A Numerical Model to Study the Effect of Temperature on Electrical Conductivity of Polymer-CNT Nanocomposites

2013 ◽  
Author(s):  
Amirhossein B. Oskouyi ◽  
Uttandraman Sundararaj ◽  
Pierre Mertiny
Keyword(s):  
Electrical Conductivity ◽  
Finite Element ◽  
Monte Carlo ◽  
Electrical Properties ◽  
Monte Carlo Model ◽  
Computational Cost ◽  
Three Dimensional ◽  
Effect Of Temperature ◽  
Tunneling Resistance ◽  
Percolation Network

In this study the effect of the temperature on the electrical conductivity of nanocomposites with carbon nanotube (CNT) fillers was investigated. A three-dimensional continuum Monte Carlo model was developed and employed first to form a CNT percolation network. CNT fillers were randomly generated and dispersed in a cubic representative volume element. Periodic boundary conditions were applied in this model to minimize size effects while decreasing computational cost. CNT fibers that connected electrically to each other through electron hopping were recognized and grouped as clusters. In addition to tunneling resistance, the effect of intrinsic CNT resistivity was considered. A three-dimensional resistor network was subsequently developed to evaluate nanocomposite electrical properties. Modeling employing the finite element method was conducted to evaluate the electrical conductivity of the percolation network. Considering the determining role of tunneling resistance on electrical conductivity of CNT based nanocomposites, as well as results obtained from experimental studies, temperature was expected to play an important role in nanocomposite electrical properties. The effect of temperature on electrical conductivity of CNT nanocomposites was thus investigated through employing the developed Monte Carlo and finite element models. Other aspects, including the electrical behavior of the polymer, tunneling resistivity and the intrinsic resistivity of CNT were considered in this study as well. The comprehensiveness of the developed modeling approach enables an evaluation of results in conjunction with experimental data in future works.

scholarly journals Monte Carlo Model for Piezoresistivity of Hybrid Nanocomposites

2017 ◽  
Vol 140 (1) ◽  
Author(s):  
Audrey Gbaguidi ◽  
Sirish Namilae ◽  
Daewon Kim
Keyword(s):  
Electrical Conductivity ◽  
Monte Carlo ◽  
Electron Tunneling ◽  
Monte Carlo Model ◽  
Hybrid Nanocomposites ◽  
Electrical Percolation ◽  
Systematic Improvement ◽  
Electromechanical Performance ◽  
Parametric Studies ◽  
Percolation Network

Hybrid nanocomposites with multiple fillers like carbon nanotubes (CNT) and graphene nanoplatelets (GNP) are known to exhibit improved electrical and electromechanical performance when compared to monofiller composites. We developed a two-dimensional Monte Carlo percolation network model for hybrid nanocomposite with CNT and GNP fillers and utilized it to study the electrical conductivity and piezoresistivity as a function of nanocomposite microstructural variations. The filler intersections are modeled considering electron tunneling as the mechanism for electrical percolation. Network modification after elastic deformation is utilized to model the nanocomposite piezoresistive behavior. Systematic improvement in electrical conductivity and piezoresistivity was observed in the hybrid nanocomposites, compared to monofiller CNT nanocomposites. Parametric studies have been performed to show the effect of GNP content, size, aspect ratio, and alignment on the percolation threshold, the conductivity, and piezoresistivity of hybrid CNT–GNP polymer composites.

Computational Cost and Accuracy in Calculating Three-Dimensional Radiative Transfer: Results for New Implementations of Monte Carlo and SHDOM

2009 ◽  
Vol 66 (10) ◽  
pp. 3131-3146 ◽  
Author(s):  
Robert Pincus ◽  
K. Franklin Evans
Keyword(s):  
Monte Carlo ◽  
Radiative Transfer ◽  
Variance Reduction ◽  
Parallel Implementation ◽  
Monte Carlo Model ◽  
Computational Cost ◽  
Three Dimensional ◽  
Computation Time ◽  
Flux Divergence ◽  
Pixel Intensity

Abstract This paper examines the tradeoffs between computational cost and accuracy for two new state-of-the-art codes for computing three-dimensional radiative transfer: a community Monte Carlo model and a parallel implementation of the Spherical Harmonics Discrete Ordinate Method (SHDOM). Both codes are described and algorithmic choices are elaborated. Two prototype problems are considered: a domain filled with stratocumulus clouds and another containing scattered shallow cumulus, absorbing aerosols, and molecular scatterers. Calculations are performed for a range of resolutions and the relationships between accuracy and computational cost, measured by memory use and time to solution, are compared. Monte Carlo accuracy depends primarily on the number of trajectories used in the integration. Monte Carlo estimates of intensity are computationally expensive and may be subject to large sampling noise from highly peaked phase functions. This noise can be decreased using a range of variance reduction techniques, but these techniques can compromise the excellent agreement between the true error and estimates obtained from unbiased calculations. SHDOM accuracy is controlled by both spatial and angular resolution; different output fields are sensitive to different aspects of this resolution, so the optimum accuracy parameters depend on which quantities are desired as well as on the characteristics of the problem being solved. The accuracy of SHDOM must be assessed through convergence tests and all results from unconverged solutions may be biased. SHDOM is more efficient (i.e., has lower error for a given computational cost) than Monte Carlo when computing pixel-by-pixel upwelling fluxes in the cumulus scene, whereas Monte Carlo is more efficient in computing flux divergence and downwelling flux in the stratocumulus scene, especially at higher accuracies. The two models are comparable for downwelling flux and flux divergence in cumulus and upwelling flux in stratocumulus. SHDOM is substantially more efficient when computing pixel-by-pixel intensity in multiple directions; the models are comparable when computing domain-average intensities. In some cases memory use, rather than computation time, may limit the resolution of SHDOM calculations.

scholarly journals Prediction of Residual Stresses in a Multipass Pipe Weld by a Novel 3D Finite Element Approach

2018 ◽  
Author(s):  
Hui Huang ◽  
Jian Chen ◽  
Blair Carlson ◽  
Hui-Ping Wang ◽  
Paul Crooker ◽  
...  
Keyword(s):  
Finite Element ◽  
Residual Stresses ◽  
High Performance ◽  
Large Scale ◽  
Graphics Processing Unit ◽  
Computational Cost ◽  
Three Dimensional ◽  
Processing Unit ◽  
Girth Welds ◽  
Welding Processes

Due to enormous computation cost, current residual stress simulation of multipass girth welds are mostly performed using two-dimensional (2D) axisymmetric models. The 2D model can only provide limited estimation on the residual stresses by assuming its axisymmetric distribution. In this study, a highly efficient thermal-mechanical finite element code for three dimensional (3D) model has been developed based on high performance Graphics Processing Unit (GPU) computers. Our code is further accelerated by considering the unique physics associated with welding processes that are characterized by steep temperature gradient and a moving arc heat source. It is capable of modeling large-scale welding problems that cannot be easily handled by the existing commercial simulation tools. To demonstrate the accuracy and efficiency, our code was compared with a commercial software by simulating a 3D multi-pass girth weld model with over 1 million elements. Our code achieved comparable solution accuracy with respect to the commercial one but with over 100 times saving on computational cost. Moreover, the three-dimensional analysis demonstrated more realistic stress distribution that is not axisymmetric in hoop direction.

Monte Carlo modeling of production of three alpha-particles in low energy n +12C interactions

2021 ◽  
Vol 36 (25) ◽  
pp. 2150182
Author(s):  
Khusniddin K. Olimov ◽  
Vladimir V. Lugovoi ◽  
Kosim Olimov ◽  
Maratbek Shodmonov ◽  
Kadyr G. Gulamov ◽  
...  
Keyword(s):  
Experimental Data ◽  
Monte Carlo ◽  
Monte Carlo Model ◽  
Three Dimensional ◽  
Alpha Particles ◽  
Incident Neutron ◽  
Excitation Energies ◽  
Collision Event ◽  
Direct Decay ◽  
Calculation Results

To describe [Formula: see text] interactions with production of three [Formula: see text]-particles at incident neutron kinetic energy of 14 MeV in a nuclear (photo) emulsion, a Monte Carlo model is proposed for four channels of decay of an excited carbon-12 nucleus into three [Formula: see text]-particles. The Monte Carlo calculation results describe well the experimental data on the distribution of the angle between the three-dimensional momenta of all pairs of [Formula: see text]-particles in a collision event, on the distribution of the angle between the projections of the momentum vectors of all pairs of [Formula: see text]-particles in collision event on each of the coordinate planes, on the distribution of the sum of the kinetic energies of all pairs of [Formula: see text]-particles in a collision event, and the distribution of projections of the momenta of [Formula: see text]-particles on the coordinate planes. The best agreement of the Monte Carlo model results with the experimental data is achieved if the direct decay [Formula: see text] and decay through the formation of an intermediate beryllium nucleus [Formula: see text] are generated with equal probabilities, while the excitation energies of 3.04 MeV, 1.04 MeV, and 0.1 MeV for the beryllium nucleus are generated with relative weights of 75%, 15%, and 10%, respectively.

Rapid manufacturing of copper-graphene composites using a novel rapid tooling technique

2020 ◽  
Vol 26 (4) ◽  
pp. 765-776 ◽  
Author(s):  
Gurminder Singh ◽  
Pulak Mohan Pandey
Keyword(s):  
Electrical Conductivity ◽  
Electrical Properties ◽  
Wear Rate ◽  
Coefficient Of Friction ◽  
Rate Coefficient ◽  
Three Dimensional ◽  
Rapid Tooling ◽  
Content Type ◽  
Three Dimensional Printing ◽  
Dimensional Printing

Purpose The purpose of this study is to study the mechanical, tribological and electrical properties of the copper-graphene (Cu-Gn) composites fabricated by a novel rapid tooling technique consist of three-dimensional printing and ultrasonic-assisted pressureless sintering (UAPS). Design/methodology/approach Four different Cu-Gn compositions with 0.25, 0.5, 1 and 1.5 per cent of graphene were fabricated using an amalgamation of three-dimensional printing and UAPS. The polymer 3d printed parts were used to prepare mould cavity and later the UAPS process was used to sinter Cu-Gn powder to acquire free-form shape. The density, hardness, wear rate, coefficient of friction and electrical conductivity were evaluated for the different compositions of graphene and compared with the pure copper. Besides, the comparison was performed with the conventional method. Findings Cu-Gn composites revealed excellent wear properties due to higher hardness, and the lubrication provided by the graphene. The electrical conductivity of the fabricated Cu-Gn composites started increasing initially but decreased afterwards with increasing the content of graphene. The UAPS fabricated composites outperformed the conventional method manufactured samples with better properties such as density, hardness, wear rate, coefficient of friction and electrical conductivity due to homogeneous mixing of metal particles and graphene. Originality/value The fabrication of Cu-Gn composite freeform shapes was found to be difficult using conventional methods. The novel technique using a combination of polymer three-dimensional printing and UAPS as rapid tooling was introduced for the fabrication of freeform shapes of Cu-Gn composites and mechanical, tribological and electrical properties were studied. The method can be used to fabricate optimized complex Cu-Gn structures with improved wear and electrical applications.

scholarly journals A 3D particle Monte Carlo approach to studying nucleation

2017 ◽  
Author(s):  
Christoph Köhn ◽  
Martin Bødker Enghoff ◽  
Henrik Svensmark
Keyword(s):  
Monte Carlo ◽  
Sulphuric Acid ◽  
Nucleation Rate ◽  
Particle Density ◽  
Monte Carlo Model ◽  
Three Dimensional ◽  
Initial Particle ◽  
Time Step ◽  
Size Dependent ◽  
Discrete Nature

Abstract. The nucleation of sulphuric acid molecules plays a key role in the formation of aerosols. We here present a three dimensional particle Monte Carlo model to study the growth of sulphuric acid clusters as well as its dependence on the ambient temperature and the initial particle density.We initiate a swarm of sulphuric acid molecules with a size of 0.15 nm with densities between 107 and 108 cm−3 at temperatures of 200 and 300 K. After every time step, we update the position and velocity of particles as a function of size-dependent diffusion coefficients. If two particles encounter, we merge them and add their volumes and masses. Inversely, we check after every time step whether a polymer evaporates liberating a molecule.We present the spatial distribution as well as the size distribution calculated from individual clusters. We also calculate the nucleation rate of clusters with a radius of 0.85 nm as a function of time, initial particle density and temperature. For 200 K, the nucleation rate increases as a function of time; for 300 K we observe an interplay between clustering and evaporation and thus the oscillation of the nucleation rate around the mean nucleation rate. The nucleation rates obtained from the presented model agree well with experimentally obtained values which serves as a benchmark of our code. In contrast to previous nucleation models, we here present for the first time a code capable of tracing individual particles and thus of capturing the physics related to the discrete nature of particles.

scholarly journals Self-learning kinetic Monte Carlo model for arbitrary surface orientations

2013 ◽  
Vol 1559 ◽  
Author(s):  
Andreas Latz ◽  
Lothar Brendel ◽  
Dietrich E. Wolf
Keyword(s):  
Monte Carlo ◽  
Specific Surface ◽  
Kinetic Monte Carlo ◽  
Monte Carlo Model ◽  
Three Dimensional ◽  
The Self ◽  
Transition Rates ◽  
Local Orientation ◽  
Realistic Potential ◽  
Self Learning

ABSTRACTWhile the self-learning kinetic Monte Carlo (SLKMC) method enables the calculation of transition rates from a realistic potential, implementations of it were usually limited to one specific surface orientation. An example is the fcc (111) surface in Latz et al. 2012, J. Phys.: Condens. Matter 24, 485005. This work provides an extension by means of detecting the local orientation, and thus allows for the accurate simulation of arbitrarily shaped surfaces. We applied the model to the diffusion of Ag monolayer islands and voids on a Ag(111) and Ag(001) surface, as well as the relaxation of a three-dimensional spherical particle.

scholarly journals Estimation of the physical properties of nanocomposites by finite-element discretization and Monte Carlo simulation

2013 ◽  
Vol 371 (1993) ◽  
pp. 20120494 ◽  
Author(s):  
P. Spanos ◽  
P. Elsbernd ◽  
B. Ward ◽  
T. Koenck
Keyword(s):  
Finite Element ◽  
Monte Carlo ◽  
Electrical Properties ◽  
Volume Fraction ◽  
Effective Conductivity ◽  
Numerical Models ◽  
Strain Curve ◽  
Identification Algorithm ◽  
Element Discretization

This paper reviews and enhances numerical models for determining thermal, elastic and electrical properties of carbon nanotube-reinforced polymer composites. For the determination of the effective stress–strain curve and thermal conductivity of the composite material, finite-element analysis (FEA), in conjunction with the embedded fibre method (EFM), is used. Variable nanotube geometry, alignment and waviness are taken into account. First, a random morphology of a user-defined volume fraction of nanotubes is generated, and their properties are incorporated into the polymer matrix using the EFM. Next, incremental and iterative FEA approaches are used for the determination of the nonlinear properties of the nanocomposite. For the determination of the electrical properties, a spanning network identification algorithm is used. First, a realistic nanotube morphology is generated from input parameters defined by the user. The spanning network algorithm then determines the connectivity between nanotubes in a representative volume element. Then, interconnected nanotube networks are converted to equivalent resistor circuits. Finally, Kirchhoff's current law is used in conjunction with FEA to solve for the voltages and currents in the system and thus calculate the effective electrical conductivity of the nanocomposite. The model accounts for electrical transport mechanisms such as electron hopping and simultaneously calculates percolation probability, identifies the backbone and determines the effective conductivity. Monte Carlo analysis of 500 random microstructures is performed to capture the stochastic nature of the fibre generation and to derive statistically reliable results. The models are validated by comparison with various experimental datasets reported in the recent literature.

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