Altug Piskin, Tolga Baklacioglu and Onder Turan
The purpose of this paper is to introduce a hybrid, metaheuristic, multimodal and multi-objective optimization tool that is needed for aerospace propulsion engineering problems.
Abstract
Purpose
The purpose of this paper is to introduce a hybrid, metaheuristic, multimodal and multi-objective optimization tool that is needed for aerospace propulsion engineering problems.
Design/methodology/approach
Multi-objective hybrid optimization code is integrated with various benchmark and test functions that are selected suitable to the difficulty level of the aero propulsion performance problems.
Findings
Ant colony and particle swarm optimization (ACOPSO) has performed satisfactorily with benchmark problems.
Research limitations/implications
ACOPSO is able to solve multi-objective and multimodal problems. Because every optimization problem has specific features, it is necessary to search their general behavior using other algorithms.
Practical implications
In addition to the optimization solving, ACOPSO enables an alternative methodology for turbine engine performance calculations by using generic components maps. The user is flexible for searching various effects of component designs along with the compressor and turbine maps.
Originality/value
A hybrid optimization code that has not been used before is introduced. It is targeted use is propulsion systems optimization and design such as Turboshaft or turbofan by preparing the necessary engine functions. A number of input parameters and objective functions can be modified accordingly.
Details
Keywords
Altug Piskin, Tolga Baklacioglu and Onder Turan
The purpose of the paper is to present component matching and off-design calculations using generic components maps.
Abstract
Purpose
The purpose of the paper is to present component matching and off-design calculations using generic components maps.
Design/methodology/approach
Multi objective hybrid optimization code is integrated with turbojet function code. Both codes are developed for the research study. Initially, methodology is applied on a numerical propulsion system simulation (NPSS) example engine cycle calculations. Effect of matching constants are shown. Later, component matching and application is done on JetCat engine. Calculations are compared with measured test data. And additional operating conditions are calculated using the matched component constants.
Findings
Obtained matching constants provided very good results with NPSS example and also JetCat test measurements. Optimization algorithm is practical for turbojet engine component matching and off-design calculations. Off-design matching provides information about the turbine and exhaust areas of an unknown turbine engine. Thus it is possible to perform off design calculations at various operating conditions. Finding detailed turbine maps is difficult than finding compressor maps. In that case characteristic turbine curve may be a good alternative.
Research limitations/implications
Selected component maps and the target engine components should be similar characteristics. For a one/two stage turbine, characteristic curves can be applied. Validation should be extended on different type of compressor and turbines.
Practical implications
Operators and researchers usually need more information about the available turbojet engines for increasing the effective usage. Generally, manufacturers do not provide such detailed information to public. This study introduces an alternative methodology for engine modeling by using generic component maps and thus obtaining information for off-design calculations. User is flexible for selecting/scaling the compressor and turbine maps.
Originality/value
A hybrid optimization code is used as a new approach. It can be used with other engine functions; for instance functions corresponding to turboshaft or turbofan engines, by modifying the engine function. Number of input parameters and objective functions can be modified accordingly.