Numerical study of solidification of A356 aluminum alloy flowing on an oblique plate with experimental validation

Molten A356 aluminum alloy flowing on an oblique plate is water cooled from underneath. The melt partially solidifies on plate wall with continuous formation of columnar dendrites. These dendrites are continuously sheared off into equiaxed/fragmented grains and carried away with the melt by producing semisolid slurry collected at plate exit. Melt pouring temperature provides required solidification whereas plate inclination enables necessary shear for producing slurry of desired solid fraction. A numerical model concerning transport equations of mass, momentum, energy and species is developed for predicting velocity, temperature, macrosegregation and solid fraction. The model uses FVM with phase change algorithm, VOF and variable viscosity. The model introduces solid phase movement with gravity effect as well. Effects of melt pouring temperature and plate inclination on hydrodynamic and thermo-solutal behaviors are studied subsequently. Slurry solid fractions at plate exit are 27%, 22%, 16%, and 10% for pouring temperatures of 620 degrees C, 625 degrees C, 630 degrees C, and 635 degrees C, respectively. And, are 27%, 25%, 22%, and 18% for plate inclinations of 30, 45, 60, and 75, respectively. Melt pouring temperature of 625 degrees C with plate inclination of 60 generates appropriate quality of slurry and is the optimum. Both numerical and experimental results are in good agreement with each other. (C) 2015 Taiwan Institute of Chemical Engineers. Published by Elsevier B.V. All rights reserved.

Lesion Detection in Breast Ultrasound Images Using Tissue Transition Analysis

Breast cancer is one of the leading cause of cancer related deaths in women and early detection is crucial for reducing mortality rates. In this paper, we present a novel and fully automated approach based on tissue transition analysis for lesion detection in breast ultrasound images. Every candidate pixel is classified as belonging to the lesion boundary, lesion interior or normal tissue based on its descriptor value. The tissue transitions are modeled using a Markov chain to estimate the likelihood of a candidate lesion region. Experimental evaluation on a clinical dataset of 135 images show that the proposed approach can achieve high sensitivity (95 %) with modest (3) false positives per image. The approach achieves very similar results (94 % for 3 false positives) on a completely different clinical dataset of 159 images without retraining, highlighting the robustness of the approach.

Disaggregation of LST over India: comparative analysis of different vegetation indices

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.