17,462 research outputs found
Uncertainty Updating in the Description of Coupled Heat and Moisture Transport in Heterogeneous Materials
To assess the durability of structures, heat and moisture transport need to
be analyzed. To provide a reliable estimation of heat and moisture distribution
in a certain structure, one needs to include all available information about
the loading conditions and material parameters. Moreover, the information
should be accompanied by a corresponding evaluation of its credibility. Here,
the Bayesian inference is applied to combine different sources of information,
so as to provide a more accurate estimation of heat and moisture fields [1].
The procedure is demonstrated on the probabilistic description of heterogeneous
material where the uncertainties consist of a particular value of individual
material characteristic and spatial fluctuations. As for the heat and moisture
transfer, it is modelled in coupled setting [2]
Heterogeneity, High Performance Computing, Self-Organization and the Cloud
application; blueprints; self-management; self-organisation; resource management; supply chain; big data; PaaS; Saas; HPCaa
Higher-order multi-scale deep Ritz method for multi-scale problems of authentic composite materials
The direct deep learning simulation for multi-scale problems remains a
challenging issue. In this work, a novel higher-order multi-scale deep Ritz
method (HOMS-DRM) is developed for thermal transfer equation of authentic
composite materials with highly oscillatory and discontinuous coefficients. In
this novel HOMS-DRM, higher-order multi-scale analysis and modeling are first
employed to overcome limitations of prohibitive computation and Frequency
Principle when direct deep learning simulation. Then, improved deep Ritz method
are designed to high-accuracy and mesh-free simulation for macroscopic
homogenized equation without multi-scale property and microscopic lower-order
and higher-order cell problems with highly discontinuous coefficients.
Moreover, the theoretical convergence of the proposed HOMS-DRM is rigorously
demonstrated under appropriate assumptions. Finally, extensive numerical
experiments are presented to show the computational accuracy of the proposed
HOMS-DRM. This study offers a robust and high-accuracy multi-scale deep
learning framework that enables the effective simulation and analysis of
multi-scale problems of authentic composite materials
Models for Metal Hydride Particle Shape, Packing, and Heat Transfer
A multiphysics modeling approach for heat conduction in metal hydride powders
is presented, including particle shape distribution, size distribution,
granular packing structure, and effective thermal conductivity. A statistical
geometric model is presented that replicates features of particle size and
shape distributions observed experimentally that result from cyclic hydride
decreptitation. The quasi-static dense packing of a sample set of these
particles is simulated via energy-based structural optimization methods. These
particles jam (i.e., solidify) at a density (solid volume fraction) of
0.665+/-0.015 - higher than prior experimental estimates. Effective thermal
conductivity of the jammed system is simulated and found to follow the behavior
predicted by granular effective medium theory. Finally, a theory is presented
that links the properties of bi-porous cohesive powders to the present systems
based on recent experimental observations of jammed packings of fine powder.
This theory produces quantitative experimental agreement with metal hydride
powders of various compositions.Comment: 12 pages, 12 figures, 2 table
- …