scholarly journals An improved performance prediction model of permanent magnet eddy current couplings based on eddy current inductance characteristics

AIP Advances ◽  
2019 ◽  
Vol 9 (3) ◽  
pp. 035350
Author(s):  
Xiaowei Yang ◽  
Yongguang Liu
Keyword(s):  
Prediction Model ◽  
Permanent Magnet ◽  
Eddy Current ◽  
Performance Prediction ◽  
Improved Performance

Improved Performance Model of Turbocharger Centrifugal Compressor

2008 ◽  
Author(s):  
Mingyang Yang ◽  
Xinqian Zheng ◽  
Yangjun Zhang ◽  
Zhigang Li
Keyword(s):  
Prediction Model ◽  
Centrifugal Compressor ◽  
Performance Prediction ◽  
Gasoline Engine ◽  
Performance Model ◽  
New Model ◽  
Compressor Impeller ◽  
Accurate Performance ◽  
Improved Performance

This paper improves the conventional performance prediction model by correlating recirculation loss at the outlet of compressor impeller with its rotational speeds. The validation is carried out on a gasoline engine turbocharger compressor. The result shows that accuracy of the new model is greatly improved over the whole operating speeds, which brings possibility to the high accurate performance prediction in off design condition and a powerful tool for the matching between turbocharger and engine.

Improved Performance Prediction Model for Turbocharger Compressor

2008 ◽  
Vol 1 (1) ◽  
pp. 1159-1166 ◽  
Author(s):  
Mingyang Yang ◽  
Xinqian Zheng ◽  
Yangjun Zhang ◽  
Zhigang Li
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Improved Performance

Performance analysis of magnetic gear with Halbach array for high power and high speed

2020 ◽  
Vol 64 (1-4) ◽  
pp. 959-967
Author(s):  
Se-Yeong Kim ◽  
Tae-Woo Lee ◽  
Yon-Do Chun ◽  
Do-Kwan Hong
Keyword(s):  
Permanent Magnet ◽  
Eddy Current ◽  
High Power ◽  
High Speed ◽  
Torque Ripple ◽  
Eddy Current Loss ◽  
Element Analysis ◽  
Current Loss ◽  
Halbach Array ◽  
Magnetic Gear

In this study, we propose a non-contact 80 kW, 60,000 rpm coaxial magnetic gear (CMG) model for high speed and high power applications. Two models with the same power but different radial and axial sizes were optimized using response surface methodology. Both models employed a Halbach array to increase torque. Also, an edge fillet was applied to the radial magnetized permanent magnet to reduce torque ripple, and an axial gap was applied to the permanent magnet with a radial gap to reduce eddy current loss. The models were analyzed using 2-D and 3-D finite element analysis. The torque, torque ripple and eddy current loss were compared in both models according to the materials used, including Sm2Co17, NdFeBs (N42SH, N48SH). Also, the structural stability of the pole piece structure was investigated by forced vibration analysis. Critical speed results from rotordynamics analysis are also presented.

Development of Interior Permanent Magnet Motors with Concentrated Windings for Reducing Magnet Eddy Current Loss

2009 ◽  
Vol 129 (11) ◽  
pp. 1022-1029 ◽  
Author(s):  
Katsumi Yamazaki ◽  
Yuji Kanou ◽  
Yu Fukushima ◽  
Shunji Ohki ◽  
Akira Nezu ◽  
...  
Keyword(s):  
Permanent Magnet ◽  
Eddy Current ◽  
Eddy Current Loss ◽  
Permanent Magnet Motors ◽  
Current Loss ◽  
Interior Permanent Magnet

Project performance prediction model linking agility and flexibility demands to project type

2021 ◽  
Author(s):  
Marco Aurélio Oliveira ◽  
Luiz V. O. Dalla Valentina ◽  
André Hideto Futami ◽  
Osmar Possamai ◽  
Carlos Alberto Flesch
Keyword(s):  
Prediction Model ◽  
Performance Prediction ◽  
Project Performance

scholarly journals Student Performance Prediction with Optimum Multilabel Ensemble Model

2021 ◽  
Vol 30 (1) ◽  
pp. 511-523
Author(s):  
Ephrem Admasu Yekun ◽  
Abrahaley Teklay Haile
Keyword(s):  
High School Students ◽  
Prediction Model ◽  
Student Performance ◽  
Performance Prediction ◽  
Transformation Method ◽  
Classification Task ◽  
Support Vector ◽  
School Students ◽  
K Nearest Neighbors ◽  
Classifier Chains

Abstract One of the important measures of quality of education is the performance of students in academic settings. Nowadays, abundant data is stored in educational institutions about students which can help to discover insight on how students are learning and to improve their performance ahead of time using data mining techniques. In this paper, we developed a student performance prediction model that predicts the performance of high school students for the next semester for five courses. We modeled our prediction system as a multi-label classification task and used support vector machine (SVM), Random Forest (RF), K-nearest Neighbors (KNN), and Multi-layer perceptron (MLP) as base-classifiers to train our model. We further improved the performance of the prediction model using a state-of-the-art partitioning scheme to divide the label space into smaller spaces and used Label Powerset (LP) transformation method to transform each labelset into a multi-class classification task. The proposed model achieved better performance in terms of different evaluation metrics when compared to other multi-label learning tasks such as binary relevance and classifier chains.

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