Existence of a Unique Solution and the Hyers–Ulam-H-Fox Stability of the Conformable Fractional Differential Equation by Matrix-Valued Fuzzy Controllers

In this paper, we consider a conformable fractional diferential equation with a constant coefcient and obtain an approximation for this equation using the Radu–Mihet method, which is derived from the alternative fxed- point theorem. Considering the matrix-valued fuzzy k-normed spaces and matrix-valued fuzzy H-Fox function as a control function, we investigate the existence of a unique solution and Hyers–Ulam-H-Fox stability for this equation. Finally, by providing numerical examples, we show the application of the obtained results

Artificial Neural Network Based Reinforcement Learning for Wind Turbine Yaw Control

This paper introduces a novel data driven yaw control algorithm synthesis method based on Reinforcement Learning (RL) for a variable pitch variable speed wind turbine. Yaw control has not been extendedly studied in the literature; in fact, most of the currently considered developments in the scope of the wind energy are oriented to the pitch and speed control. The most important drawbacks of the yaw control are the very large time constants and the strict yaw angle change rate constraints due to the high mechanical loads when the wind turbine angle is changed in order to adequate it to the wind speed orientation. An optimal yaw control algorithm needs to be designed in order to adapt the rotor orientation depending on the wind turbine dynamics and the local wind speed regime. Consequently, the biggest challenge of the yaw control algorithm is to decide the moment and the quantity of the wind turbine orientation variation to achieve the highest quantity of power at each instant, taking into account the constraints derived from the mechanical limitations of the yawing system and the mechanical loads. In this paper, a novel based algorithm based on the RL Q-Learning algorithm is introduced. The first step is to obtain a model of the power generated by the wind turbine (a real onshore wind turbine in this paper) through a power curve, that in conjunction with a conventional proportional regulator will be used to obtain a dataset that explains the actual behaviour of the real wind turbine when a variety of different yaw control commands are imposed. That knowledge is then used to learn the best control action for each different state of the wind turbine with respect to the wind direction represented by the yaw angle, storing that knowledge in a matrix Q(s,a). The last step is to model that matrix through a MultiLayer Perceptron with BackPropagation (MLP-BP) Artificial Neural Network (ANN) to avoid large matrix management and quantification problems. Once that the optimal yaw controller has been synthetized, its performance has been assessed using a number of wind speed realizations obtained using the software application TurbSim, in order to analyze how the introduced novel algorithm deals with different wind speed scenarios

CNN-based flow field prediction for bus aerodynamics analysis

Abstract The aim of this article is to evaluate the ability of a convolutional neural network (CNN) to predict velocity and pressure aerodynamic fields in heavy vehicles. For training and testing the developed CNN, various CFD simulations of three different vehicle geometries have been conducted, considering the RANS-based k-ω SST turbulent model. Two geometries correspond to the SC7 and SC5 coach models of the bus manufacturer SUNSUNDEGUI and the third one corresponds to Ahmed body. By generating different variants of these three geometries, a large number of representations of the velocity and pressure fields are obtained that will be used to train, verify, and evaluate the convolutional neural network. To improve the accuracy of the CNN, the field representations obtained are discretized as a function of the expected velocity gradient, so that in the areas where there is a greater variation in velocity, the corresponding neuron is smaller. The results show good agreement between numerical results and CNN predictions, being the CNN able to accurately represent the velocity and pressure fields with very low errors. Additionally, a substantial improvement in the computational time needed for each simulation is appreciated, reducing it by four orders of magnitude

Darcy-Forchheimer hybrid nanofluid flow over the rotating Riga disk in the presence of chemical reaction: Artificial neural network approach

The aim of present study is to examine the augmentation of thermal energy transfer in hybrid nanofluid flow caused by a rotating Riga disk in the presence of thermal radiation and chemical reaction. The silver and aluminium oxide nanoparticles are used to examine the thermal effect of water base fluid. The Darcy-Forchheimer model is considered to endorse the inertial and porous media effects and makes the model more realistic from the physical scenario. Levenberg-Marquardt backpropagation algorithm is considered to analyze the hybrid nanofluid’s properties. Using scaling group transformations, the governing partial differential equations are transformed into a system of ordinary differential equations. Resulting ordinary differential equations are solved numerically by applying a suitable shooting technique by MATLAB. The results obtained for the governing differential equations have been incorporated into a dataset on which the neural network has been trained. The effects of physical parameters have been analyzed for velocity, temperature, and concentration profiles. The determination, designing, convergence, verification, and stability of the Levenberg-Marquardt backpropagation neural network algorithm are validated on the assessment of achieved accuracy through performance, fit, regression, and error histogram plots for the discussed hybrid nanofluid. It is observed that fluid velocity reduces for enhanced Darcy-Forchheimer number, magnetic parameters and boosted for enhanced modified Hartmann number. Temperature profile increases by increasing the Brownian motion and thermophoresis parameters

Optimal data transmission and pathfinding for WSN and decentralized IoT systems using I-GWO and Ex-GWO algorithms

Efficient resource use is a very important issue in wireless sensor networks and decentralized IoT-based systems. In this context, a smooth pathfinding mechanism can achieve this goal. However, since this problem is a Non-deterministic Polynomial-time (NP-hard) problem type, metaheuristic algorithms can be used. This article proposes two new energy-efficient routing methods based on Incremental Grey Wolf Optimization (I-GWO) and Expanded Grey Wolf Optimization (Ex-GWO) algorithms to find optimal paths. Moreover, in this study, a general architecture has been proposed, making it possible for many different metaheuristic algorithms to work in an adaptive manner as well as these algorithms. In the proposed methods, a new fitness function is defined to determine the next hop based on some parameters such as residual energy, traffic, distance, buffer size and hop size. These parameters are important measurements in subsequent node selections. The main purpose of these methods is to minimize traffic, improve fault tolerance in related systems, and increase reliability and lifetime. The two metaheuristic algorithms mentioned above are used to find the best values ​​for these parameters. The suggested methods find the best path of any length for the path between any source and destination node. In this study, no ready dataset was used, and the established network and system were run in the simulation environment. As a result, the optimal path has been discovered in terms of the minimum cost of the best paths obtained by the proposed methods. These methods can be very useful in decentralized peer-to-peer and distributed systems. The metrics for performance evaluation and comparisons are i) network lifetime, ii) the alive node ratio in the network, iii) the packet delivery ratio and lost data packets, iv) routing overhead, v) throughput, and vi) convergence behavior. According to the results, the proposed methods generally choose the most suitable and efficient ways with minimum cost. These methods are compared with Genetic Algorithm Based Routing (GAR), Artificial Bee Colony Based routing (ABCbased), Multi-Agent Protocol based on Ant Colony Optimization (MAP-ACO), and Wireless Sensor Networks based on Grey Wolf optimizer. (GWO-WSN) algorithms. The simulation results show that the proposed methods outperform the others

Mechatronic Modeling and Frequency Analysis of the Drive Train of a Horizontal Wind Turbine

The relevance of renewable energies is undeniable, and among them, the importance of wind energy is capital. A lot of literature has been devoted to the control techniques that deal with the optimization of the energy produced, but the maintainability of the individual wind turbines and of the farms in general is also a fundamental factor to take into account. In this paper, the authors address the general problem of knowing in advance the resonance frequencies of the power system of a wind turbine, with the underlying idea being that those frequencies should be avoided and that resonances do not occur only due to mechanical phenomena, but also because of electrical phenomena that in turn are influenced by control and optimization techniques. Therefore, the availability of that information embedded in the optimization techniques that control a wind turbine is of major importance. The main purpose of this paper was accomplished through two related objectives: the first was to obtain a mechatronic model (using a lumped parameters model of two degrees of freedom) of the drive train in the Laplace domain oriented to subsequently perform the described analysis. The second was to use that model to determine analytically the number and the value of the resonance frequencies from the generator angular velocity in such a way that such information could be used by any control algorithm or even by the mechatronic system designers. We assessed through experimental validation using a real 100 kW wind turbine that these two objectives were reached, demonstrating that the different vibration modes were detected using only the generator angular velocity

CNN-based vane-type vortex generator modelling

The simplicity and accuracy of Computational Fluid Dynamics (CFD) tools have made them the most widely used method for solving fluid dynamics problems. However, these tools have some limitations, being the most significant the required computational resources. This fact, added to the growth of the Artificial Intelligence, has led to an increasing number of studies using data-driven methods to solve fluid dynamic problems. Flow control devices are a very popular research topic, since their implementation can significantly improve the behaviour of the flow. Among these devices, Vortex Generators (VGs) can be highlighted for their simplicity, efficiency and numerous applications. In this study, a Convolutional Neural Network (CNN) is proposed to predict the flow behaviour on the wake behind VGs. In order to obtain data for training the network, 20 different CFD simulations were conducted, considering the same inflow conditions but different vane heights and angles of attack of the VGs. The results show that the CNN is able to accurately predict the velocity and vorticity fields on the wake of the VG, being the most conflictive cases the ones with tall VGs, large angles of attack and close distances to the VGs. Additionally, the vortical structure, vortex path and velocity profiles on the vortex core of the main vortex are evaluated, showing good agreements with CFD results

Entropy generation optimization of EMHD mixed convective flow with higher order chemical reaction: Sensitivity analysis

This investigation deals with the sensitivity analysis of the unsteady, incompressible fluid flow past a vertical elongating surface using the Response Surface Methodology. The impact of Joule heating, viscous dissipation, non-uniform heat source, and higher-order chemical reaction are encountered under the influence of external electric and magnetic fields. The governing equations are modeled by the boundary layer assumptions with slip conditions, which are changed to dimensionless form by incorporation of the transformation variables. The dimensionless equations are higher-order ordinary differential equations. These ordinary differential equations are numerically simulated by the application of iterative shooting technique with Runga Kutta 4th-order numerical method. Outcomes corresponding to the fluid velocity, temperature, and concentration profile are presented in the graphs, surface plots, and contours for different influential parameters like Eckert number, electric field parameter, Prandtl number, and chemical reaction parameter. It is observed that the Nusselt number escalates with an increment in the electric field parameter and Hartmann number. The entropy formation rate is greater along the higher Hartmann number values and lesser along the higher electric field parameter values. Findings of this attempt are helpful in heat storage systems, pharmaceuticals, biological engineering, medication delivery, safer cooling surgery, cooling reactors, biosensors, magnetic cell isolation, and military fields

Robotic-Arm-Based Force Control in Neurosurgical Practice

This research proposes an optimal robotic arm speed shape in neurological surgery to minimise a cost functional that uses an adaptive scheme to determine the brain tissue force. Until now, there have been no studies or theories on the shape of the robotic arm speed in such a context. The authors have applied a robotic arm with optimal speed control in neurological surgery. The results of this research are as follows: In this article, the authors propose a control scheme that minimises a cost functional which depends on the position error, trajectory speed and brain tissue force. This work allowed us to achieve an optimal speed shape or trajectory to reduce brain retraction damage during surgery. The authors have reached two main conclusions. The first is that optimal control techniques are very well suited for robotic control of neurological surgery. The second conclusion is that several studies on functional cost parameters are needed to achieve the best trajectory speed of the robotic arm. These studies could attempt to optimise the functional cost parameters and provide a mechanical characterisation of brain tissue based on real data