k. gaurav

Modern underwater warfare necessitates the development of high-speed supercavitating torpedoes. Achieving supercavitation involves integrating a cavitator at the torpedo's front, making cavitator design a critical research area. The present study simulated supercavity formation by cavitators of various shapes attached to a heavyweight torpedo. The study involves simulations of thirteen cavitator designs with various geometrical configurations at different cavitation numbers. The simulations employ the VOF multiphase model along with the Schnerr and Sauer cavitation model to analyze supercavitation hydrodynamics. The study examines the supercavity geometry and drag characteristics for individual cavitator designs. The results reveal a significant reduction in skin friction drag by a majority of cavitators. Notably, a disc cavitator at a cavitation number of 0.09 demonstrates a remarkable 92% reduction in the coefficient of skin friction drag. However, the overall drag reduces when incorporating a cavitator, but it introduces additional pressure drag. The study found that the cavitators generating larger supercavities also yield higher pressure drag. Therefore, the supercavity should just envelop the entire torpedo, as excessively small supercavities amplify skin friction drag, while overly large ones elevate pressure drag. Ultimately, the study concludes that selecting the ideal cavitator entails a comprehensive evaluation of factors such as supercavity and torpedo geometry, reductions in skin friction drag and increments in pressure drag.

Disease prediction of a human means predicting the probability of a patient’s disease after examining the combinations of the patient’s symptoms. Monitoring a patient's condition and health information at the initial examination can help doctors to treat a patient's condition effectively. This analysis in the medical industry would lead to a streamlined and expedited treatment of patients. The previous researchers have primarily emphasized machine learning models mainly Support Vector Machine (SVM), K-nearest neighbors (KNN), and RUSboost for the detection of diseases with the symptoms as parameters. However, the data used by the prior researchers for training the model is not transformed and the model is completely dependent on the symptoms, while their accuracy is poor. Nevertheless, there is a need to design a modified model for better accuracy and early prediction of human disease. The proposed model has improved the efficacy and accuracy model, by resolving the issue of the earlier researcher’s models. The proposed model is using the medical dataset from Kaggle and transforms the data by assigning the weights based on their rarity. This dataset is then trained using a combination of machine learning algorithms: Random Forest, Long Short-Term Memory (LSTM), and SVM. Parallel to this, the history of the patient can be analyzed using LSTM Algorithm. SVM is then used to conclude, the possible disease. The proposed model has achieved better accuracy and reliability as compared to state-of-the-art methods. The proposed model is useful to contribute towards development in the automation of the healthcare industries.