Predictive Modeling

  • Michael Doyle


It is the goal of the aerospace industry to minimize the weight of structural parts without compromising the structural integrity and failure resistance of the systems they comprise. Over the past few years, more attention has been paid to composite materials as a possible solution to the challenges and tradeoffs inherent in aerospace design. In these complex composite materials, high-strength fibers provide reinforcement to polymers so that they become capable of carrying the high environmental and mechanical loads desired. Recently, reinforcing nanoparticles (nanofillers) have shown promise in the aerospace resin materials sector due to their superior mechanical and fatigue properties. In order to improve the interaction of these particles with the “host” matrix, there is a need to functionalize them for better interaction with and within the matrix. This functionalization can be guided and ultimately achieved through the use of multiscale modeling techniques and simulation, as part of a true four dimensional design space (width, height, length and material). These multiscale approaches and simulations necessitate significant computational capability. But, not only do they address the limits of conventional approaches on the number of atoms that can be simulated, but also they serve to address the time and length scales intrinsic in the atomistic approach and bring them more in line with those of the “meso-scopic regime” or real performance space. These hybrid approaches have recently shown success in solving these classes of systems. In the hybrid approach, multiple regions are defined within the configuration; some with direct atomistic interactions; some with detailed interactions defined by quantum mechanical density functional theory or semi-empirical or tight binding theory; still others defined by electrostatic or mechanical linkages; and yet others treated by a bulk or continuum representation. The objective of these methods is to predict material properties of the modified parent material when reinforced with nanoparticles using an aggregating approach, in which there are multiple connect domains of simulation. Algorithmic improvements to all of these approaches, coupled with the increasing speed of computational hardware, are making it possible to perform predictive modeling on ever larger systems. A number of methods are now available that are capable of modeling hundreds of thousands of atoms, and these results can have a significant impact on real-world engineering and failure analysis problems. This work reviews some of the modeling methods currently in use, provides illustrative examples on obtaining mechanical properties through fundamental laws, and discusses multidimensional material and device design. In this section, computational tools are illustrated that are able to bridge the gap between the characteristic time and spatial scales of the nanochemistry and the macro-scale engineering or physics of the aerospace system. Finally, discussions on the prospects for future modeling approaches will be included.


Monte Carlo Boundary Element Method Dissipative Particle Dynamic Kinetic Monte Carlo Quantum Monte Carlo 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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© Springer Science+Business Media, LLC 2009

Authors and Affiliations

  1. 1.Principal Solution Scientist, Materials Science @ AccelrysNew YorkUSA

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