Detection of Corona Faults in Switchgear by Using 1D-CNN, LSTM, and 1D-CNN-LSTM Methods.

The damaging effects of corona faults have made them a major concern in metal-clad switchgear, requiring extreme caution during operation. Corona faults are also the primary cause of flashovers in medium-voltage metal-clad electrical equipment. The root cause of this issue is an electrical breakdown of the air due to electrical stress and poor air quality within the switchgear. Without proper preventative measures, a flashover can occur, resulting in serious harm to workers and equipment. As a result, detecting corona faults in switchgear and preventing electrical stress buildup in switches is critical. Recent years have seen the successful use of Deep Learning (DL) applications for corona and non-corona detection, owing to their autonomous feature learning capability. This paper systematically analyzes three deep learning techniques, namely 1D-CNN, LSTM, and 1D-CNN-LSTM hybrid models, to identify the most effective model for detecting corona faults. The hybrid 1D-CNN-LSTM model is deemed the best due to its high accuracy in both the time and frequency domains. This model analyzes the sound waves generated in switchgear to detect faults. The study examines model performance in both the time and frequency domains. In the time domain analysis (TDA), 1D-CNN achieved success rates of 98%, 98.4%, and 93.9%, while LSTM obtained success rates of 97.3%, 98.4%, and 92.4%. The most suitable model, the 1D-CNN-LSTM, achieved success rates of 99.3%, 98.4%, and 98.4% in differentiating corona and non-corona cases during training, validation, and testing. In the frequency domain analysis (FDA), 1D-CNN achieved success rates of 100%, 95.8%, and 95.8%, while LSTM obtained success rates of 100%, 100%, and 100%. The 1D-CNN-LSTM model achieved a 100%, 100%, and 100% success rate during training, validation, and testing. Hence, the developed algorithms achieved high performance in identifying corona faults in switchgear, particularly the 1D-CNN-LSTM model due to its accuracy in detecting corona faults in both the time and frequency domains.

Heat Transfer Enhancement by Hybrid Nano Additives-Graphene Nanoplatelets/Cellulose Nanocrystal for the Automobile Cooling System (Radiator).

A radiator is used to remove a portion of the heat generated by a vehicle engine. It is challenging to efficiently maintain the heat transfer in an automotive cooling system even though both internal and external systems need enough time to keep pace with catching up with evolving engine technology advancements. The effectiveness of a unique hybrid's heat transfer nanofluid was investigated in this study. The hybrid nanofluid was mainly composed of graphene nanoplatelets (GnP), and cellulose nanocrystals (CNC) nanoparticles suspended in a 40:60 ratio of distilled water and ethylene glycol. A counterflow radiator equipped with a test rig setup was used to evaluate the hybrid nano fluid's thermal performance. According to the findings, the proposed GNP/CNC hybrid nanofluid performs better in relation to improving the efficiency of heat transfer of a vehicle radiator. The suggested hybrid nanofluid enhanced convective heat transfer coefficient by 51.91%, overall heat transfer coefficient by 46.72%, and pressure drop by 34.06% with respect to distilled water base fluid. Additionally, the radiator could reach a better CHTC with 0.01% hybrid nanofluid in the optimized radiator tube by the size reduction assessment using computational fluid analysis. In addition to downsizing the radiator tube and increasing cooling capacity over typical coolants, the radiator takes up less space and helps to lower the weight of a vehicle engine. As a result, the suggested unique hybrid graphene nanoplatelets/cellulose nanocrystal-based nanofluids perform better in heat transfer enhancement in automobiles.

Harnessing Nature's Ingenuity: A Comprehensive Exploration of Nanocellulose from Production to Cutting-Edge Applications in Engineering and Sciences.

Primary material supply is the heart of engineering and sciences. The depletion of natural resources and an increase in the human population by a billion in 13 to 15 years pose a critical concern regarding the sustainability of these materials; therefore, functionalizing renewable materials, such as nanocellulose, by possibly exploiting their properties for various practical applications, has been undertaken worldwide. Nanocellulose has emerged as a dominant green natural material with attractive and tailorable physicochemical properties, is renewable and sustainable, and shows biocompatibility and tunable surface properties. Nanocellulose is derived from cellulose, the most abundant polymer in nature with the remarkable properties of nanomaterials. This article provides a comprehensive overview of the methods used for nanocellulose preparation, structure-property and structure-property correlations, and the application of nanocellulose and its nanocomposite materials. This article differentiates the classification of nanocellulose, provides a brief account of the production methods that have been developed for isolating nanocellulose, highlights a range of unique properties of nanocellulose that have been extracted from different kinds of experiments and studies, and elaborates on nanocellulose potential applications in various areas. The present review is anticipated to provide the readers with the progress and knowledge related to nanocellulose. Pushing the boundaries of nanocellulose further into cutting-edge applications will be of particular interest in the future, especially as cost-effective commercial sources of nanocellulose continue to emerge.

An Approach for the Optimization of Thermal Conductivity and Viscosity of Hybrid (Graphene Nanoplatelets, GNPs: Cellulose Nanocrystal, CNC) Nanofluids Using Response Surface Methodology (RSM).

Response surface methodology (RSM) is used in this study to optimize the thermal characteristics of single graphene nanoplatelets and hybrid nanofluids utilizing the miscellaneous design model. The nanofluids comprise graphene nanoplatelets and graphene nanoplatelets/cellulose nanocrystal nanoparticles in the base fluid of ethylene glycol and water (60:40). Using response surface methodology (RSM) based on central composite design (CCD) and mini tab 20 standard statistical software, the impact of temperature, volume concentration, and type of nanofluid is used to construct an empirical mathematical formula. Analysis of variance (ANOVA) is applied to determine that the developed empirical mathematical analysis is relevant. For the purpose of developing the equations, 32 experiments are conducted for second-order polynomial to the specified outputs such as thermal conductivity and viscosity. Predicted estimates and the experimental data are found to be in reasonable arrangement. In additional words, the models could expect more than 85% of thermal conductivity and viscosity fluctuations of the nanofluid, indicating that the model is accurate. Optimal thermal conductivity and viscosity values are 0.4962 W/m-K and 2.6191 cP, respectively, from the results of the optimization plot. The critical parameters are 50 °C, 0.0254%, and the category factorial is GNP/CNC, and the relevant parameters are volume concentration, temperature, and kind of nanofluid. From the results plot, the composite is 0.8371. The validation results of the model during testing indicate the capability of predicting the optimal experimental conditions.

Advancing renewable fuel integration: A comprehensive response surface methodology approach for internal combustion engine performance and emissions optimization.

In the realm of internal combustion engines, there is a growing utilization of alternative renewable fuels as substitutes for traditional diesel and gasoline. This surge in demand is driven by the imperative to diminish fuel consumption and adhere to stringent regulations concerning engine emissions. Sole reliance on experimental analysis is inadequate to effectively address combustion, performance, and emission issues in engines. Consequently, the integration of engine modelling, grounded in machine learning methodologies and statistical data through the response surface method (RSM), has become increasingly significant for enhanced analytical outcomes. This study aims to explore the contemporary applications of RSM in assessing the feasibility of a wide range of renewable alternative fuels for internal combustion engines. Initially, the study outlines the fundamental principles and procedural steps of RSM, offering readers an introduction to this multifaceted statistical technique. Subsequently, the study delves into a comprehensive examination of the recent applications of alternative renewable fuels, focusing on their impact on combustion, performance, and emissions in the domain of internal combustion engines. Furthermore, the study sheds light on the advantages and limitations of employing RSM, and discusses the potential of combining RSM with other modelling techniques to optimise results. The overarching objective is to provide a thorough insight into the role and efficacy of RSM in the evaluation of renewable alternative fuels, thereby contributing to the ongoing discourse in the field of internal combustion engines.