scholarly journals Numerical simulation of electrical transport characteristics of single layer organic devices

2009 ◽  
Vol 58 (5) ◽  
pp. 3474
Author(s):  
Hu Yue ◽  
Rao Hai-Bo
Keyword(s):  
Numerical Simulation ◽  
Electrical Transport ◽  
Single Layer ◽  
Organic Devices

Electrical transport characteristics of single-layer organic devices from theory and experiment

2005 ◽  
Vol 98 (6) ◽  
pp. 063709 ◽  
Author(s):  
S. J. Martin ◽  
Alison B. Walker ◽  
A. J. Campbell ◽  
D. D. C. Bradley
Keyword(s):  
Electrical Transport ◽  
Single Layer ◽  
Organic Devices ◽  
Theory And Experiment

NUMERICAL SIMULATION AND EXPERIMENTAL STUDY OF A HARDWARE PULSE NEURAL NETWORK WITH MEMRISTOR SYNAPSES

2021 ◽  
Vol 7 (2) ◽  
pp. 223-235
Author(s):  
Alexander N. BUSYGIN ◽  
Andrey N. BOBYLEV ◽  
Alexey A. GUBIN ◽  
Alexander D. PISAREV ◽  
Sergey Yu. UDOVICHENKO
Keyword(s):  
Neural Network ◽  
Numerical Simulation ◽  
Experimental Study ◽  
Single Layer ◽  
Electrical Circuit ◽  
Learning Curves ◽  
Spiking Neural Network ◽  
New Associations ◽  
Strong Artificial Intelligence ◽  
Comparison Of The Results

This article presents the results of a numerical simulation and an experimental study of the electrical circuit of a hardware spiking perceptron based on a memristor-diode crossbar. That has required developing and manufacturing a measuring bench, the electrical circuit of which consists of a hardware perceptron circuit and an input peripheral electrical circuit to implement the activation functions of the neurons and ensure the operation of the memory matrix in a spiking mode. The authors have performed a study of the operation of the hardware spiking neural network with memristor synapses in the form of a memory matrix in the mode of a single-layer perceptron synapses. The perceptron can be considered as the first layer of a biomorphic neural network that performs primary processing of incoming information in a biomorphic neuroprocessor. The obtained experimental and simulation learning curves show the expected increase in the proportion of correct classifications with an increase in the number of training epochs. The authors demonstrate generating a new association during retraining caused by the presence of new input information. Comparison of the results of modeling and an experiment on training a small neural network with a small crossbar will allow creating adequate models of hardware neural networks with a large memristor-diode crossbar. The arrival of new unknown information at the input of the hardware spiking neural network can be related with the generation of new associations in the biomorphic neuroprocessor. With further improvement of the neural network, this information will be comprehended and, therefore, will allow the transition from weak to strong artificial intelligence.

scholarly journals Electrical Transport Properties of Single-Layer WS2

ACS Nano ◽  
2014 ◽  
Vol 8 (8) ◽  
pp. 8174-8181 ◽  
Author(s):  
Dmitry Ovchinnikov ◽  
Adrien Allain ◽  
Ying-Sheng Huang ◽  
Dumitru Dumcenco ◽  
Andras Kis
Keyword(s):  
Transport Properties ◽  
Electrical Transport ◽  
Single Layer ◽  
Electrical Transport Properties

scholarly journals Study on Retrofitting of RC Column Using Ferrocement Full and Strip Wrapping

2019 ◽  
Vol 5 (11) ◽  
pp. 2472-2485
Author(s):  
Balamuralikrishnan R. ◽  
M. Al Madhani ◽  
R. Al Madhani
Keyword(s):  
Numerical Simulation ◽  
Cross Section ◽  
Volume Fraction ◽  
Single Layer ◽  
Longitudinal Reinforcement ◽  
Element Analysis ◽  
Rc Columns ◽  
Rc Column ◽  
Concrete Form ◽  
Wrapping Technique

Ferrocement is one of the cement-based composites used for retrofitting and rehabilitation among many applications. Ferrocement is one of the reinforced concrete form with lightweight and thin composite with durability and environmental resistant that strengthen the conventional RC columns to increase its strength and serviceability. This paper examines the performance of the ferrocement wrapping in RC columns experimentally with numerical simulation using ANSYS19. Totally sixteen number of RC column of size 150 mm × 150 mm in cross section and 450 mm in length were cast and tested in laboratory. Twelve are retrofitted columns with respect to volume fraction and wrapping technique. Six columns were retrofitted by full wrapping technique and six columns of strip wrapping technique. The remaining four columns are control columns in virgin condition to compare with the retrofitted columns. Concerning the volume fraction of each specimen, the number of pre-woven mesh layers were single layer, double layer and three layers. C30 concrete grade adopted in all specimens as per ACI Committee 211-1.91 with 4H8 longitudinal reinforcement and H6 of 75mm c/c ties. As the previous researchers examined the ferrocement and proved its efficiency. This study aims to examine the ferrocement in full and strip wrapping technique to compare their efficiency to increase the strength. Finite element analysis using ANSYS19 adopted to compare the experimental data with the numerical simulation. The results are analyzed and observed that the ferrocement has increased the confinement and strength of the RC columns. 

A New Artificial Intelligence Method to Predict Water Flooding Performance in Layered Reservoir

2021 ◽  
Author(s):  
Cunliang Chen ◽  
Xiaodong Han ◽  
Wei Zhang ◽  
Yanhui Zhang ◽  
Fengjun Zhou
Keyword(s):  
Artificial Intelligence ◽  
Numerical Simulation ◽  
Computing Time ◽  
Vertical Direction ◽  
Single Layer ◽  
Oil Production ◽  
Oil Field ◽  
Production Performance ◽  
New Method ◽  
Water Flooding

Abstract The ultimate goal of oilfield development is to maximize the investment benefits. The reservoir performance prediction is directly related to oilfield investment and management. The traditional strategy based on numerical simulation has been widely used with the disadvantages of long run time and much information needed. It is necessary to form a fast and convenient method for the oil production prediction, especially for layered reservoir. A new method is proposed to predict the development indexes of multi-layer reservoirs based on the injection-production data. The new method maintains the objectivity of the data and demonstrates the superiority of the intelligent algorithm. The layered reservoir is regarded as a series of single layer reservoirs on the vertical direction. Considering the starting pressure gradient of non-Newtonian fluid flow and the variation of water content in the oil production index, the injection-production response model for single-layer reservoirs is established. Based on that, a composite model for the multi-layer reservoir is established. For model solution, particle swarm optimization is applied for optimization of the new model. A heterogeneous multi-layer model was established for validation of the new method. The results obtained from the new proposed model are in consistent with the numerical simulation results. It saves a lot of computing time with the incorporation of the artificial intelligence methods. It showed that this technique is valid and effective to predict oil performance in layered reservoir. These examples showed that the application of big data and artificial intelligence method is of great significance, which not only shortens the working time, but also obtains relatively higher accuracy. Based on the objective data of the oil field and the artificial intelligence algorithm, the prediction of oil field development data can be realized. This technique has been used in nearly 100 wells of Bohai oilfields. The results showed in this paper reveals that it is possible to estimate the production performance of the water flooding reservoirs.

Two-layer Model for Electroabsorption and Built-in Potential Measurements on a-Si:H pin Solar Cells

1996 ◽  
Vol 420 ◽  
Author(s):  
Lin Jiang ◽  
E. A. Schiff
Keyword(s):  
Solar Cells ◽  
Amorphous Silicon ◽  
Second Harmonic ◽  
Semiconductor Devices ◽  
Single Layer ◽  
Experimental Results ◽  
Layer Model ◽  
Two Layer Model ◽  
Organic Devices

AbstractModulated Electroabsorption (EA) measurements have been widely used to estimate built-in potentials (Vbi) in semiconductor devices. The method is particularly simple in devices for which the built-in potential is dropped in a single layer of the device. However, experimental results in amorphous silicon and organic devices can involve at least 2 layers. In the present paper we consider the information which can be obtained about 2-layer semiconductor devices from electroabsorption measurements. In particular we describe a 2-layer EA model appropriate to a-Si:H based pin solar cells, for which both the p+ and i layers contribute to the EA signal. We present an analysis of capacitance and second harmonic measurements which yields the EA coefficient for the p+ layer of the device, and we present measurements on a-Si:H pin devices which appear consistent with this analysis. Wavelength dependent EA then yields the built-in potential across the 2-layer device.

Prediction and Control of 32m Z-Propeller Tug Cabins Vibration and Noise

2013 ◽  
Vol 397-400 ◽  
pp. 461-464
Author(s):  
Jun Fang ◽  
Hua Bing Wen ◽  
Zhen Zhen Liu ◽  
Jian Min Dong
Keyword(s):  
Numerical Simulation ◽  
Noise Reduction ◽  
Prediction Error ◽  
Vibration Isolation ◽  
Single Layer ◽  
Isolation System ◽  
Simulation Research ◽  
Cabin Noise ◽  
Vibration Prediction ◽  
Prediction And Control

Based on statistical energy analysis method, the 32m z-propeller tug model was built by VA One software. The numerical simulation research of vibration and noise of 32m z-propeller tug cabin was carried out by VA One. In order to prove the numerical simulation validity, the simulation data was compared with that of the experiments, which shows that the vibration prediction error is lower than 15% and the noise prediction error is about 5 dB(A). Whats more, schemes of cabin noise reduction and the power plant vibration isolation were designed to make the vibration and noise levels meet the IMO and GB5980-2000 standards requirements. The sound absorption material was pasted on the bulkhead of cabins, and the average noise reduction level achieved to 7 dB(A). Then, the single-layer vibration isolation system was designed for the diesel engines, the average noise reduction level achieved to 5 dB(A). The simulations have some guiding values on the control of tug cabins vibration and noise.

scholarly journals Computer Simulation of Magnetic Skyrmions

Proceedings ◽  
2019 ◽  
Vol 46 (1) ◽  
pp. 6
Author(s):  
Vitalii Kapitan ◽  
Egor Vasiliev ◽  
Alexander Perzhu
Keyword(s):  
Magnetic Field ◽  
Numerical Simulation ◽  
Computer Simulation ◽  
External Magnetic Field ◽  
Single Layer ◽  
Metropolis Algorithm ◽  
Square Lattice ◽  
Temperature Behaviour ◽  
Magnetic Skyrmions ◽  
Moriya Interaction

In this paper, we present the results of a numerical simulation of thermodynamics for the array of Classical Heisenberg spins placed on a 2D square lattice, which effectively represents the behaviour of a single layer. Using the Metropolis algorithm, we show the temperature behaviour of the system with a competing Heisenberg and Dzyaloshinskii–Moriya interaction (DMI) in contrast with the classical Heisenberg system. We show the process of nucleation of the skyrmion depending on the value of the external magnetic field. We proposed the controlling method for the movement of skyrmions.

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