Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Applicability of several machine learning models in estimation of vortex tube trapping efficiency
A vortex tube ejector comprises a tube with a slitted crown that lies flush across the entire width of a channel bed surface. The bed and suspended loads are ejected with minimal flushing water through the slit with the same efficacy as any other alternative extractor. The whirling flow phenomena through the vortex duct are very complex, so ordinary classical models have results contrary to required design guidelines. So, the machine learning (ML) models of the artificial neural network (ANN), deep neural network (DNN), gradient boosting machine (GBM), stacked ensemble (SE), and adaptive neuro-fuzzy inference system (ANFIS) are used to predict vortex tube trapping efficiency (VTE). The input dataset takes the size of the sediment (Sz), concentration (I) of the sediment, the ratio of slit thickness to diameter of the tube (th/dia), and extraction ratio (Extro) while trapping efficiency (TE) is taken as output. Based on statistical assessments, GBM appears to be better than all proposed models. However, other proposed ML models give comparable performance. The classical models, multivariate linear, and nonlinear regression techniques also provide comparatively good results. According to sensitivity analyses, the extraction ratio is the most relevant parameter in evaluating the VTE. HIGHLIGHTS An experimental test of the vortex tube sediment ejector efficiency (VTE) has been studied.; Machine learning models are used for estimating the VTE.; The VTE by DNN, GBM, SE, NFS, NN, etc., models are compared.; The GBM model performs the best among all models.; The extraction ratio is the most sensitive parameter.;
Applicability of several machine learning models in estimation of vortex tube trapping efficiency
A vortex tube ejector comprises a tube with a slitted crown that lies flush across the entire width of a channel bed surface. The bed and suspended loads are ejected with minimal flushing water through the slit with the same efficacy as any other alternative extractor. The whirling flow phenomena through the vortex duct are very complex, so ordinary classical models have results contrary to required design guidelines. So, the machine learning (ML) models of the artificial neural network (ANN), deep neural network (DNN), gradient boosting machine (GBM), stacked ensemble (SE), and adaptive neuro-fuzzy inference system (ANFIS) are used to predict vortex tube trapping efficiency (VTE). The input dataset takes the size of the sediment (Sz), concentration (I) of the sediment, the ratio of slit thickness to diameter of the tube (th/dia), and extraction ratio (Extro) while trapping efficiency (TE) is taken as output. Based on statistical assessments, GBM appears to be better than all proposed models. However, other proposed ML models give comparable performance. The classical models, multivariate linear, and nonlinear regression techniques also provide comparatively good results. According to sensitivity analyses, the extraction ratio is the most relevant parameter in evaluating the VTE. HIGHLIGHTS An experimental test of the vortex tube sediment ejector efficiency (VTE) has been studied.; Machine learning models are used for estimating the VTE.; The VTE by DNN, GBM, SE, NFS, NN, etc., models are compared.; The GBM model performs the best among all models.; The extraction ratio is the most sensitive parameter.;
Applicability of several machine learning models in estimation of vortex tube trapping efficiency
Shubham Kumar (Autor:in) / N. K. Tiwari (Autor:in) / Subodh Ranjan (Autor:in)
2022
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
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