Davood Ramesh; Sajad Khodadadiyan; Hasan Karimi
Volume 9, Issue 1 , May 2016, , Pages 1-11
Abstract
The purpose of this paper is to present a genetic algorithm (as a software) to optimize engine main parameters through the application of "genetic algorithm" and also introduced the new and modified thermodynamic cycles with analysing their performance. This software objective function is to achieve ...
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The purpose of this paper is to present a genetic algorithm (as a software) to optimize engine main parameters through the application of "genetic algorithm" and also introduced the new and modified thermodynamic cycles with analysing their performance. This software objective function is to achieve the highest and optimum level of 'final velocity'. In this study, the strategy of using fuel booster turbopump and 2nd stage fuel pump is followed primarily to moderate the effect of cavitation on pumps. Although the use of boosterpumps increase the weight, arise pumps' rpm and possibility to reduce the tanks pressure came with a decrease in weight of propulsion system. The developed software is applied to Russian RD-180 engine in construction of propulsion system of first stage of ATLAS IIIB LV, and experimental results have been demonstrating the improvement of engine performance which results from a multi-variable sensitivity study on a staged-combustion engine will be highlighted. This algorithm is under the limitation of constraints to control the critical variation of combustion pressure, turbine rpm, and pumps cavitation margin and turbine temperature. Results show that, supply flow rate of gas generation from 2nd stage of fuel pump and divide flow rate of exhaust of fuel booster turbine to 2nd stage of fuel pump and combustion chamber, will increase the final velocity of launch vehicle.
S. Khodadadiyan; R. Farokhi; D. Ramesh
Volume 7, Issue 2 , July 2014, , Pages 75-83
Abstract
The aim of the paper is to describe a methodology of damage detection in the liquid propellant engine which is based on artificial neural networks in combination with stochastic analysis. It is assumed that the liquid propellant engine have faulty data collection system. Then a filtering algorithm for ...
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The aim of the paper is to describe a methodology of damage detection in the liquid propellant engine which is based on artificial neural networks in combination with stochastic analysis. It is assumed that the liquid propellant engine have faulty data collection system. Then a filtering algorithm for elimination perturbation data has been applied .The damage is defined as fuel and oxidizer channels clogging up. The key stone of the method is feed-forward multi layer network with back propagation algorithm. It is impossible to obtain appropriate training set for real engine, therefore stochastic analysis using mathematical model is carried out and dynamic simulation is made to get training set virtually. Engine channels clogging up leads to unwanted variation of pressure, flow rate of oxidizer and fuel and other main parameters of engine. Then variations considered as best input data for damage detection. The methodology was carried out using laboratory test.
