Generalised Pattern Search

An Extended Pattern Search (EPS) method is developed to optimize the layout and turbine geometry for offshore floating wind power systems. The EPS combines a deterministic pattern search with stochastic extensions. Three advanced models are incorporated: (1) a cost model considering investment and lifetime costs of a floating offshore wind farm comprised of WindFloat platforms; (2) a wake propagation and interaction model able to determine the reduced wind speeds downstream of rotating blades; and (3) a power model to determine power produced at each rotor, and includes a semi-continuous, discrete turbine geometry selection to optimize the rotor radius and hub height of individual turbines. The objective function maximizes profit by minimizing cost, minimizing wake interactions, and maximizing power production. A multidirectional, multiple wind speed case is modeled which is representative of real wind site conditions. Layouts are optimized within a square solution space for optimal positioning and turbine geometry for farms containing a varying number of turbines. Resulting layouts are presented; optimized layouts are biased towards dominant wind directions. Preliminary results will inform developers of best practices to include in the design and installation of offshore floating wind farms, and of the resulting cost and power production of wind farms that are computationally optimized for realistic wind conditions.

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Design Optimization for Fir-Tree Root of Turbine Blade Considering Manufacturing Variations

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.694-697.2733 ◽

2013 ◽

Vol 694-697 ◽

pp. 2733-2737

Author(s):

Qin Zhou ◽

Ming Hui Zhang ◽

Hui Yong Chen ◽

Yong Hui Xie

Keyword(s):

Particle Swarm Optimization ◽

Turbine Blade ◽

Equivalent Stress ◽

Particle Swarm ◽

Pattern Search ◽

Design System ◽

Contact Method ◽

Swarm Optimization ◽

Maximum Equivalent Stress ◽

Manufacturing Variation

An optimization design system for fir-tree root of turbine blade has been developed in this paper. In the system, a parametric model of the blade and rim was established based on the parametric design language APDL, and nonlinear contact method was used for analysis by ANSYS, meanwhile some optimization algorithms, such as Pattern Search Algorithm, Genetic Algorithm, Simulated Annealing Algorithm and Particle Swarm Optimization, were adopted to control the optimizing process. Five cases of manufacturing variation in contact surfaces between root and rim were taken into account, and the design objective was to minimize the maximum equivalent stress of root-rim by optimizing eight critical geometrical dimensions of the root and rim. As a result, the maximum equivalent stress of root-rim decreases markedly after the optimization in all cases. In consideration of both precision and computing time, particle swarm optimization is assessed as the best algorithm to solve structure optimization problem in this work. Corresponding to five different cases of manufacturing variation, the maximum equivalent stress of root and rim reduces by 7%, 8%; 27%, 24%; 27%, 22%; 25%, 19%; 10%, 14% using the Particle Swarm Optimization.

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Paralleling process of searching objects on cytological images by a template

Artificial Intelligence ◽

10.15407/jai2019.03-04.073 ◽

2019 ◽

Vol 24 (3-4) ◽

pp. 73-81

Author(s):

Berezsky O.M. ◽

◽

Pitsun O.Y. ◽

Dolynyuk T.M. ◽

Batko Y.M. ◽

...

Keyword(s):

Operating Time ◽

Pattern Search ◽

Software Module ◽

Parallel Form ◽

Result Show

Modern approaches to finding image elements are analyzed. An algorithm for searching micro-objects in histological and cytological images using a database is developed. A tiered-parallel form of parallelization of the process of micro-object pattern search is designed. Micro-object pattern search software is implemented. The obtained result show that the operating time of the software module with parallelization speeds up the processing on average by 20% for cytological images

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