Smart Edge Computing for 5 g/6 g Satellite IOT for Reducing Inter Transmission Delay

Mobile Networks and Applications - 5G/6G communication are first generation high speed wireless communication network which integrates the aerial data, terrestrial data and maritime data via...     

The preparation of an amino acid-based surfactant and its potential application as an EOR agent

Most of the synthetic surfactants investigated with the aim of enhanced chemically oil recovery in the literature have environmental drawbacks. In this work, application of an environmentally-friendly synthetic surfactant as an enhanced oil recovery agent is introduced by measuring interfacial tension of water–kerosene systems and wettability alteration of carbonate pellets. For this purpose, an amino acid-based surfactant was initially synthesized using a new synthetic approach which was subsequently confirmed by spectra of Fourier transform infrared and Proton nuclear magnetic resonance. Results showed a value of critical micelle concentration in the range of 9000–9100 ppm for this surfactant. Results also demonstrated a decrease of 38.53% in water–kerosene system interfacial tension and a 17.76% reduction in oil-wetness of the carbonate pellets.     

Prediction of hydrate equilibrium conditions using k-nearest neighbor algorithm to CO2 capture

Gas or clathrate hydrates are an important issue when they form in the oil and gas pipelines. Since the determination of the hydrate formation temperature and pressure is very difficult experimentally for every gas system and it is impossible in terms of cost and time approximately, mathematical models can be useful tools to overcome these difficulties. In this study, k-nearest neighbor model was used to predict the equilibrium conditions of hydrate formation in absorption and separation of carbon dioxide from flue gas mixture, containing carbon dioxide and nitrogen. At the training phase, temperature and composition data of nitrogen and carbon dioxide in the flue gas mixture at equilibrium conditions and the equilibrium pressures of hydrate formation were used as input and output, respectively. The error percentage less than 0.38% indicates the high accuracy of the proposed model. In this study, 80%, 85%, and 90% of the training data are examined for three numbers of nearest. For three numbers of used nearest (k = 1, k = 2 and k = 3), the value of k = 1 leads to the lowest error; so, it is selected as the best nearest in the presented model.     

Application of LS-SVM strategy to estimate H2S loading capacity in different ionic liquids and alkanolamines

Favorable properties of aqueous solutions are improved with the addition of different materials for separation of hydrogen sulfide (H₂S). Also, equilibrium data and available equations for solubility estimation of this gas are only valid for specific solutions and limited ranges of temperature and pressure. In this regard, a machine learning model based on Support vector machine (SVM) algorithm is proposed and developed with mixtures containing different amines and ionic liquids to predict H₂S solubility over wide ranges of temperature (298–434.5 K), pressure (13–9319 kPa), overall mass concentration (3.82–100%) and mixture's apparent molecular weight (18.39–556.17 g/mol). The accuracy of the performance of this network was evaluated by regression analysis on calculated and experimental data, which had not been used in network training.     

基于薄层单元模型的弹体与引信系统螺纹连接参数确定方法

螺纹连接是侵彻弹体与引信体连接的主要形式之一,直接影响着弹体与引信系统振动响应特性.针对现有数值仿真手段难以准确表征螺纹连接的问题,提出用薄层单元模拟螺纹连接的有效方案,结合理论与试验研究建立薄层单元材料参数的确定方法.基于螺纹连接弹性模型与薄层单元理论推导出薄层单元的材料参数;将薄层单元材料参数传递到有限元模型中进行...     

Workload Distribution in Fault Simulation

Simulation at the gate level is computationally very expensive.Parallel processing is one technique to reduce simulation time.Possessing knowledge of the distribution of computational activity insimulation can aid in parallelizing it efficiently. We present a newcharacterization of the distribution of the computational workload infault simulation. An empirical analysis shows that the workloaddistribution is circuit specific, and is largely independent of thevector set being simulated. An inexpensive method to predict theworkload distribution is also discussed.     

Ground plane impact on quadrifilar helix antenna performance with respect to deployment heights

Quadrifilar helix antenna (QHA) has its applications in satellite communications. This paper presents the performance optimisation of input and radiation characteristics of QHA in the presence of infinite and finite metallic ground planes. For the infinite ground plane, it has been observed that input parameters such as impedance and voltage standing wave ratio (VSWR) are stable, and the antenna has broader half power beamwidth (HPBW). Smaller metallic platforms that act as finite ground planes produce better 3‐dB axial ratio beamwidth and boresight axial ratio. Deployment of QHA on smaller metallic platforms such as nanosatellites and CubeSats enhances the circularly polarised beamwidth of the antenna with improved boresight axial ratio. However, on large low earth orbit (LEO) satellites, stable input characteristics and broader HPBW have been achieved at the cost of narrow circularly polarised beamwidth and degraded boresight axial ratio.     

Emulating Software Defined Network using Mininet-ns3-WIFI Integration for Wireless Networks

SDN enables a new networking paradigm probable to improve system efficiency where complex networks are easily managed and controlled. SDN allows network virtualization and advance programmability for customizing the behaviour of networking devices with user defined features even at run time. SDN separates network control and data planes. Intelligently controlled network management and operation, such that routing is eliminated from forwarding elements (switches) while shifting the routing logic in a centralized module named SDN Controller. Mininet is Linux based network emulator which is cost effective for implementing SDN having in built support of OpenFlow switches. This paper presents practical implementation of Mininet with ns-3 using Wi-Fi. Previous results reported in literature were limited upto 512 nodes in Mininet. Tests are conducted in Mininet by varying number of nodes in two distinct scenarios based on scalability and resource capabilities of the host system. We presented a low cost and reliable method allowing scalability with authenticity of results in real time environment. Simulation results show a marked improvement in time required for creating a topology designed for 3 nodes with powerful resources i.e. only 0.077 sec and 4.512 sec with limited resources, however with 2047 nodes required time is 1623.547 sec for powerful resources and 4615.115 sec with less capable resources respectively.

     

On the prediction of solubility of alkane in carbon dioxide using the LSSVM algorithm

The increasing global energy demand and declination of oil reservoir in recent years cause the researchers attention focus on the enhancement of oil recovery approaches. One of the extensive applicable methods for enhancement of oil recovery, which has great efficiency and environmental benefits, is carbon dioxide injection. The CO₂ injection has various effects on the reservoir fluid, which causes enhancement of recovery. One of these effects is extraction of lighter components of crude oil, which straightly depends on solubility of hydrocarbons in carbon dioxide. In order to better understand of this parameter, in this study, Least squares support vector machine (LSSVM) algorithm was developed as a novel predictive tool to estimate solubility of alkane in CO₂ as function of carbon number of alkane, carbon dioxide density, pressure, and temperature. The predicting model outputs were compared with the extracted experimental solubility from literature statistically and graphically. The comparison showed the great ability and high accuracy of developed model in prediction of solubility.     

Applying MLP-ANN as a novel and accurate method to estimate gas density

In the gas industries, to increase the degree of accuracy of calculation and estimation in different processes, the importance of accurate prediction of gas properties is highlighted. The gas density, as one of the key properties in gas engineering, has a major effect in calculations. So, in the present paper, multi-layer perceptron artificial neural network (MLP-ANN) was used to predict the gas density based on molecular weight, critical pressure and critical temperature of gas, pressure, and temperature. To this end, a total number of 1240 reliable data of gas density were gathered from literature for the training and testing phases. The MLP-ANN outputs were compared with the actual data in different manners, such as statistical and graphical analyses. The coefficient of determination (R²), average absolute relative deviation (AARD), and root mean squared error (RMSE) for overall process were calculated as 1, 0.0088444, and 0.0259, respectively. The determined parameters and graphical analysis showed that the MLP-ANN has great potential and high degree of accuracy in gas density estimation.