Çukurova Üniversitesi Mühendislik Fakültesi Dergisi 2757-9255 Çukurova Üniversitesi 10.21605/cukurovaumfd.1674303 Computational Methods in Fluid Flow, Heat and Mass Transfer (Incl. Computational Fluid Dynamics) Akışkan Akışı, Isı ve Kütle Transferinde Hesaplamalı Yöntemler (Hesaplamalı Akışkanlar Dinamiği Dahil) Isı Değiştirici Tasarım Kriterlerinin Makine Öğrenmesi Ağaç Modelleri Kullanılarak İncelenmesi An Investigation on Design Criteria of Heat Exchangers by Using Tree Models of Machine Learning Methods https://orcid.org/0009-0001-4045-5267 Ala Merve ADANA ALPARSLAN TÜRKEŞ BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ https://orcid.org/0000-0002-9565-9160 Şahin Mahir ADANA ALPARSLAN TÜRKEŞ BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ https://orcid.org/0000-0002-8006-149X Kılıç Mustafa ADANA ALPARSLAN TÜRKEŞ BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ https://orcid.org/0000-0002-8680-0636 Dişken Gökay ADANA ALPARSLAN TÜRKEŞ BİLİM VE TEKNOLOJİ ÜNİVERSİTESİ 07 02 2025 40 2 375 386 04 15 2025 05 29 2025 Copyright © 2009, Çukurova Üniversitesi Mühendislik Fakültesi Dergisi 2009 Çukurova Üniversitesi Mühendislik Fakültesi Dergisi

Isı değiştiriciler, kimya, otomotiv ve HVAC gibi çeşitli endüstrilerde yaygın olarak kullanılan kritik bileşenlerdir. Isı değiştirici tasarım kriterlerinin değerlendirilip iyileştirilmesi endüstriyel uygulamaların iyileştirilmesinde büyük önem arz etmektedir. Makine öğrenmesi ağaç modelleri, optimizasyon ve sınıflandırma tahminleri yoluyla küçük, orta veya büyük veri kümelerine sahip problemler için zaman alıcı sayısal çözümlere güçlü bir alternatif sunmaktadır. Bu çalışmada, ısı değiştirici tasarım kriterlerinin makine öğrenmesi ağaç modelleri kullanılarak incelenmesi ve her bir tasarım kriteri için en uygun modelin belirlenmesi amaçlanmaktadır. Sonuç olarak; ısı transfer hızı, güvenlik ve güvenilirlik tasarım kriteri için XGBoost modelinin, tip ve bakım kolaylığı tasarım kriteri için AdaBoost modelinin, maliyet ve pompa gücü tasarım kriteri için RF modelinin etkin çözümler sunabileceği değerlendirilmiştir. Gelecekte ısı değiştirici tasarım kriterlerinin farklı tip makine öğrenmesi metotları ile analiz edilerek daha maliyet etkin ısı değiştiricilerin tasarlanabileceği öngörülmüştür.

Heat exchangers are critical components widely used in various industries such as chemical processing, automotive, and HVAC. The evaluation and optimization of heat exchanger design criteria play a vital role in improving industrial applications. Tree-based machine learning models offer a powerful alternative to time-consuming numerical solutions by enabling optimization and classification predictions for problems involving small, medium, or large datasets. This study aims to analyze heat exchanger design criteria using tree-based machine learning models and to identify the most suitable model for each design parameter. As a result, it has been evaluated that the XGBoost model provides effective solutions for design criteria such as heat transfer rate, safety, and reliability; the AdaBoost model is more suitable for criteria such as exchanger type and ease of maintenance; and the RF model performs well for cost and pumping power. It is anticipated that in the future, analyzing heat exchanger design parameters using various machine learning approaches will enable the development of more cost-effective and efficient heat exchangers.

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