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Introduction To Electrodynamics

An Introduction to Magnetohydrodynamics

This paper presents the parameterization and optimization of two well-known airfoils. The aerodynamic shape optimization investigation includes the subsonic (NREL S-821) and transonic airfoils (RAE-2822). The class shape transformation is employed for parametrization while the genetic algorithm is used for optimization purposes. The absolute scheme of the optimization process is carried out for the minimization of the drag coefficient and maximization of lift to drag ratio. In-house MATLAB code is incorporated with a genetic algorithm to calculate the drag coefficient and lift to drag ratio of the resulting optimized airfoil. The panel method is utilized in genetic algorithm optimization code to calculate pressure distribution, lift coefficient, and lift to drag ratio for optimized airfoil shapes and validates with XFOIL and NREL experimental data. Furthermore, CFD analysis is conducted for both the original (NREL S-821) and optimized airfoil obtained. The present method shows that the optimized airfoil achieved an improvement in lift to drag ratio by 7.4% and 15.9% of S-821 and RAE-2822 airfoil, respectively, by the panel technique method and provides high design desirable stability parameters. These features significantly improve the overall aerodynamic performance of the newly optimized airfoils. Finally, the improved aerodynamics results are reported for the design of turbulence modeling and NREL phase II, Phase III, and Phase VI HAWT blades.

CFD Analysis and Shape Optimization of Airfoils Using Class Shape Transformation and Genetic Algorithm—Part I

Non-contact harmonic magnetic field detection for parallel steel pipeline localization and defects recognition

Theoretical analysis of blade coating process using simplified Phan-Thien-Tanner fluid model: An analytical study

A new mathematical model for a flexible blade coater is proposed and analysed for slip and magnetohydrodynamic (MHD) effects in blade coating process. The slip is considered at the blade surface an...

https://journals.sagepub.com/doi/pdf/10.1177/8756087919848807

Mathematical modelling for flexible blade coater with magnetohydrodynamic and slip effects in blade coating process

Research in the field of tribo-mechatronics has been gaining popularity in recent decades. The objective of the current research is to improve static/dynamics characteristics of hydrostatic bearings. Hydrostatic bearings always work in harsh environmental conditions that effect their performance, and which may even result in their failure. The current research proposes a mathematical model-based system for hydrostatic bearings that helps to improve its static/dynamic characteristics under varying conditions of performance-influencing variables such as temperature, spindle speed, external load, and clearance gap. To achieve these objectives, the capillary restrictors are replaced with servo valves, and a mathematical model is developed along with robust control design systems. The control system consists of feedforward and feedback control techniques that have not been applied before for hydrostatic bearings in the published literature. The feedforward control tries to remove a disturbance before it enters the system while feedback control achieves the objective of disturbance rejection and improves steady-state characteristics. The feedforward control is a trajectory-based controller and the feedback controller is a sliding mode controller with a PID sliding surface. The particle swarm optimization algorithm is used to tune the 6-dimensional vector of the tuning parameters with multi-objective performance criteria. Numerical investigations have been carried out to check the performance of the proposed system under varying conditions of viscosity, clearance gap, external load and the spindle speed. The comparison of our results with the published literature shows the effectiveness of the proposed system.

https://www.mdpi.com/2227-7390/9/4/388/pdf

Model-Based Design Approach to Improve Performance Characteristics of Hydrostatic Bearing Using Multivariable Optimization

During the non-contact geomagnetic detection of pipeline defects, measured signals generally contain noise, which reduces detection efficiency. Complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) has recently emerged as a signal filtering method, but its filtering performance is influenced by two parameters: the amplitude of added noise and the number of ensemble trials. To solve this issue and improve detection accuracy and distinguishability, a detection method based on improved CEEMDAN (ICEEDMAN) and the Teager energy operator (TEO) is proposed. The magnetic detection signal was first decomposed into a series of intrinsic mode functions (IMFs) by CEEMDAN with initial parameters. Signal IMFs were then distinguished using the Hurst exponent to reconstruct the preliminary filtered signal, and its maximum value (except the zero point) of the normalized autocorrelation function was defined as salp swarm algorithm (SSA) fitness. The optimal parameters that maximize fitness were found by SSA iterations, and their corresponding filtered signal was obtained. Finally, the gradient calculation and TEO were carried out to complete non-contact geomagnetic detection. The results of the simulated signal based on magnetic dipole under a noisy environment and field testing prove that ICEEMDAN denoising has better filtering performance than conventional CEEMDAN denoising methods, and ICEEMDAN-TEO has obvious advantages compared to other detection methods in the aspects of location error, peak side-lobe ratio, and integrated side-lobe ratio.

https://www.mdpi.com/2079-9292/8/3/309/pdf

Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

A new technology for steel pipeline damage detecting without removing cladding

This paper presents a harmonic magnetic field detection technology for damage identification of an in-service coated steel pipeline. Based on the principles of electromagnetic theory and magnetic field detection combined with magnetic focusing technology, an array consisting of a focusing\n detection probe and a harmonic magnetic field detection system were designed. However, the acquired detection signal includes an excitation signal in addition to the defect information. In order to make the defect information more obvious, the excitation signal needs to be removed to extract\n the defect feature. Local mean decomposition (LMD) is a new time-frequency analysis method that adaptively decomposes a signal into a set of product function (PF) combinations. The envelope of the PF is the instantaneous amplitude and the instantaneous frequency can be calculated by demodulating\n the derivative of the phase with a uniform amplitude-modulated signal. This method completely bypasses the Hilbert transform. Therefore, it does not involve the problem of negative frequencies without physical meaning. The effectiveness of LMD is demonstrated by a successful example of damage\n detection. Combining the data characteristics obtained by the experiment, the data processing algorithm suitable for the test data is written by improving the LMD. The algorithm is easy to use and has high engineering practicability.

Zia Ullah

Papers

Mathematical modelling for flexible blade coater with magnetohydrodynamic and slip effects in blade coating process

Model-Based Design Approach to Improve Performance Characteristics of Hydrostatic Bearing Using Multivariable Optimization

Non-Contact Geomagnetic Detection Using Improved Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Teager Energy Operator

A new technology for steel pipeline damage detecting without removing cladding

Research on a damage identification method of harmonic magnetic field detection in steel pipes with cladding