RECONSTRUCTION AND ANALYSIS OF JET FLOW BY DYNAMIC MODE DECOMPOSITION

Year 2023, Volume: 9 Issue: 2, 19 - 24, 31.12.2023

Mustafa Hicret Yaman , Gamze Yüksel

https://doi.org/10.22531/muglajsci.1268109

Abstract

In this study, the behavior of Turbulent Jet flow was investigated using Dynamic Mode Decomposition, which is a data-driven, dimension reduction method. Jet flow, which is an important and popular research topic in Fluid Dynamics and engineering applications, was considered as the fluid flow. A Large Eddy Simulation (LES) was performed using the openFOAM software to model the Jet flow. 180 snapshots were generated with the simulation to create a Jet Flow dataset of approximately 150 gigabytes. Firstly, the dynamic modes of the jet flow were extracted from this dataset to reveal the characteristic features of the flow. Then, state estimation for reconstruction of the flow were made. This significantly reduced the CPU and RAM requirement for processing data set and saved lots of disk space for storage. Performance measurements were made for the reconstructed images obtained as a result of the analyses. Two metrics were used for the measurements, namely Root Mean Square Error and Structural Similarity Index.

Keywords

Dynamic Mode Decomposition, Reduced Order Model, Jet Flow, Image Processing, Machine Learning

DiNAMiK MOD AYRIŞIMI İLE JET AKIŞININ YENİDEN OLUŞTURULMASI VE ANALİZİ

Abstract

Bu çalışmada, tamamen veri odaklı bir boyut indirgeme metodu olan Dinamik Mod Ayrışımı ile bir akışkanın davranışı incelenmiştir. Akış olarak, Akışkanlar Dinamiğinin ve mühendislik uygulamalarının önemli ve popüler araştırma konularından biri olan türbülanslı Jet Akışı ele alınmıştır. Akışın modellenmesi için gerekli simülasyon openFOAM yazılımı ile gerçekleştirilmiştir. Oluşturulan simülasyon ile 180 adet snapshot üretilerek akışa dair yaklaşık 150 gigabyte’lık veriseti oluşturulmuştur. Bu veriseti ile öncelikle Jet Akışının dinamik modları çıkarılarak akışın karakteristik özellikleri ortaya çıkarılmış, daha sonra akış görüntülerine dair durum tahmini ile akış yeniden oluşturulmuştur. Böylece verisetinin işlenmesi için gereken CPU ve RAM kullanımı önemli ölçüde azalmış, ayrıca sonraki işlemler için saklanacak veriye dair disk depolama alanında ciddi kazanımlar elde edilmiştir. Yapılan analizler sonucunda elde edilen yeniden oluşturulan görüntülerinin performans ölçümleri yapılmıştır. Ölçümler için iki metrik kullanılmış olup bunlar Kök Ortalama Kare Hatası ve Yapısal Benzerlik İndeksi’dir.

Keywords

Dinamik Mod Ayrışımı, Model Boyut İndirgeme (ROM), Jet Akış, Görüntü İşleme, Makine Öğrenmesi

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