مهندسی مکانیک مدرس

مهندسی مکانیک مدرس

بهینه‌سازی چندنقطه‌ای ایمپلر کمپرسور گریز از مرکز بر اساس منحنی عملکرد هدف با استفاده از یادگیری ماشین و الگوریتم ژنتیک

نوع مقاله : مقاله پژوهشی

نویسندگان
دانشکده مهندسی مکانیک، دانشکدگان فنی، دانشگاه تهران، تهران، ایران
10.48311/mme.2026.121354.83042
چکیده
در پژوهش حاضر یک روش طراحی معکوس به‌منظور بهینه‎سازی و بهبود عملکرد کمپرسورهای گریز از مرکز بر مبنای منحنی عملکرد توسعه داده شده است. ایده اصلی این روش ایجاد یک مدل جایگزین با دقت مناسب برای تخمین عملکرد کمپرسور و ترکیب آن با روش‎های بهینه‎سازی چندهدفه برای بهبود عملکرد کمپرسور در مجموعه‎ای از نقاط کارکرد است. در این روش مدل جایگزین راندمان و نسبت فشار کمپرسور را بر اساس پارامترهای هندسی آن با دقت مناسب تخمین می‎زند و الگوریتم ژنتیک هندسه‎ی کمپرسور را تا رسیدن به منحنی عملکرد دلخواه به‌صورت تکراری اصلاح می‎کند. با استفاده از این رویکرد می‎توان هندسه‎ی کمپرسور گریز از مرکز را به‌صورت معکوس و صرفاً بر اساس منحنی عملکرد هدف طراحی کرد. روش توسعه داده در این پژوهش به هندسه‎های دیگر نیز تعمیم‎پذیر است و متغیرهای مسئله از جمله پارامتر‎های هندسی و توابع هدف را می‌توان بر اساس الزامات طراحی باز تعریف کرد. استفاده از بهینه‌سازی چندنقطه‌ای در این روش باعث شده است تا کمپرسورهای ایجاد شده علاوه بر نقطه طراحی در سایر نقاط کارکرد نیز عملکرد قابل‌قبولی داشته باشند. نتایج این پژوهش نشان می‎دهد که راندمان کمپرسور در برخی از نقاط کارکرد تا 11 درصد و نسبت فشار آن تا 14 درصد بهبود یافته است. براین‌اساس می‎توان گفت که الگوریتم طراحی توسعه داده شده در این پژوهش امکان بهبود عملکرد کمپرسورهای گریز از مرکز را با دقت مناسب فراهم می‎آورد و می‎تواند به‌عنوان یک روش سریع و قابل‌اطمینان برای طراحی معکوس کمپرسورهای گریز از مرکز بر مبنای منحنی عملکرد دلخواه استفاده شود.
کلیدواژه‌ها
موضوعات

عنوان مقاله English

Multi-Point Optimization of Centrifugal Compressor Impeller Geometry Based on a Target Performance Curve Using Machine Learning and a Genetic Algorithm

نویسندگان English

Mohsen Ebrahimi
Amir Nejat
School of Mechanical Engineering, College of Engineering, University of Tehran, Tehran, Iran
چکیده English

In the present study, an inverse design method has been developed to optimize and improve the performance of centrifugal compressors based on the performance curve. The core idea of this method is to create an accurate surrogate model for predicting compressor performance and combine it with multi-objective optimization techniques to enhance performance across a range of operating points. In this approach, the surrogate model predicts the compressor efficiency and pressure ratio according to its geometric parameters with sufficient accuracy, and the genetic algorithm iteratively modifies the compressor geometry until the target performance curve is achieved. This approach allows the centrifugal compressor geometry to be inversely designed based on the desired performance curve. The proposed method is also generalizable to other geometries, and problem variables, including geometric parameters and objective functions, can be redefined according to design requirements. The use of multi-point optimization in this approach ensures that the designed compressors perform satisfactorily not only at the design point but also at off-design operating conditions.

The results of this study indicate that the compressor efficiency improved by up to 11% and the pressure ratio by up to 14% at certain operating points. Accordingly, the developed design algorithm provides a reliable and accurate means to enhance the performance of centrifugal compressors and can serve as a fast and trustworthy method for the inverse design of centrifugal compressors based on a desired performance curve.

کلیدواژه‌ها English

Centrifugal compressor
Inverse design
Neural Network
Performance improvement
Computational Fluid Dynamics
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