Wireless power transmission (WPT) is becoming a more viable idea and creative solution, particularly for the battery charging of electric vehicles (EVs). Inductive power transfer (IPT) is a widely used wireless chargi...
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Wireless power transmission (WPT) is becoming a more viable idea and creative solution, particularly for the battery charging of electric vehicles (EVs). Inductive power transfer (IPT) is a widely used wireless charging technology. Here, the energy transformation takes place between two coils that are magnetically coupled. WPT has recently increased the interest of researchers because it is such a secure, easy, and dependable method of recharging EVs. This article gives an idea to design the charging coils using WPT system by using resonant inductive coupling and also optimizes the coupling coefficient using rain optimization algorithm (ROA). Wireless power transfer based on inductive coupling may perhaps potentially be applied in many real-world applications. This article uses ANSYS MAXWELL and FEMM 4.1 software tools to analyze the results for 2D and 3D coil WPT systems. From these software simulation results, it has been illustrated that the distribution of flux between Tx and Rx is analyzed for spiral and rectangular coil shapes. The selected coupling coefficient is 0.08. At 95 kHz, the inductance is 34.76 mu H$$ \mu H $$, the mutual inductance is 9.16 mu H,$$ \mu H, $$ and the coupling coefficient is 0.2635. Highlights Design an efficient circuit to enhance the whole performance of WPT system. Self-inductance, mutual inductance, and coefficient of coupling parameter are analyzed. rain optimization algorithm is designed to optimize the parameter for enhancing the efficiency.
The migration of fine particles in oil reservoir rock is the most important cause of formation damage and permeability impairment. The mechanism of this phenomenon includes fines detachment, mobilization, agglomeratio...
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The migration of fine particles in oil reservoir rock is the most important cause of formation damage and permeability impairment. The mechanism of this phenomenon includes fines detachment, mobilization, agglomeration, and re-attachment, which lead to throat clogging and/or blocking. Therefore, accurate modeling of this complex mechanism requires a model with high solver *** this work, based on the physical features of fines migration in pore throat, the conventional DPM model of ANSYS FLUENT software was initially modified by UDF code. Then, predetermined scenarios of suspension in-jection into a two dimensional (2D) microchannel were simulated for predicting pressure drop as a function of injected pore volume using the modified DPM model. Next, based on these simulation results, a proxy model using the artificial neural network (ANN) was developed for computational cost reduction of fines migration modeling in the microchannel (pore throat). Secondly, a dual pore scale numerical model by combination of proxy model and pore network modeling approach was developed to predict pressure drop as well as perme-ability impairment in a network of microchannels (porous media). Moreover, for rapid computation of pressure drop in the network model, a meta-heuristic method of rain optimization algorithm (ROA) has been used. Finally, for model validation, a series of laboratory tests including suspension injections into the glass micromodels were conducted.A comparison of simulation results using both proxy model and dual pore scale model with experimental data shows a good agreement and according to the obtained results, a decrease in the particles mean size, and Reynolds number as well as increase in particles concentration lead to increase in pressure drop and permeability reduction. On the other hand, use of a proxy model based on artificial neural network and a meta-heuristic method of rain optimization algorithm lead to significant computational cost reduction,
Crop yield prediction is highly significant in the agricultural sector. It helps to understand the growth rate of major food crops and identify measures to improve the overall yield. The article proposes a hybrid stra...
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Crop yield prediction is highly significant in the agricultural sector. It helps to understand the growth rate of major food crops and identify measures to improve the overall yield. The article proposes a hybrid strategy called bidirectional long short term memory with black widow optimization (Bi-LSTM-BWO) for predicting the annual yield produced with improved accuracy. Initially, data augmentation is performed for the collected dataset to increase the size of the dataset and to reduce the data scarcity problem. Then, the dataset is preprocessed to improve the data's quality and remove the noise and irrelevant information. The data is cleaned, transformed, and discretized in the preprocessing stage using various techniques. Then, the preprocessed data is clustered using an enhanced K-means clustering technique. To enhance the clustering technique, the proposed technique utilized the rain optimization algorithm that automatically computes the initial centroids to improve the clustering outcome. Finally, the prediction process is performed using the proposed Bi-LSTM-BWO prediction scheme. The proposed prediction strategy efficiently predicts the annual yield with a high accuracy rate and minimizes loss. The proposed technique achieves a 99.18%, 99.81% and 99.01% accuracy rates for the summer, autumn and winter yield prediction, respectively.
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