The thermal management of a lithium-ion battery (LINB) cell in three dimensions was described in this study. For the thermal control of the LINB, two strategies were used: active and passive. For the passive heat management approach, phase change material (PCM) was employed. The LINB was put in a PCM-filled chamber. Two circular and oval chambers were placed around the LINB. In order to actively manage the heat, the LINB was thermally managed by placing the chamber and the LINB in an air duct. By changing the inlet air flow rate 0.1 cm/s to 0.4 cm/s, the temperature of the LINB, the amount of heat transfer coefficient (CHTR) and the volume fraction (VLF) of the PCM were investigated at different times (0 to 5000 s) for the two chamber shapes. Finite element method and COMSOL software were used for simulation. The results of this study showed that increasing the inlet air flow rate increases the CHTR of the LINB. Also, increasing the air speed caused the VLF of PCM to decrease. Over time, the CHTR increases and also over time, the LINB temperature increases. The hexagonal chamber resulted in a higher quantity of molten PCM than the circular chamber, particularly at t = 1200 s. 

Effect of combined air cooling and nano enhanced phase change materials on thermal management of lithium-ion batteries

Nickel oxide nanoparticles synthesis using plant extract and evaluation of their antibacterial effects on Streptococcus mutans

Predicting Compressive Strength of Manufactured-Sand Concrete Using Conventional and Metaheuristic-Tuned Artificial Neural Network

In this paper, we present a novel classifier based on fuzzy logic and wavelet transformation in the form of a neural network. This classifier includes a layer to predict the numerical feature corresponded to labels or classes. The presented classifier is implemented in brain tumor diagnosis. For feature extraction, a fractal model with four Gaussian functions is used. The classification is performed on 2000 MRI images. Regarding the results, the accuracy of the DT, KNN, LDA, NB, MLP, and SVM is 93.5%, 87.6%, 61.5%, 57.5%, 68.5%, and 43.6%, respectively. Based on the results, the presented FWNNet illustrates the highest accuracy of 100% with the fractal feature extraction method and brain tumor diagnosis based on MRI images. Based on the results, the best classifier for diagnosis of the brain tumor is FWNNet architecture. However, the second and third high-performance classifiers are the DT and KNN, respectively. Moreover, the presented FWNNet method is implemented for the segmentation of brain tumors. In this paper, we present a novel supervised segmentation method based on the FWNNet layer. In the training process, input images with a sweeping filter should be reshaped to vectors that correspond to reshaped ground truth images. In the training process, we performed a PSO algorithm to optimize the gradient descent algorithm. For this purpose, 80 MRI images are used to segment the brain tumor. Based on the results of the ROC curve, it can be estimated that the presented layer can segment the brain tumor with a high true-positive rate.

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FWNNet: Presentation of a New Classifier of Brain Tumor Diagnosis Based on Fuzzy Logic and the Wavelet-Based Neural Network Using Machine-Learning Methods.

This study used panel simultaneous equations models with a generalized method of moments (GMM) estimator to examine the three-way linkages between ecological footprint (EFP), renewable energy consumption (REC), and income in the Group of Seven (G7) countries over the period 1990–2018. The outcomes of this study demonstrate a two-way association between gross domestic product (GDP) and renewable energy. The findings confirm the presence of a bidirectional link between outcome and ecological footprint, as well as between EFP and renewable energy. The results of this study demonstrate that improving human capital positively and significantly effects income, environmental quality, and REC. Ecological footprint is not significantly impacted by economic and social globalization, whereas the impact of financial globalization is negative and significant. Trade openness is positively and significantly connected with REC and income, which could contribute to reducing environmental deterioration. In conclusion, we make policy recommendations that are based on the findings of the study.

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Exploring the Nexus of Renewable Energy, Ecological Footprint, and Economic Growth through Globalization and Human Capital in G7 Economics

Fastest-growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

A method for sizing air source heat pump considering the joint effect of outdoor air temperature and relative humidity

Energy saving and peak load shifting performance of tail water source heat pump integrated with large-scale thermal storage pool space heating system in technology park

Zhaoyang Li

Papers

Fastest-growing source prediction of US electricity production based on a novel hybrid model using wavelet transform

A method for sizing air source heat pump considering the joint effect of outdoor air temperature and relative humidity

Energy saving and peak load shifting performance of tail water source heat pump integrated with large-scale thermal storage pool space heating system in technology park