Navier-Stokes calculations with a coupled strongly implicit method. Part 2: Spline solutions

A coupled strongly implicit method is combined with a deferred-corrector spline solver for the vorticity-stream function form of the Navier-Stokes equation. Solutions for cavity, channel and cylinder flows are obtained with the fourth-order spline 4 procedure. The strongly coupled spline corrector method converges as rapidly as the finite difference calculations and also allows for arbitrary large time increments for the Reynolds numbers considered. In some cases fourth-order smoothing or filtering is required in order to suppress high frequency oscillations

High-order numerical solutions using cubic splines

The cubic spline collocation procedure for the numerical solution of partial differential equations was reformulated so that the accuracy of the second-derivative approximation is improved and parallels that previously obtained for lower derivative terms. The final result is a numerical procedure having overall third-order accuracy for a nonuniform mesh and overall fourth-order accuracy for a uniform mesh. Application of the technique was made to the Burger's equation, to the flow around a linear corner, to the potential flow over a circular cylinder, and to boundary layer problems. The results confirmed the higher-order accuracy of the spline method and suggest that accurate solutions for more practical flow problems can be obtained with relatively coarse nonuniform meshes

A pressure flux-split technique for computation of inlet flow behavior

A method for calculating the flow field in aircraft engine inlets is presented. The phenomena of inlet unstart and restart are investigated. Solutions of the reduced Navier-Stokes (RNS) equations are obtained with a time consistent direct sparse matrix solver that computes the transient flow field both internal and external to the inlet. Time varying shocks and time varying recirculation regions can be efficiently analyzed. The code is quite general and is suitable for the computation of flow for a wide variety of geometries and over a wide range of Mach and Reynolds numbers

Solution of three-dimensional afterbody flow using reduced Navier-Stokes equations

The flow over afterbody geometries was investigated using the reduced Navier-Stokes (RNS) approximation. Both pressure velocity flux-split and composites velocity primitive variable formulations were considered. Pressure or pseudopotential relaxation procedures are combined with sparse matrix or coupled strongly implicit algorithms to form a three-dimensional solver for general non-orthogonal coordinates. Three-dimensional subsonic and transonic viscous/inviscid interacting flows were evaluated. Solutions with and without regions of recirculation were obtained

Analysis of MAGSAT data of the Indian region

Progress in the development of software for reading MAGSAT data tapes and for the reduction of anomaly data, and in the preparation of data for magnetic anomaly maps is reported

Skin Lesion Analyser: An Efficient Seven-Way Multi-Class Skin Cancer Classification Using MobileNet

Skin cancer, a major form of cancer, is a critical public health problem with 123,000 newly diagnosed melanoma cases and between 2 and 3 million non-melanoma cases worldwide each year. The leading cause of skin cancer is high exposure of skin cells to UV radiation, which can damage the DNA inside skin cells leading to uncontrolled growth of skin cells. Skin cancer is primarily diagnosed visually employing clinical screening, a biopsy, dermoscopic analysis, and histopathological examination. It has been demonstrated that the dermoscopic analysis in the hands of inexperienced dermatologists may cause a reduction in diagnostic accuracy. Early detection and screening of skin cancer have the potential to reduce mortality and morbidity. Previous studies have shown Deep Learning ability to perform better than human experts in several visual recognition tasks. In this paper, we propose an efficient seven-way automated multi-class skin cancer classification system having performance comparable with expert dermatologists. We used a pretrained MobileNet model to train over HAM10000 dataset using transfer learning. The model classifies skin lesion image with a categorical accuracy of 83.1 percent, top2 accuracy of 91.36 percent and top3 accuracy of 95.34 percent. The weighted average of precision, recall, and f1-score were found to be 0.89, 0.83, and 0.83 respectively. The model has been deployed as a web application for public use at (https://saketchaturvedi.github.io). This fast, expansible method holds the potential for substantial clinical impact, including broadening the scope of primary care practice and augmenting clinical decision-making for dermatology specialists.Comment: This is a pre-copyedited version of a contribution published in Advances in Intelligent Systems and Computing, Hassanien A., Bhatnagar R., Darwish A. (eds) published by Chaturvedi S.S., Gupta K., Prasad P.S. The definitive authentication version is available online via https://doi.org/10.1007/978-981-15-3383-9_1

Characterization and its implication on beneficiation of low grade iron ore by gravity separation

Studies were undertaken on low grade iron ore sample from Noamundi iron ore mines. The objective of this study was to examine the possibility of the physical beneficiation of low grade iron ore sample by physical methods for the blast furnace route of iron production. The present investigation relies on petrography and ore mineralogical characterization, ore textures (primary, secondary, metamorphic), liberation characters and its impact on the mineral beneficiation methods to produce quality concentrate. The geological characters, alteration mineralogy, morphometric variation, ore microscopy (using model microscope with transmitted and reflected light) and thereby understanding the genesis has given proper insight into the occurrence of various minerals. In addition to this, representative samples were employed for detailed investigation by using XRD, SEM-EDS and cathodoluminescence (CL) studies for confirmation of major as well as minor ore minerals and associated gangue minerals. Investigations suggest that lateritic iron ore samples obtained from the study area are composed of hematite (two generations), goethite (two generations) and limonitic material (younger generation) in association with major gangue minerals such as clay minerals (kaolinite, illite), bauxitic minerals(gibbsite, boehmite and diaspore), cryptocrystalline silica(japer, chert) and crystalline quartz as well as apatite and collophane. Fair liberation obtained below 74 micron size. It was interesting to find that inspite of the complex mineralogy of iron ore, beneficiation results using gravity separation like multi gravity separator (MGS), particularly in finer size ranges was encouraging. The result of ore-gangue mineralogical studies were found quite useful in evaluating the separation efficacy of gravity separation process. The process mineralogical data corroborated well with beneficiation results