nonlinear modeling
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Author(s):  
Zulqurnain Sabir ◽  
Muhammad Asif Zahoor Raja ◽  
Muhammad Shoaib ◽  
R. Sadat ◽  
Mohamed R. Ali

2022 ◽  
Vol 12 (1) ◽  
pp. 73
Author(s):  
Syauqina Akmar Mohd-Shafri ◽  
Tow Leong Tiang ◽  
Choo Jun Tan ◽  
Dahaman Ishak ◽  
Mohd Saufi Ahmad

This paper investigates a nonlinear modeling optimization of 12s/8p surface-mounted permanent magnet synchronous machines (SMPMSM) with a radial magnetization pattern. The modeling is based on subdomain model (SDM) computation, where the analytical models are developed to predict the electromagnetic (EM) performances, such as, average EM torque and EM torque ripple in PM machines. A genetic algorithm is applied to the proposed model in order to search for the optimal solutions. The objective function of the optimizations is obtaining a higher average EM torque and achieving the minimum EM torque ripple. The data, viz, and the average EM torque and its ripples predicted by SDM are employed in regression analysis in order to find the model of best fit. After that, the most suitable fit of the computing equation is selected. The preliminary and optimal designs of 12s/8p PM motors are also compared in terms of parameters and motor performance. As a result, the regression model and GA framework has reduced the use of magnet materials and the EM torque ripple of the SMPMSM, making it ideal for use in an electric car. Lastly, the proposed model can determine the appropriate configuration design parameters for SMPMSM in order to achieve the best motor performance.


2022 ◽  
Vol 355 ◽  
pp. 01022
Author(s):  
Yourong Fan ◽  
Xinhua Wang ◽  
Zhe Hu ◽  
Kai Zhang

In order to solve the problem of rotor airflow interference to the wing of tiltrotor UAV, the lift and drag in the slipstream area and the free flow area were calculated respectively according to the hydrodynamics theory and CFD simulation. The longitudinal nonlinear dynamics model of tiltrotor UAV is established by Newton-Euler method. In order to solve the problem that the lift and thrust are difficult to balance the body gravity in the transition flight mode, a method for calculating the transition corridor of a tiltrotor UAV without cyclic pitch is proposed. The boundary of the transition corridor is restricted by the Angle of attack of the wing and the thrust of the rotor. Considering the requirements of UAV cruise speed, flight safety and minimum energy consumption, the optimal transition curve is selected. The result show that the designed transition curve can ensure that the lift and the rotor thrust can balance the gravity completely and the Angle of attack is in a reasonable range, and the rotor force has enough margin of safety.


2022 ◽  
Vol 119 (1) ◽  
Author(s):  
Wael M. Hassan ◽  
Medhat Elmorsy
Keyword(s):  

SIMULATION ◽  
2021 ◽  
pp. 003754972110648
Author(s):  
Enlai Zhang ◽  
Jiading Lian ◽  
Jingjing Zhang ◽  
Jiahe Lin

Aiming at the characteristics of high decibels and multiple samples for forklift noise, a subjective evaluation method of rank score comparison (RSC) based on annoyance is presented. After pre-evaluation, comprehensive evaluation and data tests on collected 50 noise samples, the annoyance grades of all noise samples were obtained, and seven psycho-acoustic parameters including linear sound pressure level (LSPL), A-weighted sound pressure level (ASPL), loudness, sharpness, roughness, impulsiveness and articulation index (AI) were determined by correlation calculation. Considering the nonlinear characteristics of human ear subjective perception, objective parameters, and annoyance were used as input and output variables correspondingly and then three nonlinear mathematical models of forklift acoustic annoyance were established using traditional artificial neural network (ANN), genetic-algorithm neural network (GANN), and particle-swarm-optimization neural network (PSONN). Moreover, the prediction accuracy of the three models was tested and compared by sample data. The results indicate that the average relative error (ARE) between the experimental and predicted values of acoustic annoyance based on PSONN model is 3.893%, which provides an effective technical support for further optimization and subjective evaluation.


2021 ◽  
Author(s):  
Julian Senoner ◽  
Torbjørn Netland ◽  
Stefan Feuerriegel

We develop a data-driven decision model to improve process quality in manufacturing. A challenge for traditional methods in quality management is to handle high-dimensional and nonlinear manufacturing data. We address this challenge by adapting explainable artificial intelligence to the context of quality management. Specifically, we propose the use of nonlinear modeling with Shapley additive explanations to infer how a set of production parameters and the process quality of a manufacturing system are related. Thereby, we contribute a measure of process importance based on which manufacturers can prioritize processes for quality improvement. Grounded in quality management theory, our decision model selects improvement actions that target the sources of quality variation. The decision model is validated in a real-world application at a leading manufacturer of high-power semiconductors. Seeking to improve production yield, we apply our decision model to select improvement actions for a transistor chip product. We then conduct a field experiment to confirm the effectiveness of the improvement actions. Compared with the average yield in our sample, the experiment returns a reduction in yield loss of 21.7%. Furthermore, we report on results from a postexperimental rollout of the decision model, which also resulted in significant yield improvements. We demonstrate the operational value of explainable artificial intelligence by showing that critical drivers of process quality can go undiscovered by the use of traditional methods. This paper was accepted by Charles Corbett, operations management.


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