Mepco Schlenk Engineering College

About: Mepco Schlenk Engineering College is a based out in . It is known for research contribution in the topics: Wavelet & Wavelet transform. The organization has 1307 authors who have published 1665 publications receiving 18690 citations.

Effect of Hybrid Nanoparticle on DI Diesel Engine Performance, Combustion, and Emission Studies

Abstract: The frequent rise in the use of diesel engines in all fields emits harmful gases such as NOx and CO, which causes significant environmental emissions, global warming, breathing problems, etc. (Sivalingam et al. 2019). In the investigation of the performance, combustion, and emission characteristics, using diesel water emulsion is mixed with hybrid nanoparticles as additives in Direct Injection (DI) diesel engine. Reducing the emission characteristics and increasing engine performance is to introduce emulsion fuels (Parthasarathy et al. 2021). The water content of 5% is added with the diesel fuel as blends of (D94% + W5%). The surfactant used is Span 80 and Tween 80 and mixed with diesel water emulsion using a mechanical stirring process. This reports on the use of cerium oxide (CeO2), aluminum oxide (Al2O3), titanium dioxide (TiO2) nanoparticles as an additive to diesel fuel. For this study, the fuel tested was prepared by blending three nanoparticles into diesel in a mass fraction of 50 ppm, 100 ppm, 150 ppm with the assistance of an ultrasonic stirrer. The diesel water emulsion is mixed with the nanoparticle and prepared as three different fuel blends such as (D90% + W5% + S5% + HBNP 50 ppm), (D90% + W5% +S5% + HBNP 100 ppm), and (D90% + W5% +S5% + HBNP 150 ppm). Based on experimental results, BTE increased by 8.3% and BSFC is reduced by 14.42% at (D94% + W5% +S1% + HB 150 ppm) blend when compared with the diesel, due to the atomization of the fuel and oxygen content available in the fuel. The emissions of CO is reduced by 10.2%; smoke, oxides of nitrogen emissions, and HC are reduced by 27.5%, 36.58%, and 27.77%, respectively, when compared with clean diesel fuel, because of microexplosion and proper atomization taking place during the combustion process. The cylinder pressure and HRR are increased by 3.2% and 2.8%, respectively, when compared with neat diesel fuel, due to increased combustion temperature and secondary atomization of fuel take places.

Fabrication of dye sensitized solarcell using chemically tuned CuO nanoparticlesprepared by sol-gel method

Abstract: Purpose: This work details about the isolation of CuO nanoflakes by a simple precipitation technique. Further it is coated over ITO substrate using spin coating technique. DSS cell capabilities were checked by placing a drop of plant dye derived from pomegranate. Design/methodology/approach: CuO nano flakes, a novel Nano sized metal oxide have been synthesized by sol-gel method. The synthesised flakes were characterized by using XRD (X-ray diffraction), FESEM, UV-VIS and PL measurements techniques. XRD studies show that, the copper oxide formed has monoclinic structure. Findings: The grain size of the synthesized copper oxide nanoflakes were measured from FESEM and found that the size was around 200 nm. The UV-VIS measurement show that the band gap of CuO nanoflakes were found to be 3.03 eV, which is in the range of a good semiconductor. Finally, the dye sensitized solar cell was fabricated and its power conversion efficiency η (PCE) was determined. Practical implications: The search for green sources or generators of energy is considered as one of the priorities in today's society and occupies many policy maker’s agenda. It is believed that nanocrystalline photovoltaic devices are becoming viable contender for large scale future solar energy converters.

Design and Analysis of Interleaved buck-boost Converter for household appliances

Abstract: DC-DC converters like switching stress and switching losses are increased, so it significantly requires an alternate choice to reduce the losses. The proposed model has interleaved buck/boost converter output voltages and also the currents are analyzed and designed. The MATLAB software is used to verify and the results are presented.

Regression Modelling of Joining Aluminium Studs to Steel with AA 1100 Interlayer

Abstract: Recently, there is a steep increase in the requirement for aluminium-steel joints in several automotive and structural applications. But joining of dissimilar metals possesses unique challenges. To overcome these issues, AA 1100 interlayer has been proposed in the friction stud welding of AISI 1030/AA 1050 joints. In the present work, it is found that the use of interlayer not only enhances the joint strength but also inhibits the formation of flash and thereby reduces the consumption of base metals. Applying full factorial design, experiments were conducted to study the influence of interlayer thickness along with other parameters such as rotational speed, friction time and friction pressure on the strength of joint. SEM evaluation on the fractured surface of the specimens was carried out. Ductile mode fracture with dimples observed with 0.95 mm interlayer. Microvoids absorb a lot of energy, and the toughness is high. Hence, the dissimilar joint made using 0.95 mm interlayer have high joint strength. Microhardness measurement across the interfacial region shows increased value near the weld line due to increased plastic deformation. Finally, regression analysis is carried out and an empirical relation has been developed to predict impact strength and axial shortening length in terms of other parameters.

Facial Emotion Recognition Based on Eye and Mouth Regions

Abstract: Machine analysis of facial emotion recognition is a challenging and an innovative research topic in human-computer intelligent interaction nowadays. The eye and the mouth regions are most essential components for facial emotion recognition. Most of the existing approaches have not utilized the eye and the mouth regions for high recognition rate. This paper proposes an approach to overcome this limitation using the eye and the mouth region-based emotion recognition using reinforced local binary patterns (LBP). The local features are extracted in each frame by using Gabor wavelet with selected scale and orientations. This feature is passed on to the ensemble classifier for detecting the location of the face region. From the signature of each pixel on the face, the eye and the mouth regions are detected using ensemble classifier. The eye and the mouth features are extracted using reinforced LBP. Multi-class Adaboost algorithm is used to select and classify these discriminative features for recognizing the emotion of the face. The developed methods are deployed on the RML, CK and FERA 2011 databases, and they exhibit significant performance improvement owing to their novel features when compared to the existing techniques.