Relevant bibliographies by topics / Fast mesoscopic model

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Academic literature on the topic 'Fast mesoscopic model'

Author: Grafiati
Published: 13 April 2024

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Journal articles on the topic "Fast mesoscopic model"

1

Sakout, Sofia, Daniel Weisz-Patrault, and Alain Ehrlacher. "Energetic upscaling strategy for grain growth. i: Fast mesoscopic model based on dissipation." Acta Materialia 196 (September 2020): 261–79. http://dx.doi.org/10.1016/j.actamat.2020.06.032.

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2

Zhou, Xuesong, Jeffrey Taylor, and Filippo Pratico. "DTALite: A queue-based mesoscopic traffic simulator for fast model evaluation and calibration." Cogent Engineering 1, no. 1 (2014): 961345. http://dx.doi.org/10.1080/23311916.2014.961345.

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3

Wang, Enjiang, José M. Carcione, and Jing Ba. "Wave simulation in double-porosity media based on the Biot-Rayleigh theory." GEOPHYSICS 84, no. 4 (2019): WA11—WA21. http://dx.doi.org/10.1190/geo2018-0575.1.

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We have developed a numerical algorithm for simulation of wave propagation in double-porosity media, where the pore space is saturated with a single fluid. Spherical inclusions embedded in a background medium oscillate to yield attenuation by mode conversion from fast P-wave energy to slow P-wave energy (mesoscopic or wave-induced fluid-flow loss). The theory is based on the Biot theory of poroelasticity and the Rayleigh model of bubble oscillations. The differential equation of the Biot-Rayleigh variable is approximated with the Zener mechanical model, which results in a memory-variable viscoelastic equation. These approximations are required to model mesoscopic losses arising from conversion of the fast P-wave energy to slow diffusive modes. The model predicts a relaxation peak in the seismic band, depending on the diameter of the patches, to model the attenuation level observed in rocks. The wavefield is obtained with a grid method based on the Fourier differential operator and a second-order time-integration algorithm. Because the presence of two slow quasistatic modes makes the differential equations stiff, a time-splitting integration algorithm is used to solve the stiff part analytically. The modeling has spectral accuracy in the calculation of the spatial derivatives.
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Needell, Zachary A., and Jessika E. Trancik. "Efficiently Simulating Personal Vehicle Energy Consumption in Mesoscopic Transport Models." Transportation Research Record: Journal of the Transportation Research Board 2672, no. 25 (2018): 163–73. http://dx.doi.org/10.1177/0361198118798244.

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Mesoscopic transport models can efficiently simulate complex travel behavior and traffic patterns over large networks, but simulating energy consumption in these models is difficult with traditional methods. As mesoscopic transport models rely on a simplified handling of traffic flow, they cannot provide the second-by-second measurement of vehicle speeds and accelerations that are required for accurately estimating energy consumption. Here we present extensions to the TripEnergy model that fill in the gaps of low-resolution trajectories with realistic, contextual driving behavior. TripEnergy also includes a vehicle energy model capable of simulating the impact of traffic conditions on energy consumption and CO2 emissions, with inputs in the form of widely available calibration data, allowing it to simulate thousands of different real-world vehicle makes and models. This design allows TripEnergy to integrate with mesoscopic transport models and to be fast enough to run on a large network with minimal additional computation time. We expect it to benefit from and enable advances in transport simulation, including optimizing traffic network controls to minimize energy, evaluating the performance of different vehicle technologies under wide-scale adoption, and better understanding the energy and climate impacts of new infrastructure and policies.
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Li, Zilong, and Yang Tang. "Mesoscopic Simulation Method for Uniaxial Compression Test of RCC Dam Material Based on DEM." Mathematical Problems in Engineering 2020 (December 17, 2020): 1–13. http://dx.doi.org/10.1155/2020/6686609.

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The roller compacted concrete (RCC) dam has become one of the most competitive dam types due to its fast construction speed, low cost, and strong adaptability. However, the macroscale compaction test can hardly reflect the mesoscopic structure on the RCC’s rolling characteristics. According to the characteristics of RCC dam materials, a numerical discrete element method (DEM) is proposed in this paper, which is used to simulate the irregular shape and proportion of RCC aggregates. Moreover, a mesoscopic parameter inversion method based on the adaptive differential evolution (ADE) algorithm is proposed to enhance the efficiency of model contact parameters determination and overcome the inconvenience and time-consumption of conventional methods. Compared with the physical test, the simulation compression curve has good consistency with the physical test curve, and the proposed method can adequately reflect the physical and mechanical properties of RCC dam materials, which provides a basis for the subsequent research on the properties of RCC dam materials under different filling times.
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6

Biscarini, Chiara, Silvia Di Francesco, Fernando Nardi, and Piergiorgio Manciola. "Detailed Simulation of Complex Hydraulic Problems with Macroscopic and Mesoscopic Mathematical Methods." Mathematical Problems in Engineering 2013 (2013): 1–14. http://dx.doi.org/10.1155/2013/928309.

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The numerical simulation of fast-moving fronts originating from dam or levee breaches is a challenging task for small scale engineering projects. In this work, the use of fully three-dimensional Navier-Stokes (NS) equations and lattice Boltzmann method (LBM) is proposed for testing the validity of, respectively, macroscopic and mesoscopic mathematical models. Macroscopic simulations are performed employing an open-source computational fluid dynamics (CFD) code that solves the NS combined with the volume of fluid (VOF) multiphase method to represent free-surface flows. The mesoscopic model is a front-tracking experimental variant of the LBM. In the proposed LBM the air-gas interface is represented as a surface with zero thickness that handles the passage of the density field from the light to the dense phase and vice versa. A single set of LBM equations represents the liquid phase, while the free surface is characterized by an additional variable, the liquid volume fraction. Case studies show advantages and disadvantages of the proposed LBM and NS with specific regard to the computational efficiency and accuracy in dealing with the simulation of flows through complex geometries. In particular, the validation of the model application is developed by simulating the flow propagating through a synthetic urban setting and comparing results with analytical and experimental laboratory measurements.
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7

Zhao, Liang, Chang-Hua Li, Fa-Ning Dang, Chu-Jun Li, and Zhong-Xing Duan. "Concrete CT Image Quick Three-Dimensional Reconstruction Research." International Journal of Pattern Recognition and Artificial Intelligence 31, no. 10 (2017): 1757005. http://dx.doi.org/10.1142/s0218001417570051.

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The research of the mechanical properties of concrete, a kind of heterogeneous composite material, was previously established on basis of the mathematical model of random aggregate, which is used to study and analyze the mesoscopic damage mechanism of concrete. Although the shape and distribution of aggregate in the model built by this method are closer to the real structure of concrete, there is still a big difference between them and the real concrete specimen. In order to solve the problem of large amount of redundant computation in the CT reconstruction of full size cube space, a fast reconstruction method based on ray-casting algorithm is proposed. First, a method integrating the new bounding box technology with the plane intersection algorithm clusters were adopted to cut the body data and ray-casting effectively, and then, the polygon scanning and conversion was utilized to reduce the number of cast rays, finally, the adaptive sampling method was used to avoid repeatedly sampling same voxel so that the reconstruction efficiency of whole algorithm and the feasibility of numerical calculation can be enhanced. The experimental results demonstrate that the proposed algorithm can greatly improve the 3D rendering speed of concrete CT without affecting the image quality. It provides a more effective and reliable approach for correctly analyzing the mesoscopic damage mechanism and mechanical characteristics of concrete.
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8

Koltcov, Sergei, Vera Ignatenko, and Sergei Pashakhin. "Fast Tuning of Topic Models: An Application of Rényi Entropy and Renormalization Theory." Proceedings 46, no. 1 (2019): 5. http://dx.doi.org/10.3390/ecea-5-06674.

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In practice, the critical step in building machine learning models of big data (BD) is costly in terms of time and the computing resources procedure of parameter tuning with a grid search. Due to the size, BD are comparable to mesoscopic physical systems. Hence, methods of statistical physics could be applied to BD. The paper shows that topic modeling demonstrates self-similar behavior under the condition of a varying number of clusters. Such behavior allows using a renormalization technique. The combination of a renormalization procedure with the Rényi entropy approach allows for fast searching of the optimal number of clusters. In this paper, the renormalization procedure is developed for the Latent Dirichlet Allocation (LDA) model with a variational Expectation-Maximization algorithm. The experiments were conducted on two document collections with a known number of clusters in two languages. The paper presents results for three versions of the renormalization procedure: (1) a renormalization with the random merging of clusters, (2) a renormalization based on minimal values of Kullback–Leibler divergence and (3) a renormalization with merging clusters with minimal values of Rényi entropy. The paper shows that the renormalization procedure allows finding the optimal number of topics 26 times faster than grid search without significant loss of quality.
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9

Guo, Yutai, Jialong He, Hui Jiang, Yuande Zhou, Feng Jin, and Chongmin Song. "A Simple Approach for Generating Random Aggregate Model of Concrete Based on Laguerre Tessellation and Its Application Analyses." Materials 13, no. 17 (2020): 3896. http://dx.doi.org/10.3390/ma13173896.

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Generating random aggregate models (RAMs) plays a key role in the mesoscopic modelling of concrete-like composite materials. The arbitrary geometry, wide gradation, and high volume ratio of aggregates pose a great challenge for fast and efficient numerical construction of concrete meso-structures. This paper presents a simple strategy for generating RAMs of concrete based on Laguerre tessellation, which mainly consists of three steps: tessellation, geometric smoothing, and scaling. The computer-assisted design (CAD) file of RAMs obtained by the proposed approach can be directly adopted for the construction of random numerical concrete samples. Combined with the image-based octree meshing algorithm, the scaled boundary finite element method (SBFEM) was adopted for an automatic stress analysis of mass concrete samples, and a parametric study was conducted to investigate the meso-structural effects on concrete elasticity properties. The modelling results successfully reproduced the increasing trend of concrete elastic modulus with the grading of coarse aggregates in literature test data and demonstrate the effectiveness of the proposed strategy.
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10

Köksal Ersöz, Elif, Julien Modolo, Fabrice Bartolomei, and Fabrice Wendling. "Neural mass modeling of slow-fast dynamics of seizure initiation and abortion." PLOS Computational Biology 16, no. 11 (2020): e1008430. http://dx.doi.org/10.1371/journal.pcbi.1008430.

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Epilepsy is a dynamic and complex neurological disease affecting about 1% of the worldwide population, among which 30% of the patients are drug-resistant. Epilepsy is characterized by recurrent episodes of paroxysmal neural discharges (the so-called seizures), which manifest themselves through a large-amplitude rhythmic activity observed in depth-EEG recordings, in particular in local field potentials (LFPs). The signature characterizing the transition to seizures involves complex oscillatory patterns, which could serve as a marker to prevent seizure initiation by triggering appropriate therapeutic neurostimulation methods. To investigate such protocols, neurophysiological lumped-parameter models at the mesoscopic scale, namely neural mass models, are powerful tools that not only mimic the LFP signals but also give insights on the neural mechanisms related to different stages of seizures. Here, we analyze the multiple time-scale dynamics of a neural mass model and explain the underlying structure of the complex oscillations observed before seizure initiation. We investigate population-specific effects of the stimulation and the dependence of stimulation parameters on synaptic timescales. In particular, we show that intermediate stimulation frequencies (>20 Hz) can abort seizures if the timescale difference is pronounced. Those results have the potential in the design of therapeutic brain stimulation protocols based on the neurophysiological properties of tissue.
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