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Öğe A magnetohydrodynamic model applied to the lower convective region in the Sun including the radial components of magnetic field and flow(Elsevier Science Bv, 2009) Cavus, HuseyinIn this work, some numerical solutions of magnetohydrodynamic equations are investigated in the presence of radial and azimuthal components of magnetic field with the use of previously developed algorithm. In this algorithm, the thin shell approximation and a special separation of variables is used to obtain the radial and latitudinal variations of physical parameters in spherical coordinates. The solutions are obtained via this separation of variables in the components of momentum transfer equation. The analysis yields three important parameters which are the sphericity. density and radial components shape parameters in the latitudinal distributions of physical variables. The magnetic field profile, used here, produces comparable magnetic fluxes found in previous works. There is a considerable change in density with respect to reference model. Other physical parameters also reveal important physical results. It is as well shown that the spherical symmetric distributions of physical parameters are broken for the region of study. (C) 2009 Elsevier B.V. All rights reserved.Öğe A viscous magnetohydrodynamic Kelvin-Helmholtz instability in the interface of two fluid layer: Part II. An application to the atmosphere of the Sun(Springer, 2019) Cavus, Huseyin; Hoshoudy, G. A.The main aim of this submission is to investigate the effects of some parameters like wave number, shear velocity, magnetic field and temperature for the growth rate of the magnetized Kelvin-Helmholtz instability (KHI) with incessant profiles through interface of two viscous fluid layers occurred in the solar atmosphere using the model of Hoshoudy et al. (Astrophys. Space Sci. 364:89, 2019). In this examination, the presence of KHI is identified for the various cases of wave number, magnetic field, shear velocity and temperature in the solar atmosphere. The sensible values of these parameters were acquired.Öğe A viscous magnetohydrodynamic Kelvin-Helmholtz instability in the interface of two fluid layers: Part I. Basic mechanism(Springer, 2019) Hoshoudy, G. A.; Cavus, Huseyin; Mahdy, A.This study investigates the combined effect of density, velocity and magnetic field gradients on the Kelvin-Helmholtz instability of two viscous fluid layers. For the linear phase of instability that refers to the early stage of development of Kelvin-Helmholtz instability, the linear growth rate and frequency are presented. With respect to our selected variables and the Atwood number, the behaviour of growth rate and frequency are analysed. It is found that, the behaviour of frequency is not affected by the magnetic field and viscous term. The velocity gradient with the small Atwood numbers tends to stabilize KHI flows, while the velocity gradient with the large Atwood numbers has destabilizing effect on KHI. The growth rate reduces with the constant magnetic field and viscous term, while it enhances with magnetic field gradient.Öğe Correlation between sunspots and interplanetary shocks measured by ACE during 1998-2014 and some estimations for the 22nd solar cycle and the years between 2015 and 2018 with artificial neural network using the Cavus 2013 model(Elsevier Sci Ltd, 2020) Cavus, Huseyin; Araz, Gokhan; Coban, Gani Caglar; Raheem, Abd-ur; Karafistan, Aysel I.The Advanced Composition Explorer (ACE) spacecraft has measured 235 solar-based interplanetary (IP) shock waves between the years of 1998-2014. These were composed of 203 fast forward (FF), 6 slow forward (SF), 21 fast reverse (FR) and 5 slow reverse (SR) type shocks. These data can be obtained from the Interplanetary Shock Database of Harvard-Smithsonian Centre for Astrophysics. The Solar Section of American Association of Variable Star Observers (AAVSO) is an organization that counts the number of the sunspots. The effects of interplanetary shock waves on some physical parameters can be computed using a hydrodynamical model. There should be some correlations between these effects and the sunspot variations. The major objective of this paper is twofold. The first one is to search these correlations with sunspots given in the database of AAVSO. As expected, high correlations between physical parameters and sunspots have been obtained and these are presented in tables below. The second objective is to make an estimation of these parameters for the 22nd solar cycle and the years between 2015 and 2018 using an artificial neural network. Predictions have been made for these years where no shock data is present using artificial intelligence. The correlations were observed to increase further when these prediction results were included. (C) 2019 COSPAR. Published by Elsevier Ltd. All rights reserved.Öğe Estimating Coronal Mass Ejection Mass and Kinetic Energy by Fusion of Multiple Deep-learning Models(Iop Publishing Ltd, 2023) Alobaid, Khalid A.; Abduallah, Yasser; Wang, Jason T. L.; Wang, Haimin; Fan, Shen; Li, Jialiang; Cavus, HuseyinCoronal mass ejections (CMEs) are massive solar eruptions, which have a significant impact on Earth. In this paper, we propose a new method, called DeepCME, to estimate two properties of CMEs, namely, CME mass and kinetic energy. Being able to estimate these properties helps better understand CME dynamics. Our study is based on the CME catalog maintained at the Coordinated Data Analysis Workshops Data Center, which contains all CMEs manually identified since 1996 using the Large Angle and Spectrometric Coronagraph (LASCO) on board the Solar and Heliospheric Observatory. We use LASCO C2 data in the period between 1996 January and 2020 December to train, validate, and test DeepCME through 10-fold cross validation. The DeepCME method is a fusion of three deep-learning models, namely ResNet, InceptionNet, and InceptionResNet. Our fusion model extracts features from LASCO C2 images, effectively combining the learning capabilities of the three component models to jointly estimate the mass and kinetic energy of CMEs. Experimental results show that the fusion model yields a mean relative error (MRE) of 0.013 (0.009, respectively) compared to the MRE of 0.019 (0.017, respectively) of the best component model InceptionResNet (InceptionNet, respectively) in estimating the CME mass (kinetic energy, respectively). To our knowledge, this is the first time that deep learning has been used for CME mass and kinetic energy estimations.Öğe Kelvin-Helmholtz instability of two finite-thickness fluid layers with continuous density and velocity profiles(Indian Acad Sciences, 2018) Hoshoudy, G. A.; Cavus, HuseyinThe effect of density and velocity gradients on the Kelvin-Helmholtz instability (KHI) of two superimposed finite-thickness fluid layers are analytically investigated. The linear normalized frequency and normalized growth rate are presented. Then, their behavior as a function of the density ratio of the light fluid to the heavy one (r) was analyzed and compared to the case of two semi-infinite fluid layers. The results showed that the values of normalized frequency of KHI for two finite-thickness fluid layers are less than their counterparts for two semi-infinite fluid layers. The behavior of normalized growth rate as a function of the velocity and density gradients capitulates to the effect of velocity gradient at the large values of (r).Öğe Magnetic Kelvin-Helmholtz instability in the solar atmosphere(Elsevier Science Bv, 2013) Cavus, Huseyin; Kazkapan, DeryaMain aim of this paper is to twofold: firstly, to investigate the formation mechanism and secondly to search the effects of fundamental parameters like magnetic field, shear velocity and wave number on the growth rate of the magnetic Kelvin-Helmholtz instability occured in the solar atmosphere. In this investigation, occurrence and unoccurrence of instability are determined for the different values of magnetic field, shear velocity and wave number in the solar atmosphere. We have obtained the critical values of shear velocity and magnetic field. (C) 2013 Elsevier B.V. All rights reserved.Öğe Modelling the magnetic field and differential rotation effects for the vicinity of the base of convective zone in the Sun(Elsevier Science Bv, 2009) Cavus, HuseyinIn this work, some numerical solutions of magnetohydrodynamics equations are investigated in the presence of differential rotation with the use of previously developed algorithm. This algorithm includes the thin shell approximation and a special separation of variables which were used to obtain the radial and latitudinal variations of physical parameters in spherical coordinates. The magnetic field profile is chosen to produce comparable magnetic fluxes found in previous works. The sphericity and density shape parameters relevant to model is determined by using two different known differential rotation profiles. It is found that the shape of variations in physical parameters is strongly dependent to magnetic field profile and there is a considerable change in density with respect to reference model. It is as well shown that the spherical symmetric distributions of physical parameters are broken for the region of study. (C) 2008 Elsevier B.V. All rights reserved.Öğe Nonlinear magneto-hydrodynamical modeling of solar envelope(Amer Inst Physics, 2006) Cavus, Huseyin; Karafistan, Aysel I.In this study, our objective was to investigate the variation of the physical parameters in the 30 % outermost convective solar layer with the use equations of magneto-hydrodynamics (MHD) for non-linear case. The anelastic approximation was used in our calculations. The variations in physical parameters were performed by Maple. Hydrostatics equilibrium state was taken as a Standard Solar Model (SSM) excluding both rotation and magnetic field.Öğe On the Effects of Viscosity on the Shock Waves for a Hydrodynamical Case-Part I: Basic Mechanism(Hindawi Ltd, 2013) Cavus, HuseyinThe interaction of shock waves with viscosity is one of the central problems in the supersonic regime of compressible fluid flow. In this work, numerical solutions of unmagnetised fluid equations, with the viscous stress tensor, are investigated for a one-dimensional shock wave. In the algorithm developed the viscous stress terms are expressed in terms of the relevant Reynolds number. The algorithm concentrated on the compression rate, the entropy change, pressures, and Mach number ratios across the shock wave. The behaviour of solutions is obtained for the Reynolds and Mach numbers defining the medium and shock wave in the supersonic limits.Öğe Predicting the physical parameters of interplanetary shock waves using Artificial Neural Networks trained on NASA's ACE and WIND spacecrafts(Institute of Electrical and Electronics Engineers Inc., 2020) Coban, Gani Caglar; Raheem, Abd-Ur; Cavus, HuseyinThis study aims to develop artificial neural networks to predict the physical parameters of the shock waves in the interplanetary (IP) environment which are closely correlated to the sunspot number as demonstrated in previous studies [1] and [2]. This is done by training the ANNs with the current available data and then use the model to predict for the years where there is no data present. For this purpose, NASA's Advanced Composition Explorer (ACE) and WIND spacecrafts are used to obtain the shock data and then physical parameters are calculated using the Cavus2013 hydrodynamical model. These physical parameters describe the properties of the IP shock waves. Predictions have been made where there is no data measured by the spacecrafts. This is achievable due to the presence of high correlation between the sunspot number and the calculated physical parameters of the shock waves. The ANNs regression is very close to 1. This is also shown in the results and proved as an increase in the correlation is observed when the predicted data is added to the actual data. © 2020 IEEE.Öğe Prediction of the SYM-H Index Using a Bayesian Deep Learning Method With Uncertainty Quantification(Amer Geophysical Union, 2024) Abduallah, Yasser; Alobaid, Khalid A.; Wang, Jason T. L.; Wang, Haimin; Jordanova, Vania K.; Yurchyshyn, Vasyl; Cavus, HuseyinWe propose a novel deep learning framework, named SYMHnet, which employs a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters for short-term forecasts of the SYM-H index based on 1- and 5-min resolution data. SYMHnet takes, as input, the time series of the parameters' values provided by NASA's Space Science Data Coordinated Archive and predicts, as output, the SYM-H index value at time point t + w hours for a given time point t where w is 1 or 2. By incorporating Bayesian inference into the learning framework, SYMHnet can quantify both aleatoric (data) uncertainty and epistemic (model) uncertainty when predicting future SYM-H indices. Experimental results show that SYMHnet works well at quiet time and storm time, for both 1- and 5-min resolution data. The results also show that SYMHnet generally performs better than related machine learning methods. For example, SYMHnet achieves a forecast skill score (FSS) of 0.343 compared to the FSS of 0.074 of a recent gradient boosting machine (GBM) method when predicting SYM-H indices (1 hr in advance) in a large storm (SYM-H = -393 nT) using 5-min resolution data. When predicting the SYM-H indices (2 hr in advance) in the large storm, SYMHnet achieves an FSS of 0.553 compared to the FSS of 0.087 of the GBM method. In addition, SYMHnet can provide results for both data and model uncertainty quantification, whereas the related methods cannot. In the past several years, machine learning and its subfield, deep learning, have attracted considerable interest. Computer vision, natural language processing, and social network analysis make extensive use of machine learning algorithms. Recent applications of these algorithms include the prediction of solar flares and the forecasting of geomagnetic indices. In this paper, we propose an innovative machine learning method that utilizes a graph neural network and a bidirectional long short-term memory network to cooperatively learn patterns from solar wind and interplanetary magnetic field parameters to provide short-term predictions of the SYM-H index. In addition, we present techniques for quantifying both data and model uncertainties in the output of the proposed method. SYMHnet is a novel deep learning method for making short-term predictions of the SYM-H index (1 or 2 hr in advance) With Bayesian inference, SYMHnet can quantify both aleatoric (data) and epistemic (model) uncertainties in making its prediction SYMHnet generally performs better than related machine learning methods for SYM-H forecastingÖğe Treatment of Viscosity in the Shock Waves Observed After Two Consecutive Coronal Mass Ejection Activities CME08/03/2012 and CME15/03/2012(Springer, 2016) Cavus, HuseyinA coronal mass ejection (CME) is one of the most the powerful activities of the Sun. There is a possibility to produce shocks in the interplanetary medium after CMEs. Shock waves can be observed when the solar wind changes its velocity from being supersonic nature to being subsonic nature. The investigations of such activities have a central place in space weather purposes, since; the interaction of shocks with viscosity is one of the most important problems in the supersonic and compressible gas flow regime (Blazek in Computational fluid dynamics: principles and applications. Elsevier, Amsterdam 2001). The main aim of present work is to achieve a search for the viscosity effects in the shocks occurred after two consecutive coronal mass ejection activities in 2012 (i.e. CME08/03/2012 and CME15/03/2012).
