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.

scholarly journals Burst Pressure Prediction of API 5L X-Grade Dented Pipelines Using Deep Neural Network

2020 ◽  
Vol 8 (10) ◽  
pp. 766
Author(s):  
Dohan Oh ◽  
Julia Race ◽  
Selda Oterkus ◽  
Bonguk Koo
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Deep Neural Network ◽  
Machine Learning Techniques ◽  
Burst Pressure ◽  
Comparison Results ◽  
Artificial Neural

Mechanical damage is recognized as a problem that reduces the performance of oil and gas pipelines and has been the subject of continuous research. The artificial neural network in the spotlight recently is expected to be another solution to solve the problems relating to the pipelines. The deep neural network, which is on the basis of artificial neural network algorithm and is a method amongst various machine learning methods, is applied in this study. The applicability of machine learning techniques such as deep neural network for the prediction of burst pressure has been investigated for dented API 5L X-grade pipelines. To this end, supervised learning is employed, and the deep neural network model has four layers with three hidden layers, and the neural network uses the fully connected layer. The burst pressure computed by deep neural network model has been compared with the results of finite element analysis based parametric study, and the burst pressure calculated by the experimental results. According to the comparison results, it showed good agreement. Therefore, it is concluded that deep neural networks can be another solution for predicting the burst pressure of API 5L X-grade dented pipelines.

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.

scholarly journals Path Loss Prediction Based on Machine Learning Techniques: Principal Component Analysis, Artificial Neural Network, and Gaussian Process

Sensors ◽  
2020 ◽  
Vol 20 (7) ◽  
pp. 1927 ◽  
Author(s):  
Han-Shin Jo ◽  
Chanshin Park ◽  
Eunhyoung Lee ◽  
Haing Kun Choi ◽  
Jaedon Park
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Artificial Neural Network ◽  
Gaussian Process ◽  
Path Loss ◽  
Principal Component ◽  
Component Analysis ◽  
Machine Learning Techniques ◽  
Shadowing Effect ◽  
Artificial Neural

Although various linear log-distance path loss models have been developed for wireless sensor networks, advanced models are required to more accurately and flexibly represent the path loss for complex environments. This paper proposes a machine learning framework for modeling path loss using a combination of three key techniques: artificial neural network (ANN)-based multi-dimensional regression, Gaussian process-based variance analysis, and principle component analysis (PCA)-aided feature selection. In general, the measured path loss dataset comprises multiple features such as distance, antenna height, etc. First, PCA is adopted to reduce the number of features of the dataset and simplify the learning model accordingly. ANN then learns the path loss structure from the dataset with reduced dimension, and Gaussian process learns the shadowing effect. Path loss data measured in a suburban area in Korea are employed. We observe that the proposed combined path loss and shadowing model is more accurate and flexible compared to the conventional linear path loss plus log-normal shadowing model.

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 Analysing 21cm signal with artificial neural network

2017 ◽  
Vol 12 (S333) ◽  
pp. 39-42
Author(s):  
Hayato Shimabukuro ◽  
Benoit Semelin
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Power Spectrum ◽  
Machine Learning Techniques ◽  
Inversion Method ◽  
Observation Data ◽  
The Neural Network ◽  
Learning Techniques ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

AbstractThe 21cm signal at epoch of reionization (EoR) should be observed within next decade. We expect that cosmic 21cm signal at the EoR provides us both cosmological and astrophysical information. In order to extract fruitful information from observation data, we need to develop inversion method. For such a method, we introduce artificial neural network (ANN) which is one of the machine learning techniques. We apply the ANN to inversion problem to constrain astrophysical parameters from 21cm power spectrum. We train the architecture of the neural network with 70 training datasets and apply it to 54 test datasets with different value of parameters. We find that the quality of the parameter reconstruction depends on the sensitivity of the power spectrum to the different parameter sets at a given redshift and also find that the accuracy of reconstruction is improved by increasing the number of given redshifts. We conclude that the ANN is viable inversion method whose main strength is that they require a sparse extrapolation of the parameter space and thus should be usable with full simulation.

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.

Sign in / Sign up

Export Citation Format

Share Document