Type Classes for Lightweight Substructural Types

Linear and substructural types are powerful tools, but adding them to standard functional programming languages often means introducing extra annotations and typing machinery. We propose a lightweight substructural type system design that recasts the structural rules of weakening and contraction as type classes; we demonstrate this design in a prototype language, Clamp. Clamp supports polymorphic substructural types as well as an expressive system of mutable references. At the same time, it adds little additional overhead to a standard Damas-Hindley-Milner type system enriched with type classes. We have established type safety for the core model and implemented a type checker with type inference in Haskell.Comment: In Proceedings LINEARITY 2014, arXiv:1502.0441

Tangling clustering instability for small particles in temperature stratified turbulence

We study particle clustering in a temperature stratified turbulence with small finite correlation time. It is shown that the temperature stratified turbulence strongly increases the degree of compressibility of particle velocity field. This results in the strong decrease of the threshold for the excitation of the tangling clustering instability even for small particles. The tangling clustering instability in the temperature stratified turbulence is essentially different from the inertial clustering instability that occurs in non-stratified isotropic and homogeneous turbulence. While the inertial clustering instability is caused by the centrifugal effect of the turbulent eddies, the mechanism of the tangling clustering instability is related to the temperature fluctuations generated by the tangling of the mean temperature gradient by the velocity fluctuations. Temperature fluctuations produce pressure fluctuations and cause particle clustering in regions with increased pressure fluctuations. It is shown that the growth rate of the tangling clustering instability is much larger than that of the inertial clustering instability. It is found that depending on the parameters of the turbulence and the mean temperature gradient there is a preferential particle size at which the particle clustering due to the tangling clustering instability is more effective. The particle number density inside the cluster after the saturation of this instability can be in several orders of magnitude larger than the mean particle number density. It is also demonstrated that the evaporation of droplets drastically change the tangling clustering instability, e.g., it increases the instability threshold in the droplet radius. The tangling clustering instability is of a great importance, e.g., in atmospheric turbulence with temperature inversions.Comment: 13 pages, 7 figures, REVTEX4-1, revised versio