Random Technology

Modern technology increasingly uses randomness to avoid lockstep behaviour and the suppression of unwanted regularity. The nature of noise is explored, and in particular the use of noise to obscure unwanted signals, either explicitly, using techniques like jitter or dither, or in the abstract. Machine-learning techniques rely on randomness to explore the solution space of a problem. Simulated evolution can be an effective way to tackle design problems, and relies on randomness.

RANS SIMULATION OF BREAKER BAR DEVELOPMENT USING A STABILIZED TURBULENCE MODEL

The results demonstrate the significant advantages of utilizing formally stabilized turbulence closure models in accurately predicting the surf zone dynamics, sediment transport, and breaker bar morphology in the shoaling region and in the outer surf zone using RANS models. Simulated evolution using a stabilized turbulence model is demonstrated to predict cross-shore breaker bar position, growth and evolution. This is in contrast to results using (otherwise identical) standard turbulence closure, which tend to flush the bar further offshore. Further improvements are still needed to increase hydrodynamic accuracy, hence sediment transport and morphological evolution, in the inner surf zone.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/R_sm_06qQGM

Are our phylomorphospace plots so terribly tangled? An investigation of disorder in data simulated under adaptive and nonadaptive models

Contemporary methods for visualizing phenotypic evolution, such as phylomorphospaces, often reveal patterns which depart strongly from a naïve expectation of consistently divergent branching and expansion. Instead, branches regularly crisscross as convergence, reversals, or other forms of homoplasy occur, forming patterns described as “birds’ nests”, “flies in vials”, or less elegantly, “a mess”. In other words, the phenotypic tree of life often appears highly tangled. Various explanations are given for this, such as differential degrees of developmental constraint, adaptation, or lack of adaptation. However, null expectations for the magnitude of disorder or “tangling” have never been established, so it is unclear which or even whether various evolutionary factors are required to explain messy patterns of evolution. I simulated evolution along phylogenies under a number of varying parameters (number of taxa and number of traits) and models (Brownian motion, Ornstein–Uhlenbeck (OU)-based, early burst, and character displacement (CD)] and quantified disorder using 2 measures. All models produce substantial amounts of disorder. Disorder increases with tree size and the number of phenotypic traits. OU models produced the largest amounts of disorder—adaptive peaks influence lineages to evolve within restricted areas, with concomitant increases in crossing of branches and density of evolution. Large early changes in trait values can be important in minimizing disorder. CD consistently produced trees with low (but not absent) disorder. Overall, neither constraints nor a lack of adaptation is required to explain messy phylomorphospaces—both stochastic and deterministic processes can act to produce the tantalizingly tangled phenotypic tree of life.

Simulated Evolution and Severe Wind Production by the 25–26 June 2015 Nocturnal MCS from PECAN

The 25–26 June 2015 nocturnal mesoscale convective system (MCS) from the Plains Elevated Convection at Night (PECAN) field project produced severe winds within an environment that might customarily be associated with elevated convection. This work incorporates both a full-physics real-world simulation and an idealized single-sounding simulation to explore the MCS’s evolution. Initially, the simulated convective systems were elevated, being maintained by wavelike disturbances and lacking surface cold pools. As the systems matured, surface outflows began to appear, particularly where heavy precipitation was occurring, with air in the surface cold pools originating from up to 4–5 km AGL. Via this progression, the MCSs exhibited a degree of self-organization (i.e., structures that are dependent upon an MCS’s particular history). The cold pools eventually became 1.5–3.5 km deep, by which point passive tracers revealed that the convection was at least partly surface based. Soon after becoming surface based, both simulations produced severe surface winds, the strongest of which were associated with embedded low-level mesovortices and their attendant outflow surges and bowing segments. The origin of the simulated mesovortices was likely the downward tilting of system-generated horizontal vorticity (from baroclinity, but also possibly friction) within the simulated MCSs’ outflow, as has been argued in a number of previous studies. Taken altogether, it appears that severe nocturnal MCSs may often resemble their cold pool-driven, surface-based afternoon counterparts.