A theoretical model is developed for the prediction of the mean-mass diameter of droplets produced by the fragmentation of liquid fuel sheet and film in a fuel-air explosive (FAE) device after the detonation of the central burster charge. This model does not contain arbitrarily assumed values for the instabilities as in presently available models. Also, adisqibution model for the initial distribution of the droplet diameter, which depends on the design parameters of the FAE device, is presented.
.Hurricanes are one of the most disastrous natural phenomena occurring on Earth that cause loss of human lives and immense damage to property. A damage assessment method has been proposed for damage caused to buildings due to Hurricane Harvey that hit the Texas region in the year 2017. The aim of our study is to predict if there is any damage to the buildings present in the postdisaster satellite images. Principal component analysis has been used for the visualization of data. The VGG16 model has been used for extracting features from the input images. K-nearest neighbor (KNN), logistic regression, decision tree, random forest, and XGBoost classification techniques have been used for classification of the images whose features have been extracted from VGG16. Best accuracy of 97% is obtained by KNN classifier for the balanced test set, and accuracy of 96% is obtained by logistic regression for the unbalanced test set.
A three dimensional study of a cold droplet impacting obliquely on a a heated solid flat surface covered by a hot liquid layer has been performed. The drop Weber number, liquid film thickness and drop impact angle are set to a range from 100 to 800, 0.1 to 0.4, and 0° to 60° respectively. The interface evolution and thermal behaviour after drop impingement is well captured using coupled level-set and volume of fluid method (CLSVOF). The code is checked against previously published results both qualitatively and quantitatively. The results show that in the case of oblique drop impact, the crown dynamics and wall heat flux distribution exhibit an asymmetric pattern, with secondary droplets generated solely on the downstream side, as opposed to normal drop impact. Based on heat flux values, two distinct region within the liquid film exist: (i) impact region around the impact point and (ii) undisturbed region far from the cavity dynamics. A parametric analysis further demonstrates that as the drop impact angle and drop Weber number increase, the asymmetric behaviour increases. As a result, significant cooling occurs when the impact angle is lower or the Weber number is higher. Furthermore, it is found that a thinner liquid film promote heat transfer from the solid surface, resulting in a higher average wall heat flux.
Introduction:
Recent advances in deep learning have aided the well-being business in Medical Imaging of numerous disorders like brain tumours, a serious malignancy caused by unregulated and aberrant cell portioning. The most frequent and widely used machine learning algorithm for visual learning and image identification is CNN. Methods: In this article, the convolutional neural network (CNN) technique is used. Augmentation of data and processing of images is used to classify scan imagery of brain MRI as malignant or benign. The performance of the proposed CNN model is compared with pre-trained models: VGG-16, ResNet-50, and Inceptionv3 using the technique which is transfer learning. Results: Even though the experiment was conducted on a relatively limited dataset, the experimental results reveal that the suggested scratched CNN model accuracy achieved is 94%, VGG-16 was extremely effective and had a very low complexity rate with an accuracy of 90%, whereas ResNet- 50 reached 86% and Inception v3 obtained 64% accuracy.
Conclusion:
When compared to previous pre-trained models, the suggested model consumes significantly less processing resources and achieves significantly higher accuracy outcomes and a reduction in losses.
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