این سایت در حال حاضر پشتیبانی نمی شود و امکان دارد داده های نشریات بروز نباشند
Journal of Applied Fluid Mechanics، جلد ۱۸، شماره ۲، صفحات ۳۹۹-۴۱۸
عنوان فارسی
چکیده فارسی مقاله
کلیدواژه‌های فارسی مقاله
عنوان انگلیسی Intelligent Prediction of Separated Flow Dynamics using Machine Learning
چکیده انگلیسی مقاله Understanding separated flow dynamics is crucial for implementing effective flow control techniques. These techniques help mitigate adverse effects on vehicle performance and environmental pollution. This research aims to improve flow control strategies by predicting separated flow dynamics solely through wall pressure measurements using artificial intelligence and numerical data. Initially, we identify numerical models that accurately replicate separated flow dynamics. Notably, the Detached Eddy Simulation (DES) model strongly agrees with experimental data, particularly in the turbulent regime at Reh= 89100, downstream of backward facing steps (BFS). Subsequently we conducted a correlational analysis that revealed a significant relationship between various wall pressure points and the velocity field, leading to the adoption of deep learning techniques such as Recurrent Neural Networks with Long Short-Term Memory (LSTM). These neural networks, tailored for time-dependent data, demonstrate high accuracy of low MSE of 13.48% using ten wall pressure points in predicting velocity magnitude contour over (BFS). To enhance predictions, Proper Orthogonal Decomposition (POD) is utilized to reduce system complexity while retaining essential dynamics, resulting in a lower MSE of 5.07%. Additionally, we identify the ideal wall pressure measurement region that accurately captures the entire dynamic behavior, achieving an acceptable MSE of 23.48% for predicting low order vorticity, with only three wall pressure points. This research aids in developing efficient flow control strategies with limited pressure data and offers valuable insights for closed-loop flow control applications.
کلیدواژه‌های انگلیسی مقاله Flow separation,Backward facing step,Instabilities,LSTM Recurrent neural network,Dynamics prediction,Machine learning
نویسندگان مقاله S. Kouah |
Research laboratory of applied and fundamental physic /Blida 1 University BP 270 Route Soumâa, Blida, Algeria

F. Fadla |
Research Laboratory of energetic, flow and transfers /AMC BP 48 Cherchell terre 42006, Tipaza, Algeria

M. Roudane |
Research laboratory of applied and fundamental physic /Blida 1 University BP 270 Route Soumâa, Blida, Algeria

نشانی اینترنتی https://www.jafmonline.net/article_2566_7e189b50bc9589c785c41961485ba0dc.pdf
فایل مقاله فایلی برای مقاله ذخیره نشده است
کد مقاله (doi)
زبان مقاله منتشر شده en
موضوعات مقاله منتشر شده
نوع مقاله منتشر شده
برگشت به: صفحه اول پایگاه   |   نسخه مرتبط   |   نشریه مرتبط   |   فهرست نشریات