methodology, detailed thermo-hydraulic analysis, sensitivity analysis and optimization are conducted on the recuperators with louver and offset-strip fins using a genetic algorithm. To assess the relationship between investment cost and profit for the recuperated systems, payback period (PBP), which incorporates all the factors is used as the universal objective function. To compare the performance of the recuperated and non-recuperated systems, exergy efficiency, exergy destruction and corresponding cost rate, fuel consumption and environmental damage cost rates, capital and operational cost rates and acquired profit rates are determined.

Based on the results, optimal PBP of the louvered-fin recuperator (147 days) is slightly lower than that with offset-strip fins (153 days). The highest profit rate is acquired by reduction of exergy destruction cost rate and corresponding decrements for louver and offset-strip fins are 2.3 and 3.9 times compared to simple cycle, respectively.

This mathematical study, for the first time, focuses on introducing a reliable methodology, which covers energetic, exergetic, economic and environmental points of view beneficial for design and selection of efficient plate-fin recuperators for micro gas turbine applications.

The purpose of this study is to describe the combustion of a magnesium particle falling into a hot oxidizer medium.

The governing equations, including mass, momentum and energy conservation equations, are numerically solved. Afterward, the influences of effective parameters on the temperature distribution and burning time are investigated. Artificial neural network (ANN) is applied to approximate the particle temperature as a function of time, diameter and porosity factor. To obtain the best arrangement of the ANN structure, an optimization process is conducted.

The results show that by considering variations of the particle size, the maximum temperature increases compared to the case in which the particle diameter is constant. Also, the ignition and burning times and the maximum temperature of the moving particle are lower than those of the motionless particle. Optimum network has the best values of regression coefficient and mean relative error whose values are found to be 0.99991 and 1.58 per cent, respectively.

In this study, particle size varies over the combustion process that leads to calculation of particle burning time. In addition, the effects of the motion and porosity of the particle are examined.

This study aims to investigate the effect of the Diamond Justice model on self-efficiency with the mediating role of job stress among the staff of Qazvin hospitals affiliated with Qazvin University of Medical Sciences. This study is a cross-sectional descriptive-analytical study conducted among the staff of Qazvin hospitals affiliated with Qazvin University of Medical Sciences in 2020.

Sampling was performed using the structural equation method. Data collection tools included three sections: demographic information, justice and self-efficiency questionnaire and job stress questionnaire. Data were finally analyzed using SPSS software version 26 and AMOS version 23 at a significance level of 0.05.

The structural equation model’s standard estimation coefficients show that all existing paths are at a significant level. Finally, the regression analysis showed that justice is inversely related to stress level (ß = −0.185, p = 0.015). Justice is directly related with self-efficiency (ß = 0.282, p < 0.001).

Justice, stress and self-efficacy have been measured in various studies among health workers. However, a fitting model showing these three variables’ interaction was necessary. Therefore, this study tries to conceptualize the multifaceted relationships of the components of these concepts by presenting a model.