Intelligent computing for the dynamics of entropy optimized nanofluidic system under impacts of MHD along thick surface
06 Sep 2021-International Journal of Modern Physics B (World Scientific Publishing Company)-Vol. 35, Iss: 26
TL;DR: This article examines entropy production (EP) of magneto-hydrodynamics viscous fluid flow model (MHD-VFFM) subject to a variable thickness surface with heat sink/source effect by utilizing the intelligent computing paradigm via artificial Levenberg–Marquardt back propagated neural networks (ALM-BPNNs).
Abstract: This article examines entropy production (EP) of magneto-hydrodynamics viscous fluid flow model (MHD-VFFM) subject to a variable thickness surface with heat sink/source effect by utilizing the inte...
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TL;DR: In this article , a scaling group transformation method is applied to the flow governing equations and three absolute invariants, third-order ordinary differential equations (ODEs) corresponding to momentum equation and second-order ODEs corresponding to energy and diffusion equations are derived.
Abstract: This work analyzes the two-dimensional flow of an incompressible magneto-hydrodynamic fluid over linear stretching sheet in the presence of suction or injection and convective boundary conditions. A scaling group transformation method is applied to the flow governing equations. The system remains invariant due to the relation between the transformation parameters. Upon finding three absolute invariants, third-order ordinary differential equations (ODEs) corresponding to momentum equation and second-order ODEs corresponding to energy and diffusion equations are derived. Shooting technique (R-K fourth-order) is applied to work out the flow equations numerically. MATLAB is used for the simulation and the results are exhibited through graphs. The computational results are validated with the published research work and a modest concurrence was found. The main outcome of this study is found to be that raising values of [Formula: see text] and [Formula: see text] decline the friction, whereas [Formula: see text] and [Formula: see text] show the opposite (increasing). The rising values of [Formula: see text] and [Formula: see text] in addition to [Formula: see text] and [Formula: see text] show a decline in friction factor. The Nusselt number values are improved as raising values of [Formula: see text] versus [Formula: see text] and [Formula: see text] versus [Formula: see text]. It is very clear the monotonically increasing [Formula: see text] versus [Formula: see text] and strictly increasing [Formula: see text] versus [Formula: see text] cases. It is very clear the mass-transfer rate is smoothly improved [Formula: see text] versus [Formula: see text] and strictly increased [Formula: see text] versus [Formula: see text].
48 citations
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TL;DR: In this paper, a mathematical model for second order velocity slip flow of Darcy-Forchheimer ferrofluid model (DF-FFM) by employing the intelligent computing paradigm via Artificial Levenberg Marquardt Method with backpropagated neural networks (ALMM-BNN) is presented.
35 citations
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TL;DR: In this paper , the authors studied the nanofluid flow under the consequences of Brownian motion, thermophoresis, and nonlinear radiation over a heated rotating disc.
Abstract: The nanofluid flow under the consequences of Brownian motion, thermophoresis, and nonlinear radiation has been numerically studied over a heated rotating disc. Arrhenius activation energy is used to describe the various aspects of heat and mass transition. The problem has been modeled in the form of a system of PDEs consist of the Maxwell and Navier Stokes equations. The system of modeled equations has been reduced to the ordinary system of dimensionless differential equations using a similarity framework. For the problem's quantitative approximation, the results have been obtained through numerical technique boundary value solver (bvp4c). The physical quantities that derive from the modeled equations are displayed and addressed. It has been perceived that the Prandtl number and radiation effect improves the heat transmission rate while improving the magnetic parameter reduces the velocity field. Furthermore, the entropy rate and Bejan number increases with the rising effect of chemical reaction, temperature differential variable, concentration ratio variable and Schmidt number.
27 citations
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TL;DR: In this article , a steady incompressible two-dimensional laminar Glauert kind wall jet is scrutinized in this study by considering nanoparticles suspension in the base liquid sodium alginate (NaAlg) with suction and wall slip boundary conditions.
Abstract: A Glauert type laminar wall jet issuing into a stationary liquid medium lying above a wall has technical uses in wall cooling and flow control. It plays a vital role in industrial applications like cooling/heating by impingement of jet, turbine blades, film cooling, mass and heat transfer phenomena. In this regard, a steady incompressible two-dimensional laminar Glauert kind wall jet is scrutinized in this study by considering nanoparticles suspension in the base liquid sodium alginate (NaAlg) with suction and wall slip boundary conditions. Further, a comparative study is done by considering aluminum alloy(AA7075) and single-walled carbon nanotube (SWCNT) as nanoparticles. The reduced ordinary differential equations (ODEs) are numerically solved by applying Runge–Kutta–Fehlberg fourth fifth-order (RKF-45) technique along with the shooting method. Results reveal that NaAlg−SWCNT Casson nanofluid shows enhanced heat transfer than NaAlg−AA7075 Casson nanoliquid for increased values of radiation parameter. The rising values of the Casson parameter deteriorate the heat transfer rate of both nanoliquids but an inverse trend is seen for improved values of radiation parameter.
18 citations
References
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TL;DR: In this paper, the authors evaluated plasma heat shock protein (Hsp) 90 in the skin of patients with systemic sclerosis (SSc) and characterized its association with SSc-related features.
Abstract: Our previous study demonstrated increased expression of Heat shock protein (Hsp) 90 in the skin of patients with systemic sclerosis (SSc). We aimed to evaluate plasma Hsp90 in SSc and characterize its association with SSc-related features. Ninety-two SSc patients and 92 age-/sex-matched healthy controls were recruited for the cross-sectional analysis. The longitudinal analysis comprised 30 patients with SSc associated interstitial lung disease (ILD) routinely treated with cyclophosphamide. Hsp90 was increased in SSc compared to healthy controls. Hsp90 correlated positively with C-reactive protein and negatively with pulmonary function tests: forced vital capacity and diffusing capacity for carbon monoxide (DLCO). In patients with diffuse cutaneous (dc) SSc, Hsp90 positively correlated with the modified Rodnan skin score. In SSc-ILD patients treated with cyclophosphamide, no differences in Hsp90 were found between baseline and after 1, 6, or 12 months of therapy. However, baseline Hsp90 predicts the 12-month change in DLCO. This study shows that Hsp90 plasma levels are increased in SSc patients compared to age-/sex-matched healthy controls. Elevated Hsp90 in SSc is associated with increased inflammatory activity, worse lung functions, and in dcSSc, with the extent of skin involvement. Baseline plasma Hsp90 predicts the 12-month change in DLCO in SSc-ILD patients treated with cyclophosphamide.
2,948 citations
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TL;DR: A novel hybrid model with the strength of fractional order derivative is presented with their dynamical features of deep learning, long-short term memory (LSTM) networks, to predict the abrupt stochastic variation of the financial market.
Abstract: Forecasting of fast fluctuated and high-frequency financial data is always a challenging problem in the field of economics and modelling. In this study, a novel hybrid model with the strength of fractional order derivative is presented with their dynamical features of deep learning, long-short term memory (LSTM) networks, to predict the abrupt stochastic variation of the financial market. Stock market prices are dynamic, highly sensitive, nonlinear and chaotic. There are different techniques for forecast prices in the time-variant domain and due to variability and uncertain behavior in stock prices, traditional methods, such as data mining, statistical approaches, and non-deep neural networks models are not suited for prediction and generalized forecasting stock prices. While autoregressive fractional integrated moving average (ARFIMA) model provides a flexible tool for classes of long-memory models. The advancement of machine learning-based deep non-linear modelling confirms that the hybrid model efficiently extracts profound features and model non-linear functions. LSTM networks are a special kind of recurrent neural network (RNN) that map sequences of input observations to output observations with capabilities of long-term dependencies. A novel ARFIMA-LSTM hybrid recurrent network is presented in which ARFIMA model-based filters having the linear tendencies better than ARIMA model in the data and passes the residual to the LSTM model that captures nonlinearity in the residual values with the help of exogenous dependent variables. The model not only minimizes the volatility problem but also overcome the over fitting problem of neural networks. The model is evaluated using PSX company data of the stock market based on RMSE, MSE and MAPE along with a comparison of ARIMA, LSTM model and generalized regression radial basis neural network (GRNN) ensemble method independently. The forecasting performance indicates the effectiveness of the proposed AFRIMA-LSTM hybrid model to improve around 80% accuracy on RMSE as compared to traditional forecasting counterparts.
219 citations
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16 Apr 2021
TL;DR: In this paper, the steady Marangoni driven boundary layer flow, heat and mass transfer characteristics of a nanofluid were studied using the Runge-Kutta-Fehlberg fourth-fifth order (RKF-45) method.
Abstract: The flow and heat transfer of non-Newtonian nanofluids has an extensive range of applications in oceanography, the cooling of metallic plates, melt-spinning, the movement of biological fluids, heat exchangers technology, coating and suspensions. In view of these applications, we studied the steady Marangoni driven boundary layer flow, heat and mass transfer characteristics of a nanofluid. A non-Newtonian second-grade liquid model is used to deliberate the effect of activation energy on the chemically reactive non-Newtonian nanofluid. By applying suitable similarity transformations, the system of governing equations is transformed into a set of ordinary differential equations. These reduced equations are tackled numerically using the Runge–Kutta–Fehlberg fourth-fifth order (RKF-45) method. The velocity, concentration, thermal fields and rate of heat transfer are explored for the embedded non-dimensional parameters graphically. Our results revealed that the escalating values of the Marangoni number improve the velocity gradient and reduce the heat transfer. As the values of the porosity parameter increase, the velocity gradient is reduced and the heat transfer is improved. Finally, the Nusselt number is found to decline as the porosity parameter increases.
163 citations
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TL;DR: The present investigation shows that strengthening of Weissenberg number uplifts the axial as well transverse fluid velocities while that of Hartmann number turns out to be a reverse trend, which imparts a reasonable, pragmatic and realistic approach to a good absorber of solar energy.
127 citations
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TL;DR: In this paper, the results of steady mixed convection flow of SiO 2 − Al 2 O 3 /water hybrid nanofluid near the stagnation point with the curved surface of radius R and mass suction S.
Abstract: The major focus of this work is to examine the results of steady mixed convection flow of SiO 2 − Al 2 O 3 /water hybrid nanofluid near the stagnation point with the curved surface of radius R and mass suction S . Hybrid nanofluid is taken into consideration by suspending a couple of distinct nanoparticles (SiO 2 and Al 2 O 3 ) into pure water. Depending on similarity variables, the governing equations with associated boundary conditions are modified to formulate a normalized boundary value problem of coupled differential equations and the MATLAB problem solver bvp4c is efficient to resolve the resulting problem. From this study it is determined that the skin friction coefficient and local Nusselt number of hybrid nanofluid improves with high values of mass suction and nanoparticles concentration while increasing curvature K declines the skin friction coefficient and gives rise to a poor performance of heat transfer. Moreover, the improvement of thermal boundary layer and velocity boundary layer take place with powerful concentration of SiO 2 and Al 2 O 3 and greater values of curvature K . Some interesting results for the flat sheet ( K → ∞ ) were also computed.
120 citations