Influence of Biosynthesized Nanoparticles Addition and Fibre Content on the Mechanical and Moisture Absorption Behaviour of Natural Fibre Composite

This study looks at how incorporating nanofiller into sisal/flax-fibre-reinforced epoxy-based hybrid composites affects their mechanical and water absorption properties. The green Al2O3 NPs are generated from neem leaves in a proportion of leaf extract to an acceptable aluminium nitrate combination. Both natural fibres were treated with different proportions of NaOH to eliminate moisture absorption. The following parameters were chosen as essential to achieving the objectives mentioned above: (i) 0, 5, 10, and 15% natural fibre concentrations; (ii) 0, 2, 4, and 6% aluminium powder concentrations; and (iii) 0, 1, 3, and 5% NaOH concentrations. Compression moulding was used to create the hybrid nanocomposites and ASTM standards were used for mechanical testing such as tension, bending, and impact. The findings reveal that combining sisal/flax fibre composites with nanofiller improved the mechanical features of the nanocomposite. The sisal and flax fibre hybridised successfully, with 10% fibres and 4% aluminium filler. The water absorption of the hybrids rose as the fibre weight % increased, and during the next 60 h, all of the specimens achieved equilibrium. The failed samples were examined using scanning electron Microscopic (SEM) images better to understand the composite’s failure in the mechanical experimentations. Al2O3 NPs were confirmed through XRD, UV spectroscope and HPLC analysis. According to the HPLC results, the leaf’s overall concentrations of flavonoids (gallocatechin, carnosic acid, and camellia) are determined to be 0.250 mg/g, 0.264 mg/g, and 0.552 mg/g, respectively. The catechin concentration is higher than the phenolic and caffeic acid levels, which could have resulted in a faster rate of reduction among many of the varying configurations, 4 wt.% nano Al2O3 particle, 10 wt.% flax and sisal fibres, as well as 4 h of NaOH with a 5 wt.% concentration, producing the maximum mechanical properties (59.94 MPa tension, 149.52 Mpa bending, and 37.9 KJ/m2 impact resistance). According to the results, it can be concluded that botanical nutrients may be used effectively in the manufacturing of nanomaterials, which might be used in various therapeutic and nanoscale applications

Effectiveness of LRB in Curved Bridge Isolation: A Numerical Study

Lead Rubber Bearings (LRBs) represent one of the most widely employed devices for the seismic protection of structures. However, the effectiveness of the same in the case of curved bridges has not been judged well because of the complexity involved in curved bridges, especially in controlling torsional moments. This study investigates the performance of an LRB-isolated horizontally curved continuous bridge under various seismic loadings. The effectiveness of LRBs on the bridge response control was determined by considering various aspects, such as the changes in ground motion characteristics, multidirectional effects, the degree of seismic motion, and the variation of incident angles. Three recorded ground motions were considered in this study, representing historical earthquakes with near-field, far-field, and forward directivity effects. The effectiveness of the bidirectional behavior considering the interaction effect of the bearing and pier was also studied. The finite element method was adopted. A sensitivity study of the bridge response related to the bearing design parameters was carried out for the considered ground motions. The importance of non-linearity and critical design parameters of LRBs were assessed. It was found that LRBs resulted in a significant increase in deck displacement for Turkey ground motion, which might be due to the forward directivity effect. The bi-directional effect is crucial for the curved bridge as it enhances the displacement significantly compared to uni-directional motion

Unified Power Control of Permanent Magnet Synchronous Generator Based Wind Power System with Ancillary Support during Grid Faults

A unified active power control scheme is devised for the grid-integrated permanent magnet synchronous generator-based wind power system (WPS) to follow the Indian electricity grid code requirements. The objective of this paper is to propose control schemes to ensure the continuous integration of WPS into the grid even during a higher percentage of voltage dip. In this context, primarily a constructive reactive power reference is formulated to raise and equalize the point of common coupling (PCC) potential during symmetrical and asymmetrical faults, respectively. A simple active power reference is also proposed to inject a consistent percentage of generated power even during faults without violating system ratings. Eventually, the efficacy of the proposed scheme is demonstrated in terms of PCC voltage enhancement, DC-link potential, grid real, and reactive power oscillation minimization using the PSCAD/ EMTDC software

Statistical experiment analysis of wear and mechanical behaviour of abaca/sisal fiber-based hybrid composites under liquid nitrogen environment

Ice accretion on various onshore and offshore infrastructures imparts hazardous effects sometimes beyond repair, which may be life-threatening. Therefore, it has become necessary to look for ways to detect and mitigate ice. Some ice mitigation techniques have been tested or in use in aviation and railway sectors, however, their applicability to other sectors/systems is still in the research phase. To make such systems autonomous, ice protection systems need to be accompanied by reliable ice detection systems, which include electronic, mechatronics, mechanical, and optical techniques. Comparing the benefits and limitations of all available methodologies, Infrared Thermography (IRT) appears to be one of the useful, non-destructive, and emerging techniques as it offers wide area monitoring instead of just point-based ice monitoring. This paper reviews the applications of IRT in the field of icing on various subject areas to provide valuable insights into the existing development of an intelligent and autonomous ice mitigation system for general applications

Experimental probe into an automative engine run on waste cooking oil biodiesel blend at varying engine speeds

The present work attempts to evaluate the performance of an automotive diesel engine run on waste cooking oil biodiesel (WCO) blend at variable engine speeds. The composition of the blend (B40) used in the study is 40% WCO and 60% diesel by volume and the engine used for the experimentation is a naturally aspirated, watercooled and direct injection type having a compression ratio of 18:1. The engine settings used in the study are an injection timing (IT) of 150 bTDC and a fuel injection pressure (IP) of 500 bar. The performance and emissions characteristics of the automotive engine are studied at various loads of 20%, 40%, 60%, 80% and 100% and at different engine speeds of 1500, 1800 and 2400 rpm. The first two rotational speeds are chosen to study the stationary power generation capabilities of the blend, while the feasibility of blend for automotive applications has been evaluated at 2400 rpm. Experiments have also been conducted on the engine run on mineral diesel fuel in order to make a comparative analysis. At full load, the maximum brake thermal efficiency (BTE) is found to be 21.51%, 25.48% and 23.56% for the blend at 1500, 1800 and 2400 rpm, respectively. At 2400 rpm and at 20% and 40% loads, the blend shows an absolute improvement in BTE of 0.17% and 0.03%, respectively over diesel fuel. On an average, there is a decrease of carbon monoxide (CO) emissions by 87.5%, 22.22% and 14.28% at 1500, 1800 and 2400 rpm as compared to diesel fuel. At 1500 and 2400 rpm, there is an average absolute increase in hydrocarbon (HC) emissions by 1.6 ppm and 9.6 ppm, respectively; while at 1800 rpm, an average decrease in HC emissions by 4 ppm is observed vis-a-vis diesel fuel. While emissions of oxides of nitrogen (NOx) as compared to diesel fuel increased on an average by 19.43%, 26.09% and 1.01% at 1500, 1800 and 2400 rpm, respectively

Selection of Response Reduction Factor Considering Resilience Aspect

The selection of an adequate response reduction factor (R) in the seismic design of a reinforced concrete building is critical to the building’s seismic response. To construct a robust structure, the R factor should be chosen based on the building’s resilience performance. Since no background was provided for the selection of R factors, the study focuses on the right selection of R factors in relation to the building’s functionality, performance level, and resilience. In this study, a high-rise building with multiple R factors (R = 3, 4, 5, and 6) is developed. Five potential recovery paths (RP-1 to RP-5) that matched the realistic scenario were used to estimate the building’s functionality. The building was subjected to uni and bi-directional loadings, and two design levels, Design Basic Earthquake (DBE) and Maximum Considered Earthquake were used to monitor the building’s response. According to the findings, a decrease in the lateral design force with the highest R results in a high ductility requirement and a substantial loss of resilience. The maximum R factor can be recommended under uni-directional loading up to 6, in which the building’s resilience is almost 50%, whereas under bi-directional loading and taking the recommended R factor decreased from 6 to 4

Enhancing mechanical performance of TiO2 filler with Kevlar/epoxy-based hybrid composites in a cryogenic environment: a statistical optimization study using RSM and ANN methods

This research aims to investigate the mechanical performance of the different weight proportions of nano-TiO2 combined with Kevlar fiber-based hybrid composites under cryogenic conditions. The following parameters were thus considered: (i) Kevlar fiber mat type (100 and 200 gsm); (ii) weight proportions of TiO2 nanofiller (2 and 6 wt%); and (iii) cryogenic processing time (10–30 min at −196°C). The composites were fabricated through compression molding techniques. After fabrication, the mechanical characteristics of the prepared nanocomposites—such as tensile, bending, and impact properties—were evaluated. The optimal mechanical strength of nanofiller-based composites was analyzed using response surface methodology (RSM) and artificial neural networks (ANNs). Compositions, such as four weight percentages of nano-TiO2 filler, 200 gsm of the Kevlar fiber mat, and 20 min of cryogenic treatment, were shown to produce the maximum mechanical strength (65.47 MPa of tensile, 97.34 MPa of flexural, and 52.82 J/m2 of impact). This is because residual strains are produced at low temperatures (cryogenic treatment) due to unstable matrices and fiber contraction. This interfacial stress helps maintain a relationship between the reinforcement and resin and improves adhesion, leading to improved results. Based on statistical evaluation, the ratio of correlation (R2), mean square deviation, and average error function of the experimental and validation data sets of the experimental models were analyzed. The ANN displays 0.9864 values for impact, 0.9842 for flexural, and 0.9764 for tensile. ANN and RSM models were used to forecast the mechanical efficiency of the suggested nanocomposites with up to 95% reliability

Transfer Learning Based Fault Detection for Suspension System Using Vibrational Analysis and Radar Plots

The suspension system is of paramount importance in any automobile. Thanks to the suspension system, every journey benefits from pleasant rides, stable driving and precise handling. However, the suspension system is prone to faults that can significantly impact the driving quality of the vehicle. This makes it essential to find and diagnose any faults in the suspension system and rectify them immediately. Numerous techniques have been used to identify and diagnose suspension faults, each with drawbacks. This paper’s proposed suspension fault detection system aims to detect these faults using deep transfer learning techniques instead of the time-consuming and expensive conventional methods. This paper used pre-trained networks such as Alex Net, ResNet-50, Google Net and VGG16 to identify the faults using radar plots of the vibration signals generated by the suspension system in eight cases. The vibration data were acquired using an accelerometer and data acquisition system placed on a test rig for eight different test conditions (seven faulty, one good). The deep learning model with the highest accuracy in identifying and detecting faults among the four models was chosen and adopted to find defects. The results state that VGG16 produced the highest classification accuracy of 96.70%

Tanning Wastewater Sterilization in the Dark and Sunlight Using Psidium guajava Leaf-Derived Copper Oxide Nanoparticles and Their Characteristics

Employing Psidium guajava (P. guajava) extract from leaves, copper oxide nanoparticles (CuO NPs), likewise referred to as cupric oxide and renowned for their sustainable and harmless biogenesis, have the possibility of being useful for the purification of pollutants as well as for medicinal purposes. The current study examined the generated CuO NPs and their physical qualities by using ultraviolet−visible (UV) spectroscopy. The distinctive peak at 265 nm of the CuO NP production was originally seen. Additionally, an X-ray diffraction (XRD) investigation was conducted to identify the crystalline arrangement of the produced CuO NPs, and a Fourier transform infrared (FTIR) spectroscopy examination was performed to validate the functional compounds of the CuO NPs. Additionally, the synthesized nanoparticles’ catalytic activities (wastewater treatment) were analyzed in dark and sunlight modes. The catalytic properties of CuO NPs in total darkness resulted in 64.21% discoloration, whereas exposure to sunshine increased the nanomaterials′ catalyst performance to 92.31%. By lowering Cr(VI), Ni, Pb, Co, and Cd in sewage by proportions of 91.4, 80.8, 68.26, 73.25, and 72.4% accordingly, the CuO NP demonstrated its effectiveness as a nanosorbent. Total suspended solids (TSS), total dissolved solids (TDS), chemical oxygen demand (COD), biological demand for oxygen (BOD), and conductance were all successfully reduced by nanotreatment of tanning effluents, with proportion reductions of 93.24, 88.62, 94.21, 87.5, and 98.3%, correspondingly

Deep Learning for Enhanced Fault Diagnosis of Monoblock Centrifugal Pumps: Spectrogram-Based Analysis

Abstract The reliable operation of monoblock centrifugal pumps (MCP) is crucial in various industrial applications. Achieving optimal performance and minimizing costly downtime requires effectively detecting and diagnosing faults in critical pump components. This study proposes an innovative approach that leverages deep transfer learning techniques. An accelerometer was adopted to capture vibration signals emitted by the pump. These signals are then converted into spectrogram images which serve as the input for a sophisticated classification system based on deep learning. This enables the accurate identification and diagnosis of pump faults. To evaluate the effectiveness of the proposed methodology, 15 pre-trained networks including ResNet-50, InceptionV3, GoogLeNet, DenseNet-201, ShuffleNet, VGG-19, MobileNet-v2, InceptionResNetV2, VGG-16, NasNetmobile, EfficientNetb0, AlexNet, ResNet-18, Xception, ResNet101 and ResNet-18 were employed. The experimental results demonstrate the efficacy of the proposed approach with AlexNet exhibiting the highest level of accuracy among the pre-trained networks. Additionally, a meticulous evaluation of the execution time of the classification process was performed. AlexNet achieved 100.00% accuracy with an impressive execution (training) time of 17 s. This research provides invaluable insights into applying deep transfer learning for fault detection and diagnosis in MCP. Using pre-trained networks offers an efficient and precise solution for this task. The findings of this study have the potential to significantly enhance the reliability and maintenance practices of MCP in various industrial settings