Combustion monitoring system

Study of combustion process in an internal combustion engine to improve the performance of the engine. Flame speed is used to study the stability of combustion process in the engine. A simplified setup of a non compressed air-gas(LPG) mixture is used in this project . The Idea is to determine the flame speed of the combustion dependent on the Fan speed Ion sensor is used to measure the flame speed.The experimental result is compared with the theoretical book value

A Comparative Genome-Wide Transcriptome Analysis of Glucocorticoid Responder and Non-Responder Primary Human Trabecular Meshwork Cells

Aim: To investigate genes and pathways involved in differential glucocorticoid (GC) responsiveness in human trabecular meshwork (HTM) cells using RNA sequencing. Methods: Using paired human donor eyes, human organ-cultured anterior segment (HOCAS) was established in one eye to characterize GC responsiveness based on intra ocular pressure (IOP) change and, in the other eye, primary HTM cell culture was established. For RNA sequencing, total RNA extracted from GC-responder (GC-R) and non-responder (GC-NR) cells after dexamethasone (DEX) or ethanol (ETH) treatment for 7d was used. Differentially expressed genes (DEGs) were compared among five groups and validated. Results: In total, 616 and 216 genes were identified as significantly dysregulated in Group #1 and #2 (#1: ETH vs. DEX-treated GC-R; #2: ETH vs. DEX-treated GC-NR), respectively. Around 80 genes were commonly dysregulated in Group #3 (overlapping DEGs between #1 and #2), whereas 536 and 136 genes were uniquely expressed in GC-R (#4) and GC-NR HTM (#5) cells, respectively. Pathway analysis revealed that WNT signaling, drug metabolism cytochrome p450, cell adhesion, TGF-β signaling, and MAPK signaling were associated with GC responsiveness. Conclusion: This is the first study reporting distinct gene signatures and their associated pathways for GC-R and GC-NR HTM cells. WNT and MAPK signaling are potential therapeutic targets for the management of GC-induced glaucoma

A Comprehensive Review on Tribological Behaviourof Hybrid Nanocellulose-CuO as Nanolubricantfor Piston Ring Cylinder Liner Application

Friction coefficient and wear rate are crucial performance for the development of nanolubricants, which are of great significance for realizing energy conservation and prolonging service life of mechanical components. Herein, this review systematically describes the progress on nanocellulose (CNC) and Copper (II) oxide (CuO) as effective nanoparticles for developments of nanolubricant as oil additives for improving the anti-friction and wear resistant for piston ring-cylinder liner contact. Additionally, we point out several problems existing in the applications of CuO and CNC additives propose the possible solutions. Finally, the research prospects of CNC-CuO in the field of tribology are prospected

Graphene as an alternative additive in automotive cooling system

The project represents graphene can be used as an alternative additive in the automotive cooling system. Thus, graphene nanofluids have been prepared at 0.1, 0.3 and 0.5% volume concentrations. Afterward, measurement of various thermophysical properties of nanofluid such as thermal conductivity, density, viscosity, and specific has been done. The obtaining data has been analyzed and compared with graphene oxide, titanium oxide, aluminium oxide, silicon carbide, and copper oxide nanofluid to figure out the best nanofluid that can absorb more heat to protect the car engine from overheating. In, summary, the overall best nanofluid among these six would be graphene oxide, with the best thermal conductivity, specific heat capacity, and one of the lowest viscosities. As for comparison among graphene all volume concentrations, the 0.1% graphene nanofluid demonstrated the best with high thermal conductivity and low viscosity

Analysis of Correlation of Induced Frequency and Cream Skimming Efficiency through Ultrasonic Technology

Ultrasonic cream separator is a very new technology especially in the dairy industry. Ultrasonic separator machine is an eco-friendly technology that could boost the separation process and it could act as supplement to heat-based technology. However, ultrasonic separators are well developed in other industries such as sludge separation, emulsion breaking, de-oiling and sewage disposal. This technology has been implemented in the food industries recently too. The primary function of an ultrasonic cream separator machine is to separate the milk into two products i.e., cream, and skimmed milk. It is an instantaneous process which saves up workload, manpower, time, and cost. This machine intends to coagulate the fat particles with one another once a certain frequency is induced in the milk. Cream, which is lighter molecule will coagulate and float, whereas the heavier molecules of milk such as protein and minerals will sink. Upon separation, the fat molecules can be scooped out or separated through flushing from below. This machine is at a very agile stage that leads to inefficient cream skimming which leads to fluctuation of fat particles coagulation that leads to inefficient cream skimming. The intention of this study is to evaluate the factors that have a direct relationship with low performance of cream skimming via ultrasonic cream separation and come up with an ideal possible solution to enhance the process of cream separation

The Implementation of Transfer Learning by Convolution Neural Network (CNN) for Recognizing Facial Emotions

The primary objective of this study is to develop a real-time system that can predict the emotional states of an individual who commonly undergoes various experiences. The primary methodology suggested in this research for detecting facial expressions involves the integration of transfer learning techniquesthat incorporate convolutional neural networks (CNNs), along with a parameterization approach that minimizes the number of parameters. The FER-2013, JAFFE, and CK+ datasets were jointly used to train the CNN architecture for real-time detection, which broadened the range of emotional expressions that may be recognized. The proposed model has the capability to identify various emotions, including but not limited to happiness, fear, surprise, anger, contempt, sadness, and neutrality. Several methods were employed to assess the efficacy of the model's performance in this study. The experimental results indicate that the proposed approach surpasses previous studies in terms of both speed and accuracy

Comprehensive investigation and prediction model for mechanical properties of coconut wood–polylactic acid composites filaments for FDM 3D printing

Fused deposition modeling (FDM) is a practical 3D printing technology to print thermoplastic and composite materials. The FDM 3D printing process has gained substantial attention due to its capability to produce complex and accurate components. Recently, the wood particles-based flament in 3D printing has become a subject of interest, which is due to their prominent advantages, such as thermal resistivity, corrosion resistivity, biodegradable characteristics, and being environmentally friendly. Therefore, this research study aims to investigate the mechanical properties and statistical prediction model development for coconut wood–polylactic acid (PLA). The experimental investigation was carried out according to the ASTM standards (tensile—ASTM D638 Type 1, compression—ASTM D695, and bending—ASTM D790) at diferent infll densities (25, 50, and 70%) and fve diferent infll patterns. The obtained results proved that concentric infll pattern accompanied by 75% infll percentage achieved the most outstanding tensile and bending behavior. For compression testing, grid infll pattern accompanied by 75% infll percentage exhibits maximum compression properties. In overall, the octagram spiral infll pattern shows the weakest properties among all the infll patterns. The experimental results were further analyzed using response surface methodology to identify the efectiveness of studied parameters on mechanical properties and to derive a mathematical model. The derived mathematical models related to studied mechanical properties have been proposed to predict the desired mechanical properties with respect to the variation of infll patterns and percentages

Thermal stability and performance evaluation of hitec molten salt for high-temperature energy storage applications

The quest for advanced materials in thermal energy storage (TES) has become paramount in a world grappling with pressing demands for sustainable and reliable energy solutions. Among these materials, molten salts have emerged as up-and-coming contenders, owing to their exceptional thermal properties and wide operational temperature ranges. HITEC, a eutectic blend of sodium nitrate, sodium nitrite, and potassium nitrate, distinguishes itself as a superior choice due to its unique amalgamation of favorable thermal characteristics. This comprehensive review delves into the thermal properties of HITEC molten salt and its manifold applications in thermal energy storage, illuminating its potential as a pivotal element in addressing contemporary global challenges. The review examines HITEC's specific heat capacity, thermal conductivity, and thermal stability, presenting critical insights into its efficacy as a TES medium. Such comprehension fosters the advancement of Sustainable Development Goal 7. The article explores strides made in HITEC-based TES systems, underscoring inventive engineering approaches and burgeoning technologies that bolster progress towards Sustainable Development Goal 9. Furthermore, the article discusses challenges associated with HITEC molten salts, such as corrosion and material compatibility issues, and investigates ongoing research efforts to overcome these limitations. A comparative evaluation of HITEC with other molten salt mixtures elucidates its competitive advantages. This review consolidates knowledge about HITEC molten salt for thermal energy storage applications, providing valuable perspectives for researchers, engineers, and policymakers dedicated to advancing sustainable energy technologies. The review underscores the pivotal role of HITEC molten salt in advancing thermal energy storage technologies, directly influencing the achievement of several SDGs

An overview on synthesis, stability, opportunities and challenges of nanofluids

Nanofluids are the novel class of nanomaterials suspension in base fluids. A nanoscale colloidal dispersion which containing nanoparticles is called nanofluids. Nanofluids have shown enormously distinctive features, unique properties by offering the unexampled possibility for many applications. Nanofluids are a new generation of heat transfer fluids that have attracted researchers' attention from diverse fields due to their anomalous thermal behaviour and potential applications. Improved thermophysical properties of nanofluids including the thermal conductivity, viscosity, diffusivity, heat transfer coefficient are the most fundamental properties of nanofluids in the field of nanotechnology. Among all of the exciting properties of nanofluids, long-term stability is the first basic requirement in nanofluid research for maintaining their enhanced and outstanding thermophysical properties. This paper provides the ongoing advancement on the investigation of nanofluids including the preparation method, approaches to improve the stability, the assessment technique for the stability focusing challenges and opportunities of further improvement towards the performance of nanofluids