Journal of Engineering

This study addresses the intricate interplay of magnetohydrodynamics (MHD), thermal radiation, and porous media effects, which are crucial in numerous engineering applications, including aerospace, energy systems, and environmental processes. The development of a mass-based hybrid nanofluid model signifies a novel approach, potentially yielding more accurate predictions and insights into the thermal behavior of fluids in diverse scenarios. Thus, the current research explores the heat transfer characteristics of a unique nanofluid known as TiO₂ (titania)-CuO (copper oxide)/H₂O (water) hybrid nanofluid. This nanofluid flows past a static or moving wedge considering the impact of thermal radiation and magnetic field in the appearance of porous medium. To calculate the effective thermophysical attributions of the hybrid (TiO₂-CuO) nanofluid, a mass-based strategy is employed. This approach involves analyzing the masses of both the first and second nanoparticles, along with the mass of the base fluid, as essential input parameters. The proposed mathematical model is modified to a dimensionless form by applying similarity transformations. The numerical solution is obtained by utilizing the bvp4c built-in function within the MATLAB environment. Graphs illustrate the influence of various parameters on temperature and velocity trends, including the magnetic field parameter and heat absorption/generation parameter as well as the thermal radiation parameter. It is noted that along with the enhancement in the values of parameters related to porous medium or magnetic field, the velocity of the hybrid nanofluid improves. This occurs when the moving wedge parameter’s value is below 1. Conversely, when the moving wedge parameter’s value exceeds 1, the velocity of the hybrid nanofluid decreases. The shape factor is more effective in the temperature profile for developed inputs of heat absorption/generation parameter. A juxtaposition of enhancement in heat transfer rate due to nanofluid (TiO₂/H₂O) and hybrid nanofluid (TiO₂-CuO/H₂O) is likewise presented. The main outcome indicates that the hybrid nanofluid exhibits superior thermal conductivity relative to the conventional nanofluid.

The lack of concrete tensile stress endurance led to the invention of engineered cementitious composite. However, the absence of gravel from the mixture in addition to the high binder content may lead to high shrinkage strain. Therefore, a radical solution to this problem is worth to be anticipated. The importance of this research lies in investigating the long-term drying shrinkage strain of the engineered cementitious composite since there is a lack of information regarding this behavior. Mixes of 30 and 60 MPa strengths were produced with polyvinyl alcohol fibers PVA-ECC and polypropylene fibers PP-ECC. The drying shrinkage strain of PVA-ECC mixes has been compared to PP-ECC mixes for both short term (0–28 days) and long term (0–360 days). Results indicated that all PVA-ECC mixes exhibited lower drying shrinkage strains than PP-ECC mixes. The ultimate drying shrinkage strain was recorded to be 1200 microstrain at 28 days. The increment in drying shrinkage strain after 28 days was 5.6% in PVA-ECC mixes when compared to that in PP-ECC mixes which was 6.77%. For high strength levels, the drying shrinkage strain at the age of 360 days declared a reduction of 3.5% for PVA-ECC compared to PP-ECC mixes. Also, it was lower for mixes with 60 MPa (6.3%) than for mixes with 30 MPa (7.6%). Therefore, despite the higher cement content for mixes with 60 MPa strength, the higher fiber volume fraction and the higher PVA solution percentage restricted the drying shrinkage strain increment. The research also addresses some mechanical tests of engineered cementitious composite concrete such as compressive strength, flexural strength, and strain capacity that may provide a strong relation to the drying shrinkage behavior of the different mixes. The scanning electron microscope images were involved in this research to declare the impact of fiber types on the microstructure of the ECC mixes.

It is quite evident that some of the experimental research works have been carried out by researchers in the area of spiral polishing using abrasive for surface finishing improvement but most of the experiment has been employed on Steel or Alloy Steel as a material. Very few number of research works have been performed by researchers across the globe on the Spiral Polishing Method of Titanium holes made by Electrical Discharge Machining (EDM). Therefore, experimental investigations were carried out in the area of Spiral Polishing and Finishing of EDM-drilled holes of various materials to meet the desired goal of demands on the surface quality. This experimentation aims to develop a novel method with spiral polishing using abrasive flow finishing. To explore the search investigation and find out the better surface finishing for through holes made on EDM, the process parameters have been designed using the Taguchi L₁₆ orthogonal array with input parameters such as Current (I), Pulse on time (T_on), and Pulse off time (T_off). The addition of Boron Carbide Powder with Handwash, Glycerine, Shampoo, and Liquid Soap is used to decrease the Surface Roughness (Ra) with a ratio of 5 : 1. The Taguchi technique is used to assess the P/M process parameter setting for a given signal to noise (S/N) ratio.

Tire pressure monitoring system (TPMS) has a critical role in safeguarding vehicle safety by monitoring tire pressure levels. Keeping the accurate tire pressure is necessary for confirming comfortable driving and safety, and improving fuel consumption. Tire problems can result from various factors, such as road surface conditions, weather changes, and driving activities, emphasizing the importance of systematic tire checks. This study presents a novel method for tire condition monitoring using weightless neural networks (WNN), which mimic neural processes using random-access memory (RAM) components, supporting fast and precise training. Wilkes, Stonham, and Aleksander Recognition Device (WiSARD), a type of WNN, stands out for its capability in classification and pattern recognition, gaining from its ability to avoid repetitive training and residual formation. For vibration data acquisition from tires, cost-effective micro-electro-mechanical system (MEMS) sensors are employed, offering a more economical solution than piezoelectric sensors. This approach yields a variety of features, such as autoregressive moving average (ARMA), statistical and histogram features. The J48 decision tree algorithm plays a critical role in selecting essential features for classification, which are subsequently divided into training and testing sets, crucial for assessing the WiSARD classifier’s efficacy. Hyperparameter optimization of the WNN leads to improved classification accuracy and shorter computation times. In practical tests, the WiSARD classifier, when optimally configured, achieved an impressive 97.92% accuracy with histogram features in only 0.008 seconds, showcasing the capability of WNN to enhance tire technology and the accuracy and efficiency of tire monitoring and maintenance.

Artificial neural networks (ANNs) have gained prominence among contemporary computing techniques due to their capacity to handle complicated stochastic datasets and nonlinear modelling in combined gas and combined cycle power (COGAS) plants. Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. The first implementation of the simultaneous adoption of three types of ANNs using Levenberg–Marquardt (LM), Bayesian regularisation (BR), and scaled conjugate gradient (SCG) configurations for training and assessing a combined cycle power plant output is presented. The dataset used in this research is a 9568-unit full combined cycle power plant basis load dataset, accessible through the public UCI Machine Learning Repository. It incorporates ambient temperature, exhaust vacuum, ambient pressure, and relative humidity as input parameters to predict the electric output power. The most accurate and dependable electric power predictions could be identified for 70% of the total data, of which 6698 were trained, 15% were tested, and 15% were validated (2870). By using the three training techniques, namely, LM, BR, and SCG, the parameterized networks are studied, increasing the number of hidden layers from 20 to 500. The lowest root-mean-square error value for a multilayer perceptron (MLP) architecture is 3.631%, which is lower than the values of 4.17%, 4.35%, and 4.63% for comparable MLP structures (20 to 500), documented in the literature. The LM and BR algorithms outperform SCG. These adopted algorithms could be a cutting-edge application in the power plant industry and other real-world applications for reliable solutions, to satisfy emerging societal needs with environmental benefits.

This study aims to develop a biocompatible and bioactive food-packaging composite film material. The material is based on bacterial cellulose (BC) microfibers from coconut jelly biomass combined with olive oil as a carrier of antibacterial properties. A composite membrane was fabricated with 20%, 30%, and 40 wt % glycerol and separately impregnated with 1%, 2%, and 3 wt % olive oil in the presence of BC. The results of SEM image structure and morphology show that the membrane was successfully fabricated with uniform distribution of BC, without losing its natural structure. The film was initially applied to preserve apples, and the research results showed that the mass index did not change significantly (ranging from 1.13 to 5.1 g); the hardness through the bearing test results showed a decrease. The preservation time of the composite membranes, which are based on bacterial cellulose combined with glycerol and vegetable residues, extends up to 20 days. The membrane with the highest concentration of vitamin C is soaked in BC/glycerol 30%/olive oil 2% membrane and the lowest is soaked in BC membrane.