Program for calculating the macroscopic properties of a substance via the MINDO/3 semiempirical approximation

1986 ◽  
Vol 26 (6) ◽  
pp. 942-943
Author(s):  
V. V. Lobanov
Keyword(s):  
Semiempirical Approximation ◽  
Macroscopic Properties

scholarly journals The Effects of Periodicity Assumptions in Porous Media Modelling

2021 ◽  
Author(s):  
T. O. M. Forslund ◽  
I. A. S. Larsson ◽  
J. G. I. Hellström ◽  
T. S. Lundström
Keyword(s):  
Fluid Flow ◽  
Lattice Boltzmann ◽  
Single Unit ◽  
Unsteady Flows ◽  
Unit Cell ◽  
Porous Bed ◽  
Bed Porosity ◽  
Macroscopic Properties ◽  
Boltzmann Method ◽  
Method Model

AbstractThe effects of periodicity assumptions on the macroscopic properties of packed porous beds are evaluated using a cascaded Lattice-Boltzmann method model. The porous bed is modelled as cubic and staggered packings of mono-radii circular obstructions where the bed porosity is varied by altering the circle radii. The results for the macroscopic properties are validated using previously published results. For unsteady flows, it is found that one unit cell is not enough to represent all structures of the fluid flow which substantially impacts the permeability and dispersive properties of the porous bed. In the steady region, a single unit cell is shown to accurately represent the fluid flow across all cases studied

A New “Bounce-Normal” Boundary in DPD Calculations for the Reduction of Density Fluctuations

2008 ◽  
Author(s):  
Emadaldin Moeendarbary ◽  
K. Y. Lam ◽  
T. Y. Ng
Keyword(s):  
Shear Stress ◽  
Protein Separation ◽  
Dissipative Particle Dynamics ◽  
Computational Cost ◽  
Colloidal Suspensions ◽  
Density Fluctuations ◽  
Polymer Chains ◽  
New Method ◽  
Mems Devices ◽  
Macroscopic Properties

Dissipative Particle Dynamics (DPD) is a mesoscopic fluid modeling method, which facilitates the simulation of the statics and dynamics of complex fluid systems at physically interesting length and time scales. Currently, there are various applications of DPD, such as colloidal suspensions, multi-phase flow, rheology of polymer chains, DNA macromolecular suspension, etc., which employ this technique for their numerical simulation. The DPD technique is capable of modeling macroscopic properties of the bulk flow very well, but difficulties arise if the flows are confined through wall-bounded regions, or when different boundaries simultaneously exist in the simulation domain. These boundaries cause negative effects on the macroscopic temperature, density and velocity profiles, as well as the shear stress and pressure distributions. In particular, the interaction of DPD particles with solid boundaries causes large density fluctuations at the near wall regions. This density distortion leads to pronounced fluctuations in the pressure and shear stress, which are not actually present. To overcome these serious deficiencies, we introduce a new method in this work, which uses a combination of randomly distributed wall particles and a novel reflection adaptation at the wall. This new methodology is simple to implement and incurs no additional computational cost. More importantly, it does not cause any distortion in the macroscopic properties. This novel reflection adaptation is a novel version of the bounce back reflection, which we shall term the bounce-normal reflection. The most important characteristic of this method is that it reduces density fluctuations near the boundaries without affecting the velocity and temperature profiles. This new method is easily applicable to any wall-bounded problem with stationary boundaries and it has a very good consistency with macroscopic features. The eventual objective of this numerical development work is to investigate suspension flow through micro/nano channels of fluidic NEMS/MEMS devices, with applications to DNA and protein separation. These micro/nano channel devices, consisting of many entropic traps, are designed and fabricated for the separation of proteins and long DNA molecules.

scholarly journals Optimizing machine learning models for granular NdFeB magnets by very fast simulated annealing

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Hyeon-Kyu Park ◽  
Jae-Hyeok Lee ◽  
Jehyun Lee ◽  
Sang-Koog Kim
Keyword(s):  
Machine Learning ◽  
Simulated Annealing ◽  
Permanent Magnets ◽  
Supervised Machine Learning ◽  
Support Vector ◽  
Micromagnetic Simulations ◽  
Ndfeb Magnets ◽  
Average Grain Size ◽  
Macroscopic Properties ◽  
Very Fast Simulated Annealing

AbstractThe macroscopic properties of permanent magnets and the resultant performance required for real implementations are determined by the magnets’ microscopic features. However, earlier micromagnetic simulations and experimental studies required relatively a lot of work to gain any complete and comprehensive understanding of the relationships between magnets’ macroscopic properties and their microstructures. Here, by means of supervised learning, we predict reliable values of coercivity (μ0Hc) and maximum magnetic energy product (BHmax) of granular NdFeB magnets according to their microstructural attributes (e.g. inter-grain decoupling, average grain size, and misalignment of easy axes) based on numerical datasets obtained from micromagnetic simulations. We conducted several tests of a variety of supervised machine learning (ML) models including kernel ridge regression (KRR), support vector regression (SVR), and artificial neural network (ANN) regression. The hyper-parameters of these models were optimized by a very fast simulated annealing (VFSA) algorithm with an adaptive cooling schedule. In our datasets of randomly generated 1,000 polycrystalline NdFeB cuboids with different microstructural attributes, all of the models yielded similar results in predicting both μ0Hc and BHmax. Furthermore, some outliers, which deteriorated the normality of residuals in the prediction of BHmax, were detected and further analyzed. Based on all of our results, we can conclude that our ML approach combined with micromagnetic simulations provides a robust framework for optimal design of microstructures for high-performance NdFeB magnets.

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