Yingli Zhu^1,*, Jiachi Xie¹, Mingwei Zhu¹, Jun Zhang², Miaomiao Li³

Energy Engineering, Vol.121, No.5, pp. 1161-1172, 2024, DOI:10.32604/ee.2024.041205

Abstract The open ratio of a current collector has a great impact on direct methanol fuel cell (DMFC) performance. Although a number of studies have investigated the influence of the open ratio of DMFC current collectors, far too little attention has been given to how geometry (including the shape and feature size of the flow field) affects a current collector with an equal open ratio. In this paper, perforated and parallel current collectors with an equal open ratio of 50% and different feature sizes are designed, and the corresponding experimental results are shown to explain the… More >

Kaicheng Liu^1,3,*, Chen Liang², Xiaoyang Dong², Liping Liu¹

Energy Engineering, Vol.121, No.4, pp. 933-949, 2024, DOI:10.32604/ee.2023.043658

Abstract Due to the unpredictable output characteristics of distributed photovoltaics, their integration into the grid can lead to voltage fluctuations within the regional power grid. Therefore, the development of spatial-temporal coordination and optimization control methods for distributed photovoltaics and energy storage systems is of utmost importance in various scenarios. This paper approaches the issue from the perspective of spatiotemporal forecasting of distributed photovoltaic (PV) generation and proposes a Temporal Convolutional-Long Short-Term Memory prediction model that combines Temporal Convolutional Networks (TCN) and Long Short-Term Memory (LSTM). To begin with, an analysis of the spatiotemporal distribution patterns of More >

Zaihe Yang^1,*, Shuling Wang¹, Runhang Zhu¹, Jiao Cui², Ji Su², Liling Chen³

Energy Engineering, Vol.121, No.3, pp. 807-820, 2024, DOI:10.32604/ee.2023.028167

Abstract To address the scheduling problem involving energy storage systems and uncertain energy, we propose a method based on multi-stage robust optimization. This approach aims to regulate the energy storage system by using a multi-stage robust optimal control method, which helps overcome the limitations of traditional methods in terms of time scale. The goal is to effectively utilize the energy storage power station system to address issues caused by unpredictable variations in environmental energy and fluctuating load throughout the day. To achieve this, a mathematical model is constructed to represent uncertain energy sources such as photovoltaic More >

Zhihua Liu, Shengquan Liu^*, Jian Zhang

CMC-Computers, Materials & Continua, Vol.78, No.1, pp. 411-433, 2024, DOI:10.32604/cmc.2023.046237

Abstract Network intrusion detection systems (NIDS) based on deep learning have continued to make significant advances. However, the following challenges remain: on the one hand, simply applying only Temporal Convolutional Networks (TCNs) can lead to models that ignore the impact of network traffic features at different scales on the detection performance. On the other hand, some intrusion detection methods consider multi-scale information of traffic data, but considering only forward network traffic information can lead to deficiencies in capturing multi-scale temporal features. To address both of these issues, we propose a hybrid Convolutional Neural Network that supports… More >

Cheng Han^1,*, Zhengguang Xu^1,2

CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.1, pp. 515-536, 2024, DOI:10.32604/cmes.2023.043464

Abstract A prediction framework based on the evolution of pattern motion probability density is proposed for the output prediction and estimation problem of non-Newtonian mechanical systems, assuming that the system satisfies the generalized Lipschitz condition. As a complex nonlinear system primarily governed by statistical laws rather than Newtonian mechanics, the output of non-Newtonian mechanics systems is difficult to describe through deterministic variables such as state variables, which poses difficulties in predicting and estimating the system’s output. In this article, the temporal variation of the system is described by constructing pattern category variables, which are non-deterministic variables.… More >

Junying Wei^*, Gang Li, Chenrui Zhang, Wenwen Chang, Jidai Wang

Frontiers in Heat and Mass Transfer, Vol.21, pp. 199-214, 2023, DOI:10.32604/fhmt.2023.041793

Abstract To study the complex internal flow field variation and output characteristics of a variable base radius scroll expander, this paper uses dynamic mesh techniques and computational fluid dynamics (CFD) methods to perform transient numerical simulations of a variable base radius scroll expander. Analysis of the flow field in the working cavity of a variable base radius scroll expander at different spindle angles and the effect of different profiles, speeds and pressures on the output characteristics of the scroll expander. The results of the study show that due to the periodic blocking of the inlet by… More >

Amit V. Patel¹, Arpan Desai¹, Issa Elfergani^2,3,*, Hiren Mewada⁴, Chemseddine Zebiri⁵, Keyur Mahant¹, Jonathan Rodriguez², Raed Abd-Alhameed³

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.3, pp. 2867-2879, 2023, DOI:10.32604/cmes.2023.026200

Abstract A four-element compact dual-band patch antenna having a common ground plane operating at 28/38 GHz is proposed for millimeter-wave communication systems in this paper. The multiple-input-multiple-output (MIMO) antenna geometry consists of a slotted ellipse enclosed within a hollow circle which is orthogonally rotated with a connected partial ground at the back. The overall size of the four elements MIMO antenna is 2.24λ × 2.24λ (at 27.12 GHz). The prototype of four-element MIMO resonator is designed and printed using Rogers RT Duroid 5880 with ε_r = 2.2 and loss tangent = 0.0009 and having a thickness of More >

Mohammed Yunus^*, Mohammad S. Alsoufi

Frontiers in Heat and Mass Transfer, Vol.12, pp. 1-8, 2019, DOI:10.5098/hmt.12.11

Abstract Swift cooling systems, improved microprocessor chips, processors’ performance and power usage have increased production of an enormous amount of heat and high operating temperatures due to excess heat flux density in the field of microelectronics. A rapid cooling of electronic circuits and heat dissipation for the same size of pipe with the present technology as nano size circuits critically generate high heat flux beyond 100 W/cm2 is currently the challenging task with which we are presented. Cooling in the form of heat transfer should be managed using both thermal conductivity (evaporation) and phase transition (condensation)… More >

A. Richard William^1,*, J. Senthilkumar², Y. Suresh², V. Mohanraj²

Computer Systems Science and Engineering, Vol.47, No.1, pp. 777-790, 2023, DOI:10.32604/csse.2023.031753

Abstract In cloud computing Resource allocation is a very complex task. Handling the customer demand makes the challenges of on-demand resource allocation. Many challenges are faced by conventional methods for resource allocation in order to meet the Quality of Service (QoS) requirements of users. For solving the about said problems a new method was implemented with the utility of machine learning framework of resource allocation by utilizing the cloud computing technique was taken in to an account in this research work. The accuracy in the machine learning algorithm can be improved by introducing Bat Algorithm with… More >

Ming Li^1,*, Cairen Furifu¹, Chengyang Ge², Yunping Zheng¹, Shunfu Lin², Ronghui Liu²

Energy Engineering, Vol.120, No.8, pp. 1775-1801, 2023, DOI:10.32604/ee.2023.027215

Abstract As an effective carrier of integrated clean energy, the microgrid has attracted wide attention. The randomness of renewable energies such as wind and solar power output brings a significant cost and impact on the economics and reliability of microgrids. This paper proposes an optimization scheme based on the distributionally robust optimization (DRO) model for a microgrid considering solar-wind correlation. Firstly, scenarios of wind and solar power output scenarios are generated based on non-parametric kernel density estimation and the Frank-Copula function; then the generated scenario results are reduced by K-means clustering; finally, the probability confidence interval More >