Numerical Simulation and Neural Network Study Using an Upstream Cylinder for Flow Control of an Airfoil

2019 ◽  
Author(s):  
Meihua Zhang ◽  
Zhongquan Charlie Zheng ◽  
Yangliu Liu ◽  
Xiaoyu Jiang
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Flow Control ◽  
Translational Motion ◽  
Back Propagation ◽  
Test Method ◽  
Immersed Boundary ◽  
Control Parameters ◽  
Reference Case ◽  
Drag Ratio

Abstract Flow behaviors of a downstream object can be affected significantly by an upstream object in close proximity. This concept is used for flow control in this study to maximize the lift/drag ratio on a NACA0012 airfoil. A cylinder with cross-flow translational motion is placed upstream of the airfoil. Numerical simulations are carried out with an immersed-boundary method to solve the incompressible, viscous flow at the Reynolds number of 2000. Control parameters that influence the dynamics of flow around the airfoil are systematically investigated, including the oscillating frequency and amplitude of the upstream cylinder, the distances between the cylinder and the airfoil, and the diameter of the cylinder. To obtain sample data properly and efficiently for carrying out the neural network study, the idea of the orthogonal test method is used to set the control parameters in the numerical simulation. The combination of the back-propagation neural network algorithm and the genetic algorithm is applied to find the optimal value of the lift/drag ratio and the corresponding control parameters. The results show that when the cylinder oscillating frequency increases, the ratio increases until negative coefficients occur; when the distance between the cylinder and the airfoil increases or the amplitude of oscillating cylinder increases, the ratio decreases first and then increases; and when the cylinder diameter increases, the ratio increases. Compared to the reference case, the optimized lift/drag ratio increases 178%.

scholarly journals Effect of the location of a gas flow control element in an ejector unit on the flow pattern

2020 ◽  
Vol 2020 (3) ◽  
pp. 54-63
Author(s):  
O.D. Ihnatev ◽  
◽  
H.M. Shevelova ◽  
Keyword(s):  
Numerical Simulation ◽  
Flow Control ◽  
Flow Pattern ◽  
Flow Rate ◽  
Gas Flow ◽  
Energy Carrier ◽  
Control Element ◽  
Gas Flows ◽  
Control Parameters ◽  
Processing Technologies

This article is devoted to a numerical simulation of the flow in a jet mill ejector equipped with a gas flow control element. This element is a channel wherefrom an additional gas flow enters the accelerating tube of the ejector. The gas flows in the mill ejector are controlled using the energy of additional gas flows, thus increasing the velocity of the main flow at the outlet of the ejector accelerating tube and producing a protective layer around the tube walls to prevent their wear. At the same time, there is no substantiation for the choice of optimal control parameters, a methodology, or scientific methods for gas flow control in the ejector channels. The purpose of this work is to investigate the effect of the location of the gas flow control element on gas-dynamic ejector performance and the flow pattern in the ejector channels. A numerical study was carried out using the Ansys Fluent software package and the SST k-? turbulence model. In the course of the study, the pressure of the additional gas flow and the distance from the accelerating tube inlet to the energy carrier supply channel were varied. The angle of the additional gas flow was 20 ?. The numerical simulation gave flow patterns in the ejector as a function of the location of the gas flow control element. Streamlines of the additional gas flow were constructed. The article presents the average flow velocity at the accelerating tube outlet and the energy carrier flow rate as a function of the pressure of the additional flow of the energy carrier and the location of the gas flow control element and the maximum values of the average outlet velocity for given pressure ranges. The article substantiates the choice of the gas flow control parameters that maximize the velocity of the mixed flow at the accelerating tube outlet at a minimum gas flow rate. The results may be used in improving material processing technologies.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

Comparison of an Artificial Neural Network and a Multiple Linear Regression in Predicting the Heat of Combustion of Diesel Fuel Based on Hydrocarbon Groups

2020 ◽  
Vol 71 (6) ◽  
pp. 66-74
Author(s):  
Younis M. Younis ◽  
Salman H. Abbas ◽  
Farqad T. Najim ◽  
Firas Hashim Kamar ◽  
Gheorghe Nechifor
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Diesel Fuel ◽  
Back Propagation ◽  
Second Step ◽  
Heating Value ◽  
Network Training ◽  
Marquardt Algorithm ◽  
Levenberg Marquardt ◽  
Propagation Network

A comparison between artificial neural network (ANN) and multiple linear regression (MLR) models was employed to predict the heat of combustion, and the gross and net heat values, of a diesel fuel engine, based on the chemical composition of the diesel fuel. One hundred and fifty samples of Iraqi diesel provided data from chromatographic analysis. Eight parameters were applied as inputs in order to predict the gross and net heat combustion of the diesel fuel. A trial-and-error method was used to determine the shape of the individual ANN. The results showed that the prediction accuracy of the ANN model was greater than that of the MLR model in predicting the gross heat value. The best neural network for predicting the gross heating value was a back-propagation network (8-8-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.98502 for the test data. In the same way, the best neural network for predicting the net heating value was a back-propagation network (8-5-1), using the Levenberg�Marquardt algorithm for the second step of network training. R = 0.95112 for the test data.

An Anatomy of a Hybrid Color Descriptor with a Neural Network Model to Enhance the Retrieval Accuracy of an Image Retrieval System

2019 ◽  
Vol 13 ◽  
Author(s):  
Shikha Bhardwaj ◽  
Gitanjali Pandove ◽  
Pawan Kumar Dahiya
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Image Retrieval ◽  
Hybrid System ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Retrieval Accuracy ◽  
Color Descriptor ◽  
Benchmark Datasets ◽  
Color Moment

Background: In order to retrieve a particular image from vast repository of images, an efficient system is required and such an eminent system is well-known by the name Content-based image retrieval (CBIR) system. Color is indeed an important attribute of an image and the proposed system consist of a hybrid color descriptor which is used for color feature extraction. Deep learning, has gained a prominent importance in the current era. So, the performance of this fusion based color descriptor is also analyzed in the presence of Deep learning classifiers. Method: This paper describes a comparative experimental analysis on various color descriptors and the best two are chosen to form an efficient color based hybrid system denoted as combined color moment-color autocorrelogram (Co-CMCAC). Then, to increase the retrieval accuracy of the hybrid system, a Cascade forward back propagation neural network (CFBPNN) is used. The classification accuracy obtained by using CFBPNN is also compared to Patternnet neural network. Results: The results of the hybrid color descriptor depict that the proposed system has superior results of the order of 95.4%, 88.2%, 84.4% and 96.05% on Corel-1K, Corel-5K, Corel-10K and Oxford flower benchmark datasets respectively as compared to many state-of-the-art related techniques. Conclusion: This paper depict an experimental and analytical analysis on different color feature descriptors namely, Color moment (CM), Color auto-correlogram (CAC), Color histogram (CH), Color coherence vector (CCV) and Dominant color descriptor (DCD). The proposed hybrid color descriptor (Co-CMCAC) is utilized for the withdrawal of color features with Cascade forward back propagation neural network (CFBPNN) is used as a classifier on four benchmark datasets namely Corel-1K, Corel-5K and Corel-10K and Oxford flower.

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