In the present study, erosion wear of a 90 o pipe bend has been investigated using the Computational fluid dynamics code FLUENT. Solid particles were tracked to evaluate the erosion rate along with k-ɛ turbulent model for continuous/fluid phase flow field. Spherical shaped sand particles of size 183 µm and 277 µm of density 2631 kg/m 3 are injected from the inlet surface at velocity ranging from 0.5 to 8 ms -1 at two different concentrations. By considering the interaction between solid-liquid, effect of velocity, particle size and concentration were studied. Erosion wear was increased exponential with velocity, particles size and concentrations. Predicted results with CFD have revealed well in agreement with experimental results. The magnitude and location of maximum erosion wear were more severe in bend rather than the straight pipe.
This paper presents the machine learning based machine translation system for Hindi to English, which learns the semantically correct corpus. The quantum neural based pattern recognizer is used to recognize and learn the pattern of corpus, using the information of part of speech of individual word in the corpus, like a human. The system performs the machine translation using its knowledge gained during the learning by inputting the pair of sentences of Devnagri-Hindi and English. To analyze the effectiveness of the proposed approach, 2600 sentences have been evaluated during simulation and evaluation. The accuracy achieved on BLEU score is 0.7502, on NIST score is 6.5773, on ROUGE-L score is 0.9233, and on METEOR score is 0.5456, which is significantly higher in comparison with Google Translation and Bing Translation for Hindi to English Machine Translation.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.