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The interface characteristics of the matrix and fibers significantly influence the evolution of residual stress in composite materials. In this study, we provide a methodology for reducing the residual stress in laminated composites by modifying the thermomechanical properties at the fiber–matrix interface. A hydrothermal chemical growth method was used to grow Zinc Oxide nanowires on the carbon fibers. We then utilized a novel digital image correlation approach to evaluate strains and residual stresses, in situ, throughout the autoclave curing of composites. We find that interface modification results in the reduction of residual stress and an increase in laminate strength and stiffness. Upon growing ZnO NWs on the carbon fibers, the maximum in situ in-plane strain components were reduced by approximately 55% and 31%, respectively, while the corresponding maximum residual stresses were decreased by 50.8% and 49.33% for the cross-play laminate [0°/90°] layup in the x and y directions, respectively. For the [45°/-45°] angle ply layup in the x-direction, the strain was decreased by 27.3%, and the maximum residual stress was reduced by 41.5%, whereas in the y-direction, the strain was decreased by 166.3%, and the maximum residual stress was reduced by 17.8%. Furthermore, mechanical testing revealed that the tensile strength for the [45°/-45°] and [0°/90°] laminates increased by 130% and 20%, respectively, with the interface modification. 

Pedestrian dynamics is an approach for modeling the fine-scaled movement of people. It is finding increasing application in the analysis of infection risk for directly transmitted diseases during air travel. A parameter sweep is often needed to evaluate infection risk for a variety of possible scenarios to account for inherent variability in human behavior. A low discrepancy parameter sweep was recently introduced to reduce the computational effort by one to three orders of magnitude. However, it has the following limitations: (i) a low overhead parallelization leads to significant load imbalance, and (ii) the convergence rate worsens with dimension. This paper examines whether pseudorandom and hybrid sequences can overcome these defects and whether the convergence criteria can be changed to yield accurate solutions faster. We simulate the deplaning process of an airplane using different parameter sweep strategies and evaluate their relative computational efficiencies. Our results show that hybrid and pseudorandom parameter sweeps are advantageous for moderate accuracy, while a low discrepancy sweep is preferable for high accuracy. Our results also show that the convergence criteria could be relaxed substantially to yield accurate solutions around a factor of 20 faster. They promise to help a variety of applications that employ large parameter sweeps for modeling infection risk. 

Residual stresses are detrimental to composite structures as they induce processing defects like debonding, delamination, and matrix cracking which significantly decrease their load-bearing capability. In this research, a new in-situ approach using digital image correlation is utilized to analyze the effect of the cure cycle modification on residual stress evolution during processing. It was found that the modified cure cycle comprising abrupt cooling after gelation reduces the residual stresses. Five different layup configurations are investigated to examine the effect of fiber direction. A maximum average residual stress reduction of 31.8% is observed for the balanced unsymmetric [30/-30/60/-60] laminate. The residual stress reduction results in an increase in failure strength between 4 and 12% in the different layups and can lead up to a 22% increase in first-ply failure strength. 

COVID-19 pandemic has resulted in an over 60 % reduction in airtravel worldwide according to some estimates. The high economic and public perception costs of potential superspreading during air-travel necessitates research efforts that model, explain and mitigate disease spread. The long-duration exposure to infected passengers and the limited air circulation in the cabin are considered to be responsible for the infection spread during flight. Consequently, recent public health measures are primarily based on these aspects. However, a survey of recent on-flight outbreaks indicates that some aspects of the COVID-19 spread, such as long-distance superspreading, cannot be explained without also considering the movement of people. Another factor that could be influential but has not gained much attention yet is the unpredictable passenger behavior. Here, we use a novel infection risk model that is linked with pedestrian dynamics to accurately capture these aspects of infection spread. The model is parameterized through spatiotemporal analysis of a recent superspreading event in a restaurant in China. The passenger movement during boarding and deplaning, as well as the in-plane movement, are modeled with social force model and agent-based model respectively. We utilize the model to evaluate what-if scenarios on the relative effectiveness of policies and procedures such as masking, social distancing, as well as synergistic effects by combining different approaches in airplanes and other contexts. We find that in certain instances independent strategies can combine synergistically to reduce infection probability, by more than a sum of individual strategies