Simulations of Shocked Methane Including Self-consistent Semiclassical Quantum Nuclear Effects

2013 ◽  
Vol 1582 ◽  
Author(s):  
Tingting Qi ◽  
Evan J. Reed
Keyword(s):  
Computational Cost ◽  
Atomistic Simulations ◽  
Thermal Bath ◽  
New Approach ◽  
Multi Scale ◽  
Self Consistent ◽  
Shock Hugoniot ◽  
Hugoniot Curves ◽  
Bose Einstein ◽  
Nuclear Effects

ABSTRACTA methodology is described for atomistic simulations of shock-compressed materials that incorporates quantum nuclear effects on the fly. We introduce a modification of the multi-scale shock technique (MSST) that couples to a quantum thermal bath described by a colored noise Langevin thermostat. The new approach, which we call QB-MSST, is of comparable computational cost to MSST and self-consistently incorporates quantum heat capacities and Bose-Einstein harmonic vibrational distributions. As a first test, we study shock-compressed methane using the ReaxFF potential. The Hugoniot curves predicted from the new approach are found comparable with existing experimental data. We find that the self-consistent nature of the method results in the onset of chemistry at 40% lower pressure on the shock Hugoniot than observed with classical molecular dynamics. The temperature change associated with quantum heat capacity is determined to be the primary factor in this shift.

On the Use of Quantum Thermal Bath in Unimolecular Fragmentation Simulations

2019 ◽  
Author(s):  
Riccardo Spezia ◽  
Hichem Dammak
Keyword(s):  
Degrees Of Freedom ◽  
Molecular Simulations ◽  
Zero Point ◽  
Thermal Bath ◽  
Unimolecular Dissociation ◽  
Point Energy ◽  
Rotational Degrees Of Freedom ◽  
The Difference ◽  
Nuclear Effects

<div> <div> <div> <p>In the present work we have investigated the possibility of using the Quantum Thermal Bath (QTB) method in molecular simulations of unimolecular dissociation processes. Notably, QTB is aimed in introducing quantum nuclear effects with a com- putational time which is basically the same as in newtonian simulations. At this end we have considered the model fragmentation of CH4 for which an analytical function is present in the literature. Moreover, based on the same model a microcanonical algorithm which monitor zero-point energy of products, and eventually modifies tra- jectories, was recently proposed. We have thus compared classical and quantum rate constant with these different models. QTB seems to correctly reproduce some quantum features, in particular the difference between classical and quantum activation energies, making it a promising method to study unimolecular fragmentation of much complex systems with molecular simulations. The role of QTB thermostat on rotational degrees of freedom is also analyzed and discussed. </p> </div> </div> </div>

scholarly journals Data-Informed Decomposition for Localized Uncertainty Quantification of Dynamical Systems

Vibration ◽  
2020 ◽  
Vol 4 (1) ◽  
pp. 49-63
Author(s):  
Waad Subber ◽  
Sayan Ghosh ◽  
Piyush Pandita ◽  
Yiming Zhang ◽  
Liping Wang
Keyword(s):  
Dynamical Systems ◽  
Computational Cost ◽  
Three Dimensional ◽  
Region Of Interest ◽  
System Response ◽  
Computational Domain ◽  
User Interest ◽  
Numerical Resolution ◽  
Multi Scale ◽  
Operation Conditions

Industrial dynamical systems often exhibit multi-scale responses due to material heterogeneity and complex operation conditions. The smallest length-scale of the systems dynamics controls the numerical resolution required to resolve the embedded physics. In practice however, high numerical resolution is only required in a confined region of the domain where fast dynamics or localized material variability is exhibited, whereas a coarser discretization can be sufficient in the rest majority of the domain. Partitioning the complex dynamical system into smaller easier-to-solve problems based on the localized dynamics and material variability can reduce the overall computational cost. The region of interest can be specified based on the localized features of the solution, user interest, and correlation length of the material properties. For problems where a region of interest is not evident, Bayesian inference can provide a feasible solution. In this work, we employ a Bayesian framework to update the prior knowledge of the localized region of interest using measurements of the system response. Once, the region of interest is identified, the localized uncertainty is propagate forward through the computational domain. We demonstrate our framework using numerical experiments on a three-dimensional elastodynamic problem.

scholarly journals A thermal resistances-based approach for thermal-elastohydrodynamic calculations in point contacts

2017 ◽  
Vol 232 (11) ◽  
pp. 2088-2102 ◽  
Author(s):  
Eduardo de la Guerra Ochoa ◽  
Javier Echávarri Otero ◽  
Enrique Chacón Tanarro ◽  
Benito del Río López
Keyword(s):  
Friction Coefficient ◽  
Film Thickness ◽  
Thermal Effects ◽  
Good Accuracy ◽  
Computational Cost ◽  
Point Contact ◽  
Elastohydrodynamic Lubrication ◽  
Point Contacts ◽  
New Approach ◽  
Ball On Disc

This article presents a thermal resistances-based approach for solving the thermal-elastohydrodynamic lubrication problem in point contact, taking the lubricant rheology into account. The friction coefficient in the contact is estimated, along with the distribution of both film thickness and temperature. A commercial tribometer is used in order to measure the friction coefficient at a ball-on-disc point contact lubricated with a polyalphaolefin base. These data and other experimental results available in the bibliography are compared to those obtained by using the proposed methodology, and thermal effects are analysed. The new approach shows good accuracy for predicting the friction coefficient and requires less computational cost than full thermal-elastohydrodynamic simulations.

scholarly journals Macro-meso scale simulations of 3D woven composite reinforcements during the forming process

2021 ◽  
Author(s):  
Jie Wang ◽  
Peng Wang ◽  
Nahiène Hamila ◽  
Philippe Boisse
Keyword(s):  
Computational Cost ◽  
Constitutive Law ◽  
Forming Process ◽  
Multi Scale ◽  
Rtm Process ◽  
Macroscopic Simulation ◽  
Deformations And Orientations ◽  
High Computational Cost ◽  
Meso Scale ◽  
Composite Reinforcements

During the forming stage in the RTM process, deformations and orientations of yarns at the mesoscopic scale are essential to evaluate mechanical behaviors of final composite products and calculate the permeability of the reinforcement. However, due to the high computational cost, it is very difficult to carry out a mesoscopic draping simulation for the entire reinforcement. In this paper, a macro-meso scale simulation of composite reinforcements is presented in order to predict mesoscopic deformations of the fabric in a reasonable calculation time. The proposed multi-scale method allows linking the macroscopic simulation of the reinforcement with the mesoscopic modelling of the RVE through a macromeso embedded analysis. On the base of macroscopic simulations using a hyperelastic constitutive law of the reinforcement, an embedded mesoscopic geometry is first deduced from the macroscopic simulation of the draping. To overcome the inconvenience of the macro-meso embedded solution which leads to unreal excessive yarn extensions, local mesoscopic simulations based on the embedded analysis are carried out on a single RVE by defining specific boundary conditions. Finally, the multi-scale forming simulations are investigated in comparison with the experimental results, illustrating the efficiency of the proposed approach, in terms of accuracy and CPU time.

Helical Magnetic Cavities: Kinetic Model and Comparison with MMS Observations

2021 ◽  
Author(s):  
Jinghuan Li ◽  
Xuzhi Zhou ◽  
Fan Yang ◽  
Anton V. Artemyev ◽  
Qiugang Zong
Keyword(s):  
Magnetic Field ◽  
Kinetic Model ◽  
Pitch Angle ◽  
Maxwell Equations ◽  
Equilibrium Solution ◽  
Space Plasma ◽  
Azimuthal Magnetic Field ◽  
Particle Distributions ◽  
Multi Scale ◽  
Self Consistent

&lt;p&gt;Magnetic cavities are sudden depressions of magnetic field strength widely observed in the space plasma environments, which are often accompanied by plasma density and pressure enhancement. To describe these cavities, a self-consistent kinetic model has been proposed as an equilibrium solution to the Vlasov-Maxwell equations. However, observations from the Magnetospheric Multi-Scale (MMS) constellation have shown the existence of helical magnetic cavities characterized by the presence of azimuthal magnetic field, which could not be reconstructed by the aforementioned model. Here, we take into account another invariant of motion, the canonical axial momentum, to construct the particle distributions and accordingly modify the equilibrium model. The reconstructed magnetic cavity shows excellent agreement with the MMS1 observations not only in the electromagnetic field and plasma moment profiles but also in electron pitch-angle distributions. With the same set of parameters, the model also predicts signatures of the neighboring MMS3 spacecraft, matching its observations satisfactorily.&lt;/p&gt;

Relative time seislet transform

Geophysics ◽  
2020 ◽  
Vol 85 (2) ◽  
pp. V223-V232 ◽  
Author(s):  
Zhicheng Geng ◽  
Xinming Wu ◽  
Sergey Fomel ◽  
Yangkang Chen
Keyword(s):  
Seismic Data ◽  
Computational Cost ◽  
Lifting Scheme ◽  
Real Data ◽  
Data Sets ◽  
Relative Time ◽  
New Approach ◽  
New Formulation ◽  
Wavelet Lifting ◽  
Seismic Images

The seislet transform uses the wavelet-lifting scheme and local slopes to analyze the seismic data. In its definition, the designing of prediction operators specifically for seismic images and data is an important issue. We have developed a new formulation of the seislet transform based on the relative time (RT) attribute. This method uses the RT volume to construct multiscale prediction operators. With the new prediction operators, the seislet transform gets accelerated because distant traces get predicted directly. We apply our method to synthetic and real data to demonstrate that the new approach reduces computational cost and obtains excellent sparse representation on test data sets.

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