55 research outputs found
Optimization of post weld heat treatment cycle of fiber laser welded bainitic steel
No full textAutomobile industry has always been in look out for advanced materials that would account for greater crash resistance, high fatigue strength, optimum ductility and longer service life despite heavy mechanical loads applied on these engine components. These critical requirements are met through maintaining the complex microstructures and optimum phase constituents. The retention of microstructural constituents has always been a key parameter while fabricating these advanced automobile materials by fusion welding process. Carbide free bainitic steels are emerging out to be the candidate materials for automobile applications. Owing to their microstructure consisting fine bainitic ferrite laths that are interwoven with retained austenite in their lath boundaries. The fine Bainitic laths provide enough strength and the retained austenite phase facilitates the desired ductility. The current paper critically discusses the microstructural and microhardness variation across the zones during Fiber Laser welding of bainitic steel sheets. Keeping the phase transformations during welding in view, post weld heat treatments were undertaken. The welded steel was austenitized at 820 OC, rapidly cooled to 390 OC, and soaked at different durations before furnace cooing. The microstructure variation and microhardness profiling were done at all these heat treatment conditions. Basing on the analyses, the heat treatment cycle has been optimized
Fatigue behaviour of FDM-3D printed polymers, polymeric composites and architected cellular materials
Get PDFPolymer-based materials are increasingly produced through fused deposition modelling (FDM) – an additive manufacturing process, due to its intrinsic advantages in manufacturing complex shapes and structures at low overhead costs. The versatility of this technology has attracted several industries to print complex geometrical structures. This underlines the importance of studying the mechanical strength of FDM printed polymeric materials, especially their fatigue behaviour in cyclic loading conditions. Conventionally manufactured polymeric materials (e.g. injection moulding) have superior fatigue performance than FDM printed materials. Unlike conventionally manufactured polymers, FDM-made polymers have layer by layer adhesion and the influence of printing parameters make fatigue analysis complex and critical. The influences of printing parameters and printing material characteristics have a significant impact on the fatigue behaviour of these materials. The underlying mechanism behind the fatigue of FDM printed polymers is crucial for the assessment of these materials in structural applications. However, the fatigue behaviour of FDM printed polymeric materials has not been reviewed in detail. Therefore, this article aims to evaluate 3D printed polymeric materials’ fatigue properties. The importance of fatigue in the FDM printed biomedical materials is also reviewed, and more importantly, the novel FDM printed architected cellular material fatigue properties are also introduced. © 2020 The Author(s
Design Techniques For Ultra-Low Noise And Low Power Low Dropout (LDO) Regulators
Get PDFabstract: Modern day deep sub-micron SOC architectures often demand very low supply noise levels. As supply voltage decreases with decreasing deep sub-micron gate length, noise on the power supply starts playing a dominant role in noise-sensitive analog blocks, especially high precision ADC, PLL, and RF SOC's. Most handheld and portable applications and highly sensitive medical instrumentation circuits tend to use low noise regulators as on-chip or on board power supply. Nonlinearities associated with LNA's, mixers and oscillators up-convert low frequency noise with the signal band. Specifically, synthesizer and TCXO phase noise, LNA and mixer noise figure, and adjacent channel power ratios of the PA are heavily influenced by the supply noise and ripple. This poses a stringent requirement on a very low noise power supply with high accuracy and fast transient response. Low Dropout (LDO) regulators are preferred over switching regulators for these applications due to their attractive low noise and low ripple features. LDO's shield sensitive blocks from high frequency fluctuations on the power supply while providing high accuracy, fast response supply regulation.
This research focuses on developing innovative techniques to reduce the noise of any generic wideband LDO, stable with or without load capacitor. The proposed techniques include Switched RC Filtering to reduce the Bandgap Reference noise, Current Mode Chopping to reduce the Error Amplifier noise & MOS-R based RC filter to reduce the noise due to bias current. The residual chopping ripple was reduced using a Switched Capacitor notch filter. Using these techniques, the integrated noise of a wideband LDO was brought down to 15µV in the integration band of 10Hz to 100kHz. These techniques can be integrated into any generic LDO without any significant area overhead.Dissertation/ThesisMasters Thesis Electrical Engineering 201
Genetic algorithms with immigrants and memory schemes for dynamic shortest path routing problems in mobile ad hoc networks
Get PDFThis article is posted here with permission of IEEE - Copyright @ 2010 IEEEIn recent years, the static shortest path (SP) problem has been well addressed using intelligent optimization techniques, e.g., artificial neural networks, genetic algorithms (GAs), particle swarm optimization, etc. However, with the advancement in wireless communications, more and more mobile wireless networks appear, e.g., mobile networks [mobile ad hoc networks (MANETs)], wireless sensor networks, etc. One of the most important characteristics in mobile wireless networks is the topology dynamics, i.e., the network topology changes over time due to energy conservation or node mobility. Therefore, the SP routing problem in MANETs turns out to be a dynamic optimization problem. In this paper, we propose to use GAs with immigrants and memory schemes to solve the dynamic SP routing problem in MANETs. We consider MANETs as target systems because they represent new-generation wireless networks. The experimental results show that these immigrants and memory-based GAs can quickly adapt to environmental changes (i.e., the network topology changes) and produce high-quality solutions after each change.This work was supported by the Engineering
and Physical Sciences Research Council of U.K. underGrant EP/E060722/
Author Correction: Comprehensive analysis of chromothripsis in 2,658 human cancers using whole-genome sequencing
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Determination of magnetic domain state of carbon coated iron nanoparticles via Fe-57 zero-external-field NMR
No full textThe magnetic domain state of carbon coated iron nanopowder (Fe@C) was studied by the internal field nuclear magnetic resonance (IFNMR) at 77 K using the spin echo technique. The structure and magnetic properties of the sample were further characterized by scanning electron microscopy (SEM), X-ray diffraction (XRD), Mossbauer spectroscopy, vibrating sample magnetometry (VSM), thermogravimetric analysis (TGA) and Raman Spectroscopy. The obtained IFNMR results of Fe@C powder were compared with that of micron sized carbonyl iron (CI) and electrolytic iron (EI) powders. The calculated critical size of the single domain iron particles in Fe@C is similar to 16 nm. A higher enhancement in echo amplitude was observed due to better response of the domain walls of multidomain particles in comparison to the single domain particles. The echo signal of CI and EI particles exhibit a single narrow intense peak corresponding to the domain walls, whereas Fe@C exhibits two low amplitude peaks at two different frequencies: a low frequency (46.6 MHz) peak corresponds to the response of the domain walls of the multidomain particles and the other high frequency (47.2 MHz) signal (a shoulder) corresponding to the response of the magnetic nuclei inside the domain. Our results help in determining the domain state of iron-based magnetic particles using Fe-57-IFNMR. (C) 2018 Elsevier B.V. All rights reserved
Impact of period and timescale of FDDA analysis nudging on the numerical simulation of tropical cyclones in the Bay of Bengal
No full textIn this study, the impact of four-dimensional data assimilation (FDDA) analysis nudging is examined on the prediction of tropical cyclones (TC) in the Bay of Bengal to determine the optimum period and timescale of nudging. Six TCs (SIDR: November 13–16, 2007; NARGIS: April 29–May 02, 2008; NISHA: November 25–28, 2008; AILA: May 23–26, 2009; LAILA: May 18–21, 2010; JAL: November 04–07, 2010) were simulated with a doubly nested Weather Research and Forecasting (WRF) model with a horizontal resolution of 9 km in the inner domain. In the control run for each cyclone, the National Centers for Environmental Prediction (NCEP) Global Forecast System (GFS) analysis and forecasts at 0.5_ resolution are used for initial and boundary conditions. In the FDDA experiments available surface, upper air observations obtained from NCEP Atmospheric Data Project (ADP) data sets were used for assimilation after merging with the first guess through objective analysis procedure. Analysis nudging experiments with different nudging periods (6, 12, 18, and 24 h) indicated a period of 18 or 24 h of nudging during the pre-forecast stage provides maximum impact on simulations in terms of minimum track and intensity forecasts. To determine the optimum timescale of nudging, two cyclone cases (NARGIS: April 28–May 02, 2008; NISHA: November 25–28, 2008) were simulated varying the inverse timescales as 1.0e-4 to 5.0e-4 s−1 in steps of 1.0e-4 s−1. A positive impact of assimilation is found on the simulated characteristics with a nudging coefficient of either 3.0e-4 or 4.0e-4 s−1 which corresponds to a timescale of about 1 h for nudging dynamic (u,v) and thermodynamical (t,q) fields.Authors sincerely thank Dr. Satyamurty Director, EIRSG, Dr. B. Venkatrman, AD, RSEG and Dr. R. Baskaran Head, RIAS for their encouragement and support in carrying out the study. The first author acknowledges the Space Applications Centre, Ahmadabad for the award of the Junior Research Fellowship under MeghaTropiques-Utilization Project under which this research was carried out. The WRF-ARW model was obtained from NCAR. The NCEP GFS analysis and forecasts were downloaded from NCEP. The QSCAT data were obtained from NASA. The prepbufr surface upper air observations are taken from the online archives of NCAR. Authors thank the anonymous reviewers for critical reviews and valuable comments, which helped to improve the manuscript
Evaluation of MODIS LAI/FPAR Product Collection 6. Part 1: Consistency and Improvements
Get PDFAs the latest version of Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation (FPAR) products, Collection 6 (C6) has been distributed since August 2015. This collection is evaluated in this two-part series with the goal of assessing product accuracy, uncertainty and consistency with the previous version. In this first paper, we compare C6 (MOD15A2H) with Collection 5 (C5) to check for consistency and discuss the scale effects associated with changing spatial resolution between the two collections and benefits from improvements to algorithm inputs. Compared with C5, C6 benefits from two improved inputs: (1) L2G–lite surface reflectance at 500 m resolution in place of reflectance at 1 km resolution; and (2) new multi-year land-cover product at 500 m resolution in place of the 1 km static land-cover product. Global and seasonal comparison between C5 and C6 indicates good continuity and consistency for all biome types. Moreover, inter-annual LAI anomalies at the regional scale from C5 and C6 agree well. The proportion of main radiative transfer algorithm retrievals in C6 increased slightly in most biome types, notably including a 17% improvement in evergreen broadleaf forests. With same biome input, the mean RMSE of LAI and FPAR between C5 and C6 at global scale are 0.29 and 0.091, respectively, but biome type disagreement worsens the consistency (LAI: 0.39, FPAR: 0.102). By quantifying the impact of input changes, we find that the improvements of both land-cover and reflectance products improve LAI/FPAR products. Moreover, we find that spatial scale effects due to a resolution change from 1 km to 500 m do not cause any significant differences
ESTIMATION OF PRECIPITATION DURING THE PERIOD OF SOUTH WEST MONSOON USING NUMERICAL METHODS UNDER C-PROGRAMMING IJCET © I A E M E
Get PDFABSTRACT This paper briefly focuses on the literature review of an unsupervised machine learning competitive topology preserving Self organizing maps through Ho-Kashyap rule which is the best approach in precipitation prediction to solve the data reduction with fine resolution of atmospheric models through the extensive classifying nature of the proposed model. The Model of precipitation prediction via neural networks explains that self organizing maps along with Ho-Kashyap Rule of pattern results in carrying forward the weather forecasting phenomenon at input level. The classifier provides space to separate the non linear regression pattern resulted in defining a decision pattern, reveals the precipitation prediction of low time and less number of hidden layers. Every predicate model takes input data, Processes with specified levels to variable level and counter with a variable set reduction to reach the decision of prediction in a more confirm levels. To provide a critical modeling design using non liner regression namely a support vector machine which is very non interpretable neural network designed as a two stage activity in prediction with more than three kernel functions for improving the performance of the SVMs experts in predicting the future weather happenings through its classifier. Recording the input data set parameters of weather compared with a MLP (multilayer preceptron), SVM has more esteemed performance in forecasting the parameter with respect to others in a two stage procedur
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