Experimental assessment and modeling of solar air heater with V shape roughness on absorber plate

A roughness of the absorber plate can improve the efficiency of a solar air heater. To boost the efficiency of triangle solar air heaters, this research presents the results of a comparison study between with and without rib roughness on absorber plates. Both use black paint with graphene nanoparticles infused into it, coating an absorber plate. Both numerical and experimental methods have been used to examine the impact of surface roughness on friction factors and heat transport properties. ANSYS 14.5 software module and RNG turbulence, k-€ model is used to conduct a three-dimensional simulation and solve the governing equations in the turbulent situation. Based on experimental data, it has been established that smooth plates are more efficient in converting heat into useful work than rough ones, on average, by a factor of 4.82 and 4.46, respectively. The length of the duct in the solar air heater mitigates the temperature gradient seen in the simulation result. The roughness of V-shaped ribs has a far larger effect on the heat transfer and friction factor properties than do variations in relative roughness pitch (P/e) and Reynolds number (Re). Experimental observations supported by modeling and simulation confirms that triangular duct absorber surface roughness provides improved outcome

Decision-making algorithm based on Pythagorean fuzzy environment with probabilistic hesitant fuzzy set and Choquet integral

The Pythagorean Probabilistic Hesitant Fuzzy (PyPHF) Environment is an amalgamation of the Pythagorean fuzzy set and the probabilistic hesitant fuzzy set that is intended for some unsatisfactory, ambiguous, and conflicting situations where each element has a few different values created by the reality of the situation membership hesitant function and the falsity membership hesitant function with probability. The decision-maker can efficiently gather and analyze the information with the use of a strategic decision-making technique. In contrast, ambiguity will be a major factor in our daily lives while gathering information. We describe a decision-making technique in the PyPHF environment to deal with such data uncertainty. The fundamental operating principles for PyPHF information under Choquet Integral were initially established in this study. Then, we put up a set of new aggregation operator names, including Pythagorean probabilistic hesitant fuzzy Choquet integral average and Pythagorean probabilistic hesitant fuzzy Choquet integral geometric aggregation operators. Finally, we explore a multi-attribute decision-making (MADM) algorithm based on the suggested operators to address the issues in the PyPHF environment. To demonstrate the work and contrast the findings with those of previous studies, a numerical example is provided. Additionally, the paper provides sensitivity analysis and the benefits of the stated method to support and reinforce the research

Lane Line Detection and Object Scene Segmentation Using Otsu Thresholding and the Fast Hough Transform for Intelligent Vehicles in Complex Road Conditions

An Otsu-threshold- and Canny-edge-detection-based fast Hough transform (FHT) approach to lane detection was proposed to improve the accuracy of lane detection for autonomous vehicle driving. During the last two decades, autonomous vehicles have become very popular, and it is constructive to avoid traffic accidents due to human mistakes. The new generation needs automatic vehicle intelligence. One of the essential functions of a cutting-edge automobile system is lane detection. This study recommended the idea of lane detection through improved (extended) Canny edge detection using a fast Hough transform. The Gaussian blur filter was used to smooth out the image and reduce noise, which could help to improve the edge detection accuracy. An edge detection operator known as the Sobel operator calculated the gradient of the image intensity to identify edges in an image using a convolutional kernel. These techniques were applied in the initial lane detection module to enhance the characteristics of the road lanes, making it easier to detect them in the image. The Hough transform was then used to identify the routes based on the mathematical relationship between the lanes and the vehicle. It did this by converting the image into a polar coordinate system and looking for lines within a specific range of contrasting points. This allowed the algorithm to distinguish between the lanes and other features in the image. After this, the Hough transform was used for lane detection, making it possible to distinguish between left and right lane marking detection extraction; the region of interest (ROI) must be extracted for traditional approaches to work effectively and easily. The proposed methodology was tested on several image sequences. The least-squares fitting in this region was then used to track the lane. The proposed system demonstrated high lane detection in experiments, demonstrating that the identification method performed well regarding reasoning speed and identification accuracy, which considered both accuracy and real-time processing and could satisfy the requirements of lane recognition for lightweight automatic driving systems

Smart android based home automation system using internet of things (IoT)

Recently, home automation system has getting significant attention because of the fast and advanced technology, making daily living more convenient. Almost everything has been digitalized and automated. The development of home automation will become easier and more popular because of the use of the Internet of Things (IoT). This paper described various interconnection systems of actuators, sensors to enable multiple home automation implementations. The system is known as HAS (Home automation system). It operates by connecting the robust Application Programming Interface (API), which is the key to a universal communication method. The HAS used devices, often implemented the actuators or sensors that have an upwards communication network followed by HAS (API). Most of the devices of the HAS (home automation system) used Raspberry Pi boards and ESP8285 chips. A smartphone application has been developed that allows users to control a wide range of home appliances and sensors from their smartphones. The application is user-friendly, adaptable, and beneficial for consumers and disabled people. It has the potential to be further extended via the use of various devices. The main objectives of this work are to make our home automation system, more secure and intelligent. HAS is a highly effective and efficient computational system that may be enhanced with a variety of devices and add-ons

Cross electromagnetic nanofluid flow examination with infinite shear rate viscosity and melting heat through Skan-Falkner wedge

This demonstration of study focalizes the melting transport and inclined magnetizing effect of cross fluid with infinite shear rate viscosity along the Skan-Falkner wedge. Transport of energy analysis is brought through the melting process and velocity distribution is numerically achieved under the influence of the inclined magnetic dipole effect. Moreover, this study brings out the numerical effect of the process of thermophoresis diffusion and Brownian motion. The infinite shear rate of viscosity model of cross fluid reveals the set of partial differential equations (PDEs). Similarity transformation of variables converts the PDEs system into nonlinear ordinary differential equations (ODEs). Furthermore, a numerical bvp4c process is imposed on these resultant ODEs for the pursuit of a numerical solution. From the debate, it is concluded that melting process cases boost the velocity of fluid and velocity ratio parameter. The augmentation of the minimum value of energy needed to activate or energize the molecules or atoms to activate the chemical reaction boosts the concentricity inclined magnetized flow, infinite shear rate viscosity, Brownian motion, 2-D cross fluid, melting process of energy, thermophoresis diffusion melting of energy.Campus Chiclay

A comparative analysis of generalized and extended (G′G)-Expansion methods for travelling wave solutions of fractional Maccari's system with complex structure

Fractional partial differential equations emerge as a prominent research area in recent times owing to their ability to depict intricate physical phenomena. Discovering travelling wave solutions for fractional partial differential equations is an arduous task, and several mathematical approaches devise to address this issue. This investigation aims to compare two distinguished methods, namely, the generalized (G′G)-Expansion and the extended (G′G)-Expansion, in discovering the most optimal travelling wave solutions for fractional partial differential equations. Our observations indicate that the generalized (G′G)-Expansion method surpasses the extended (G′G)-Expansion method regarding the count of travelling wave solutions obtained. Moreover, the generalized (G′G)-Expansion method furnishes a more comprehensive and in-depth comprehension of physical phenomena by revealing a greater number of travelling wave solutions. This exploration validates the effectiveness of the generalized (G′G)-Expansion method in resolving intricate fractional partial differential equations and underscores its potential for further investigation and application in a variety of fields. Lastly, this study demonstrates the effectiveness of the proposed approaches in discovering travelling wave solutions and shed light on the intricate behavior of waves through plotted graphs, thereby contributing to the body of knowledge on this subject

Multi-Objective Quantum-Inspired Seagull Optimization Algorithm

Objective solutions of multi-objective optimization problems (MOPs) are required to balance convergence and distribution to the Pareto front. This paper proposes a multi-objective quantum-inspired seagull optimization algorithm (MOQSOA) to optimize the convergence and distribution of solutions in multi-objective optimization problems. The proposed algorithm adopts opposite-based learning, the migration and attacking behavior of seagulls, grid ranking, and the superposition principles of quantum computing. To obtain a better initialized population in the absence of a priori knowledge, an opposite-based learning mechanism is used for initialization. The proposed algorithm uses nonlinear migration and attacking operation, simulating the behavior of seagulls for exploration and exploitation. Moreover, the real-coded quantum representation of the current optimal solution and quantum rotation gate are adopted to update the seagull population. In addition, a grid mechanism including global grid ranking and grid density ranking provides a criterion for leader selection and archive control. The experimental results of the IGD and Spacing metrics performed on ZDT, DTLZ, and UF test suites demonstrate the superiority of MOQSOA over NSGA-II, MOEA/D, MOPSO, IMMOEA, RVEA, and LMEA for enhancing the distribution and convergence performance of MOPs

Risk Probabilistic Characteristics for Contaminated Porcelain Insulator in the Egyptian Sinai Desert

Transmission lines in the desert are exposed to the desert environment, which includes sandstorms as one of its hallmarks. A conductive layer develops with prolonged sand deposition and the presence of moisture, ambient humidity, and dew. The ensuing leakage current causes surface discharge, which limits the life of the insulator and interrupting the power supply. The locations of power lines in the Egyptian Sinai desert, where sandstorms are known to occur frequently, are exposed to such a risk. In order to estimate the danger of insulator failure, this paper studies the flow of leakage current on porcelain insulators that have been contaminated with sand. This work relies on accurate data collected and published in a prior study regarding Sinai, which mainly focused on contaminating sand’s grain sizes. Porcelain insulator is simulated using finite element method to determine the leakage current that results on its contaminated surface. The probabilistic characteristics of the insulator’s leakage current are derived using Monte Carlo technique, allowing for the risk assessment of insulator failure. This assessment can be used to justify the suitability of using this kind of insulator in Sinai

Multi-Objective Quantum-Inspired Seagull Optimization Algorithm

Objective solutions of multi-objective optimization problems (MOPs) are required to balance convergence and distribution to the Pareto front. This paper proposes a multi-objective quantum-inspired seagull optimization algorithm (MOQSOA) to optimize the convergence and distribution of solutions in multi-objective optimization problems. The proposed algorithm adopts opposite-based learning, the migration and attacking behavior of seagulls, grid ranking, and the superposition principles of quantum computing. To obtain a better initialized population in the absence of a priori knowledge, an opposite-based learning mechanism is used for initialization. The proposed algorithm uses nonlinear migration and attacking operation, simulating the behavior of seagulls for exploration and exploitation. Moreover, the real-coded quantum representation of the current optimal solution and quantum rotation gate are adopted to update the seagull population. In addition, a grid mechanism including global grid ranking and grid density ranking provides a criterion for leader selection and archive control. The experimental results of the IGD and Spacing metrics performed on ZDT, DTLZ, and UF test suites demonstrate the superiority of MOQSOA over NSGA-II, MOEA/D, MOPSO, IMMOEA, RVEA, and LMEA for enhancing the distribution and convergence performance of MOPs

Optimistic multigranulation roughness of a fuzzy set based on soft binary relations over dual universes and its application

A fascinating extension of Pawlak rough set theory to handle uncertainty is multigranulation roughness, which has been researched by several researchers over dual universes. In light of this, we proposed a novel optimistic multigranulation roughness of a fuzzy set based on soft binary relations over dual universes and established two types of approximations of a fuzzy set with respect to forsets and aftersets of the finite number of soft binary relations in this article. We obtain two sets of fuzzy soft sets in this way, referred to as the lower approximation and upper approximation with respect to the aftersets and the foresets, respectively. Next, we look into some of the lower and higher approximations of the newly multigranulation rough set model's algebraic properties. Both the roughness and accuracy measurements were defined. In order to show our suggested model, we first develop a decision-making algorithm. Then, we give an example from a variety of applications