Multi Objective Optimisation of an Aero Engine Rotor System Using Nondominated Sorting Genetic Algorithm (NSGA)

2017 ◽  
Author(s):  
Joseph Shibu Kalloor ◽  
Ch. Kanna Babu ◽  
Girish K. Degaonkar ◽  
K. Shankar
Keyword(s):  
Genetic Algorithm ◽  
Critical Speed ◽  
Rotor System ◽  
Pareto Optimal ◽  
Multi Objective ◽  
Rayleigh Beam ◽  
Unbalance Response ◽  
Aero Engine ◽  
Nondominated Sorting ◽  
Engine Rotor

A comprehensive multi-objective optimisation methodology is presented and applied to a practical aero engine rotor system. A variant of Nondominated Sorting Genetic Algorithm (NSGA) is employed to simultaneously minimise the weight and unbalance response of the rotor system with restriction imposed on critical speed. Rayleigh beam is used in Finite Element Method (FEM) implemented in-house developed MATLAB code for analysis. The results of practical interest are achieved through bearing-pedestal model and eigenvalue based Rayleigh damping model. Pareto optimal solutions generated and best solution selected with the help of response surface approximation of the Pareto optimal front. The outcome of the paper is a minimum weight and minimum unbalance response rotor system which satisfied the critical speed constraints.

scholarly journals Modeling Cantilever Branch Structure of Aero-Engine Power Turbine and Analyzing Its Influence

2018 ◽  
Vol 36 (4) ◽  
pp. 728-734 ◽  
Author(s):  
Yebao Xia ◽  
Xingmin Ren ◽  
Yongfeng Yang
Keyword(s):  
Critical Speed ◽  
Vibration Mode ◽  
Structural Parameters ◽  
Rotor System ◽  
Unbalance Response ◽  
Power Turbine ◽  
Aero Engine ◽  
Turbine Component ◽  
Calculation Results ◽  
Turbine Disks

In the power turbine component of an aero-engine, there exists a unique cantilever branch structure, on which turbine disks are mounted. Due to the cantilever's characteristics, this structure exhibits a vibration of large amplitude; thus its characteristics need to be studied in detail.In this paper, the motion equations combining the structure and the shaft were deduced; then its vibration mode was given, and the criticl speed was computed; finally the unbalance response of an integrated rotor system was simulated.The calculation results are compared with the simulation results without considering the branch structure.Some key parameters' influences are studied thoroughly, e.g., the branch shaft's length, the flange's offset and the installation orientation. As the results show, the branch structure has a large influence on the vibration mode and critical speed of the rotor system, thus it should not be simplified and ignored in modelling; After adjusting the branch structure's parameters, the characteristics of a vibration mode do not change, and the effects of branch structural parameters on critical speed are closely related to the corresponding vibration mode; the bending stiffness and the critical speed of the rotor system both decreased with increasing branch shaft's length; if reducing the flange's offset and fabricating the branch structure reversely, a sharp increase in the unbalance response of the turbine disc will occur. In conclusion, the dynamical characteristics of the integrated rotor system can be optimized through reasonably designing the branch structure.

Multi-objective optimization of a recuperative gas turbine cycle using non-dominated sorting genetic algorithm

2011 ◽  
Vol 225 (8) ◽  
pp. 1041-1051 ◽  
Author(s):  
H Sayyaadi ◽  
H R Aminian
Keyword(s):  
Genetic Algorithm ◽  
Gas Turbine ◽  
Heat Exchangers ◽  
Mixed Integer ◽  
Design Stage ◽  
Tube Length ◽  
Pareto Optimal ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
Gas Turbine Cycle

A regenerative gas turbine cycle with two particular tubular recuperative heat exchangers in parallel is considered for multi-objective optimization. It is assumed that tubular recuperative heat exchangers and its corresponding gas cycle are in design stage simultaneously. Three objective functions including the purchased equipment cost of recuperators, the unit cost rate of the generated power, and the exergetic efficiency of the gas cycle are considered simultaneously. Geometric specifications of the recuperator including tube length, tube outside/inside diameters, tube pitch, inside shell diameter, outer and inner tube limits of the tube bundle and the total number of disc and doughnut baffles, and main operating parameters of the gas cycle including the compressor pressure ratio, exhaust temperature of the combustion chamber and the air mass flowrate are considered as decision variables. Combination of these objectives anddecision variables with suitable engineering and physical constraints (including NO x and CO emission limitations) comprises a set of mixed integer non-linear problems. Optimization programming in MATLAB is performed using one of the most powerful and robust multi-objective optimization algorithms, namely non-dominated sorting genetic algorithm. This approach is applied to find a set of Pareto optimal solutions. Pareto optimal frontier is obtained, and a final optimal solution is selected in a decision-making process.

scholarly journals Analisis Perbandingan Aggregat Of Function (AOF) dengan Non-Dominated Sorting Genetic Algorithm (NSGA-II) dalam Menentukan Optimasi Multi-Objective pada Penjadwalan Mesin Produksi Flow Shop

2018 ◽  
Vol 17 (2) ◽  
pp. 158
Author(s):  
Fifin Sonata ◽  
Dede Prabowo Wiguna
Keyword(s):  
Genetic Algorithm ◽  
Flow Shop ◽  
Total Tardiness ◽  
Pareto Optimal ◽  
Nsga Ii ◽  
Trade Off ◽  
Multi Objective

Penjadwalan mesin produksi dalam dunia industri memiliki peranan penting sebagai bentuk pengambilan keputusan. Salah satu jenis sistem penjadwalan mesin produksi adalah sistem penjadwalan mesin produksi tipe flow shop. Dalam penjadwalan flow shop, terdapat sejumlah pekerjaan (job) yang tiap-tiap job memiliki urutan pekerjaan mesin yang sama. Optimasi penjadwalan mesin produksi flow shop berkaitan dengan penyusunan penjadwalan mesin yang mempertimbangkan 2 objek yaitu makespan dan total tardiness. Optimasi kedua permasalahan tersebut merupakan optimasi yang bertolak belakang sehingga diperlukan model yang mengintegrasikan permasalahan tersebut dengan optimasi multi-objective A Fast Elitist Non-Dominated Sorting Genetic Algorithm for Multi-Objective Optimazitaion : NSGA-II. Dalam penelitian ini akan dibandingkan 2 buah metode yaitu Aggregat Of Function (AOF) dengan NSGA-II agar dapat terlihat nilai solusinya. Penyelesaian penjadwalan mesin produksi flow shop dengan algoritma NSGA-II untuk membangun jadwal dengan meminimalkan makespan dan total tardiness.Tujuan yang ingin dicapai adalah mengetahui bahwa model yang dikembangkan akan memberikan solusi penjadwalan mesin produksi flow shop yang efisien berupa solusi pareto optimal yang dapat memberikan sekumpulan solusi alternatif bagi pengambil keputusan dalam membuat penjadwalan mesin produksi yang diharapkan. Solusi pareto optimal yang dihasilkan merupakan solusi optimasi multi-objective yang optimal dengan trade-off terhadap seluruh objek, sehingga seluruh solusi pareto optimal sama baiknya.

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