ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS

Akin PAKSOY, (Türkiye Petrol Rafinerileri A.Ş., İzmit Rafinesi, İzmit, Türkiye)
Selin ARADAG (TOBB Ekonomi ve Teknoloji Üniversitesi, Makine Mühendisliği Bölümü, Ankara, Türkiye)
Isı Bilimi ve Tekniği Dergisi
10 3
Yıl: 2015 Cilt: 35 Sayı: 2 Sayfa Aralığı: 1 - 18 Metin Dili: Türkçe İndeks Tarihi: 29-07-2022

ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS

Öz:
Bu çalışmada, Reynolds sayısının 100 ile 10000 arasında değiştiği, iki boyutlu ve zamana bağlı kapak güdümlü kavite akışlarının Hesaplamalı Akışkanlar Dinamiği (HAD) çalışmaları özgün olarak geliştirilen HAD kodlarının uygulanmasıyla incelenmiştir. Akışın zamana bağımlı davranışı sinüssel hız profili uygulanarak tetiklenmiştir. Akış alanında gözlemlenen yapılar düşük boyutlu modelleme tekniği olan Dikgen Ayrıştırma Yöntemi (DAY) kullanılarak incelenmiş olup, bu yapılar frekans derecelerine (akış alanının bütününü ifade etmeye yönelik olan katkılarına, enerji içeriklerine) göre ayrıştırılmıştır. DAY sonuçlarına göre, akım fonksiyonu veri grubu olarak kullanıldığında, toplam enerji içeriğinin %99a yakın kısmı sadece en yüksek enerji içeriğine sahip ilk dört DAY kipi kullanılarak ifade edilebilmektedir. Buna karşılık, x-yönündeki hız veri grubu kullanıldığında en yüksek enerji içeriğine sahip ilk dört kipin kullanılmasıyla toplam enerji içeriğinin % 9095lik bir kısmı ifade edilebilmektedir. Ayrıca, çalışmada değişik Reynolds sayılarının uygulandığı durumlar için Yapay Sinir Ağı (YSA) uygulaması yapılarak kip genlikleri de tahmin edilmiştir. HAD kullanılarak belirli akış durumları için yeterince bilgi toplandıktan sonra, YSA ile bütünleştirilen yaklaşım sayesinde farklı akış koşulları için gerçek zamanlı kontrol uygulamaları için pratik olmayan HAD analizlerine gerek duyulmadan akış alanında neler olduğuna ilişkin bilgileri tahmin etmek mümkün olmaktadır.
Anahtar Kelime:

Konular: Termodinamik

KAPAK GÜDÜMLÜ KAVİTE AKIŞLARINDA ZAMANA BAĞLI DAVRANIŞIN YAPAY SİNİR AĞLARI TABANLI TAHMİNİ

Öz:
In this study, computational fluid dynamics (CFD) analyses of the two-dimensional, time-dependent liddriven cavity flows, for Reynolds numbers ranging from 100 to 10000, are performed by using an in-house developed CFD code. The unsteady behavior of the flow is triggered using a sinusoidal lid velocity profile. The flow structure is further investigated with the application of a reduced order modeling technique, Proper Orthogonal Decomposition (POD), and the structures present in the flow, are separated according to their frequency (energy) content. POD results show that when the stream function formation is used as a data ensemble, about 99% of the total energy content can be modeled by considering only the most energetic first four POD modes; whereas, this value remains at a range between 9095% for the x-direction velocity data ensemble. What is more, an Artificial Neural Network (ANN) based approach is developed to predict mode amplitudes for flows with different Reynolds numbers. Once enough information is obtained with the help of CFD of few flow cases, the ANN integrated approach presented herein helps to predict what is happening in the flow for different flow cases without requiring further CFD simulations, which are not practical in real-time flow control applications.
Anahtar Kelime:

Konular: Termodinamik
Belge Türü: Makale Makale Türü: Araştırma Makalesi Erişim Türü: Erişime Açık
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APA PAKSOY A, ARADAG S (2015). ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. , 1 - 18.
Chicago PAKSOY Akin,ARADAG Selin ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. (2015): 1 - 18.
MLA PAKSOY Akin,ARADAG Selin ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. , 2015, ss.1 - 18.
AMA PAKSOY A,ARADAG S ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. . 2015; 1 - 18.
Vancouver PAKSOY A,ARADAG S ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. . 2015; 1 - 18.
IEEE PAKSOY A,ARADAG S "ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS." , ss.1 - 18, 2015.
ISNAD PAKSOY, Akin - ARADAG, Selin. "ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS". (2015), 1-18.
APA PAKSOY A, ARADAG S (2015). ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. Isı Bilimi ve Tekniği Dergisi, 35(2), 1 - 18.
Chicago PAKSOY Akin,ARADAG Selin ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. Isı Bilimi ve Tekniği Dergisi 35, no.2 (2015): 1 - 18.
MLA PAKSOY Akin,ARADAG Selin ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. Isı Bilimi ve Tekniği Dergisi, vol.35, no.2, 2015, ss.1 - 18.
AMA PAKSOY A,ARADAG S ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. Isı Bilimi ve Tekniği Dergisi. 2015; 35(2): 1 - 18.
Vancouver PAKSOY A,ARADAG S ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS. Isı Bilimi ve Tekniği Dergisi. 2015; 35(2): 1 - 18.
IEEE PAKSOY A,ARADAG S "ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS." Isı Bilimi ve Tekniği Dergisi, 35, ss.1 - 18, 2015.
ISNAD PAKSOY, Akin - ARADAG, Selin. "ARTIFICIAL NEURAL NETWORK BASED PREDICTION OF TIME-DEPENDENT BEHAVIOR FOR LID-DRIVEN CAVITY FLOWS". Isı Bilimi ve Tekniği Dergisi 35/2 (2015), 1-18.
Sayfa Oluşturulma Tarihi
17-04-2024 3:29

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