scholarly journals Development of thermal conductivity measuring device using single operational amplifier and its modeling by artificial neural network (ANN)

2017 ◽  
Vol 8 (2) ◽  
pp. 83-88
Author(s):  
SHIVMURTI SRIVASTAV
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Operational Amplifier ◽  
Measuring Device ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

Prediction of the Density & Thermal Conductivity of Light Bricks on the Effect of Aluminium Elements Using Artificial Neural Network (ANN)

2019 ◽  
Vol 964 ◽  
pp. 270-279
Author(s):  
Zulkifli ◽  
Gede Panji
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Raw Materials ◽  
Simulation Method ◽  
Acoustic Properties ◽  
Main Element ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Element Variation

Indonesia with abundant limestone raw materials, lightweight brick is the most important component in building construction, so it needs a light brick product that qualifies in thermal, mechanical and acoustic properties. In this paper raised the lightweight brick domains that qualify on the properties of thermal conductivity as building wall components.The advantage of low light density brick (500-650 kg/m3), more economical, suitable for high rise building can reduce the weight of 30-40% in compared to conventional brick (clay brick). To obtain AAC type lightweight brick product that qualifies for low thermal and density properties to the effect of Aluminum (Al) additive element variation using artificial neural network (ANN). The composition of the main elements of lightweight brick O (29-45 % wt), Si (25-35% wt) and Ca (20-40 % wt). Mixing ratio of the main element of light brick (Ca, O and Si) with Aluminum additive element (Al), is done by simulation method of artificial neural network (ANN), Al additive element as a porosity regulator is formed. The simulation of thermal conductivity to the influence of main element variation: Ca (22-32 % wt), Si (12-33 % wt). Simulation of thermal conductivity to effect of additive Al variation (1-7 % wt). Simulation of thermal conductivity to density variation (500-1200 kg/m3). The simulated results of four AAC brick samples showed the thermal conductivity (0.145-0.192 W/m.K) to the influence of qualified Aluminum additives (2.10-6.75 % wt). Additive Al the higher the lower density value (higher porosity) additive Al smaller than 2.10 % wt does not meet the requirements in the simulation.Thermal conductivity of AAC light brick sample (0.184 W/m.K) the influence of the main elements that qualify Ca (20.32-30.35 % wt) and Si (26.57 % wt). Simulation of artificial neural network (ANN) of light brick shows that maximum allowable Si content of 26.57 % wt, Ca content is in the range 20.32-30.35 % wt, and the minimum content of aluminum in brick is light at 2.10 % wt. ANN tests performed to predict the thermal conductivity of light brick samples obtained results of the average AAC light brick thermal conductivity of 0.151 W/m.K. The best performance with Artificial Neural Network (ANN) characteristics has a validation MSE of 0.002252.

scholarly journals Using artificial neural network to predict dry density of soil from thermal conductivity

2017 ◽  
Vol 64 (3) ◽  
pp. 169-180 ◽  
Author(s):  
Oluseun Adetola Sanuade ◽  
Rasheed Babatunde Adesina ◽  
Joel Olayide Amosun ◽  
Akindeji Opeyemi Fajana ◽  
Olayiwola Grace Olaseeni
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Dry Density ◽  
Grid Pattern ◽  
Southwestern Nigeria ◽  
Ogun State ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Thermal Conductivities

Abstract Artificial neural network (ANN) was used to predict the dry density of soil from its thermal conductivity. The study area is a farmland located in Abeokuta, Ogun State, Southwestern Nigeria. Thirty points were sampled in a grid pattern, and the thermal conductivities were measured using KD-2 Pro thermal analyser. Samples were collected from 20 sample points to determine the dry density in the laboratory. MATLAB was used to perform the ANN analysis in order to predict the dry density of soil. The ANN was able to predict dry density with a root-mean-square error (RMSE) of 0.50 and a correlation coefficient (R2) of 0.80. The validation of our model between the actual and predicted dry densities shows R2 to be 0.99. This fit shows that the model can be applied to predict the dry density of soil in study areas where the thermal conductivities are known.

Application of Artificial Neural Network (ANN) for the prediction of thermal conductivity of oxide–water nanofluids

Nano Energy ◽  
2012 ◽  
Vol 1 (2) ◽  
pp. 290-296 ◽  
Author(s):  
Giovanni A. Longo ◽  
Claudio Zilio ◽  
Elena Ceseracciu ◽  
Monica Reggiani
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

scholarly journals Prediction of thermal conductivity of aqueous solution at high pressures by using artificial neural network

2014 ◽  
Vol 20 (4) ◽  
pp. 565-569
Author(s):  
Ali Amooey ◽  
Maryam Ahangarian ◽  
Farshad Rezazadeh
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Aqueous Solution ◽  
Artificial Neural Network ◽  
Regression Analysis ◽  
High Pressures ◽  
Ann Model ◽  
Artificial Neural ◽  
Artificial Neural Network Ann ◽  
Hidden Layer

The objective of this study is to predict thermal conductivity of aqueous solution with artificial neural network (ANN) model with three inputs (pressure, temperature and concentration). A feed forward artificial neural network with three neurons in its hidden layer is recommended to predict thermal conductivity and the accuracy of this method evaluated by regression analysis between the predicted and experimental value and it shows desired result.

Development of an Artificial Neural Network-Based Procedure for the Verification of Traffic Speed Deflectometer Measurements

2021 ◽  
pp. 036119812110057
Author(s):  
Hossam Abohamer ◽  
Mostafa A. Elseifi ◽  
Zia U. A. Zihan ◽  
Zhong Wu ◽  
Nathan Kebede ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
The United States ◽  
Coefficient Of Determination ◽  
Ann Model ◽  
Measuring Device ◽  
Spatial Coverage ◽  
Artificial Neural ◽  
Traffic Speed ◽  
Artificial Neural Network Ann

Since the 1980s, the falling weight deflectometer (FWD) has been the primary deflection-measuring device in the United States to evaluate the structural conditions of in-service pavements. However, the stop and go nature of the FWD limits its application at the network level. In the early 2000s, the traffic speed deflectometer (TSD) was introduced as an alternate deflection-measuring device for network-level applications. TSD collects deflection measurements while traveling at traffic speed, which provides improved spatial coverage and no traffic disturbance. The verification of TSD measurements is of great interest as many agencies move toward widespread implementation. This study aims at developing a reliable and straightforward procedure for the verification of TSD measurements using limited FWD measured deflection measurements. The verification procedure employs a trained artificial neural network (ANN) model to shift TSD deflections to their corresponding FWD deflections. The ANN model was trained and verified based on FWD and TSD measurements from two deflection-testing programs. The developed model accurately predicted FWD measurements with a coefficient of determination (R2) of 0.994. The suitability of the proposed verification procedure was evaluated using statistical and engineering-based measures and showed acceptable accuracy. Results also validated that the proposed method could be used to verify TSD measurements before its use for conducting deflection measurements at the network level.

scholarly journals Performance evaluation of natural esters and dielectric correlation assessment using artificial neural network (ANN)

2020 ◽  
Vol 10 (05) ◽  
pp. 2050025 ◽  
Author(s):  
Raymon Antony Raj ◽  
Ravi Samikannu ◽  
Abid Yahya ◽  
Modisa Mosalaosi
Keyword(s):  
Neural Network ◽  
Thermal Conductivity ◽  
Artificial Neural Network ◽  
Repetition Rate ◽  
Unsaturated Fatty Acids ◽  
Mineral Oil ◽  
Transformer Oil ◽  
International Electrotechnical Commission ◽  
Artificial Neural ◽  
Artificial Neural Network Ann

The performance of correlation between the dielectric parameters of Baobab Oil (BAO) and Mongongo Oil (MGO) is evaluated using Artificial Neural Network (ANN). The BAO and MGO naturally own high Unsaturated Fatty Acids (UFAs) and are highly biodegradable. The temperature studies and dielectric studies are carried out and found that the Natural Esters (NEs) show a reliable performance over mineral oil-based Transformer Oil (TO). Further the endurance test, Partial Discharge Inception Voltage (PDIV) repetition rate and drop after 30 days, dielectric measurements are done as per the standards of IEC (International Electrotechnical Commission) and ASTM (American Society for Testing and Materials). The NEs show stable performance under PDIV and show minimum repetition rate when compared to the TO. The C[Formula: see text]H[Formula: see text] or Kerosene (KER) and NEs mixture prove that the NE-based transformer fluids show lesser tendency to hydro peroxidation. The C[Formula: see text]H[Formula: see text] acts as a thinning agent and reduces the ageing rate of the NEs, and this leads to slower rate of water saturation. This in turn increases the thermal conductivity of the oil and nearly a 30-days thermal ageing of the oil samples at 90[Formula: see text]C shows better strength of liquid insulation. The performance of association between the dielectric properties like breakdown voltage and water content, dissipation factor and thermal conductivity prove that the NEs show consistent performance and is a better substitute for the mineral oil-based TO.

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