2024/12/12 by Wenlong Shi, Yang Jiao, Shi, Wenlong +3
Physics and Astronomy · Computer Science · #Random lasers and scattering media #Face and Expression Recognition #Machine Learning and ELM
paper · pdf · doi:10.48550/arxiv.2412.08974
Rigorous theories connecting physical properties of a heterogeneous material to its microstructure offer a promising avenue to guide the computational material design and optimization. We present here an efficient Fourier-space based computational framework and employ a variety of analytical χV(k) functions that satisfy all known necessary conditions to construct 3D disordered stealthy hyperuniform, standard hyperuniform, nonhyperuniform, and antihyperuniform two-phase heterogeneous material systems at varying phase volume fractions. We show that a rich spectrum of distinct structures within each of the above classes of materials can be generated by tuning correlations in the system across length scales. We present the first realization of antihyperuniform two-phase heterogeneous materials in 3D, which are characterized by a power-law autocovariance function χV(r) and contain clusters of dramatically different sizes and morphologies. We also determine the diffusion spreadability \cal S(t) and estimate the fluid permeability k associated with all of the constructed materials directly from the corresponding χV(k) functions. We find that varying the length-scale parameter within each class of χV(k) functions can also lead to orders of magnitude variation of \cal S(t) at intermediate and long time scales. Moreover, we find that increasing solid volume fraction ϕ1 and correlation length a in the constructed media generally leads to a decrease in the dimensionless fluid permeability k/a2. These results indicate the feasibility of employing parameterized χV(k) for designing composites with targeted transport properties.