Research on time sequence prediction of the flow field structure of supersonic cascade channels in wide range based on artificial neural network

2022 ◽  
Vol 34 (1) ◽  
pp. 016106
Author(s):  
Yunfei Li ◽  
Ziao Wang ◽  
Weiyu Jiang ◽  
Zongqi Xie ◽  
Chen Kong ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Flow Field ◽  
Field Structure ◽  
Time Sequence ◽  
Sequence Prediction ◽  
Wide Range ◽  
Artificial Neural

scholarly journals Predictive modelling of soils’ hydraulic conductivity using artificial neural network and multiple linear regression

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Charles Gbenga Williams ◽  
Oluwapelumi O. Ojuri
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Hydraulic Conductivity ◽  
Linear Regression ◽  
Multiple Linear Regression ◽  
Coarse Grained ◽  
Soil Types ◽  
Wide Range ◽  
Artificial Neural ◽  
Input Variables

AbstractAs a result of heterogeneity nature of soils and variation in its hydraulic conductivity over several orders of magnitude for various soil types from fine-grained to coarse-grained soils, predictive methods to estimate hydraulic conductivity of soils from properties considered more easily obtainable have now been given an appropriate consideration. This study evaluates the performance of artificial neural network (ANN) being one of the popular computational intelligence techniques in predicting hydraulic conductivity of wide range of soil types and compared with the traditional multiple linear regression (MLR). ANN and MLR models were developed using six input variables. Results revealed that only three input variables were statistically significant in MLR model development. Performance evaluations of the developed models using determination coefficient and mean square error show that the prediction capability of ANN is far better than MLR. In addition, comparative study with available existing models shows that the developed ANN and MLR in this study performed relatively better.

Development of a Hybrid Artificial Neural Network and Genetic Algorithm Model for Regime Identification of Slurry Transport in Pipelines

2009 ◽  
Vol 4 (1) ◽  
Author(s):  
Sandip K Lahiri ◽  
Kartik Chandra Ghanta
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Artificial Neural Network ◽  
Operating Conditions ◽  
Misclassification Error ◽  
Wide Range ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Pipeline Design ◽  
Hybrid Artificial Neural Network

Four distinct regimes were found existent (namely sliding bed, saltation, heterogeneous suspension and homogeneous suspension) in slurry flow in pipeline depending upon the average velocity of flow. In the literature, few numbers of correlations has been proposed for identification of these regimes in slurry pipelines. Regime identification is important for slurry pipeline design as they are the prerequisite to apply different pressure drop correlation in different regime. However, available correlations fail to predict the regime over a wide range of conditions. Based on a databank of around 800 measurements collected from the open literature, a method has been proposed to identify the regime using artificial neural network (ANN) modeling. The method incorporates hybrid artificial neural network and genetic algorithm technique (ANN-GA) for efficient tuning of ANN meta parameters. Statistical analysis showed that the proposed method has an average misclassification error of 0.03%. A comparison with selected correlations in the literature showed that the developed ANN-GA method noticeably improved prediction of regime over a wide range of operating conditions, physical properties, and pipe diameters.

Artificial Neural Network and the Taguchi Method Application for Robust Wheatstone Bridge Design

1994 ◽  
Author(s):  
Jung-eui Hong ◽  
Cihan H. Dagli ◽  
Kenneth M. Ragsdell
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Wheatstone Bridge ◽  
Parameter Design ◽  
System Response ◽  
Measurement Circuit ◽  
Wide Range ◽  
Reduce Measurement Error ◽  
On Line ◽  
Artificial Neural

Abstract The primary function of the Wheatstone bridge is to measure an unknown resistance. The elements of this well-known measurement circuit will take on different values depending upon the range and accuracy required for a particular application. The Taguchi approach to parameter design is used to select values for the measurement circuit elements so as to reduce measurement error. Next we introduce the use of an artificial neural network to extrapolate limited experimental results to predict system response over a wide range of applications. This approach can be employed for on-line quality control of the manufacture of such device.

Burst Pressure Prediction of Cylindrical Vessels Using Artificial Neural Network

2020 ◽  
Vol 142 (3) ◽  
Author(s):  
Abolfazl Zolfaghari ◽  
Moein Izadi
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Pressure Vessel ◽  
Pressure Vessels ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Outer Diameter ◽  
Wide Range ◽  
Artificial Neural ◽  
Predictor Model

Abstract Pressure vessel plays an important role in wide range of applications to store gas or liquid substances. In order to design a pressure vessel safely, one of the main factors which has to be considered is selection of proper burst pressure perdition criterion. Due to large range of available materials in manufacturing of the vessels under different working conditions, several criteria to forecast burst pressure of the vessels have been developed and used by designers. Choosing the most proper criterion based on working condition and the material is a vital task to meet design requirements because inappropriate criterion may lead to unsafe vessel or over design. This issue makes not only pressure vessel design more complex but also maintenance planning, especially for designers who do not have enough experience, is a challenging task. Therefore, lack of a burst pressure predictor model, which is able to determine the pressure more accurately for wide range of materials and applications, has been remained unsolved. To evaluate machine learning techniques in prediction of burst pressure of pressure vessels, in this paper, a new model based on artificial neural network (ANN) has been proposed and developed. Input parameters of the model include internal and outer diameter, thickness, ultimate and yield strength; output is burst pressure. The obtained results showed that the constructed model has a good potential to be used as more applicable model compared to current models in design of pressure vessels.

CFD-based blade shape optimization of MGT-70(3)axial flow compressor

2019 ◽  
Vol 30 (6) ◽  
pp. 3307-3321 ◽  
Author(s):  
Mohammad Reza Pakatchian ◽  
Hossein Saeidi ◽  
Alireza Ziamolki
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Shape Optimization ◽  
Fluid Dynamic ◽  
Axial Compressor ◽  
Axial Flow ◽  
Design Parameters ◽  
Content Type ◽  
Wide Range ◽  
Artificial Neural

Purpose This study aims at enhancing the performance of a 16-stage axial compressor and improving the operating stability. The adopted approaches for upgrading the compressor are artificial neural network, optimization algorithms and computational fluid dynamics. Design/methodology/approach The process starts with developing several data sets for certain 2D sections by means of training several artificial neural networks (ANNs) as surrogate models. Afterward, the trained ANNs are applied to the 3D shape optimization along with parametrization of the blade stacking line. Specifying the significant design parameters, a wide range of geometrical variations are considered by implementation of appropriate number of design variables. The optimized shapes are analyzed by applying computational fluid dynamic to obtain the best geometry. Findings 3D optimal results show improvements, especially in the case of decreasing or elimination of near walls corner separations. In addition, in comparison with the base geometry, numerical optimization shows an increase of 1.15 per cent in total isentropic efficiency in the first four stages, which results in 0.6 per cent improvement for the whole compressor, even while keeping the rest of the stages unchanged. To evaluate the numerical results, experimental data are compared with obtained data from simulation. Based on the results, the highest absolute relative deviation between experimental and numerical static pressure is approximately 7.5 per cent. Originality/value The blades geometry of an axial compressor used in a heavy-duty gas turbine is optimized by applying artificial neural network, and the results are compared with the base geometry numerically and experimentally.

scholarly journals Artificial Neural Network as a Universal Model of Nonlinear Dynamical Systems

2021 ◽  
Vol 17 (1) ◽  
pp. 5-21
Author(s):  
P. V. Kuptsov ◽  
◽  
A. V. Kuptsova ◽  
N. V. Stankevich ◽  
◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Dynamical Systems ◽  
Network Model ◽  
Numerical Solutions ◽  
Nonlinear Dynamical Systems ◽  
Universal Model ◽  
Dynamical Equations ◽  
Wide Range ◽  
Artificial Neural

We suggest a universal map capable of recovering the behavior of a wide range of dynamical systems given by ODEs. The map is built as an artificial neural network whose weights encode a modeled system. We assume that ODEs are known and prepare training datasets using the equations directly without computing numerical time series. Parameter variations are taken into account in the course of training so that the network model captures bifurcation scenarios of the modeled system. The theoretical benefit from this approach is that the universal model admits applying common mathematical methods without needing to develop a unique theory for each particular dynamical equations. From the practical point of view the developed method can be considered as an alternative numerical method for solving dynamical ODEs suitable for running on contemporary neural network specific hardware. We consider the Lorenz system, the Rössler system and also the Hindmarch – Rose model. For these three examples the network model is created and its dynamics is compared with ordinary numerical solutions. A high similarity is observed for visual images of attractors, power spectra, bifurcation diagrams and Lyapunov exponents.

scholarly journals Identification of Variable Importance for Predictions of Mortality From COVID-19 Using AI Models for Ontario, Canada

2021 ◽  
Vol 9 ◽  
Author(s):  
Brett Snider ◽  
Edward A. McBean ◽  
John Yawney ◽  
S. Andrew Gadsden ◽  
Bhumi Patel
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Logistic Regression ◽  
Random Forest ◽  
Census Data ◽  
Age Groups ◽  
Area Under The Curve ◽  
Medical Systems ◽  
Wide Range ◽  
Artificial Neural

The Severe Acute Respiratory Syndrome Coronavirus 2 pandemic has challenged medical systems to the brink of collapse around the globe. In this paper, logistic regression and three other artificial intelligence models (XGBoost, Artificial Neural Network and Random Forest) are described and used to predict mortality risk of individual patients. The database is based on census data for the designated area and co-morbidities obtained using data from the Ontario Health Data Platform. The dataset consisted of more than 280,000 COVID-19 cases in Ontario for a wide-range of age groups; 0–9, 10–19, 20–29, 30–39, 40–49, 50–59, 60–69, 70–79, 80–89, and 90+. Findings resulting from using logistic regression, XGBoost, Artificial Neural Network and Random Forest, all demonstrate excellent discrimination (area under the curve for all models exceeded 0.948 with the best performance being 0.956 for an XGBoost model). Based on SHapley Additive exPlanations values, the importance of 24 variables are identified, and the findings indicated the highest importance variables are, in order of importance, age, date of test, sex, and presence/absence of chronic dementia. The findings from this study allow the identification of out-patients who are likely to deteriorate into severe cases, allowing medical professionals to make decisions on timely treatments. Furthermore, the methodology and results may be extended to other public health regions.

scholarly journals The Effect of Different Cover and Flooring Materials on Climatic Comfort in Landscape Design

2022 ◽  
Vol 9 (sp) ◽  
pp. 2571-2580
Author(s):  
Orhun Soydan ◽  
Ahmet Benliay
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Network Model ◽  
Temperature Difference ◽  
Neural Network Model ◽  
Artificial Neural Network Model ◽  
Ambient Air ◽  
Maximum Temperature ◽  
Wide Range ◽  
Artificial Neural

In this study, it is aimed to understand the effects of structural and vegetative elements that can be used in landscape designs on the temperature factor, which will greatly affect the climatic comfort, by using artificial neural networks. In this context, measurements were carried out in the morning (08:00-09:00), noon (13:00-14:00) and evening (17:00-18:00) of a total of 100 days, 50 days in each of the winter and summer seasons, at 7 randomly selected points in the Akdeniz University Campus. In these measurements, the temperature difference values of 11 cover elements on 7 different floor covering types were measured, and the ambient air temperature, humidity and wind values were also determined. The temperature differences between the areas where the flooring elements are exposed to direct sun and the shadow effect of different plant and cover elements were determined using an infrared laser thermometer. These values were processed with Neural Designer software and possible temperature difference prediction values were created for 57.750 different alternatives with the help of artificial neural network model from 837 sets of data. Evaluation shows that the maximum temperature difference is 15.6°C at noon in the summer months in the red tartan flooring material and Callistemon viminalis cover material. While the artificial neural network model predicts that there will be a high 2-3° C temperature difference for the alternatives, it has made predictions for temperature differences between 0-10°C in winter and 0-16°C in summer months. Although the temperature differences that will occur in the noon hours are distributed over a wide range of values, it seems that the morning and evening forecasts are concentrated between 0-7°C values. Also, it has been determined that the wind and humidity in the environment are more important factors than the ambient temperature in terms of temperature differences.

scholarly journals Usporedna studija predviđanja koeficijenta molekularne difuzije za polarni i nepolarni binarni plin pomoću neuronskih mreža i višestrukih linearnih regresija

2019 ◽  
Vol 68 (11-12) ◽  
pp. 573-582 ◽  
Author(s):  
Naima Melzi ◽  
Hamid Zentou ◽  
Maamar Laidi ◽  
Salah Hanini ◽  
Yamina Ammi ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Molecular Diffusion ◽  
Mean Squared Error ◽  
External Validation ◽  
Multiple Linear Regressions ◽  
Mean Squared Errors ◽  
Wide Range ◽  
Artificial Neural ◽  
Linear Regressions

In the current study, an artificial neural network (ANN) and multiple linear regressions (MLR) have been used to develop predictive models for the estimation of molecular diffusion coefficients of 1252 polar and non-polar binary gases at multiple pressures over a wide range of temperatures and substances. The quality and reliability of each method were estimated in terms of the correlation coefficient (R), mean squared errors (MSE), root mean squared error (RMSE), and in terms of external validation coefficients (Q2ext). The comparison between the artificial neural network (ANN) and the multiple linear regressions (MLR) revealed that the neural network models showed a good predicting ability with lower errors (the roots of the mean squared errors in the total database were 0.1400 for ANN1 and 0.1300 for ANN2), and (root mean squared errors in the total databases were 0.5172 for MLR1 and 0.5000 for MLR2).

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