Design-Optimization Approach to Multistage Axial Contra-Rotating Turbines

Volume 6B: Turbomachinery ◽

10.1115/gt2013-94762 ◽

2013 ◽

Author(s):

Ernesto Sozio ◽

Tom Verstraete ◽

Guillermo Paniagua

Keyword(s):

High Speed ◽

Differential Evolution Algorithm ◽

Design Procedure ◽

Second Step ◽

Optimization Approach ◽

Rocket Engines ◽

Multi Stage ◽

Through Flow ◽

Evolution Algorithm ◽

Turbine Airfoils

Air Turbo Rocket engines, suitable for high-speed propulsion, require compact turbomachinery. This paper presents the design of an innovative multi-stage turbine mounted at the hub of a counter-rotating fan. Hence, the turbine airfoils are required to deliver high torque at low peripheral speeds. The design methodology specifically developed for this fourteen-stage turbine relies on two successive optimization cycles. The first one is based on a through-flow 1D code. This optimization cycle explores a vast set of possible design solutions. In a second step, an optimization using a 3D high fidelity RANS defines the 3D airfoil geometry. In order to accelerate the entire design procedure, a special routine was developed to morph the 1D results into the required info for the 3D optimization. Both the 1D and 3D optimizations are based on differential evolution algorithm.

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Frequency optimization of laminated functionally graded carbon nanotube reinforced composite quadrilateral plates using smoothed FEM and evolution algorithm

Journal of Composite Materials ◽

10.1177/0021998317737831 ◽

2017 ◽

Vol 52 (14) ◽

pp. 1971-1986 ◽

Cited By ~ 7

Author(s):

T Vo-Duy ◽

T Truong-Thi ◽

V Ho-Huu ◽

T Nguyen-Thoi

Keyword(s):

Carbon Nanotube ◽

Optimization Problem ◽

Volume Fraction ◽

Differential Evolution Algorithm ◽

Composite Plates ◽

Functionally Graded ◽

Optimization Approach ◽

Reinforced Composite ◽

Design Variables ◽

Evolution Algorithm

The paper presents an efficient numerical optimization approach to deal with the optimization problem for maximizing the fundamental frequency of laminated functionally graded carbon nanotube-reinforced composite quadrilateral plates. The proposed approach is a combination of the cell-based smoothed discrete shear gap method (CS-DSG3) for analyzing the first natural frequency of the functionally graded carbon nanotube reinforced composite plates and a global optimization algorithm, namely adaptive elitist differential evolution algorithm (aeDE), for solving the optimization problem. The design variables are the carbon nanotube orientation in the layers and constrained in the range of integer numbers belonging to [−900 900]. Several numerical examples are presented to investigate optimum design of quadrilateral laminated functionally graded carbon nanotube reinforced composite plates with various parameters such as carbon nanotube distribution, carbon nanotube volume fraction, boundary condition and number of layers.

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Novel hybrid discrete differential evolution algorithm for the multi-stage multi-purpose batch plant scheduling problem

Applied Soft Computing ◽

10.1016/j.asoc.2021.108262 ◽

2021 ◽

pp. 108262

Author(s):

Yuxin Han ◽

Xueli Yan ◽

Xingsheng Gu

Keyword(s):

Differential Evolution ◽

Differential Evolution Algorithm ◽

Scheduling Problem ◽

Multi Stage ◽

Batch Plant ◽

Evolution Algorithm ◽

Discrete Differential Evolution

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An Improved Differential Evolution Algorithm for Data Stream Clustering

International Journal of Electrical and Computer Engineering (IJECE) ◽

10.11591/ijece.v9i4.pp2659-2667 ◽

2019 ◽

Vol 9 (4) ◽

pp. 2659

Author(s):

Bhaskar Adepu ◽

Jayadev Gyani ◽

G. Narsimha

Keyword(s):

Differential Evolution ◽

Data Stream ◽

Concept Drift ◽

Differential Evolution Algorithm ◽

Optimization Approach ◽

Stream Clustering ◽

Data Stream Clustering ◽

Evolution Algorithm ◽

Improved Differential Evolution Algorithm ◽

Measure Estimate

A Few algorithms were actualized by the analysts for performing clustering of data streams. Most of these algorithms require that the number of clusters (K) has to be fixed by the customer based on input data and it can be kept settled all through the clustering process. Stream clustering has faced few difficulties in picking up K. In this paper, we propose an efficient approach for data stream clustering by embracing an Improved Differential Evolution (IDE) algorithm. The IDE algorithm is one of the quick, powerful and productive global optimization approach for programmed clustering. In our proposed approach, we additionally apply an entropy based method for distinguishing the concept drift in the data stream and in this way updating the clustering procedure online. We demonstrated that our proposed method is contrasted with Genetic Algorithm and identified as proficient optimization algorithm. The performance of our proposed technique is assessed and cr eates the accuracy of 92.29%, the precision is 86.96%, recall is 90.30% and F-measure estimate is 88.60%.

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