Blade Shape Optimization of Transonic Axial Flow Fan in Terms of Sectional Profiles and Stacking Line

2014 ◽  
Author(s):  
Peng Song ◽  
Jinju Sun ◽  
Ke Wang
Keyword(s):  
Surrogate Model ◽  
Optimization Problem ◽  
Pressure Ratio ◽  
Axial Flow ◽  
Optimization Method ◽  
Global Optimization Method ◽  
Blade Shape ◽  
Blade Tip ◽  
Overall Efficiency ◽  
Performance Gains

Transonic axial flow fan has relatively high blade tip speed and produces higher pressure ratio than the subsonic. However, considerable losses are brought about by the shock waves close to blade tip and over part of span, leading to deteriorated overall efficiency and operating flow range. It is generally acknowledged that modifications of blade stacking line (axially sweep and tangentially lean) and sectional profiles can help to control spanwise distribution of blade loading, reduce shock loss and secondary flow, and extend the operating flow range. The present study is to maximize the comprehensive benefits of simultaneously optimizing the sectional profiles and stack line by means of a global optimization method with reduced cost. In contrast with previous studies, it is of two distinguished features. First, in blade geometry parameterization, both sectional profiles and stacking line are varied to provide more flexible blade shape variation and subsequently permit more optimization performance gains. Secondly, with simultaneous variation of sectional profiles and stacking line, number of optimization variables and nonlinearity of optimization problem will increase largely. How to obtain a global optimal solution and also reduce the computation become the major concerns. For this purpose, a global optimization method proposed by us is used. It includes an improved CCEA (Cooperative Co-Evolution Algorithm) optimizer, adaptively updated kriging surrogate model, and one-stage Expected Improvement (EI) approach that permits adaptive sampling. At initial stage, a coarse surrogate model is constructed with small number of samples. During the optimization process, some new samples are identified, evaluated, and then used to refine the model and conduct further optimal searching. In the optimization process, the accuracy of the surrogate model is improved based on its own characteristics of optimization problem and this permits the optimizer to conduct the aim-oriented optimal searches. In such a manner, the surrogate model sustains high-level of accuracy while uses fewer samples, thus the blade optimization and computations are significantly reduced. The optimization is conducted for NASA Rotor67 at design flow rate with a single workstation of DELL 7500. It is demonstrated that the optimized blade design produces significant performance gains at design condition (where the overall efficiency and pressure ratio are increased respectively by 1.27 and 6.53 points) and also at off-design conditions.

Synthesis of Controllers for MIMO Systems with Time Response Specifications

2014 ◽  
Vol 3 (3) ◽  
pp. 25-52 ◽  
Author(s):  
Maher Ben Hariz ◽  
Wassila Chagra ◽  
Faouzi Bouani
Keyword(s):  
Global Optimization ◽  
Optimization Problem ◽  
Closed Loop ◽  
Controller Design ◽  
Mimo Systems ◽  
Optimal Solution ◽  
Optimization Method ◽  
Time Response ◽  
Step Response ◽  
Global Optimization Method

This paper proposes the design of fixed low order controllers for Multi Input Multi Output (MIMO) decoupled systems. The simplified decoupling is used as a decoupling system technique due to its advantages compared to other decoupling methods. The main objective of the proposed controllers is to satisfy some desired closed loop step response performances such as the settling time and the overshoot. The controller design is formulated as an optimization problem which is non convex and it takes in account the desired closed loop performances. Therefore, classical methods used to solve the non convex optimization problem can generate a local solution and the resulting control law is not optimal. Thus, the thought is to use a global optimization method in order to obtain an optimal solution which will guarantee the desired time response specifications. In this work the Generalized Geometric Programming (GGP) is exploited as a global optimization method. The key idea of this method consists in transforming an optimization problem, initially, non convex to a convex one by some mathematical transformations. Simulation results and a comparison study between the presented approach and a Proportional Integral (PI) controller are given in order to shed light the efficiency of the proposed controllers.

Optimal Design and Aerodynamic Study of Leaned Transonic Axial Flow Fan Rotors

2014 ◽  
Author(s):  
Seyed Reza Razavi ◽  
Masoud Boroomand
Keyword(s):  
Numerical Simulation ◽  
Optimization Problems ◽  
Pressure Ratio ◽  
Axial Flow ◽  
Target Function ◽  
Computer Code ◽  
Optimization Method ◽  
Tangential Direction ◽  
Operating Range ◽  
Safe Operating

Multi-Objective Optimization Problems (MOP) are very usual and complicated subjects in Turbomachinery and there are several methodologies for optimizing these problems. Genetic Algorithm (GA) and Artificial Neural Network (ANN) are the most popular ones to solve MOP. In this study, optimization was done for leaned rotor blades to achieve maximum performance parameters including specifically stage pressure ratio, efficiency and operating range. By bending an existing transonic rotor which is well-known as NASA rotor-67 in tangential direction, effect of leaning on performance and aerodynamic parameters of transonic axial-flow compressor rotors was studied. To understand all effects of lean angle, an organized investigation including numerical simulation of basic rotor, implementation of curvatures on basic rotor, numerical simulation of leaned blades and optimization were applied. Various levels of lean angles were implemented to basic rotor and by employing a three dimensional compressible turbulent model, the operating parameters were achieved. Afterwards, the results were used as input data of optimization computer code. Finally, the ANN optimization method was used to achieve maximum stage pressure ratio, efficiency and safe operating range. it was found that the Optimized leaned blades according to their target function had positive or negative optimized angles and the optimized lean angles effectively increased the safe operating range about 12% and simultaneously increase the pressure ratio and efficiency by 4% and 5%, respectively.

Numerical Simulation of Steady Air Injection Flow Control Effects on a Transonic Axial Flow Compressor

2012 ◽  
Vol 224 ◽  
pp. 352-357
Author(s):  
Islem Benhegouga ◽  
Ce Yang
Keyword(s):  
Mass Flow Rate ◽  
Mass Flow ◽  
Flow Rate ◽  
Pressure Ratio ◽  
Axial Flow ◽  
Leading Edge ◽  
Air Injection ◽  
Axial Flow Compressor ◽  
Blade Tip ◽  
Flow Compressor

In this work, steady air injection upstream of the blade leading edge was used in a transonic axial flow compressor, NASA rotor 37. The injectors were placed at 27 % upstream of the axial chord length at blade tip, the injection mass flow rate is 3% of the chock mass flow rate, and 3 yaw angles were used, respectively -20°, -30°, and -40°. Negative yaw angles were measured relative to the compressor face in opposite direction of rotational speeds. To reveal the mechanism, steady numerical simulations were performed using FINE/TURBO software package. The results show that the stall mass flow can be decreased about 2.5 %, and an increase in the total pressure ratio up to 0.5%.

Distortion Tolerance: By Design Instead of by Accident

1969 ◽  
Author(s):  
C. E. Langston
Keyword(s):  
Adverse Effects ◽  
Peak Pressure ◽  
Pressure Ratio ◽  
Axial Flow ◽  
Chord Length ◽  
Individual Stage ◽  
Inlet Distortion ◽  
Blade Tip ◽  
And Performance ◽  
Selection Of

A variety of techniques are available today that may be employed by the compressor designer to minimize the adverse effects of inlet distortion. The effects of individual stage rotor matching and blade chord length have been qualitatively indicated by isolated airfoil data. These effects have been verified through testing of several multistage axial flow compressors. Matching of early compressor stages well below their peak pressure ratio has been shown to significantly reduce the sensititivty of the entire compressor-to-inlet distortion. Careful selection of blade geometry by the designer may be used to provide a favorable balance between distortion tolerance and performance. In addition, increases in rotor blade chord length and use of honeycomb blade tip shrouds have been shown to further reduce the compressor’s sensititivity to distortion. Finally, the association of inlet distortion with turbulence is established. This association assures the designer that accommodations made for time-average spatial distortion will be effective in combating the adverse effects of inlet turbulence.

scholarly journals Shape optimization of a blended-wing-body underwater glider using surrogate-based global optimization method IESGO-HSR

2020 ◽  
Vol 103 (3) ◽  
pp. 003685042095014
Author(s):  
Pengcheng Ye ◽  
Guang Pan
Keyword(s):  
Global Optimization ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Optimization Method ◽  
Hydrodynamic Performance ◽  
Optimum Shape ◽  
Underwater Glider ◽  
Global Optimization Method ◽  
Shape Optimization Problem ◽  
Blended Wing Body

As a novel flying-wing configuration underwater glider, the blended-wing-body underwater glider (BWBUG) has the satisfactory hydrodynamic performance in comparison to the conventional cylindrical autonomous underwater gliders (AUGs). The complicated shape optimization of BWBUG is significant for improving its hydrodynamic efficiency while it has to require huge computation time and efforts. A novel surrogate-based shape optimization (SBSO) framework is proposed to deal with the BWBUG shape optimization problem for improving the optimization efficiency and quality. During the optimization search, the parametric geometric model of the BWBUG is constructed depending on seven specific sectional airfoils, with the planar surface being unaltered. Moreover, an improved ensemble of surrogates based global optimization method using a hierarchical design space reduction strategy (IESGO-HSR) is used for optimizing the chosen sectional airfoils. The optimum shape of BWBUG can be obtained using all sectional airfoils which are successfully optimized. The maximum lift to drag ratio (LDR) of the optimal BWBUG is improved by 24.32% with acceptable computational resources. The optimization results show that the proposed SBSO framework is more superior and efficient in handling the BWBUG shape optimization problem.

Three-Dimensional Blade Shape Optimization for a Transonic Axial Flow Compressor Through Incorporating Surrogate Model and Sequential Sampling

2018 ◽  
Author(s):  
Xuesong Wang ◽  
Jinju Sun ◽  
Peng Song ◽  
Youwei He ◽  
Da Xu
Keyword(s):  
Surrogate Model ◽  
Pareto Front ◽  
Sequential Sampling ◽  
Axial Flow ◽  
Aerodynamic Performance ◽  
Axial Flow Compressor ◽  
Overall Performance ◽  
Flow Compressor ◽  
High Level ◽  
Performance Gains

High level aerodynamic performance has been always expected for the axial flow compressors, and it is the consistent goal for axial flow compressor research. To achieve such a goal, the incorporation of CFD with optimization algorithm and surrogate model in blade geometry optimization has become a common practice and been used extensively. But the conventional surrogate model based on merely initial sampling often deviates from the real optimization problem during optimization process and then brings the optimizer to search locally, leading to the compromised optimal results. There are yet much to do to improve such optimization design method. An optimization method of surrogate model being updated by sequential sampling strategy is developed to achieve global optimal design geometry and permit high-level of aerodynamic performance for axial flow compressor. Preliminary Kriging surrogate model is constructed with a small number of selected DOE samplings, where the multiple optimization objective functions are obtained based on CFD simulations. The optimization is performed on the surrogate model with NSGA-II optimization algorithm and Pareto fronts successively obtained. To improve the surrogate model, the MSE (Mean Squared Error) criteria is used to select the refinement point from the newest Pareto front, and it is used to update and improve the surrogate model gradually during the optimization. Such adaptive feature of the surrogate model has enabled the optimizer to search globally. The method is used to optimize transonic Rotor 37 at design flow rate, where the blade shape is varied simultaneously in terms of sweep and lean, and the geometry is optimized. In the converged Pareto front, abundant candidate designs with significant performance gains are produced. Three points over the Pareto front are selected and analyzed to take an insight into the optimization effectiveness. Overall performance curves of optimized geometries are predicted over the entire flow range and they are significantly improved compared with the original ones. Significant overall performance gains arising from the blade optimization are supported by the improved flow behavior. The overall pressure ratio or efficiency gains of the optimized blades are attributed to the significant improvement in the radial distribution of aerodynamic parameters. Further research shows that the shock structure is changed and separation zone is reduced with the optimized blades, which are the major reasons for the improvement of the aerodynamic performance of optimized blades.

Three-Dimensional Aerodynamic Optimization of a Multi-Stage Axial Compressor

2016 ◽  
Author(s):  
Tao Ning ◽  
Chun-wei Gu ◽  
Xiao-tang Li ◽  
Tai-qiu Liu
Keyword(s):  
Genetic Algorithm ◽  
Surrogate Model ◽  
Pressure Ratio ◽  
Three Dimensional ◽  
Axial Compressor ◽  
Optimization Method ◽  
Operating Point ◽  
Design Parameters ◽  
Aerodynamic Optimization ◽  
Stall Margin

An optimization method combined of a genetic algorithm, an artificial neural network, a CFD solver and a blade generator, is developed in this research and applied in the three-dimensional blading design of a newly designed highly-loaded 5-stage axial compressor. The adaptive probabilities of crossover and mutation, non-uniform mutation operator and elitism operator are employed to improve the convergence of the genetic algorithm. Considering both the optimization efficiency and effectiveness, a mixture of high-fidelity multistage CFD method and approximate surrogate model of the feed-forward ANN is used to evaluate the fitness. In particular, the database is updated dynamically and used to re-train the surrogate model of ANN for improving the accuracy for predicting. The last stator of the compressor is optimized at the near stall operating point. The tip bow with relative bow height Hb and bow angle αb are treated as design parameters. The adiabatic efficiency as well as the penalty of mass flow and total pressure ratio constitute the objective functions to be maximized. The optimum (Hb = 0.881, αb = 14.7°) obtains 0.4% adiabatic efficiency increase for the whole compressor at the optimized operating point. The detailed aerodynamic is compared between the baseline and optimized stator, and the mechanism is analyzed. The optimized version obtains 5.1% increase in stall margin and maintains the efficiency at the design point.

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