Study on the influence factors of short-time thermal response test based on artificial neural network

Ground source heat pumps (GSHPs) have been widely applied worldwide in recent years because of their high efficiency and environmental friendliness. An accurate estimation of the thermal conductivity of rock and soil layers is important in the design of GSHP systems. The distributed thermal response test (DTRT) method incorporates the standard test with a pair of fiber optic-distributed temperature sensors in the U-tube to accurately calculate the layered thermal conductivity of the rock/soil. In this work, in situ layered thermal conductivity was initially obtained by DTRT for four boreholes in the study region. A series of laboratory tests was also conducted on the rock samples obtained from drilling. Then, an artificial neural network (ANN) model was developed to predict the layered thermal conductivity on the basis of the DTRT results. The primary modeling factors were water content, density, and porosity. The results showed that the ANN models can predict the layered thermal conductivity with an absolute error of less than 0.1 W/(m·K). Finally, the trained ANN models were used to predict the layered thermal conductivity for another study region, in which only the effective thermal conductivity was measured with the thermal response test (TRT). To verify the accuracy of the prediction, the product of pipe depth and layered thermal conductivity was suggested to represent heat transfer capacity. The results showed that the discrepancies between the TRT and ANN models were 5.43% and 6.37% for two boreholes, respectively. The results prove that the proposed method can be used to determine layered thermal conductivity.

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Low-Cost Hardware Implementation of Human Blood Identification Device Using Feed-Forward Artificial Neural Network on FPGA

10.31227/osf.io/dp63x ◽

2018 ◽

Author(s):

Rizki Eka Putri ◽

Denny Darlis

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Low Cost ◽

Image Resolution ◽

Blood Type ◽

Feed Forward ◽

Testing Facility ◽

System Input ◽

Artificial Neural ◽

Short Time

This article was under review for ICELTICS 2018 -- In the medical world there is still service dissatisfaction caused by lack of blood type testing facility. If the number of tested blood arise, a lot of problems will occur so that electronic devices are needed to determine the blood type accurately and in short time. In this research we implemented an Artificial Neural Network on Xilinx Spartan 3S1000 Field Programable Gate Array using XSA-3S Board to identify the blood type. This research uses blood sample image as system input. VHSIC Hardware Discription Language is the language to describe the algorithm. The algorithm used is feed-forward propagation of backpropagation neural network. There are 3 layers used in design, they are input, hidden1, and output. At hidden1layer has two neurons. In this study the accuracy of detection obtained are 92%, 92%, 92%, 90% and 86% for 32x32, 48x48, 64x64, 80x80, and 96x96 pixel blood image resolution, respectively.

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Temperature Rise Prediction of Oil-Air Lubricated Angular Contact Ball Bearings Using Artificial Neural Network

Recent Patents on Mechanical Engineering ◽

10.2174/2212797612666190530115739 ◽

2019 ◽

Vol 12 (3) ◽

pp. 248-261

Author(s):

Baomin Wang ◽

Xiao Chang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Temperature Rise ◽

High Speed ◽

Ball Bearing ◽

Influence Factors ◽

Ball Bearings ◽

Ann Model ◽

Angular Contact Ball Bearing ◽

Artificial Neural

Background: Angular contact ball bearing is an important component of many high-speed rotating mechanical systems. Oil-air lubrication makes it possible for angular contact ball bearing to operate at high speed. So the lubrication state of angular contact ball bearing directly affects the performance of the mechanical systems. However, as bearing rotation speed increases, the temperature rise is still the dominant limiting factor for improving the performance and service life of angular contact ball bearings. Therefore, it is very necessary to predict the temperature rise of angular contact ball bearings lubricated with oil-air. Objective: The purpose of this study is to provide an overview of temperature calculation of bearing from many studies and patents, and propose a new prediction method for temperature rise of angular contact ball bearing. Methods: Based on the artificial neural network and genetic algorithm, a new prediction methodology for bearings temperature rise was proposed which capitalizes on the notion that the temperature rise of oil-air lubricated angular contact ball bearing is generally coupling. The influence factors of temperature rise in high-speed angular contact ball bearings were analyzed through grey relational analysis, and the key influence factors are determined. Combined with Genetic Algorithm (GA), the Artificial Neural Network (ANN) model based on these key influence factors was built up, two groups of experimental data were used to train and validate the ANN model. Results: Compared with the ANN model, the ANN-GA model has shorter training time, higher accuracy and better stability, the output of ANN-GA model shows a good agreement with the experimental data, above 92% of bearing temperature rise under varying conditions can be predicted using the ANNGA model. Conclusion: A new method was proposed to predict the temperature rise of oil-air lubricated angular contact ball bearings based on the artificial neural network and genetic algorithm. The results show that the prediction model has good accuracy, stability and robustness.

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Identification of Short Duration Voltage Variations Based on Short Time Fourier Transform and Artificial Neural Network

2020 International Electronics Symposium (IES) ◽

10.1109/ies50839.2020.9231815 ◽

2020 ◽

Author(s):

Dimas Okky Anggriawan ◽

Endro Wahjono ◽

Indhana Sudiharto ◽

Aji Akbar Firdaus ◽

Dianing Novita Nurmala Putri ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Fourier Transform ◽

Short Duration ◽

Short Time Fourier Transform ◽

Artificial Neural ◽

Short Time

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Artificial Neural Network Analysis Method of Concrete Performance Based on Virtual Data Samples

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.34-35.462 ◽

2010 ◽

Vol 34-35 ◽

pp. 462-466

Author(s):

Jun Wei Song ◽

Yan Shi

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Bp Neural Network ◽

Influence Factors ◽

Forecast Performance ◽

Virtual Samples ◽

Artificial Neural Network Analysis ◽

Artificial Neural ◽

Predicted Values ◽

The Relationship

The relationship between concrete performance and influence factors is uncertain and nonlinear. Accordingly, present BP neural network and virtual samples are presented to predict concrete performance in this paper. At first neural network and matters which need attention are introduced, And frost resistance forecasting model and impermeability model are built up, which are three-tier BP neural network of 6-13-2,4-9-1.The results show that the predicted values are ideal, and artificial neural network as one of the methods to forecast performance of concrete is appropriate.

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Short-time thermal response test based on a 3-D numerical model

Journal of Physics Conference Series ◽

10.1088/1742-6596/395/1/012056 ◽

2012 ◽

Vol 395 ◽

pp. 012056 ◽

Cited By ~ 1

Author(s):

F Bozzoli ◽

G Pagliarini ◽

S Rainieri ◽

L Schiavi

Keyword(s):

Numerical Model ◽

Thermal Response ◽

Thermal Response Test ◽

Response Test ◽

Short Time

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Study on the Prediction Meth of Automobile Braking Distance Based on Artificial Neural Network

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.734.515 ◽

2015 ◽

Vol 734 ◽

pp. 515-521

Author(s):

Pei Ye ◽

Xiu Mei Zhang ◽

Tao Jiang

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Prediction Model ◽

Influence Factors ◽

The Road ◽

The Neural Network ◽

Artificial Neural Network Method ◽

Braking Distance ◽

Brake Performance ◽

Artificial Neural

The automobile braking distance is one of the important indexes to measure the brake performance, so the study of automobile braking distance is very important. Domestic and foreign scholars research on automobile brake performance and braking distance, and achieved fruitful results, but there are only little research on the braking distance predicting of the car. In the paper, the process of automobile braking, effect of the braking distance, the influence factors and the road adhesion coefficient are studied. In the paper, it also discussed the effective methods to calculate the braking distance. On these basses, the author puts forward the prediction model of automobile braking distance with artificial neural network method. In this model, the author takes the running state and parameters of the car as samples for the input and output. After training, the author gets the curing prediction model based on each layer of network weights and threshold of the neural network.

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Adsorption of dicamba and MCPA onto MIL-53(Al) metal–organic framework: response surface methodology and artificial neural network model studies

RSC Advances ◽

10.1039/d0ra07969c ◽

2020 ◽

Vol 10 (70) ◽

pp. 43213-43224

Author(s):

Hamza Ahmad Isiyaka ◽

Khairulazhar Jumbri ◽

Nonni Soraya Sambudi ◽

Zakariyya Uba Zango ◽

Nor Ain Fathihah Abdullah ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Metal Organic Framework ◽

Adsorption Parameters ◽

Model Studies ◽

Neural Network Prediction ◽

Metal Organic ◽

Network Prediction ◽

Artificial Neural ◽

Short Time

Rapid equilibration within a short time, high adsorption capacity, optimization, multivariate interaction of adsorption parameters and artificial neural network prediction model.

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