Investigation of thermal effects on mechanical properties of 9Cr-1Mo to 9Cr-1Mo welded joints for high-temperature applications

Modified 9CR-1MO usage has been highly increased in recent years mainly under high-temperature application. This paper focusses on microstructure without post heat treatment and hardness, stress analysis of weld zone, heat affected zone and parent metal with and without post heat treatment. Here, among many suitable welding processes, we have been chosen Tungsten Inert Gas Welding with an appropriate filler rod ER308L.The post heat treatment is been carried out by an annealing process. The study of microstructure for 9cr-1mo steel mainly describes size and shape of grains by the Scanning Electronic Microscope, micro, and macro structure examination. The tensile and hardness strength of the 9cr-1mo steel has been determined by using suitable equipment throughout the base metal and weld zone. The microstructure is uniform and free from a dendritic pattern, the ductility of material more after annealing and the hardness of material become uniform overall except in HAZ.     

Steady-state temperature distribution in a tetragonal solid

ABSTRACT

The main objective of the project is to use a simple numerical technique to find out the temperature distribution in each of the three planes of the block. For many practical problems, numerical methods to solve partial differential equations (PDEs) are required. Conventional finite element or finite-difference codes have a difficulty to obtain precise solutions because of the need for an exceedingly fine mesh, which leads to often prohibitive CPU time. While conventional methods exhibit such a difficulty, some practical problems even require solutions guaranteed. The Laplace equation is one of the important PDEs in physics and engineering, describing the phenomenology of electrostatics among others, and various problems for the Laplace equation require highly precise and verified solutions. We present an alternative approach based on numerical method by computer programming in order to find the temperature distribution in the three-dimensional solid.     

Solar energy performance analysis of basin-type solar still under the effect of vacuum pressure

ABSTRACT

This paper deals with a solar still for distilled water, using the heat of the sun to evaporate, cool and then collect the water. They are used in areas where drinking water is unavailable, so that clean water is obtained from dirty water or from plants by exposing them to sunlight; vacuum pressure is delivered to still-type solar. The reason for selecting vacuum pressure is that during the monsoon and cloudy days, solar intensity will not be high as it leads to evaporation at lower temperature saline water under the soar still. The basin area for the production of 5 litres per day of fresh water is determined as 1.44?m²and the solar still basin area of 1.44?m² and 21.5° tilt angle are designed. Solar energy be may used full to alternate in electrical power and to purify water in future. Finally, the performance was analysed for the solar still with a vacuum pressure at 0.6 bar. The outcome of the theoretical analysis shows that adoption of 0.6 bar pressure inside the solar still improves the average performance by 30%. In order to overcome this problem, vacuum could be applied for the better performance of the solar still during the low solar intensity periods.     

Water-photolysis properties of micron-length highly-ordered titania nanotube-arrays

We report the water photoelectrolysis and photoelectrochemical properties of the titania nanotube arrays as a function of nanotube crystallinity, length (up to 6.4 microm), and pore size. Most noteworthy of our results, under 320-400 nm illumination (98 mW/cm2) the titania nanotube-array photoanodes (area 1 cm2), pore size 110 nm, wall thickness 20 nm, and 6 microm length, generate hydrogen by water photoelectrolysis at a rate of 7.6 mL/hr, with a photoconversion efficiency of 12.25%. The energy-time normalized hydrogen evolution rate is 80 mL/hrW, the largest reported hydrogen photoelectrolysis generation rate for any material system by a factor of four. The highly-ordered nanotubular architecture appears to allow for superior charge separation and charge transport, with a calculated quantum efficiency of over 80% for incident photons with energies larger than the titania bandgap.     

A new image representation algorithm inspired by image submodality models, redundancy reduction, and learning in biological vision

We develop a new biologically motivated algorithm for representing natural images using successive projections into complementary subspaces. An image is first projected into an edge subspace spanned using an ICA basis adapted to natural images which captures the sharp features of an image like edges and curves. The residual image obtained after extraction of the sharp image features is approximated using a mixture of probabilistic principal component analyzers (MPPCA) model. The model is consistent with cellular, functional, information theoretic, and learning paradigms in visual pathway modeling. We demonstrate the efficiency of our model for representing different attributes of natural images like color and luminance. We compare the performance of our model in terms of quality of representation against commonly used basis, like the discrete cosine transform (DCT), independent component analysis (ICA), and principal components analysis (PCA), based on their entropies. Chrominance and luminance components of images are represented using codes having lower entropy than DCT, ICA, or PCA for similar visual quality. The model attains considerable simplification for learning from images by using a sparse independent code for representing edges and explicitly evaluating probabilities in the residual subspace.     

Integrating Visual Analytics Support for Grounded Theory Practice in Qualitative Text Analysis

We present an argument for using visual analytics to aid Grounded Theory methodologies in qualitative data analysis. Grounded theory methods involve the inductive analysis of data to generate novel insights and theoretical constructs. Making sense of unstructured text data is uniquely suited for visual analytics. Using natural language processing techniques such as parts‐of‐speech tagging, retrieving information content, and topic modeling, different parts of the data can be structured and semantically associated, and interactively explored, thereby providing conceptual depth to the guided discovery process. We review grounded theory methods and identify processes that can be enhanced through visual analytic techniques. Next, we develop an interface for qualitative text analysis, and evaluate our design with qualitative research practitioners who analyze texts with and without visual analytics support. The results of our study suggest how visual analytics can be incorporated into qualitative data analysis tools, and the analytic and interpretive benefits that can result.     

De-tethering of high aspect ratio metallic and polymeric MEMS/NEMS parts for the direct pick-and-place assembly of 3D microsystem

We report our study on several de-tethering methods for various high aspect ratio metallic and polymeric MEMS and NEMS parts including 5:1 aspect ratio 50 μm thick metallic (nickel) MEMS parts, 3:1 aspect ratio 1 μm thick sub-micron (350 nm) feature size metallic NEMS actuators, and 10:1 aspect ratio 100 μm thick polymer/metal bi-layer MEMS actuators. Resistive heating was found to be effective for the de-tethering of high aspect ratio metallic MEMS parts. In order to de-tether metallic NEMS parts and polymer/metal bi-layer devices, we performed the milling of tethers using a focused ion beam. Very low current (20 pA) ion beam was found to be effective means of de-tethering the metallic NEMS parts. Relatively larger current (0.3–20 nA) ion beam was found to be good for the polymer/metal bi-layer parts. We demonstrated 3D assembly and complete packaging of the de-tethered high aspect ratio metallic and metal/polymer bilayer MEMS parts.     

Finding “persistent rules”: Combining association and classification results

Different data mining algorithms applied to the same data can result in similar findings, typically in the form of rules. These similarities can be exploited to identify especially powerful rules, in particular those that are common to the different algorithms. This research focuses on the independent application of association and classification mining algorithms to the same data to discover common or similar rules, which are deemed “persistent-rules”. The persistent-rule discovery process is demonstrated and tested against two data sets drawn from the American National Election Studies: one data set used to predict voter turnout and the second used to predict vote choice.     

Likelihood ratio-based biometric score fusion

Multibiometric systems fuse information from different sources to compensate for the limitations in performance of individual matchers. We propose a framework for the optimal combination of match scores that is based on the likelihood ratio test. The distributions of genuine and impostor match scores are modeled as finite Gaussian mixture model. The proposed fusion approach is general in its ability to handle 1) discrete values in biometric match score distributions, 2) arbitrary scales and distributions of match scores, 3) correlation between the scores of multiple matchers, and 4) sample quality of multiple biometric sources. Experiments on three multibiometric databases indicate that the proposed fusion framework achieves consistently high performance compared to commonly used score fusion techniques based on score transformation and classification.     

An Iterative Processing Scheme for Enhancing the Map Reduce Using Map Information Storage in Wireless Environment

Wireless Personal Communications - The intention is to design the schemes for the enhancements for the enhancement of behaviour of map reduction scheme when it is employed for the iterative...