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In order to fully harness the outstanding mechanical properties of carbon nanotubes (CNT) as fiber reinforcements, it is essential to understand the nature of load transfer in the fiber matrix interfacial region of CNT-based composites. With controlled experimentation on nanoscale interfaces far off, molecular dynamics (MD) is evolving as the primary method to model these systems and processes. While MD is capable of simulating atomistic behavior in a deterministic manner, the extremely small length and time scales modeled by MD necessitate multiscale approaches. To study the atomic scale interface effects on composite behavior, we herein develop a hierarchical multiscale methodology linking molecular dynamics and the finite element method through atomically informed cohesive zone model parameters to represent interfaces. Motivated by the successful application of pullout tests in conventional composites, we simulate fiber pullout tests of carbon nanotubes in a given matrix using MD. The results of the pullout simulations are then used to evaluate cohesive zone model parameters. These cohesive zone models (CZM) are then used in a finite element setting to study the macroscopic mechanical response of the composites. Thus, the method suggested explicitly accounts for the behavior of nanoscale interfaces existing between the matrix and CNT. The developed methodology is used to study the effect of interface strength on stiffness of the CNT-based composite.

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Reducing the number of contacts between passengers on an airplane can potentially curb the spread of infectious diseases. In this paper, a social force based pedestrian movement model is formulated and applied to evaluate the movement and contacts among passengers during boarding and deplaning of an airplane. Within the social force modeling framework, we introduce location dependence on the self-propelling momentum of pedestrian particles. The model parameters are varied over a large design space and the results are compared with experimental observations to validate the model. This model is then used to assess the different approaches to minimize passenger contacts during boarding and deplaning of airplanes. We find that smaller aircrafts are effective in reducing the contacts between passengers. Column wise deplaning and random boarding are found to be two strategies that reduced the number of contacts during passenger movement, and can potentially lower the likelihood of infection spread.

In this paper we develop a multiscale model combining social-force-based pedestrian movement with a population level stochastic infection transmission dynamics framework. The model is then applied to study the infection transmission within airplanes and the transmission of the Ebola virus through casual contacts. Drastic limitations on air-travel during epidemics, such as during the 2014 Ebola outbreak in West Africa, carry considerable economic and human costs. We use the computational model to evaluate the effects of passenger movement within airplanes and air-travel policies on the geospatial spread of infectious diseases. We find that boarding policy by an airline is more critical for infection propagation compared to deplaning policy. Enplaning in two sections resulted in fewer infections than the currently followed strategy with multiple zones. In addition, we found that small commercial airplanes are better than larger ones at reducing the number of new infections in a flight. Aggregated results indicate that passenger movement strategies and airplane size predicted through these network models can have significant impact on an event like the 2014 Ebola epidemic. The methodology developed here is generic and can be readily modified to incorporate the impact from the outbreak of other directly transmitted infectious diseases.