This article presents a technique for analyzing the time variability of numerical simulations of two-dimensional Rayleigh Bénard convection at large aspect ratio and intermediate Rayleigh number.
In the numerical FE method, the variability of the fiber dispersion of the individual SFRC beams is determined by identifying the stress-strain relation in each mesh based on the proposed parameter from the X-ray images.
Until recent years, the evaluation of the uncertainties consisted of numerical simulations in which variability and lack-of-knowledge uncertainties were combined in Monte Carlo simulations.
Supposing that the relationship between response variability and numerical magnitude reflects a compression of the underlying number scale (hypothesis 2 in the introduction), we distorted the two number scales so that the standard deviations would be constant on that scale.
Furthermore, by observing the fact that for stable proteins the protein abundance will be in excess of the k d, we are able to derive a novel, clearer and simpler numerical expression for protein variability, and which gives essentially the same results.
Numerical results show that spatial variability in both elastic modulus and undrained shear strength (cohesion) of supporting rock layers affect tip stiffness.
The numerical results indicate that the variability imposed by the proposed stochastic method negatively affected the similarity between the synthesized joint trajectories and the captured ones, with an average decrease in correlation of 0.02 ± 0.02 points against dance sequences synthesized without variability, among all metric resolutions and for both dance styles.
We conduct numerical experiments to estimate the variability of 1D linear and nonlinear soil amplifications due to the uncertainty in shallow S-wave velocity profiles derived from surface-wave phase velocity inversions using the Markov-chain Monte Carlo method.
In addition to results based on this experimental field data, we also provide extensive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the GC times GC biomarker ROI image of the MW pre-spill sample (sample (#1) in Table S1).
In addition to results based on this experimental field data, we also provide extentive perturbation analysis of the PTM method over numerical simulations that introduce random variability of peak locations over the ((GC times GC)) biomarker ROI image of the MW pre-spill sample (sample (#1) in Additional file 4: Table S1).
