We evaluate the thermal conductivity of a model nanofluid at various volume fractions of nanoparticles with equilibrium (EMD) and non-equilibrium (NEMD) molecular dynamics simulations. The Green-Kubo formalism is used for the EMD simulations while a net heat flux is imposed on the system for the NEMD simulations. The nanoparticle-nanoparticle, fluid-fluid and fluid-nanoparticle interactions are all taken as Lennard-Jones potentials. An empirical parameter is added to the attractive part of the potential to control the hydrophilicity of the nanoparticles, hence controlling how well dispersed are the nanoparticles in the base fluid. The results show that the aggregation of the nanoparticles does not have a measurable effect on the conductivity of the nanofluid. Nanofluids with volume fractions of 2% and 3% show an enhanced conductivity with respect to the bulk fluid. Surprisingly, nanofluids with higher volume fractions did not show any enhancement of the conductivity.
Reconstructing network dynamics from data is crucial for predicting the changes in the dynamics of complex systems such as neuron networks; however, previous research has shown that the reconstruction is possible under strong constraints such as the need for lengthy data or small system size. Here, we present a recovery scheme blending theoretical model reduction and sparse recovery to identify the governing equations and the interactions of weakly coupled chaotic maps on complex networks, easing unrealistic constraints for real-world applications. Learning dynamics and connectivity lead to detecting critical transitions for parameter changes. We apply our technique to realistic neuronal systems with and without noise on a real mouse neocortex and artificial networks.
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