scholarly journals Metamodel-Based Optimization of the Labyrinth Seal

2017 ◽  
Vol 64 (1) ◽  
pp. 75-91 ◽  
Author(s):  
Sebastian Rulik ◽  
Włodzimierz Wróblewski ◽  
Daniel Frączek
Keyword(s):  
Neural Network ◽  
Evolutionary Algorithm ◽  
Response Surface ◽  
Optimization Algorithm ◽  
Goodness Of Fit ◽  
Computation Time ◽  
Initial Response ◽  
Labyrinth Seal ◽  
Geometrical Parameters ◽  
Marquardt Algorithm

Abstract The presented paper concerns CFD optimization of the straight-through labyrinth seal with a smooth land. The aim of the process was to reduce the leakage flow through a labyrinth seal with two fins. Due to the complexity of the problem and for the sake of the computation time, a decision was made to modify the standard evolutionary optimization algorithm by adding an approach based on a metamodel. Five basic geometrical parameters of the labyrinth seal were taken into account: the angles of the seal’s two fins, and the fin width, height and pitch. Other parameters were constrained, including the clearance over the fins. The CFD calculations were carried out using the ANSYS-CFX commercial code. The in-house optimization algorithm was prepared in the Matlab environment. The presented metamodel was built using a Multi-Layer Perceptron Neural Network which was trained using the Levenberg-Marquardt algorithm. The Neural Network training and validation were carried out based on the data from the CFD analysis performed for different geometrical configurations of the labyrinth seal. The initial response surface was built based on the design of the experiment (DOE). The novelty of the proposed methodology is the steady improvement in the response surface goodness of fit. The accuracy of the response surface is increased by CFD calculations of the labyrinth seal additional geometrical configurations. These configurations are created based on the evolutionary algorithm operators such as selection, crossover and mutation. The created metamodel makes it possible to run a fast optimization process using a previously prepared response surface. The metamodel solution is validated against CFD calculations. It then complements the next generation of the evolutionary algorithm.

scholarly journals A NEURAL NETWORK NOISE PREDICTION MODEL FOR TEHRAN URBAN ROADS

2018 ◽  
Vol 26 (2) ◽  
pp. 88-97 ◽  
Author(s):  
Ali Mansourkhaki ◽  
Mohammadjavad Berangi ◽  
Majid Haghiri ◽  
Mohammadreza Haghani
Keyword(s):  
Neural Network ◽  
Regression Model ◽  
Network Model ◽  
Neural Network Model ◽  
Goodness Of Fit ◽  
Measurement Data ◽  
Reflection Factor ◽  
Road Traffic Noise ◽  
The Neural Network ◽  
Marquardt Algorithm

Over the last decades, the number of motor vehicles has increased dramatically in Iran, where different traffic characteristics and urban structures are notable. In the present study, a multilayer perceptron neural network model trained with the Levenberg-Marquardt algorithm was used for predicting the equivalent sound level (LAeq) originating from traffic. Fifty-one samples were collected from different areas of Tehran. Input parameters consisted of total traffic volume per hour, average speed of vehicles, percentage of each category of vehicles, road gradient, density of buildings around the road section and a new parameter named “Building Reflection Factor”. These data were randomly used with 80, 10 and 10 percentiles respectively for training, validation and testing of the Artificial Neural Network (ANN). Results yielded by the ANN model were compared with field measurement data, a proposed regression model and some classical well-known models. Our study indicated that the prediction error of the neural network model was much less than that of the regression model and other classical models. Moreover, a statistical t-test was applied for evaluating the goodness-of-fit of the proposed model and proved that the neural network model is highly efficient in estimating road traffic noise levels.

scholarly journals Comparison of Artificial Neural Network Model and Response Surface Methodology for Springback Prediction

2021 ◽  
Vol 343 ◽  
pp. 03012
Author(s):  
Iliass El Mrabti ◽  
Abdelhamid Touache ◽  
Abdelhadi El Hakimi ◽  
Abderahim Chamat
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Response Surface Methodology ◽  
Response Surface ◽  
Goodness Of Fit ◽  
Ann Model ◽  
Goodness Of Fit Test ◽  
Artificial Neural ◽  
Input Variables ◽  
Springback Prediction

In sheet metal manufacturing, the ability to predict failures, such as springback, wrinkling and thinning, are of high importance. The objective of this study is to compare the response surface methodology (RSM) and the artificial neural network (ANN) model for predicting springback during the deep drawing process. In this investigation, friction coefficient, punch speed and blank holder force were considered as input variables. Sample data were planned by the complete factorial design and obtained via numerical simulation. To compare the RSM and ANN models, a goodness of-fit test was performed. The results of the two methods are promising and it is found that the ANN results are more accurate than the RSM results.

scholarly journals artificial bee colony optimization algorithm for adaptive power scheduling in an isolated system

2021 ◽  
Vol 21 (2) ◽  
pp. 1168
Author(s):  
Vijo M. Joy ◽  
S. Krishnakumar
Keyword(s):  
Neural Network ◽  
Energy Management ◽  
Optimization Algorithm ◽  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Artificial Bee Colony Optimization ◽  
Bee Colony ◽  
Power Scheduling ◽  
Marquardt Algorithm ◽  
Bee Colony Optimization

<p>The objective of this work is to solve the power scheduling problems for efficient energy management by assigning the optimal values. Artificial neural networks are used widely in the field of energy management and load scheduling. The backpropagation technique is used for the feed-forward neural network training and the levenberg-marquardt algorithm is used to minimize the errors. The slow speed of convergence and getting stuck in local minima are some negatives of backpropagation in complex computation. To overcome these drawbacks an innovative meta-heuristic search algorithm called modified artificial bee colony optimization algorithm is used. A hybrid neural network is introduced in this work. The simulation result shows that the efficiency of the system is improved when hybrid optimization is used. With this method, the system achieves an optimal accuracy of 99.23%.</p>

Wear volume prediction of AISI H13 die steel using response surface methodology and artificial neural network

2020 ◽  
Vol 14 (2) ◽  
pp. 6789-6800
Author(s):  
Vishal Jagota ◽  
Rajesh Kumar Sharma
Keyword(s):  
Neural Network ◽  
Heat Treatment ◽  
Artificial Neural Network ◽  
Response Surface ◽  
Sliding Wear ◽  
Austenitizing Temperature ◽  
Tempering Temperature ◽  
Die Steel ◽  
Artificial Neural ◽  
Tempering Time

Resistance to wear of hot die steel is dependent on its mechanical properties governed by the microstructure. The required properties for given application of hot die steel can be obtained with control the microstructure by heat treatment parameters. In the present paper impact of different heat treatment parameters like austenitizing temperature, tempering time, tempering temperature is studied using response surface methodology (RSM) and artificial neural network (ANN) to predict sliding wear of H13 hot die steel. After heat treating samples at austenitizing temperature of 1020°C, 1040°C and 1060°C; tempering temperature 540°C, 560°C and 580°C; tempering time 1hour, 2hours and 3hours, experimentation on pin-on-disc tribo-tester is done to measure the sliding wear of H13 die steel. Box-Behnken design is used to develop a regression model and analysis of variance technique is used to verify the adequacy of developed model in case of RSM. Whereas, multi-layer feed-forward backpropagation architecture with input layer, single hidden layer and an output layer is used in ANN. It was found that ANN proves to be a better tool to predict sliding wear with more accuracy. Correlation coefficient R2 of the artificial neural network model is 0.986 compared to R2 of 0.957 for RSM. However, impact of input parameter interactions can only be analysed using response surface method. In addition, sensitivity analysis is done to determine the heat treatment parameter exerting most influence on the wear resistance of H13 hot die steel and it showed that tempering time has maximum influence on wear volume, followed by tempering temperature and austenitizing temperature. The prediction models will help to estimate the variation in die lifetime by finding the amount of wear that will occur during use of hot die steel, if the heat treatment parameters are varied to achieve different properties.

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.

Research on Power Load Forecasting Model Based on Hybrid Algorithm Optimizing BP Neural Network

2014 ◽  
Vol 8 (1) ◽  
pp. 723-728 ◽  
Author(s):  
Chenhao Niu ◽  
Xiaomin Xu ◽  
Yan Lu ◽  
Mian Xing
Keyword(s):  
Neural Network ◽  
Optimization Algorithm ◽  
Load Forecasting ◽  
Optimal Solution ◽  
Electric Power System ◽  
Local Optimum ◽  
Good Prediction ◽  
Ant Colony Optimization Algorithm ◽  
The Neural Network ◽  
Power Load

Short time load forecasting is essential for daily planning and operation of electric power system. It is the important basis for economic dispatching, scheduling and safe operation. Neural network, which has strong nonlinear fitting capability, is widely used in the load forecasting and obtains good prediction effect in nonlinear chaotic time series forecasting. However, the neural network is easy to fall in local optimum, unable to find the global optimal solution. This paper will integrate the traditional optimization algorithm and propose the hybrid intelligent optimization algorithm based on particle swarm optimization algorithm and ant colony optimization algorithm (ACO-PSO) to improve the generalization of the neural network. In the empirical analysis, we select electricity consumption in a certain area for validation. Compared with the traditional BP neutral network and statistical methods, the experimental results demonstrate that the performance of the improved model with more precise results and stronger generalization ability is much better than the traditional methods.

scholarly journals Creep-Based Reliability Evaluation of Turbine Blade-Tip Clearance with Novel Neural Network Regression

Materials ◽  
2019 ◽  
Vol 12 (21) ◽  
pp. 3552 ◽  
Author(s):  
Chun-Yi Zhang ◽  
Jing-Shan Wei ◽  
Ze Wang ◽  
Zhe-Shan Yuan ◽  
Cheng-Wei Fei ◽  
...  
Keyword(s):  
Neural Network ◽  
High Pressure ◽  
Reliability Analysis ◽  
Response Surface ◽  
Response Surface Method ◽  
Tip Clearance ◽  
Strong Nonlinearity ◽  
Surface Method ◽  
Blade Tip ◽  
Blade Tip Clearance

To reveal the effect of high-temperature creep on the blade-tip radial running clearance of aeroengine high-pressure turbines, a distributed collaborative generalized regression extremum neural network is proposed by absorbing the heuristic thoughts of distributed collaborative response surface method and the generalized extremum neural network, in order to improve the reliability analysis of blade-tip clearance with creep behavior in terms of modeling precision and simulation efficiency. In this method, the generalized extremum neural network was used to handle the transients by simplifying the response process as one extremum and to address the strong nonlinearity by means of its nonlinear mapping ability. The distributed collaborative response surface method was applied to handle multi-object multi-discipline analysis, by decomposing one “big” model with hyperparameters and high nonlinearity into a series of “small” sub-models with few parameters and low nonlinearity. Based on the developed method, the blade-tip clearance reliability analysis of an aeroengine high-pressure turbine was performed subject to the creep behaviors of structural materials, by considering the randomness of influencing parameters such as gas temperature, rotational speed, material parameters, convective heat transfer coefficient, and so forth. It was found that the reliability degree of the clearance is 0.9909 when the allowable value is 2.2 mm, and the creep deformation of the clearance presents a normal distribution with a mean of 1.9829 mm and a standard deviation of 0.07539 mm. Based on a comparison of the methods, it is demonstrated that the proposed method requires a computing time of 1.201 s and has a computational accuracy of 99.929% over 104 simulations, which are improvements of 70.5% and 1.23%, respectively, relative to the distributed collaborative response surface method. Meanwhile, the high efficiency and high precision of the presented approach become more obvious with the increasing simulations. The efforts of this study provide a promising approach to improve the dynamic reliability analysis of complex structures.

scholarly journals An image denoising method based on BP neural network optimized by improved whale optimization algorithm

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Chunzhi Wang ◽  
Min Li ◽  
Ruoxi Wang ◽  
Han Yu ◽  
Shuping Wang
Keyword(s):  
Neural Network ◽  
Image Denoising ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Weight Coefficient ◽  
Whale Optimization Algorithm ◽  
Denoising Method ◽  
Training Time ◽  
Whale Optimization ◽  
City Construction

AbstractAs an important part of smart city construction, traffic image denoising has been studied widely. Image denoising technique can enhance the performance of segmentation and recognition model and improve the accuracy of segmentation and recognition results. However, due to the different types of noise and the degree of noise pollution, the traditional image denoising methods generally have some problems, such as blurred edges and details, loss of image information. This paper presents an image denoising method based on BP neural network optimized by improved whale optimization algorithm. Firstly, the nonlinear convergence factor and adaptive weight coefficient are introduced into the algorithm to improve the optimization ability and convergence characteristics of the standard whale optimization algorithm. Then, the improved whale optimization algorithm is used to optimize the initial weight and threshold value of BP neural network to overcome the dependence in the construction process, and shorten the training time of the neural network. Finally, the optimized BP neural network is applied to benchmark image denoising and traffic image denoising. The experimental results show that compared with the traditional denoising methods such as Median filtering, Neighborhood average filtering and Wiener filtering, the proposed method has better performance in peak signal-to-noise ratio.

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