کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6904103 | 1446996 | 2018 | 52 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Efficiency of bio- and socio-inspired optimization algorithms for axial turbomachinery design
ترجمه فارسی عنوان
کارایی الگوریتم بهینه سازی زیستی و اجتماعی الهام گرفته از طراحی توربوماشین محوری
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کلمات کلیدی
بهینه سازی، توربو شفت محوری طراحی معکوس، الگوریتم بهینه سازی بیولوژیک و اجتماعی الهام گرفته، برنامه ریزی خطی متوالی،
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
نرم افزارهای علوم کامپیوتر
چکیده انگلیسی
Turbomachinery design is a complex problem which requires a lot of experience. The procedure may be speed up by the development of new numerical tools and optimization techniques. The latter rely on the parameterization of the geometry, a model to assess the performance of a given geometry and the definition of an objective functions and constraints to compare solutions. In order to improve the reference machine performance, two formulations including the off-design have been developed. The first one is the maximization of the total nominal efficiency. The second one consists to maximize the operation area under the efficiency curve. In this paper five optimization methods have been assessed for axial pump design: Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Cuckoo Search (CS), Teaching Learning Based Optimization (TLBO) and Sequential Linear Programming (SLP). Four non-intrusive methods and the latter intrusive. Given an identical design point and set of constraints, each method proposed an optimized geometry. Their computing time, the optimized geometry and its performances (flow rate, head (H), efficiency (η), net pressure suction head (NPSH) and power) are compared. Although all methods would converge to similar results and geometry, it is not the case when increasing the range and number of constraints. The discrepancy in geometries and the variety of results are presented and discussed. The computational fluid dynamics (CFD) is used to validate the reference and optimized machines performances in two main formulations. The most adapted approach is compared with some existing approaches in literature.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Applied Soft Computing - Volume 64, March 2018, Pages 282-306
Journal: Applied Soft Computing - Volume 64, March 2018, Pages 282-306
نویسندگان
Mohamed Abdessamed Ait Chikh, Idir Belaidi, Sofiane Khelladi, José Paris, Michael Deligant, Farid Bakir,