Optimization of aircraft propulsion system design and performance by means of metaherustic methods

Within the scope of this thesis; a novel multi-dimensional and multi-objective hybrid optimization algorithm ACOPSO (Ant Colony Optimization – Particle Swarm Optimization) has been developed by utilizing the Ant Colony and Particle Swarm Optimization algorithms. With this algorithm, it is targeted to solve non-linear, multi-modal challenging optimization problems, especially encountered in the aerospace propulsion system design and optimization. In the second phase, performance optimiza-tion of turbojet, turboshaft, turboprop and turbofan engines is performed using this code. Particularly, problems related to the gas turbine engine off-design matching and performance improvement have been investigated. As an example to the design study; a well known commercial turboprop engine and its turboshaft and turbofan variants have been modeled and their performance calculations have been performed. At first, single shaft turboprop engine off-design calculations at take-off, cruise and loiter conditions have been realized with ACOPSO. Next, this engine has been converted to the free turbine turboshaft configuration. Afterwards, the maximum thrust that can be obtained by converting this turboshaft engine into an unmixed flow turbofan engine is achieved. A fan performance map obtained from the literature and scaled using the optimum fan pressure ratio, by-pass ratio and air mass flow rates, and off-design operating conditions of the turbofan engine have been calculated.