کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
9825473 1521789 2005 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Classification of two-phase flow regimes via image analysis and a neuro-wavelet approach
موضوعات مرتبط
مهندسی و علوم پایه مهندسی انرژی مهندسی انرژی و فناوری های برق
پیش نمایش صفحه اول مقاله
Classification of two-phase flow regimes via image analysis and a neuro-wavelet approach
چکیده انگلیسی
A non-intrusive method of two-phase flow identification is investigated in this paper. It is based on image processing of data obtained partly from dynamic neutron radiography recordings of real two-phase flow in a heated metal channel, and partly by visible light from a two-component mixture of water and air. Classification of the flow regime types is performed by an artificial neural network (ANN) algorithm. The input data to the ANN are some statistical moments of the wavelet pre-processed pixel intensity data of the images. The pre-processing used in this paper consists of a one-step multiresolution analysis of the 2-D image data. The investigations of the neutron radiography images, where all four flow regimes are represented, show that bubbly and annular flows can be identified with a high confidence, but slug and churn-turbulent flows are more often mixed up in between themselves. The reason for the faulty identifications, at least partially, lies in the insufficient quality of these images. In the measurements with air-water two-component mixture, only bubbly and slug flow regimes were available, and these were identified with nearly 100% success ratio. The maximum success ratio attainable was approximately the same whether the raw data was used without wavelet preprocessing or with a wavelet preprocessing of the input data. However, the use of wavelet preprocessing decreased the training time (number of epochs) with about a factor 100.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Progress in Nuclear Energy - Volume 46, Issues 3–4, 2005, Pages 348-358
نویسندگان
, , ,