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Showing 2 results for Arshi
Improvement of simple and regenerative gas turbine using simple and ejector-absorption refrigeration
L. Garooci Farshi, A. H. Mosafa , S. M. Seyed Mahmoudi ,
Volume 19, Issue 7 (IJES 2008)
Abstract

The exhaust gases of gas turbine power plant carry a significant amount of thermal energy that is usually expelled to the atmosphere this causes a reduction in net work and efficiency of gas turbine. On the other hand, the generated power and efficiency of gas turbine plants depend largely on the temperature of the inlet air, So that they both increase as the inlet air temperature decreases. The mentioned two problems can be solved by installing an absorption refrigeration cycle (ARC) at gas turbine inlet, working with thermal energy of exhaust gases. In this research, effect of inlet air cooling on gas turbine performance is studied. The work shows that, the net work and the efficiency will increase by 6-10% and 1-5% respectively for every 10°C decrease of inlet temperature. Since, coefficient of performance (COP) of ARC is low, with high pressure ratios in simple gas turbine (SGT) and with low pressure ratios in regenerative gas turbine (RGT), thermal energy of exhaust gases can not supply all the needed thermal energy for refrigeration cycle. The results show that, when an ejector is included in refrigeration cycle, the need for external energy source required for refrigeration cycle is reduced . 


A nonlinear model to solve multiple attribute decision-making problems with interval-valued neutrosophic numbers
Maryam Arshi, Abdollah Hadi-Vencheh, Adel Aazami, Ali Jamshidi,
Volume 35, Issue 4 (IJIEPR 2024)
Abstract
Linguistic variables (LVs) provide a reliable expression of cognitive information. By inheriting the advantages of LVs, we can express uncertain and incomplete cognitive information in multiple attribute decision-making (MADM), and they do so better than existing methods.  In the decision-making process, we can consider decision experts’ (DEs’) bounded rationality, such as cognition toward loss caused by the DEs’ cognitive limitations during the decision process. Therefore, it is necessary to propose a novel cognitive decision approach to handle MADM problems in which the cognitive information is expressed by LVs. In this paper, we employ LVs to represent uncertain and hesitant cognitive information. Then, we propose a mathematical programming approach to solve the MADM problems where attributes or cognitive preferences are not independent.  Moreover, the validity and superiority of the presented approach are verified by dealing with a practical problem. 

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