Journal of Modeling and Optimization

Numerical Study of Air-Flow Phenomena Through a Baffled Rectangular Micro-Channel

Sandip Saha — Wed, 15 Dec 2021 00:00:00 +0000

The aim of this study is to investigate the heat transfer characteristics of turbulent airflow phenomena in a rectangular micro-channel in presence of two plane shaped (type-1) and diamond shaped (type-2) baffles which will help to develop various heat exchanger models. Finite volume method has been used to solve the governing equations and the FLUENT software has been employed to visualize the simulation results. For both the baffles, the profile of flow structure, normalized velocity profile, normalized friction factor and average Nusselt number have been investigated with the variations of Reynolds number ranges between [10,000-50,000]. In terms of fluid flow and heat transfer phenomena, it has been found that in the presence of diamond shaped baffles (type-2) are more convenient than plane shaped baffles.

Closed-Loop Drive Detection and Diagnosis of Multiple Combined Faults in Induction Motor Through Model-Based and Neuro-Fuzzy Network Techniques

Imadeddine Harzelli, Abdelhamid Benakcha, Tarek Ameid, Arezki Menacer — Wed, 15 Dec 2021 00:00:00 +0000

In this paper, a fault detection and diagnosis approach adopted for an input-output feedback linearization (IOFL) control of induction motor (IM) drive is proposed. This approach has been employed to detect and identify the simple and mixed broken rotor bars and static air-gap eccentricity faults right from the start its operation by utilizing advanced techniques. Therefore, two techniques are applied: the model-based strategy, which is an online method used to generate residual stator current signal in order to indicate the presence of possible failures by means of the sliding mode observer (SMO) in the closed-loop drive. However, this strategy is not able to recognise the fault types and it can be affected by the other disturbances. Therefore, the offline method using the multi-adaptive neuro-fuzzy inference system (MANAFIS) technique is proposed to identify the faults and distinguish them. However, the MANAFIS required a relevant database to achieve satisfactory results. Hence, the stator current analysis based on the HFFT combination of the Hilbert transform (HT) and Fast Fourier transform (FFT) is applied to extract the amplitude of harmonics due to defects occur and used them as an input data set for the MANFIS under different loads and fault severities. The simulation results show the efficiency of the proposed techniques and its ability to detect and diagnose any minor faults in a closed-loop drive of IM.

Supervised Learning for Binary Classification on US Adult Income

Li-Pang Chen — Wed, 15 Dec 2021 00:00:00 +0000

In this project, various binary classification methods have been used to make predictions about US adult income level in relation to social factors including age, gender, education, and marital status. We first explore descriptive statistics for the dataset and deal with missing values. After that, we examine some widely used classification methods, including logistic regression, discriminant analysis, support vector machine, random forest, and boosting. Meanwhile, we also provide suitable R functions to demonstrate applications. Various metrics such as ROC curves, accuracy, recall and F-measure are calculated to compare the performance of these models. We find the boosting is the best method in our data analysis due to its highest AUC value and the highest prediction accuracy. In addition, among all predictor variables, we also find three variables that have the largest impact on the US adult income level.