Model of Magnetically Shielded Ferrite-Cored Eddy Current Sensor

Computationally fast electromagnetic models of eddy current sensors are required in model-based measurements, machine interpretation approaches or in the sensor design phase. If a sensor geometry allows it, the analytical approach to the modeling has significant advantages in comparison to numerical methods, most notably less demanding implementation and faster computation. In this paper, we studied an eddy current sensor consisting of a transmitter coil with a finitely long I ferrite core, which was screened with a finitely thick magnetic shield. The sensor was placed above a conductive and magnetic half-layer. We used vector magnetic potential formulation of the problem with a truncated region eigenfunction expansion, and obtained expressions for the transmitter coil impedance and magnetic potential in all subdomains. The modeling results are in excellent agreement with the results using the finite element method. The model was also compared with the impedance measurement in the frequency range from 5 kHz to 100 kHz and the agreement is within 3% for the resistance change due to the presence of the half-layer and 1% for the inductance change. The presented model can be used for measurement of properties of metallic objects, sensor lift-off or nonconductive coating thickness.

Comparative Study of Electric Machines for Stirling Generator Application

The choice of a machine for an application and a given specification remains a complex problem. This will involve, for example, bringing together criteria such as: performance, space saving, economical, reliable, little acoustic noise and others. The best machine selection to fulfill all constraints is an important step for the project to be realized. This work focus on Stirling Engine based Generator and study all types of rotating machines that can be employed for maximum electric power production. Analytical electromagnetic models where developed for all types of rotating machines that satisfied minimum requirement for the project by solving Maxwell equations. The purpose is to develop the design model and combine electromagnetic and thermal study of the machines. Finite Element Method is used to compare the performances of the generators for the best choice. Results show that for applications not requiring bigger output power, the major criteria for the selection is the optimal magnetic induction created by the inducer in the stationary part of the machine. For application such as Stirling generators, permanent magnet (PM) machine satisfy many comparison criteria such as maximum power at low speed, torque density, high efficiency. Beyond exposing a selection method for a project, this work lay down a step-by-step method for engineers and scientists for the crucial stage of design and conception work

Control-oriented modeling for the electrodynamic levitation with permanent magnet Halbach array

Poor stability of the permanent magnet electrodynamic levitation hinders its application in the maglev field. Therefore, building a control-oriented model to improve its stability is most challenging. However, intractable electromagnetic models leading to an implicit relationship between levitation force and gap, yields a barrier for model-based controller design. To solve the above-mentioned problem, this paper develops a control-oriented model by two stages. Specifically, the first stage is to show an explicit formula of the levitation force with regard to the levitation gap by neglecting end effect; meanwhile the “maximum–minimum rectification” method is put forward to evaluating an accurate levitation force. The second stage is to bring forth the control-oriented model on basis of the estimated levitation force. Although the paper focus mainly on the development of the control-oriented model, an example of PD controller is provided to verify its validation. Experiment results demonstrate the estimated levitation force is highly consistent with the real one. Simulation results show that the control-oriented model is sufficiently reliable. The research bridges the gap between the physical model and the model-based controller for the electrodynamic levitation with permanent magnet Halbach array.

Development of Refined Electromagnetic Models of Reciprocating Electric Generators with Permanent Magnets

The analysis of scientific papers devoted to the mathematical description of electric generators of reciprocating motion with permanent magnets demonstrated that the proposed mathematical models of this type of generators are based on the theory of magnetic circuits. Such mathematical models are based on a simplified representation of the magnetic system and the magnetic field in the form of a magnetic circuit with corresponding magnetic conductivities. However, unlike traditional rotary type electric machines, electric generators of reciprocating motion have a number of features, the omission of which in mathematical modeling causes the increase of the cost of their creation (due to the duration of the design and experimental refinement of the generators). Therefore, at the initial stages of electromagnetic calculation and solving optimization problems, it is necessary to use adequate mathematical models to improve the accuracy of calculations of the parameters of these generators. For this purpose, a mathematical model based on field theory can be used; however, its main drawback is the complexity of its application for solving optimization problems. In this regard, to improve the accuracy of calculations of the parameters of electric generators of reciprocating motion with permanent magnets, it is proposed to use refining coefficients (coefficients of scattering and buckling of the magnetic flux) in mathematical models based on the theory of magnetic circuits. The authors have developed refined electromagnetic models of electric generators of reciprocating motion with permanent magnets, which make it possible to obtain the main parameters of generators at the initial stages of electromagnetic calculation and when solving optimization problems with acceptable accuracy. A distinctive feature of the refined electromagnetic models of generators is the consideration of the scattering and buckling coefficients of the magnetic flux in the magnetic system that change during the simulation.

Investigation into the Thermal Behavior and Loadability Characteristic of a YASA-AFPM Generator via an Improved 3-D Coupled Electromagnetic-Thermal Approach

The objective of this paper is to investigate the thermal behaviour and loadability characteristic of a yokeless and segmented armature axial-flux permanent-magnet (YASA-AFPM) generator, which uses an improved 3-D coupled electromagnetic-thermal approach. Firstly, a 1-kW YASA-AFPM generator is modelled and analysed by using the proposed approach; the transient and steady-state temperatures of different parts of the generator are determined. To improve the modelling accuracy, the information is exchanged between the thermal and electromagnetic models at each step of the co-simulation, considering both the accurate calculation of losses and the impacts of temperature rise on the temperature-dependent characteristics of the materials. Then, by using the proposed approach, the impact of the slot opening width and the turn number of stator segments on the generator loadability are investigated. After that, the experimental tests are performed. The results reveal the effectiveness and accuracy of the approach to predict the machine loadability and thermal behavior.

Selected Aspects of Decreasing Weight of Motor Dedicated to Wheel Hub Assembly by Increasing Number of Magnetic Poles

Decreasing the mass of a wheel hub motor by improving the design of a motor’s electromagnetic circuit is discussed in this paper. The authors propose to increase the number of magnetic pole pairs. They present possibilities of mass reduction obtained by these means. They also analyze the impact of design changes on losses and temperature distribution in motor elements. Lab tests of a constructed prototype, as well as elaborated conjugate thermal-electromagnetic models of the prototype motor and modified motor (i.e., motor with increased number of magnetic poles) were used in the investigation. Simulation models were verified by tests on the prototype. Results of calculations for two motors, differing by the number of pair poles, were compared over a wide operational range specific to the motor application in the electric traction. A detailed analysis of the operational range for these motors was also made.

Modeling and Theoretical Analysis of GNSS-R Soil Moisture Retrieval Based on the Random Forest and Support Vector Machine Learning Approach

Global Navigation Satellite System-Reflectometry (GNSS-R) as a microwave remote sensing technique can retrieve the Earth’s surface parameters using the GNSS reflected signal from the surface. These reflected signals convey the surface features and therefore can be utilized to detect certain physical properties of the reflecting surface such as soil moisture content (SMC). Up to now, a serial of electromagnetic models (e.g., bistatic radar and Fresnel equations, etc.) are employed and solved for SMC retrieval. However, due to the uncertainty of the physical characteristics of the sites, complexity, and nonlinearity of the inversion process, etc., it is still challenging to accurately retrieve the soil moisture. The popular machine learning (ML) methods are flexible and able to handle nonlinear problems. It can dig out and model the complex interactions between input and output and ultimately make good predictions. In this paper, two typical ML methods, specifically, random forest (RF) and support vector machine (SVM), are employed for SMC retrieval from GNSS-R data of self-designed experiments (in situ and airborne). A comprehensive simulated dataset involving different types of soil is constructed firstly to represent the complex interactions between the variables (reflectivity, elevation angle, dielectric constant, and SMC) for the requirement of training ML regression models. Correspondingly, the main task of soil moisture retrieval (regression) is addressed. Specifically, the post-processed data (reflectivity and elevation angle) from sensor acquisitions are used to make predictions by these two adopted ML methods and compared with the commonly used GNSS-R retrieval method (electromagnetic models). The results show that the RF outperforms the SVM method, and it is more suitable for handling the inversion problem. Moreover, the RF regression model built by the comprehensive dataset demonstrates satisfactory accuracy and strong universality, especially when the soil type is not uniform or unknown. Furthermore, the typical task of detecting water/soil (classification) is discussed. The ML algorithms demonstrate a high potential and efficiency in SMC retrieval from GNSS-R data.