Multi-Thermal Desing Optimization of a Shell and Tube Condenser through Global Best Algorithm

Bu çalışmada Global Eniyi Arama algoritması gövde borulu düzenli bir kondenserin termal tasarımını oluşturmak için kullanılmıştır. Konvensiyonel optimizasyon algoritmaları tarafından sağlanan tasarım süreci, zaman alıcı olmasının yanısıra ekonomik açıdan da beklenen sonuçları sağlayamayabilmektedir. Literatür çalışmaları Global Eniyi Arama algoritması gibi stokastik optimizasyon algoritmalarının herhangi bir ısı değiştiricinin termal tasarımında uygulanmasının literatürde yapılan diğer çalışmalardan elde edilen sonuçlara dayanarak oldukça olumlu çıktılar verdiğini göstermektedir. Bu çalışmada ilk olarak, Global Eniyi Arama algoritmasının optimizasyon performansı 10 adet optimizasyon test fonksiyonu kullanılarak değerlendirilmiştir. Literatür çalışmalarından alınan bir örnek optimizasyon problemi Global Eniyi Arama algoritması ile birlikte Diferansiyel Evrim ve Parçacık Sürü Optimizasyon algoritmaları tarafından minimum toplam ısı değiştirici maliyeti ve maksimum toplam ısı transferi katsayısı gibi amaç fonksiyonlarını optimize etmek için tek ve çok amaçlı optimizasyon yöntemleri kullanılarak çözülmüştür. Global Eniyi Arama algoritması diğer karşılaştırılan algoritmalardan daha olumlu sonuçlar elde etmekle kalmamış ayrıca örnek optimizasyon probleminde tasarlanan değerlerin gelişmesinde önemli bir rol oynamıştır. Çok amaçlı optimizasyon için birbirine üstünlük kuramayan sonuçlardan oluşan Pareto eğrisi inşa edilmiş ve eğri üzerindeki en iyi sonuç LINMAP, TOPSIS ve Shannon'un entropi teorisi gibi üç önemli karar verme mekanizması tarafından seçilmiştir. Çalışmanın sonunda ise hassasiyet analizi uygulanarak tasarım parametrelerinin optimizasyon amaç fonskiyonları üzerindeki değişimsel etkileri gözlemlenmiştir.

Gövde Boru Tipli Kondenserlerin Global Eniyi Arama Algoritmasıyla Çok Amaçlı Termal Tasarım Optimizasyonu

This study considers Global Best Algorithm (GBEST) for thermoeconomic design of a shell and tube condenser. Design process sustained by the traditional procedures involves tedious and exhaustive iterative calculations which sometimes becomes time consuming and may not lead to economically optimum configuration. Literature studies have shown that solution strategy offered by stochastic optimization methods such as Global Best Algorithm over thermal design of any kind of heat exchanger is promising solution strategy according to the optimum results found in each study. Firstly, optimization performance of the GBEST is assessed with ten benchmark problems and numerical outcomes are compared with those obtained from different literature optimization methods. A case study taken from literature has been solved by GBEST along with famous optimizers of Particle Swarm Optimization and Differential Evolution in the framework of single and multi objective optimization so as to optimize the problem objectives of total cost of heat exchanger and average overall heat transfer coefficient. GBEST not only finds more favourable results than those obtained from the compared optimization algorithms, but also improves the preliminary design taken from literature study. Pareto curve is constructed for multi objective optimization and best solution on the curve is selected by three renowned decision making methods of LINMAP, TOPSIS, and Shannon's entropy theory. Finally, a sensitivity analysis has been performed in order to observe the variational influences of design parameters over optimization objectives.

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