MHD Stability of Streaming Jet Using Artificial Intelligence Technique

2012 ◽  
Vol 28 (3) ◽  
pp. 453-459
Author(s):  
Mostafa A. M. Abdeen ◽  
Alfaisal A. Hasan
Keyword(s):  
Magnetic Field ◽  
Artificial Intelligence ◽  
Mathematical Formulation ◽  
Transverse Magnetic ◽  
Field Variation ◽  
Ann Model ◽  
Eigen Value ◽  
Mhd Stability ◽  
Artificial Neural Network Ann ◽  
The Stability

AbstractMathematical formulation for Magnetohydrodynamic (MHD) stability of a streaming cylindrical model penetrated by varying transverse magnetic field is presented. Eigen value relation is derived and discussed analytically. In the current paper, Artificial Neural Network (ANN) model, one of the artificial intelligence techniques, is developed to simulate the stability of streaming jet penetrated by magnetic field. The ANN results presented in the current study showed that ANN technique, with less effort and time, is very efficiently capable of simulating and predicting the effect of magnetic field variation and axial exterior field on the stability of the streaming jet. The influence of magnetic field has a stabilizing effect for all short and long wavelengths. However the streaming is strongly destabilizing.

scholarly journals Rainfall Reliability Evaluation for Stability of Municipal Solid Waste Landfills on Slope

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Fu-Kuo Huang ◽  
Grace S. Wang ◽  
Yueh-Lin Tsai
Keyword(s):  
Municipal Solid Waste ◽  
Solid Waste ◽  
Engineering Properties ◽  
Ann Model ◽  
First Order ◽  
Reliability Method ◽  
Municipal Solid Waste Landfills ◽  
Artificial Neural Network Ann ◽  
The Stability ◽  
Solid Waste Landfills

A method to assess the reliability for the stability of municipal solid waste (MSW) landfills on slope due to rainfall infiltration is proposed. Parameter studies are first done to explore the influence of factors on the stability of MSW. These factors include rainfall intensity, duration, pattern, and the engineering properties of MSW. Then 100 different combinations of parameters are generated and associated stability analyses of MSW on slope are performed assuming that each parameter is uniform distributed around its reason ranges. In the following, the performance of the stability of MSW is interpreted by the artificial neural network (ANN) trained and verified based on the aforementioned 100 analysis results. The reliability for the stability of MSW landfills on slope is then evaluated and explored for different rainfall parameters by the ANN model with first-order reliability method (FORM) and Monte Carlo simulation (MCS).

Prediction and optimization of stability parameters for titanium dioxide nanofluid using response surface methodology and artificial neural networks

2013 ◽  
Vol 20 (4) ◽  
pp. 319-330 ◽  
Author(s):  
Ali Sadollah ◽  
Azadeh Ghadimi ◽  
Ibrahim H. Metselaar ◽  
Ardeshir Bahreininejad
Keyword(s):  
Response Surface Methodology ◽  
Response Surface ◽  
Process Parameters ◽  
Lauryl Sulfate ◽  
Ann Model ◽  
Stability Parameters ◽  
Weight Percentage ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
The Stability

AbstractThe effect of various process parameters on the stability of TiO2 nanofluid, which can mostly be defined as zeta potential and particle size, was studied using response surface methodology (RSM) by the design of experiments and was predicted through a trained artificial neural network (ANN). The process parameters studied were weight percentage of surfactant (sodium lauryl sulfate) (0.01–0.2 wt%) and the value of pH (10–12). Central composite design and the RSM were employed to develop a mathematical model as well as to define the optimum condition. A three-layered feed-forward ANN model was designed and used for the prediction of the stability parameters. From the analysis of variance, the significant factors that affected the experimental design responses were also identified. The predicted stability parameters using the RSM and ANNs were compared using figures and tables. It is shown that the trained ANN outperformed the RSM in terms of accuracy and prediction of obtained results.

scholarly journals Air-Aided Shear on a Thin Film Subjected to a Transverse Magnetic Field of Constant Strength: Stability and Dynamics

2013 ◽  
Vol 2013 ◽  
pp. 1-24 ◽  
Author(s):  
Mohammed Rizwan Sadiq Iqbal
Keyword(s):  
Magnetic Field ◽  
Inertial Force ◽  
Transverse Magnetic ◽  
Nonlinear Analyses ◽  
Weakly Nonlinear ◽  
Weakly Nonlinear Theory ◽  
Constant Strength ◽  
Long Time ◽  
The Stability ◽  
The One

The effect of air shear on the hydromagnetic instability is studied through (i) linear stability, (ii) weakly nonlinear theory, (iii) sideband stability of the filtered wave, and (iv) numerical integration of the nonlinear equation. Additionally, a discussion on the equilibria of a truncated bimodal dynamical system is performed. While the linear and weakly nonlinear analyses demonstrate the stabilizing (destabilizing) tendency of the uphill (downhill) shear, the numerics confirm the stability predictions. They show that (a) the downhill shear destabilizes the flow, (b) the time taken for the amplitudes corresponding to the uphill shear to be dominated by the one corresponding to the zero shear increases with magnetic fields strength, and (c) among the uphill shear-induced flows, it takes a long time for the wave amplitude corresponding to small shear values to become smaller than the one corresponding to large shear values when the magnetic field intensity increases. Simulations show that the streamwise and transverse velocities increase when the downhill shear acts in favor of inertial force to destabilize the flow mechanism. However, the uphill shear acts oppositely. It supports the hydrostatic pressure and magnetic field in enhancing films stability. Consequently, reduced constant flow rates and uniform velocities are observed.

An exact solution in the stability of m. h. d. Couette flow

1972 ◽  
Vol 327 (1569) ◽  
pp. 269-278 ◽  
Keyword(s):  
Magnetic Field ◽  
Exact Solution ◽  
Couette Flow ◽  
Stability Problem ◽  
Axial Magnetic Field ◽  
Narrow Gap ◽  
Mhd Stability ◽  
Arbitrary Magnetic Field ◽  
The Stability ◽  
Conducting Cylinders

The MHD stability problem for dissipative Couette flow in a narrow gap between corotating, conducting cylinders with an axial magnetic field is solved exactly. Results are presented for an arbitrary magnetic field; in particular, previous results on the zero and infinite magnetic field limits are verified.

scholarly journals An Artificial Intelligence Approach for Italian EVOO Origin Traceability through an Open Source IoT Spectrometer

Foods ◽  
2020 ◽  
Vol 9 (6) ◽  
pp. 834 ◽  
Author(s):  
Simona Violino ◽  
Luciano Ortenzi ◽  
Francesca Antonucci ◽  
Federico Pallottino ◽  
Cinzia Benincasa ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Olive Oil ◽  
Open Source ◽  
Geographical Origin ◽  
Virgin Olive Oil ◽  
Extra Virgin Olive Oil ◽  
Ann Model ◽  
First Choice ◽  
Phenolic Components ◽  
Artificial Neural Network Ann

Extra virgin olive oil (EVOO) represents a crucial ingredient of the Mediterranean diet. Being a first-choice product, consumers should be guaranteed its quality and geographical origin, justifying the high purchasing cost. For this reason, it is important to have new reliable tools able to classify products according to their geographical origin. The aim of this work was to demonstrate the efficiency of an open source visible and near infra-red (VIS-NIR) spectrophotometer, relying on a specific app, in assessing olive oil geographical origin. Thus, 67 Italian and 25 foreign EVOO samples were analyzed and their spectral data were processed through an artificial intelligence algorithm. The multivariate analysis of variance (MANOVA) results reported significant differences (p < 0.001) between the Italian and foreign EVOO VIS-NIR matrices. The artificial neural network (ANN) model with an external test showed a correct classification percentage equal to 94.6%. Both the MANOVA and ANN tested methods showed the most important spectral wavelengths ranges for origin determination to be 308–373 nm and 594–605 nm. These are related to the absorption of phenolic components, carotenoids, chlorophylls, and anthocyanins. The proposed tool allows the assessment of EVOO samples’ origin and thus could help to preserve the “Made in Italy” from fraud and sophistication related to its commerce.

scholarly journals Artificial intelligence application for rapid fabrication of size-tunable PLGA microparticles in microfluidics

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Safa A. Damiati ◽  
Damiano Rossi ◽  
Haakan N. Joensson ◽  
Samar Damiati
Keyword(s):  
Artificial Intelligence ◽  
Learning Technology ◽  
Particle Sizes ◽  
Ann Model ◽  
Microfluidic Systems ◽  
Polymeric Particles ◽  
Plga Microparticles ◽  
Key Factor ◽  
Ann Models ◽  
Artificial Neural Network Ann

Abstract In this study, synthetic polymeric particles were effectively fabricated by combining modern technologies of artificial intelligence (AI) and microfluidics. Because size uniformity is a key factor that significantly influences the stability of polymeric particles, therefore, this work aimed to establish a new AI application using machine learning technology for prediction of the size of poly(d,l-lactide-co-glycolide) (PLGA) microparticles produced by diverse microfluidic systems either in the form of single or multiple particles. Experimentally, the most effective factors for tuning droplet/particle sizes are PLGA concentrations and the flow rates of dispersed and aqueous phases in microfluidics. These factors were utilized to develop five different and simple in structure artificial neural network (ANN) models that are capable of predicting PLGA particle sizes produced by different microfluidic systems either individually or jointly merged. The systematic development of ANN models allowed ultimate construction of a single in silico model which consists of data for three different microfluidic systems. This ANN model eventually allowed rapid prediction of particle sizes produced using various microfluidic systems. This AI application offers a new platform for further rapid and economical exploration of polymer particles production in defined sizes for various applications including biomimetic studies, biomedicine, and pharmaceutics.

scholarly journals Applications of Artificial Intelligence Techniques to Enhance Sustainability of Industry 4.0: Design of an Artificial Neural Network Model as Dynamic Behavior Optimizer of Robotic Arms

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-10 ◽  
Author(s):  
Aydin Azizi
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Artificial Neural Network ◽  
Industrial Robots ◽  
Optimal Parameters ◽  
Ann Model ◽  
Robotic Arms ◽  
Revolute Joints ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Industrial robots have a great impact on increasing the productivity and reducing the time of the manufacturing process. To serve this purpose, in the past decade, many researchers have concentrated to optimize robotic models utilizing artificial intelligence (AI) techniques. Gimbal joints because of their adjustable mechanical advantages have been investigated as a replacement for traditional revolute joints, especially when they are supposed to have tiny motions. In this research, the genetic algorithm (GA), a well-known evolutionary technique, has been adopted to find optimal parameters of the gimbal joints. Since adopting the GA is a time-consuming process, an artificial neural network (ANN) architecture has been proposed to model the behavior of the GA. The result shows that the proposed ANN model can be used instead of the complex and time-consuming GA in the process of finding the optimal parameters of the gimbal joint.

Ideal magnetohydrodynamic stability in stellarators with subsonic equilibrium flow

2021 ◽  
Author(s):  
Carolin Nuehrenberg
Keyword(s):  
Mathematical Formulation ◽  
Radial Electric Field ◽  
Weak Form ◽  
Unstable Flow ◽  
Magnetic Surfaces ◽  
Ideal Mhd ◽  
Mhd Stability ◽  
Ideal Magnetohydrodynamic ◽  
The Stability ◽  
The Ideal

Abstract The effect of a subsonic flow, inherent to most stellarators because of a radial electric field, on their ideal magnetohydrodynamic (MHD) stability properties is studied employing the quasi-Lagrangian picture developed by Frieman and Rotenberg [1960 Rev. Mod. Phys. 32, 898]. The Mach number of the perpendicular ExB flow in stellarators is of order 0.01 and, therefore, admits the usage of a subsonic approximation in form of a static equilibrium. A mathematical formulation of the weak form of the stability equation with flow has been implemented in the ideal-MHD stability code CAS3D. This formulation uses magnetic coordinates and does not involve any derivatives across magnetic surfaces. In addition to the expected Doppler shift of frequencies, properties of the spectrum of the ideal MHD force operator, which are already known for tokamaks, but now also shown in the stellarator case, are: firstly, the appearance of unstable flow-induced continua stemming from the coupling of sound and Alfven continuum branches with equal mode numbers; and, secondly, the existence of flow-induced, global, stable modes near extrema of sound continuum branches, the extrema, in turn, being generated by the influence of a sheared flow on the static sound continua.

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