Search results for: simulink simulation model

Authors: M. Cardenas-Garcia, P. P. Gonzalez-Perez

Abstract:

Intercellular communication is a necessary condition for cellular functions and it allows a group of cells to survive as a population. Throughout this interaction, the cells work in a coordinated and collaborative way which facilitates their survival. In the case of cancerous cells, these take advantage of intercellular communication to preserve their malignancy, since through these physical unions they can send signs of malignancy. The Wnt/β-catenin signaling pathway plays an important role in the formation of intercellular communications, being also involved in a large number of cellular processes such as proliferation, differentiation, adhesion, cell survival, and cell death. The modeling and simulation of cellular signaling systems have found valuable support in a wide range of modeling approaches, which cover a wide spectrum ranging from mathematical models; e.g., ordinary differential equations, statistical methods, and numerical methods– to computational models; e.g., process algebra for modeling behavior and variation in molecular systems. Based on these models, different simulation tools have been developed from mathematical ones to computational ones. Regarding cellular and molecular processes in cancer, its study has also found a valuable support in different simulation tools that, covering a spectrum as mentioned above, have allowed the in silico experimentation of this phenomenon at the cellular and molecular level. In this work, we simulate and explore the complex interaction patterns of intercellular communication in cancer cells using the Cellulat bioinformatics tool, a computational simulation tool developed by us and motivated by two key elements: 1) a biochemically inspired model of self-organizing coordination in tuple spaces, and 2) the Gillespie’s algorithm, a stochastic simulation algorithm typically used to mimic systems of chemical/biochemical reactions in an efficient and accurate way. The main idea behind the Cellulat simulation tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation in intra and intercellular signaling networks. Unlike most of the cell signaling simulation tools, such as E-Cell, BetaWB and Cell Illustrator which provides abstractions to model only intracellular behavior, Cellulat is appropriate for modeling both intracellular signaling and intercellular communication, providing the abstractions required to model –and as a result, simulate– the interaction mechanisms that involve two or more cells, that is essential in the scenario discussed in this work. During the development of this work we made evident the application of our computational simulation tool (Cellulat) for the modeling and simulation of intercellular communication between normal and cancerous cells, and in this way, propose key molecules that may prevent the arrival of malignant signals to the cells that surround the tumor cells. In this manner, we could identify the significant role that has the Wnt/β-catenin signaling pathway in cellular communication, and therefore, in the dissemination of cancer cells. We verified, using in silico experiments, how the inhibition of this signaling pathway prevents that the cells that surround a cancerous cell are transformed.

Keywords: cancer cells, in silico approach, intercellular communication, key molecules, modeling and simulation

Procedia PDF Downloads 251

Authors: Jae Won Shin

Abstract:

We develop an evaluated nuclear data based photonuclear reaction model of GEANT4 for a more accurate simulation of photon-induced neutron production. The evaluated photonuclear data libraries from the ENDF/B-VII.1 are taken as input. Incident photon energies up to 140 MeV which is the threshold energy for the pion production are considered. For checking the validity of the use of the data-based model, we calculate the photoneutron production cross-sections and yields and compared them with experimental data. The results obtained from the developed model are found to be in good agreement with the experimental data for (γ,xn) reactions.

Keywords: ENDF/B-VII.1, GEANT4, photoneutron, photonuclear reaction

Procedia PDF Downloads 275

Authors: S. M. Mahbobur Rahman

Abstract:

Whole building energy simulation models are widely used for predicting future energy consumption, performance diagnosis and optimum control.  Black box building energy modeling approach has been heavily studied in the past decade. The thermal response of a building can also be modeled using a network of interconnected resistors (R) and capacitors (C) at each node called R-C network. In this study, a model building, Case 600, as described in the “Standard Method of Test for the Evaluation of Building Energy Analysis Computer Program”, ASHRAE standard 140, is studied along with a 3R2C thermal network model and the ASHRAE clear sky solar radiation model. Although building an energy model involves two important parts of building component i.e., the envelope and internal mass, the effect of building internal mass is not considered in this study. All the characteristic parameters of the building envelope are evaluated as on Case 600. Finally, monthly building energy consumption from the thermal network model is compared with a simple-box energy model within reasonable accuracy. From the results, 0.6-9.4% variation of monthly energy consumption is observed because of the south-facing windows.

Keywords: ASHRAE case study, clear sky solar radiation model, energy modeling, thermal network model

Procedia PDF Downloads 146

Authors: Boukelkoul Lahcen

Abstract:

The cumulative costs for O&M may represent as much as 65%-90% of the turbine's investment cost. Nowadays the cost effectiveness concept becomes a decision-making and technology evaluation metric. The cost of energy metric accounts for the effect replacement cost and unscheduled maintenance cost parameters. One key of the proposed approach is the idea of maintaining the WTs which can be captured via use of a finite state Markov chain. Such a model can be embedded within a probabilistic operation and maintenance simulation reflecting the action to be done. In this paper, an approach of estimating the cost of O&M is presented. The finite state Markov model is used for decision problems with number of determined periods (life cycle) to predict the cost according to various options of maintenance.

Keywords: cost, finite state, Markov model, operation and maintenance

Procedia PDF Downloads 533

Authors: S. Yunus, A. Suratman, N. Mohamad Nor, M. Othman

Abstract:

This paper presents the measurement and simulation results by Finite Element Method (FEM) for earth resistance (R_DC) for interconnected vertical ground rod configurations. The soil resistivity was measured using the Wenner four-pin Method, and R_DC was measured using the Fall of Potential (FOP) method, as outlined in the standard. Genetic Algorithm (GA) is employed to interpret the soil resistivity to that of a 2-layer soil model. The same soil resistivity data that were obtained by Wenner four-pin method were used in FEM for simulation. This paper compares the results of R_DC obtained by FEM simulation with the real measurement at field site. A good agreement was seen for R_DC obtained by measurements and FEM. This shows that FEM is a reliable software to be used for design of earthing systems. It is also found that the parallel rod system has a better performance compared to a similar setup using a grid layout.

Keywords: earthing system, earth electrodes, finite element method, genetic algorithm, earth resistances

Procedia PDF Downloads 110

Authors: Hassan Athari, Abdurrahim Bolukbasi, Dogan Ciloglu

Abstract:

This study represented the respiratory alveoli system, and determined the trajectory of inhaled particles more accurately using the modified three-dimensional model with deformable walls of alveoli. The study also considered the tissue tension in the model to demonstrate the effect of lung. Tissue tensions are transferred by the lung parenchyma and produce the pressure gradient. This load expands the alveoli and establishes a sub-ambient (vacuum) pressure within the lungs. Thus, at the alveolar level, the flow field and movement of alveoli wall lead to an integrated effect. In this research, we assume that the three-dimensional alveolus has a visco-elastic tissue (walls). For accurate investigation of pulmonary tissue mechanical properties on particle transport and alveolar flow field, the actual relevance between tissue movement and airflow is solved by two-way FSI (Fluid Structure Interaction) simulation technique in the alveolus. Therefore, the essence of real simulation of pulmonary breathing mechanics can be achieved by developing a coupled FSI computational model. We, therefore conduct a series of FSI simulations over a range of tissue models and breathing rates. As a result, the fluid flows and streamlines have changed during present flexible model against the rigid models and also the two-way coupling particle trajectories have changed against the one-way particle coupling.

Keywords: FSI, two-way particle coupling, alveoli, CDF

Procedia PDF Downloads 260

Authors: Badreddine Chemali, Boualem Tiliouine

Abstract:

This article presents the main results of a numerical investigation on the uncertainty of dynamic response of structures with statistically correlated random damping Gamma distributed. A computational method based on a Linear Statistical Model (LSM) is implemented to predict second order statistics for the response of a typical industrial building structure. The significance of random damping with correlated parameters and its implications on the sensitivity of structural peak response in the neighborhood of a resonant frequency are discussed in light of considerable ranges of damping uncertainties and correlation coefficients. The results are compared to those generated using Monte Carlo simulation techniques. The numerical results obtained show the importance of damping uncertainty and statistical correlation of damping coefficients when obtaining accurate probabilistic estimates of dynamic response of structures. Furthermore, the effectiveness of the LSM model to efficiently predict uncertainty propagation for structural dynamic problems with correlated damping parameters is demonstrated.

Keywords: correlated random damping, linear statistical model, Monte Carlo simulation, uncertainty of dynamic response

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Authors: Hakiki Kheira, Belhamiti Omar

Abstract:

In this paper, we propose a new compartmental fractional order model for the simulation of epidemic obesity dynamics. Using the Legendre wavelet method combined with the decoupling and quasi-linearization technique, we demonstrate the validity and applicability of our model. We also present some fractional differential illustrative examples to demonstrate the applicability and efficiency of the method. The fractional derivative is described in the Caputo sense.

Keywords: Caputo derivative, epidemiology, Legendre wavelet method, obesity

Procedia PDF Downloads 422

Authors: S. F. Wong, S. S. Dol

Abstract:

Automotive designers have been trying to use dimples to reduce drag in vehicles. In this work, a car model has been applied with dimple surface with a parameter called dimple ratio DR, the ratio between the depths of the half dimple over the print diameter of the dimple, has been introduced and numerically simulated via k-ε turbulence model to study the aerodynamics performance with the increasing depth of the dimples The Ahmed body car model with 25 degree slant angle is simulated with the DR of 0.05, 0.2, 0.3 0.4 and 0.5 at Reynolds number of 176387 based on the frontal area of the car model. The geometry of dimple changes the kinematics and dynamics of flow. Complex interaction between the turbulent fluctuating flow and the mean flow escalates the turbulence quantities. The maximum level of turbulent kinetic energy occurs at DR = 0.4. It can be concluded that the dimples have generated extra turbulence energy at the surface and as a result, the application of dimples manages to reduce the drag coefficient of the car model compared to the model with smooth surface.

Keywords: aerodynamics, boundary layer, dimple, drag, kinetic energy, turbulence

Procedia PDF Downloads 315

Authors: Abdellahi Cheikh

Abstract:

With the emergence of quantum computers with very powerful capabilities, the security of the exchange of shared keys between two interlocutors poses a big problem in terms of the rapid development of technologies such as computing power and computing speed. Therefore, the Diffie-Hellmann (DH) algorithm is more vulnerable than ever. No mechanism guarantees the security of the key exchange, so if an intermediary manages to intercept it, it is easy to intercept. In this regard, several studies have been conducted to improve the security of key exchange between two interlocutors, which has led to interesting results. The modification made on our model Diffie-Hellman-RSA-AES (DRA), which encrypts the information exchanged between two users using the three-encryption algorithms DH, RSA and AES, by using stenographic photos to hide the contents of the p, g and ClesAES values that are sent in an unencrypted state at the level of DRA model to calculate each user's public key. This work includes a comparative study between the DRA model and all existing solutions, as well as the modification made to this model, with an emphasis on the aspect of reliability in terms of security. This study presents a simulation to demonstrate the effectiveness of the modification made to the DRA model. The obtained results show that our model has a security advantage over the existing solution, so we made these changes to reinforce the security of the DRA model.

Keywords: Diffie-Hellmann, DRA, RSA, advanced encryption standard

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Authors: Jiangxia Han, Liang Xue, Keda Chen

Abstract:

Despite the significant progress over the last decades in reservoir simulation using numerical discretization, meshing is complex. Moreover, the high degree of freedom of the space-time flow field makes the solution process very time-consuming. Therefore, we present Physics-Informed Convolutional Neural Networks(PICNN) as a hybrid scientific theory and data method for reservoir modeling. Besides labeled data, the model is driven by the scientific theories of the underlying problem, such as governing equations, boundary conditions, and initial conditions. PICNN integrates governing equations and boundary conditions into the network architecture in the form of a customized convolution kernel. The loss function is composed of data matching, initial conditions, and other measurable prior knowledge. By customizing the convolution kernel and minimizing the loss function, the neural network parameters not only fit the data but also honor the governing equation. The PICNN provides a methodology to model and history-match flow and transport problems in porous media. Numerical results demonstrate that the proposed PICNN can provide an accurate physical solution from a limited dataset. We show how this method can be applied in the context of a forward simulation for continuous problems. Furthermore, several complex scenarios are tested, including the existence of data noise, different work schedules, and different good patterns.

Keywords: convolutional neural networks, deep learning, flow and transport in porous media, physics-informed neural networks, reservoir simulation

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Authors: Giovanni Masala, Stefania Marica

Abstract:

Forecasting electricity load plays a crucial role regards decision making and planning for economical purposes. Besides, in the light of the recent privatization and deregulation of the power industry, the forecasting of future electricity load turned out to be a very challenging problem. Empirical data about electricity load highlights a clear seasonal behavior (higher load during the winter season), which is partly due to climatic effects. We also emphasize the presence of load periodicity at a weekly basis (electricity load is usually lower on weekends or holidays) and at daily basis (electricity load is clearly influenced by the hour). Finally, a long-term trend may depend on the general economic situation (for example, industrial production affects electricity load). All these features must be captured by the model. The purpose of this paper is then to build an hourly electricity load model. The deterministic component of the model requires non-linear regression and Fourier series while we will investigate the stochastic component through econometrical tools. The calibration of the parameters’ model will be performed by using data coming from the Italian market in a 6 year period (2007- 2012). Then, we will perform a Monte Carlo simulation in order to compare the simulated data respect to the real data (both in-sample and out-of-sample inspection). The reliability of the model will be deduced thanks to standard tests which highlight a good fitting of the simulated values.

Keywords: ARMA-GARCH process, electricity load, fitting tests, Fourier series, Monte Carlo simulation, non-linear regression

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Authors: Zabihullah Mahdi, Khwaja Naweed Seddiqi

Abstract:

Afghanistan is located in a tectonically complex and dynamic area, surrounded by rocks that originated on the mother continent of Gondwanaland. The northern Afghanistan basin, which runs along the country's northern border, has the potential for petroleum generation and accumulation. The Amu Darya basin has the largest petroleum potential in the region. Sedimentation occurred in the Amu Darya basin from the Jurassic to the Eocene epochs. Kashkari oil field is located in northern Afghanistan's Amu Darya basin. The field structure consists of a narrow northeast-southwest (NE-SW) anticline with two structural highs, the northwest limb being mild and the southeast limb being steep. The first oil production well in the Kashkari oil field was drilled in 1976, and a total of ten wells were drilled in the area between 1976 and 1979. The amount of original oil in place (OOIP) in the Kashkari oil field, based on the results of surveys and calculations conducted by research institutions, is estimated to be around 140 MMbbls. The objective of this study is to increase recoverable oil reserves in the Kashkari oil field through the implementation of low-salinity water flooding (LSWF) enhanced oil recovery (EOR) technique. The LSWF involved conducting a core flooding laboratory test consisting of four sequential steps with varying salinities. The test commenced with the use of formation water (FW) as the initial salinity, which was subsequently reduced to a salinity level of 0.1%. Afterwards, the numerical simulation model of core scale oil recovery by LSWF was designed by Computer Modelling Group’s General Equation Modeler (CMG-GEM) software to evaluate the applicability of the technology to the field scale. Next, the Kahskari oil field simulation model was designed, and the LSWF method was applied to it. To obtain reasonable results, laboratory settings (temperature, pressure, rock, and oil characteristics) are designed as far as possible based on the condition of the Kashkari oil field, and several injection and production patterns are investigated. The relative permeability of oil and water in this study was obtained using Corey’s equation. In the Kashkari oilfield simulation model, three models: 1. Base model (with no water injection), 2. FW injection model, and 3. The LSW injection model were considered for the evaluation of the LSWF effect on oil recovery. Based on the results of the LSWF laboratory experiment and computer simulation analysis, the oil recovery increased rapidly after the FW was injected into the core. Subsequently, by injecting 1% salinity water, a gradual increase of 4% oil can be observed. About 6.4% of the field, is produced by the application of the LSWF technique. The results of LSWF (salinity 0.1%) on the Kashkari oil field suggest that this technology can be a successful method for developing Kashkari oil production.

Keywords: low salinity water flooding, immiscible displacement, kashkari oil field, twophase flow, numerical reservoir simulation model

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Authors: Suparman

Abstract:

This paper addresses the problem of the signal segmentation within a Bayesian framework by using reversible jump MCMC algorithm. The signal is modelled by piecewise constant Moving-Average (MA) model where the numbers of segments, the position of change-point, the order and the coefficient of the MA model for each segment are unknown. The reversible jump MCMC algorithm is then used to generate samples distributed according to the joint posterior distribution of the unknown parameters. These samples allow calculating some interesting features of the posterior distribution. The performance of the methodology is illustrated via several simulation results.

Keywords: piecewise, moving-average model, reversible jump MCMC, signal segmentation

Procedia PDF Downloads 227

Authors: Judson P. LaGrone

Abstract:

Simulation-based education in curricula was once a luxurious component of nursing programs but now serves as a vital element of an individual’s learning experience. A debriefing occurs after the simulation scenario or clinical experience is completed to allow the instructor(s) or trained professional(s) to act as a debriefer to guide a reflection with a purpose of acknowledging, assessing, and synthesizing the thought process, decision-making process, and actions/behaviors performed during the scenario or clinical experience. Debriefing is a vital component of the simulation process and educational experience to allow the learner(s) to progressively build upon past experiences and current scenarios within a safe and welcoming environment with a guided dialog to enhance future practice. The aim of this integrative review was to assess current practices of debriefing models in simulation-based education for health care professionals and students. The following databases were utilized for the search: CINAHL Plus, Cochrane Database of Systemic Reviews, EBSCO (ERIC), PsycINFO (Ovid), and Google Scholar. The advanced search option was useful to narrow down the search of articles (full text, Boolean operators, English language, peer-reviewed, published in the past five years). Key terms included debrief, debriefing, debriefing model, debriefing intervention, psychological debriefing, simulation, simulation-based education, simulation pedagogy, health care professional, nursing student, and learning process. Included studies focus on debriefing after clinical scenarios of nursing students, medical students, and interprofessional teams conducted between 2015 and 2020. Common themes were identified after the analysis of articles matching the search criteria. Several debriefing models are addressed in the literature with similarities of effectiveness for participants in clinical simulation-based pedagogy. Themes identified included (a) importance of debriefing in simulation-based pedagogy, (b) environment for which debriefing takes place is an important consideration, (c) individuals who should conduct the debrief, (d) length of debrief, and (e) methodology of the debrief. Debriefing models supported by theoretical frameworks and facilitated by trained staff are vital for a successful debriefing experience. Models differed from self-debriefing, facilitator-led debriefing, video-assisted debriefing, rapid cycle deliberate practice, and reflective debriefing. A reoccurring finding was centered around the emphasis of continued research for systematic tool development and analysis of the validity and effectiveness of current debriefing practices. There is a lack of consistency of debriefing models among nursing curriculum with an increasing rate of ill-prepared faculty to facilitate the debriefing phase of the simulation.

Keywords: debriefing model, debriefing intervention, health care professional, simulation-based education

Procedia PDF Downloads 142

Authors: Han Bing, Yin Zhenjie

Abstract:

In order to analyze the status of a diesel engine, as well as conduct fault prediction, a new prediction model based on a gray system is proposed in this paper, which takes advantage of the neural network and the genetic algorithm. The proposed GBPGA prediction model builds on the GM (1.5) model and uses a neural network, which is optimized by a genetic algorithm to construct the error compensator. We verify our proposed model on the diesel faulty simulation data and the experimental results show that GBPGA has the potential to employ fault prediction on diesel.

Keywords: fault prediction, neural network, GM(1, 5) genetic algorithm, GBPGA

Procedia PDF Downloads 307

Authors: Zabihullah Mahdi, Khwaja Naweed Seddiqi

Abstract:

Afghanistan is located in a tectonically complex and dynamic area, surrounded by rocks that originated on the mother continent of Gondwanaland. The northern Afghanistan basin, which runs along the country's northern border, has the potential for petroleum generation and accumulation. The Amu Darya basin has the largest petroleum potential in the region. Sedimentation occurred in the Amu Darya basin from the Jurassic to the Eocene epochs. Kashkari oil field is located in northern Afghanistan's Amu Darya basin. The field structure consists of a narrow northeast-southwest (NE-SW) anticline with two structural highs, the northwest limb being mild and the southeast limb being steep. The first oil production well in the Kashkari oil field was drilled in 1976, and a total of ten wells were drilled in the area between 1976 and 1979. The amount of original oil in place (OOIP) in the Kashkari oil field, based on the results of surveys and calculations conducted by research institutions, is estimated to be around 140 MMbbls. The objective of this study is to increase recoverable oil reserves in the Kashkari oil field through the implementation of low-salinity water flooding (LSWF) enhanced oil recovery (EOR) technique. The LSWF involved conducting a core flooding laboratory test consisting of four sequential steps with varying salinities. The test commenced with the use of formation water (FW) as the initial salinity, which was subsequently reduced to a salinity level of 0.1%. Afterward, the numerical simulation model of core scale oil recovery by LSWF was designed by Computer Modelling Group’s General Equation Modeler (CMG-GEM) software to evaluate the applicability of the technology to the field scale. Next, the Kahskari oil field simulation model was designed, and the LSWF method was applied to it. To obtain reasonable results, laboratory settings (temperature, pressure, rock, and oil characteristics) are designed as far as possible based on the condition of the Kashkari oil field, and several injection and production patterns are investigated. The relative permeability of oil and water in this study was obtained using Corey’s equation. In the Kashkari oilfield simulation model, three models: 1. Base model (with no water injection), 2. FW injection model, and 3. The LSW injection model was considered for the evaluation of the LSWF effect on oil recovery. Based on the results of the LSWF laboratory experiment and computer simulation analysis, the oil recovery increased rapidly after the FW was injected into the core. Subsequently, by injecting 1% salinity water, a gradual increase of 4% oil can be observed. About 6.4% of the field is produced by the application of the LSWF technique. The results of LSWF (salinity 0.1%) on the Kashkari oil field suggest that this technology can be a successful method for developing Kashkari oil production.

Keywords: low-salinity water flooding, immiscible displacement, Kashkari oil field, two-phase flow, numerical reservoir simulation model

Procedia PDF Downloads 41

Authors: Yasser F. Hassan

Abstract:

Deep learning structure is a branch of machine learning science and greet achievement in research and applications. Cellular neural networks are regarded as array of nonlinear analog processors called cells connected in a way allowing parallel computations. The paper discusses how to use deep learning structure for representing neural cellular automata model. The proposed learning technique in cellular automata model will be examined from structure of deep learning. A deep automata neural cellular system modifies each neuron based on the behavior of the individual and its decision as a result of multi-level deep structure learning. The paper will present the architecture of the model and the results of simulation of approach are given. Results from the implementation enrich deep neural cellular automata system and shed a light on concept formulation of the model and the learning in it.

Keywords: cellular automata, neural cellular automata, deep learning, classification

Procedia PDF Downloads 199

Authors: Farzaneh Khorram

Abstract:

This paper endeavors to construct a block model to assess grinding energy consumption (CCE) and pinpoint blocks with the highest potential for energy usage during the grinding process within a specified region. Leveraging geostatistical techniques, particularly joint estimation, or simulation, based on geometallurgical data from various mineral processing stages, our objective is to forecast CCE across the study area. The dataset encompasses variables obtained from 2754 drill samples and a block model comprising 4680 blocks. The initial analysis encompassed exploratory data examination, variography, multivariate analysis, and the delineation of geological and structural units. Subsequent analysis involved the assessment of contacts between these units and the estimation of CCE via cokriging, considering its correlation with SPI. The selection of blocks exhibiting maximum CCE holds paramount importance for cost estimation, production planning, and risk mitigation. The study conducted exploratory data analysis on lithology, rock type, and failure variables, revealing seamless boundaries between geometallurgical units. Simulation methods, such as Plurigaussian and Turning band, demonstrated more realistic outcomes compared to cokriging, owing to the inherent characteristics of geometallurgical data and the limitations of kriging methods.

Keywords: geometallurgy, multivariate analysis, plurigaussian, turning band method, cokriging

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Authors: Ahmad Rouhani, Masood Jabbari, Sima Honarmand

Abstract:

This paper introduces a method to optimal design of a hybrid Wind/Photovoltaic/Fuel cell generation system for a typical domestic load that is not located near the electricity grid. In this configuration the combination of a battery, an electrolyser, and a hydrogen storage tank are used as the energy storage system. The aim of this design is minimization of overall cost of generation scheme over 20 years of operation. The Matlab/Simulink is applied for choosing the appropriate structure and the optimization of system sizing. A teaching learning based optimization is used to optimize the cost function. An overall power management strategy is designed for the proposed system to manage power flows among the different energy sources and the storage unit in the system. The results have been analyzed in terms of technics and economics. The simulation results indicate that the proposed hybrid system would be a feasible solution for stand-alone applications at remote locations.

Keywords: hybrid energy system, optimum sizing, power management, TLBO

Procedia PDF Downloads 579

Authors: Anicet Dansou

Abstract:

Textile reinforced concrete (TRC)is increasingly used nowadays in various fields, in particular civil engineering, where it is mainly used for the reinforcement of damaged reinforced concrete structures. TRC is a composite material composed of multi- or uni-axial textile reinforcements coupled with a fine-grained cementitious matrix. The TRC composite is an alternative solution to the traditional Fiber Reinforcement Polymer (FRP) composite. It has good mechanical performance and better temperature stability but also, it makes it possible to meet the criteria of sustainable development better.TRCs are highly anisotropic composite materials with nonlinear hardening behavior; their macroscopic behavior depends on multi-scale mechanisms. The characterization of these materials through numerical simulation has been the subject of many studies. Since TRCs are multiscale material by definition, numerical multi-scale approaches have emerged as one of the most suitable methods for the simulation of TRCs. They aim to incorporate information pertaining to microscale constitute behavior, mesoscale behavior, and macro-scale structure response within a unified model that enables rapid simulation of structures. The computational costs are hence significantly reduced compared to standard simulation at a fine scale. The fine scale information can be implicitly introduced in the macro scale model: approaches of this type are called non-classical. A representative volume element is defined, and the fine scale information are homogenized over it. Analytical and computational homogenization and nested mesh methods belong to these approaches. On the other hand, in classical approaches, the fine scale information are explicitly introduced in the macro scale model. Such approaches pertain to adaptive mesh refinement strategies, sub-modelling, domain decomposition, and multigrid methods This research presents the main principles of numerical multiscale approaches. Advantages and limitations are identified according to several criteria: the assumptions made (fidelity), the number of input parameters required, the calculation costs (efficiency), etc. A bibliographic study of recent results and advances and of the scientific obstacles to be overcome in order to achieve an effective simulation of textile reinforced concrete in civil engineering is presented. A comparative study is further carried out between several methods for the simulation of TRCs used for the structural reinforcement of reinforced concrete structures.

Keywords: composites structures, multiscale methods, numerical modeling, textile reinforced concrete

Procedia PDF Downloads 109

Authors: Adrienne Kline, Jaydip Desai

Abstract:

Electroencephalogram (EEG) is a noninvasive technique that registers signals originating from the firing of neurons in the brain. The Emotiv EEG Neuroheadset is a consumer product comprised of 14 EEG channels and was used to record the reactions of the neurons within the brain to two forms of stimuli in 10 participants. These stimuli consisted of auditory and visual formats that provided directions of ‘right’ or ‘left.’ Participants were instructed to raise their right or left arm in accordance with the instruction given. A scenario in OpenViBE was generated to both stimulate the participants while recording their data. In OpenViBE, the Graz Motor BCI Stimulator algorithm was configured to govern the duration and number of visual stimuli. Utilizing EEGLAB under the cross platform MATLAB®, the electrodes most stimulated during the study were defined. Data outputs from EEGLAB were analyzed using IBM SPSS Statistics® Version 20. This aided in determining the electrodes to use in the development of a brain-machine interface (BMI) using real-time EEG signals from the Emotiv EEG Neuroheadset. Signal processing and feature extraction were accomplished via the Simulink® signal processing toolbox. An Arduino™ Duemilanove microcontroller was used to link the Emotiv EEG Neuroheadset and the right and left Mecha TE™ Hands.

Keywords: brain-machine interface, EEGLAB, emotiv EEG neuroheadset, OpenViBE, simulink

Procedia PDF Downloads 502

Authors: T. Kobayashi, K. Shiba, T. Kaburagi, Y. Kurihara

Abstract:

For the elderly living alone, falls can be a serious problem encountered in daily life. Some elderly people are unable to stand up without the assistance of a caregiver. They may become unconscious after a fall, which can lead to serious aftereffects such as hypothermia, dehydration, and sometimes even death. We treat the subject as an inverted pendulum and model its angle from the equilibrium position and its angular velocity. As the model is non-linear, we implement the filtering method with a particle filter which can estimate true states of the non-linear model. In order to evaluate the accuracy of the particle filter estimation results, we calculate the root mean square error (RMSE) between the estimated angle/angular velocity and the true values generated by the simulation. The experimental results give the highest accuracy RMSE of 0.0141 rad and 0.1311 rad/s for the angle and angular velocity, respectively.

Keywords: fall, microwave Doppler sensor, non-linear dynamics model, particle filter

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Authors: R. Pilafkan, M. Kaffash Irzarahimi, S. F. Asbaghian Namin

Abstract:

The biaxial buckling behavior of single-layered graphene sheets (SLGSs) is studied in the present work. To consider the size-effects in the analysis, Eringen’s nonlocal elasticity equations are incorporated into classical plate theory (CLPT). A Generalized Differential Quadrature Method (GDQM) approach is utilized and numerical solutions for the critical buckling loads are obtained. Then, molecular dynamics (MD) simulations are performed for a series of zigzag SLGSs with different side-lengths and with various boundary conditions, the results of which are matched with those obtained by the nonlocal plate model to numerical the appropriate values of nonlocal parameter relevant to each type of boundary conditions.

Keywords: biaxial buckling, single-layered graphene sheets, nonlocal elasticity, molecular dynamics simulation, classical plate theory

Procedia PDF Downloads 278

Authors: Dicko Ali Hamadi, Tong-Yette Nicolas, Gilles Benjamin, Faure Francois, Palombi Olivier

Abstract:

A novel hybrid model of the lumbar spine, allowing fast static and dynamic simulations of the disc pressure and the spine mobility, is introduced in this work. Our contribution is to combine rigid bodies, deformable finite elements, articular constraints, and springs into a unique model of the spine. Each vertebra is represented by a rigid body controlling a surface mesh to model contacts on the facet joints and the spinous process. The discs are modeled using a heterogeneous tetrahedral finite element model. The facet joints are represented as elastic joints with six degrees of freedom, while the ligaments are modeled using non-linear one-dimensional elastic elements. The challenge we tackle is to make these different models efficiently interact while respecting the principles of Anatomy and Mechanics. The mobility, the intradiscal pressure, the facet joint force and the instantaneous center of rotation of the lumbar spine are validated against the experimental and theoretical results of the literature on flexion, extension, lateral bending as well as axial rotation. Our hybrid model greatly simplifies the modeling task and dramatically accelerates the simulation of pressure within the discs, as well as the evaluation of the range of motion and the instantaneous centers of rotation, without penalizing precision. These results suggest that for some types of biomechanical simulations, simplified models allow far easier modeling and faster simulations compared to usual full-FEM approaches without any loss of accuracy.

Keywords: hybrid, modeling, fast simulation, lumbar spine

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Authors: Mehdi Homayouni, Karim Adinehvand, Bakhtiar Azadbakht

Abstract:

In this study, we used Monte Carlo code (MCNP4C) that is a general method, for simulation, electron source and electric field, a disc source with 0.05 cm radius in direct of anode are used, radius of disc source show focal spot of X-ray tube that here is 0.05 cm. In this simulation, the anode is from tungsten with 18.9 g/cm3 density and angle of the anode is 18°. We simulated X-ray tube for 140 kv. For increasing of speed data acquisition, we use F5 tally. With determination the exact position of F5 tally in the program, outputs are acquired. In this spectrum the start point is about 0.02 Mev, the absorption edges are about 0.06 Mev and 0.07 Mev, and average energy is about 0.05 Mev.

Keywords: X-spectrum, simulation, Monte Carlo, tube

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Authors: Zahra Emami, H. Reza Mesgarzade, A. Morad Ghorbami, S. Reza Motahari

Abstract:

This paper presents a method for calculation of parasitic elements consisting of leakage inductance and parasitic capacitance in a high voltage pulse transformer. The parasitic elements of pulse transformers significantly influence the resulting pulse shape of a power modulator system. In order to prevent the effects on the pulse shape before constructing the transformer an electrical model is needed. The technique procedures for computing these elements are based on finite element analysis. The finite element model of pulse transformer is created using software "Ansys Maxwell 3D". Finally, the transformer parasitic elements is calculated and compared with the value obtained from the actual test and pulse modulator is simulated and results is compared with actual test of pulse modulator. The results obtained are very similar with the test values.

Keywords: pulse transformer, simulation, modeling, Maxwell 3D, modulator

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Authors: Martin Kendra, Tomas Skrucany, Jozef Gnap, Jan Ponicky

Abstract:

Paper deals with the modeling and simulation of energy consumption and GHG production of two different modes of regional passenger transport – road and railway. These two transport modes use the same type of fuel – diesel. Modeling and simulation of the energy consumption in transport is often used due to calculation satisfactory accuracy and cost efficiency. Paper deals with the calculation based on EN standards and information collected from technical information from vehicle producers and characteristics of tracks. Calculation included maximal theoretical capacity of bus and train and real passenger’s measurement from operation. Final energy consumption and GHG production is calculated by using software simulation. In evaluation of the simulation is used system ‘well to wheel’.

Keywords: bus, consumption energy, GHG, production, simulation, train

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Authors: Makhlouf Mourad, Medkour Mihoub, Bouchher Omar, Messabih Sidi Mohamed, Benrachedi Khaled

Abstract:

This work is the modeling and simulation of fluid flow (liquid) through porous media. This type of flow occurs in many situations of interest in applied sciences and engineering, fluid (oil) consists of several individual substances in pure, single-phase flow is incompressible and isothermal. The porous medium is isotropic, homogeneous optionally, with the rectangular format and the flow is two-dimensional. Modeling of hydrodynamic phenomena incorporates Darcy's law and the equation of mass conservation. Correlations are used to model the density and viscosity of the fluid. A finite volume code is used in the discretization of differential equations. The nonlinearity is treated by Newton's method with relaxation coefficient. The results of the simulation of the pressure and the mobility of liquid flowing through porous media are presented, analyzed, and illustrated.

Keywords: Darcy equation, middle porous, continuity equation, Peng Robinson equation, mobility

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Authors: Sachin Kamble, Shradha Gawankar

Abstract:

This paper presents the findings of successful implementation of Business Process Reengineering (BPR) of cement dispatch activities in a cement manufacturing plant located in India. Simulation model was developed for the purpose of identifying and analyzing the areas for improvement. The company was facing a problem of low throughput rate and subsequent forced stoppages of the plant leading to a high production loss of 15000MT per month. It was found from the study that the present systems and procedures related to the in-plant logistics plant required significant changes. The major recommendations included process improvement at the entry gate, reducing the cycle time at the security gate and installation of an additional weigh bridge. This paper demonstrates how BPR can be implemented for improving the in-plant logistics process. Various recommendations helped the plant to increase its throughput by 14%.

Keywords: in-plant logistics, cement logistics, simulation modelling, business process re-engineering, supply chain management

Procedia PDF Downloads 300