Using an Artificial Neural Network to Predict Flame Spread across Electrical Wires

Abstract There is currently a global-scale transition from fossil fuel energy technologies towards increasing use of electrically driven energy technologies, especially transportation and heat, fueled by renewable energy sources, which is making fire safety in electrically powered systems increasingly important. The work presented here provides a coherent understanding of flame spread parametric trends and associated fire safety issues in electrical systems for structural, transportation, and space applications. This understanding was obtained through use of an artificial neural network (ANN) that was trained to predict the flame spread rate along “laboratory” wires of different sizes and compositions (copper, nichrome, iron, and stainless-steel tube cores and HDPE, LDPE, and ETFE insulation sheaths) and exposed to different ambient conditions (varying flows, pressure, oxygen concentration, orientation, and gravitational strength). For these predictions, a comprehensive data base of 1200 data points was created by incorporating flame spread rate results from both in-house experiments (400 data points) as well external experiments from other sources (800 data points). The predictions from the ANN showed that it is possible to merge together various data sets, including results from horizontal, inclined, vertical, and microgravity experiments, and obtain unified predictive results. While these initial results are very encouraging with an overall average error rate of 14%, they also show that future improvements to the ANN could still be made to increase prediction accuracy.

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A Simple Heat Transfer Correlation for Three Inlet Configurations Using Artificial Neural Network in the Complex Transition Flow Regime

Heat Transfer: Volume 1 ◽

10.1115/ht2003-47109 ◽

2003 ◽

Author(s):

A. J. Ghajar ◽

L. M. Tam ◽

S. C. Tam

Keyword(s):

Neural Network ◽

Heat Transfer ◽

Artificial Neural Network ◽

Least Squares Method ◽

Transition Flow ◽

Transfer Coefficients ◽

Transition Flow Regime ◽

Bell Mouth ◽

Data Points ◽

Artificial Neural

Local forced and mixed heat transfer coefficients were measured by Ghajar and Tam (1994) along a stainless steel horizontal circular tube fitted with reentrant, square-edged, and bell-mouth inlets under uniform wall heat flux condition. For the experiments the Reynolds, Prandtl, and Grashof numbers varied from about 280 to 49000, 4 to 158, and 1000 to 2.5×105, respectively. The heat transfer transition regions were established by observing the change in the heat transfer behavior. The data in the transition region were correlated by using the traditional least squares method. The correlation predicted the transitional data with an average absolute deviation of about 8%. However, 30% of the data were predicted with 10 to 20% deviation. The reason is due to the abrupt change in the heat transfer characteristic and its intermittent behavior. Since the value of heat transfer coefficient has a direct impact on the size of the heat exchanger, a more accurate correlation has been developed using the artificial neural network (ANN). A total of 1290 data points (441 for reentrant, 416 for square-edged, and 433 for bell mouth) were used. The accuracy of the new correlation is excellent with the majority of the data points predicted with less than 10% deviation.

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Application of Artificial Neural Network to Building Compartment Design for Fire Safety

Intelligent Data Engineering and Automated Learning – IDEAL 2006 - Lecture Notes in Computer Science ◽

10.1007/11875581_32 ◽

2006 ◽

pp. 265-274 ◽

Cited By ~ 2

Author(s):

Eric Wai Ming Lee ◽

Po Chi Lau ◽

Kitty Kit Yan Yuen

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fire Safety ◽

Artificial Neural

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Estimation of Axle Torque for an Agricultural Tractor Using an Artificial Neural Network

Sensors ◽

10.3390/s21061989 ◽

2021 ◽

Vol 21 (6) ◽

pp. 1989

Author(s):

Wan-Soo Kim ◽

Dae-Hyun Lee ◽

Yong-Joo Kim ◽

Yeon-Soo Kim ◽

Seong-Un Park

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Physical Properties ◽

Low Cost ◽

Model Performance ◽

Soil Physical Properties ◽

Simple Method ◽

Slip Ratio ◽

Data Points ◽

Artificial Neural

The objective of this study was to develop a model to estimate the axle torque (AT) of a tractor using an artificial neural network (ANN) based on a relatively low-cost sensor. ANN has proven to be useful in the case of nonlinear analysis, and it can be applied to consider nonlinear variables such as soil characteristics, unlike studies that only consider tractor major parameters, thus model performance and its implementation can be extended to a wider range. In this study, ANN-based models were compared with multiple linear regression (MLR)-based models for performance verification. The main input data were tractor engine parameters, major tractor parameters, and soil physical properties. Data of soil physical properties (i.e., soil moisture content and cone index) and major tractor parameters (i.e., engine torque, engine speed, specific fuel consumption, travel speed, tillage depth, and slip ratio) were collected during a tractor field experiment in four Korean paddy fields. The collected soil physical properties and major tractor parameter data were used to estimate the AT of the tractor by the MLR- and ANN-based models: 250 data points were used for developing and training the model were used, the 50 remaining data points were used to test the model estimation. The AT estimated with the developed MLR- and ANN-based models showed agreement with actual measured AT, with the R2 value ranging from 0.825 to 0.851 and from 0.857 to 0.904, respectively. These results suggest that the developed models are reliable in estimating tractor AT, while the ANN-based model showed better performance than the MLR-based model. This study can provide useful results as a simple method using ANNs based on relatively inexpensive sensors that can replace the existing complex tractor AT measurement method is emphasized.

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Application of Artificial Neural Network to Fire Safety Engineering

Handbook on Decision Making - Intelligent Systems Reference Library ◽

10.1007/978-3-642-13639-9_15 ◽

2010 ◽

pp. 369-395 ◽

Cited By ~ 3

Author(s):

Eric Wai Ming Lee

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fire Safety ◽

Safety Engineering ◽

Fire Safety Engineering ◽

Artificial Neural

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Modeling of a planar SOFC performances using artificial neural network

Indonesian Journal of Electrical Engineering and Computer Science ◽

10.11591/ijeecs.v15.i3.pp1645-1652 ◽

2019 ◽

Vol 15 (3) ◽

pp. 1645

Author(s):

N. A. Zambri ◽

Norhafiz Salim ◽

A. Mohamed ◽

Ili Najaa Aimi Mohd Nordin

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fuel Cell ◽

Multilayer Perceptron ◽

Electrical Power ◽

Neural Net ◽

Neural Network Approach ◽

Energy Technologies ◽

Artificial Neural ◽

Work Model

The Planar Solid Oxide Fuel Cell (PSOFC) is one of the renewable energy technologies that is important as the main source for distributed generation and can play a significant role in the conventional electrical power generation. PSOFC stack modeling is performed in order to provide a platform for the optimal design of fuel cell systems. It is explained by the structure and operating principle of the PSOFC for the modeling purposes. PSOFC model can be developed using Artificial Neural Network approach. The data required to train the neural net-work model is generated by simulating the existing PSOFC model in the MATLAB/ Simulink software. The Radial Basis Function (RBF) and Multilayer Perceptron (MLP) neural networks are the most useful techniques in many applications and will be applied in developing the PSOFC model. A detailed analysis is presented on the best ANN network that gives the greatest results on the performances of the PSOFC. The simulation results show that Multilayer Perceptron (MLP) gives the best outcomes of the PSOFC performance based on the smallest errors and good regression analysis.

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MARINE FIRE-DANGEROUS SITUATIONS FACTORS’ VALUES FORECASTING BASED ON ARTIFICIAL NEURAL NETWORK

Issues of radio electronics ◽

10.21778/2218-5453-2020-7-8-34-40 ◽

2020 ◽

pp. 34-40

Author(s):

A. E. Romanov

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fire Safety ◽

Learning Algorithm ◽

Air Density ◽

Algorithmic Support ◽

Information Management Systems ◽

Artificial Neural ◽

Perceptron Learning ◽

Perceptron Learning Algorithm

The article describes the procedure of marine fire-dangerous situations factors’ values forecasting based on artificial neural network. These factors are temperature, optical air density, aerosol concentration. Given procedure is flexible and can be expanded for other factors of fire-safety state of monitored object. Artificial neural network with architecture of three-layer perceptron is used for forecasting. The article gives a common scheme for realization of fire-dangerous situations factors’ values forecasting, substantiates the choice of used artificial neural network’s architecture, gives perceptron learning algorithm. As a result of given procedure execution factors’ values forecasting is implemented for prevention of fire-dangerous situation and the adoption of early actions. In case of integration of the developed procedure inside ship information management systems’ algorithmic support is capable of dramatically raise effectiveness of decisions made while providing fire safety on ships.

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Estimating the Heat Capacity of Non-Newtonian Ionanofluid Systems Using ANN, ANFIS, and SGB Tree Algorithms

Applied Sciences ◽

10.3390/app10186432 ◽

2020 ◽

Vol 10 (18) ◽

pp. 6432 ◽

Cited By ~ 3

Author(s):

Reza Daneshfar ◽

Amin Bemani ◽

Masoud Hadipoor ◽

Mohsen Sharifpur ◽

Hafiz Muhammad Ali ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Capacity ◽

Mean Squared Error ◽

Gradient Boosting ◽

Acentric Factor ◽

Inference System ◽

Stochastic Gradient Boosting ◽

Data Points ◽

Artificial Neural

This work investigated the capability of multilayer perceptron artificial neural network (MLP–ANN), stochastic gradient boosting (SGB) tree, radial basis function artificial neural network (RBF–ANN), and adaptive neuro-fuzzy inference system (ANFIS) models to determine the heat capacity (Cp) of ionanofluids in terms of the nanoparticle concentration (x) and the critical temperature (Tc), operational temperature (T), acentric factor (ω), and molecular weight (Mw) of pure ionic liquids (ILs). To this end, a comprehensive database of literature reviews was searched. The results of the SGB model were more satisfactory than the other models. Furthermore, an analysis was done to determine the outlying bad data points. It showed that most of the experimental data points were located in a reliable zone for the development of the model. The mean squared error and R2 were 0.00249 and 0.987, 0.0132 and 0.9434, 0.0320 and 0.8754, and 0.0201 and 0.9204 for the SGB, MLP–ANN, ANFIS, and RBF–ANN, respectively. According to this study, the ability of SGB for estimating the Cp of ionanofluids was shown to be greater than other models. By eliminating the need for conducting costly and time-consuming experiments, the SGB strategy showed its superiority compared with experimental measurements. Furthermore, the SGB displayed great generalizability because of the stochastic element. Therefore, it can be highly applicable to unseen conditions. Furthermore, it can help chemical engineers and chemists by providing a model with low parameters that yields satisfactory results for estimating the Cp of ionanofluids. Additionally, the sensitivity analysis showed that Cp is directly related to T, Mw, and Tc, and has an inverse relation with ω and x. Mw and Tc had the highest impact and ω had the lowest impact on Cp.

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Generation of Synthetic Sonic Slowness Logs from Real-Time Drilling Sensors Using Artificial Neural Network

Journal of Energy Resources Technology ◽

10.1115/1.4052412 ◽

2021 ◽

pp. 1-13

Author(s):

Abdulazeez Abdulraheem

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Real Time ◽

Shear Wave ◽

Wave Velocity ◽

Shear Wave Velocity ◽

High Accuracy ◽

Drilling Data ◽

Data Points ◽

Artificial Neural

Abstract The acoustic data in terms of compressional and shear wave velocity provide important petrophysical information about the rock. The sonic data is a significant input that is commonly used for deriving geomechanical parameters. Understanding the geomechanical properties of reservoir rock is essential during the drilling, development, production, and stimulation of an oil or gas reservoir. Among them, Young's modulus and Poisson's ratio are the most important elastic parameters. These properties are usually estimated from bulk density, compressional and shear wave velocity log data. Sonic data acquisition is usually achieved through dipole sonic imager log or laboratory testing on core samples which is costly and time-consuming. Acquiring sonic data from wireline logs is not feasible approach all the time; as the wireline log, specially shear-wave log, may not be recorded for every well. However, drilling data is available in a real-time for every well using real-time drilling sensors. The main objective of this paper is to predict sonic slowness logs in real-time based on the drilling data using artificial neural network (ANN). The data used in this study were recorded during the drilling of 12 ¼” hole sections from two wells. Many formations of different lithology were penetrated while drilling these sections of over 3000 ft vertical interval. The drilling and sonic datasets were recorded and preprocessed before using them for the ANN model. 2900 data points from the first well were used for building and testing the model. The input parameters included weight on bit (WOB), torque (T), standpipe pressure (SPP), pipe speed (PS), rate of penetration (ROP), and mud flow rate (Q). Another dataset of 2000 data points from the second well that was drilled in the same field was used to validate the model. The predictions were compared with sonic logs that were obtained after the drilling operation and the results appear to be highly promising for future applications. The sonic slowness ANN models showed a high accuracy for the model building (training and testing). Validation of these models was carried out using an unseen dataset. The results using the validation dataset for the compressional slowness model yielded a coefficient of determination (R2) of 0.983 and average absolute percentage error (AAPE) of less than 1.25%. For the shear slowness model, R2 was higher than 0.994 and AAPE less than 1.175%. The study offers empirical correlations that can be utilized to estimate the sonic slowness logs by engineers without the need to employ ANN software. The new shear slowness correlation was compared with other widely used correlations and the results showed high accuracy.

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Application of artificial neural network (ANN-MLP) for the prediction of fouling resistance in heat exchanger to MgO-water and CuO-water nanofluids

Water Science & Technology ◽

10.2166/wst.2021.253 ◽

2021 ◽

Author(s):

Ahmed Benyekhlef ◽

Brahim Mohammedi ◽

Djamel Hassani ◽

Salah Hanini

Keyword(s):

Neural Network ◽

Experimental Data ◽

Artificial Neural Network ◽

Heat Exchanger ◽

Coefficient Of Determination ◽

Percentage Error ◽

Inlet Temperature ◽

Fouling Resistance ◽

Data Points ◽

Artificial Neural

Abstract In this work an artificial neural network model was developed with the aim of predicting fouling resistance for heat exchanger, the network was designed and trained by means of 375 experimental data points that were selected from the literature. This data points contains 6 inputs, including time, volumetric concentration, heat flux, mass flow rate, inlet temperature, thermal conductivity and fouling resistance as an output. The experimental data are used for training, testing and validation the ANN using multiple layer perceptron (MLP). The comparison of statistical criteria of different networks shows that the optimal structure for predicting the fouling resistance of the nanofluid is the MLP network with 20 hidden neurons, which has been trained with Levenberg–Marquardt (LM) algorithm. The accuracy of the model was assessed based on three known statistical metrics including mean square error (MSE), mean absolute percentage error (MAPE) and coefficient of determination (R2). The obtained model was found with the performance of {MSE = 6.5377 × 10−4, MAPE = 2.40% and R2 = 0.99756} for the training stage, {MSE = 3.9629 × 10−4, MAPE = 1.8922% and R2 = 0.99835} for the test stage and {MSE = 5.8303 × 10−4, MAPE = 2.57% and R2 = 0.99812} for the validation stage. In order to control the fouling procedure, and after conducting a sensitivity analysis, it found that all input variables have strong effect on the estimation of the fouling resistance.

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