Surrogate based design optimisation of composite aerofoil cross-section for helicopter vibration reduction

2012 ◽  
Vol 116 (1181) ◽  
pp. 709-725 ◽  
Author(s):  
M. S. Murugan ◽  
R. Ganguli ◽  
D. Harursampath
Keyword(s):  
Objective Function ◽  
Cross Section ◽  
Rotor Blade ◽  
Vibration Reduction ◽  
Substantial Reduction ◽  
Response Surfaces ◽  
Design Optimisation ◽  
Baseline Design ◽  
Real Coded Genetic Algorithm ◽  
Helicopter Vibration

AbstractDesign optimisation of a helicopter rotor blade is performed. The objective is to reduce helicopter vibration and constraints are put on frequencies and aeroelastic stability. The ply angles of the D-spar and skin of the composite rotor blade with NACA 0015 aerofoil section are considered as design variables. Polynomial response surfaces and space filling experimental designs are used to generate surrogate models of the objective function with respect to cross-section properties. The stacking sequence corresponding to the optimal cross-section is found using a real-coded genetic algorithm. Ply angle discretisation of 1°, 15°, 30° and 45° are used. The mean value of the objective function is used to find the optimal blade designs and the resulting designs are tested for variance. The optimal designs show a vibration reduction of 26% to 33% from the baseline design. A substantial reduction in vibration and an aeroelastically stable blade is obtained even after accounting for composite material uncertainty.

Flexible Genetic Algorithm Based Optimal Power Flow of Power Systems

2018 ◽  
Vol 24 (3) ◽  
pp. 84
Author(s):  
Hassan Abdullah Kubba ◽  
Mounir Thamer Esmieel
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Power Flow ◽  
Optimal Power Flow ◽  
Reactive Power ◽  
Test System ◽  
Multi Objective ◽  
Optimal Power ◽  
Blending Method ◽  
Real Coded Genetic Algorithm

Nowadays, the power plant is changing the power industry from a centralized and vertically integrated form into regional, competitive and functionally separate units. This is done with the future aims of increasing efficiency by better management and better employment of existing equipment and lower price of electricity to all types of customers while retaining a reliable system. This research is aimed to solve the optimal power flow (OPF) problem. The OPF is used to minimize the total generations fuel cost function. Optimal power flow may be single objective or multi objective function. In this thesis, an attempt is made to minimize the objective function with keeping the voltages magnitudes of all load buses, real output power of each generator bus and reactive power of each generator bus within their limits. The proposed method in this thesis is the Flexible Continuous Genetic Algorithm or in other words the Flexible Real-Coded Genetic Algorithm (RCGA) using the efficient GA's operators such as Rank Assignment (Weighted) Roulette Wheel Selection, Blending Method Recombination operator and Mutation Operator as well as Multi-Objective Minimization technique (MOM). This method has been tested and checked on the IEEE 30 buses test system and implemented on the 35-bus Super Iraqi National Grid (SING) system (400 KV). The results of OPF problem using IEEE 30 buses typical system has been compared with other researches.     

scholarly journals Optimization of the cross-sectional shape of the belts of trihedral lattice supports

2019 ◽  
Vol 7 (4) ◽  
pp. 5-8
Author(s):  
Linar Sabitov ◽  
Ilnar Baderddinov ◽  
Anton Chepurnenko
Keyword(s):  
Objective Function ◽  
Nonlinear Optimization ◽  
Cross Section ◽  
Optimization Methods ◽  
Cross Sectional ◽  
Maximum Value ◽  
Axial Moment ◽  
The Cross ◽  
Nonlinear Optimization Methods ◽  
Sectional Shape

The article considers the problem of optimizing the geometric parameters of the cross section of the belts of a trihedral lattice support in the shape of a pentagon. The axial moment of inertia is taken as the objective function. Relations are found between the dimensions of the pentagonal cross section at which the objective function takes the maximum value. We introduce restrictions on the constancy of the consumption of material, as well as the condition of equal stability. The solution is performed using nonlinear optimization methods in the Matlab environment.

Experimental Characterization of the Evolution of Global Flow Structure in the Passage of an Axial Compressor

2021 ◽  
Author(s):  
Ayush Saraswat ◽  
Subhra Shankha Koley ◽  
Joseph Katz
Keyword(s):  
Flow Structure ◽  
Rotor Blade ◽  
Incidence Angle ◽  
Substantial Reduction ◽  
Axial Compressor ◽  
Suction Side ◽  
Rotor Wake ◽  
Tip Leakage ◽  
Blade Tip

Abstract Ongoing experiments conducted in a one-and-half stages axial compressor installed in the JHU refractive index-matched facility investigate the evolution of flow structure across blade rows. After previously focusing only on the rotor tip region, the present stereo-PIV (SPIV) measurements are performed in a series of axial planes covering an entire passage across the machine, including upstream of the IGV, IGV-rotor gap, rotor-stator gap, and downstream of the stator. The measurements are performed at flow rates corresponding to pre-stall condition and best efficiency point (BEP). Data are acquired for various rotor-blade orientations relative to the IGV and stator blades. The results show that at BEP, the wakes of IGV and rotor are much more distinct and the wake signatures of one row persists downstream of the next, e.g., the flow downstream of the stator is strongly affected by the rotor orientation. In contrast, under pre-stall conditions, the rotor orientation has minimal effect on the flow structure downstream of the stator. However, the wakes of the stator blades, where the axial momentum is low, are now wider. For both conditions, the flow downstream of the rotor is characterized by two regions of axial momentum deficit in addition to the rotor wake. A deficit on the pressure side of the rotor wake tip is associated with the tip leakage vortex (TLV) of the previous rotor blade, and is much broader at pre-stall condition. A deficit on the suction side of the rotor wake near the hub appears to be associated with the hub vortex generated by the neighboring blade, and is broader at BEP. At pre-stall, while the axial momentum upstream of the rotor decreases over the entire tip region, it is particularly evident near the rotor blade tip, where the instantaneous axial velocity becomes intermittently negative. Downstream of the rotor, there is a substantial reduction in mean axial momentum in the upper half of the passage, concurrently with an increase in the circumferential velocity. Consequently, the incidence angle upstream of the stator increases in certain regions by up to 30 degrees. These observations suggest that while the onset of the stall originates from the rotor tip flow, one must examine its impact on the flow structure in the stator passage as well.

Design Optimization by Integration of Evolutionary Search and Cumulative Approximation

2005 ◽  
Author(s):  
Kikuo Fujita ◽  
Tomoki Ushiro ◽  
Noriyasu Hirokawa
Keyword(s):  
Design Optimization ◽  
Computational Cost ◽  
Optimal Solution ◽  
Response Surfaces ◽  
Evolutionary Search ◽  
Design Problems ◽  
Real Coded Genetic Algorithm ◽  
Cumulative Function ◽  
Gradient Based ◽  
New Framework

This paper proposes a new design optimization framework by integrating evolutionary search and cumulative function approximation. While evolutionary algorithms are robust even under multi-peaks, rugged natures, etc., their computational cost is inferior to ordinary schemes such as gradient-based methods. While response surface techniques such as quadratic approximation can save computational cost for complicated design problems, the fidelity of solution is affected by density of samples. The new framework simultaneously performs evolutionary search and constructs response surfaces. That is, in its early phase the search is performed over roughly but globally approximated surfaces with the relatively small number of samples, and in its later phase the search is performed intensively around promising regions, which are revealed in the preceded phases, over response surfaces enhanced with additional samples. This framework is expected to be able to robustly find the optimal solution with less sampling. An optimization algorithm is implemented by combining a real-coded genetic algorithm and a Voronoi diagram based cumulative approximation, and it is applied to some numerical examples for discussing its potential and promises.

scholarly journals Analysis of Toothbrush Rig Parameter Estimation Using Different Model Orders in Real-Coded Genetic Algorithm (RCGA)

2018 ◽  
Vol 7 (4.30) ◽  
pp. 443 ◽  
Author(s):  
Ainul, H.M.. Y ◽  
Salleh, S. M ◽  
Halib, N ◽  
Taib, H. ◽  
Fathi, M. S
Keyword(s):  
Genetic Algorithm ◽  
Parameter Estimation ◽  
Objective Function ◽  
Optimization Technique ◽  
Convergence Time ◽  
Generation Gap ◽  
Model Order ◽  
Real Coded Genetic Algorithm ◽  
The Mean ◽  
Conventional Genetic Algorithm

System identification is a method to build a model for a dynamic system from the experimental data. In this paper, optimization technique was applied to optimize the objective function that lead to satisfying solution which obtain the dynamic model of the system. Real-coded genetic algorithm (RCGA) as a stochastic global search method was applied for optimization. Hence, the model of the plant was represented by the transfer function from the identified parameters obtained from the optimization process. For performance analysis of toothbrush rig parameter estimation, there were six different model orders have been considered where each of model order has been analyzed for 10 times. The influence of conventional genetic algorithm parameter - generation gap has been investigated too. The statistical analysis was used to evaluate the performance of the model based on the objective function which is the Mean Square Error (MSE). The validation test-through correlation analysis was used to validate the model. The model of model order 2 is chosen as the best model as it has fulfilled the criteria involved in selecting the accurate model. Generation gap used was 0.5 has shorten the algorithm convergence time without affecting the model accuracy.

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