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Science! references
The following page contains a quick list of references. These introduce the scientific concepts which motivate the project, and provide relevant mathematical background. Most articles are open-access, and selected for readability.
A real‐space cellular automaton laboratory (Rozier & Narteau, 2014) | Paper presenting ReSCAL. Demonstrations of cellular automaton transitions, lattice gas fluid dynamics modelling, and avalanching for sand dunes.
Setting the length and time scales of a cellular automaton dune model from the analysis of superimposed bed forms (Narteau et al, 2009) | First paper presenting ReSCAL. How do you compare the size and speed of a modeled dune to a real one? Turns out that's a tricky question with an empirical answer. Paper also reviews cellular automaton mechanics and linear stability analysis.
Mean sediment residence time in barchan dunes (Zhang et al 2014) | Sample parameters scaling ReSCAL for a range of real dunes (Table 2).
Snow bedforms: a review, new data and a formation model (Filhol & Sturm, 2015) | Sections 1 and 2 present an excellent overview, with photos and descriptions of snow bedforms, as well as a brief summary of their effects on climate.
The next most accessible references are Kelly Kochanski's proposals and pre-prints (not yet available online).
The following two papers are older reviews for the curious:
Surface structures in snow (Doumani, 1967) | Short descriptive paper with many photos from Antarctica; one of the first papers on snow bedforms.
Studies on interaction of wind and dry snow surface (Kobayashi 1979) | Long paper full of clever experiments and insights; skim photos and schematics. Good reference alongside Filhol & Sturm if you see an interesting feature and want to know if anyone has thought about if before.
These papers, generated using ReSCAL, are examples of types of studies we might do on snow dunes.
Phase diagrams of dune shape and orientation depending on sand availability (Gao et al, 2015) | Example parameter space exploration, validated with study of satellite photos of dunes in the Sahara.
Morphology and dynamics of star dunes from numerical modelling (Zhang et al, 2012) | First model of a previously-unmodelled type of dune.
Self-organization is driven by either (1) critical processes, (2) activator-inhibitor relationships, or (3) fluxes of energy. Each of these concepts has generated its own field of mathematics. Highlight papers from each are below.
(1) How Nature Works (Bak, 1996) | Very well written book presenting the concepts of self-organization and criticality, and demonstrating that self-organized systems are self-similar or fractal. First chapter is short, well-written, and worth reading.
(2) Kelly wrote an essay summarizing her favorite references here.
(3) The constructal law and the evolution of design in nature (Bejan & Lorente, 2011) | New (and controversial) idea: natural systems self-organize to maximize their access to flow (of... something, generally assumed to be entropy).
Dissipative adaptation in driven self-assembly (England, 2015) | Newer (and more controversial) idea: systems not only self-organize in order to dissipate energy effectively, but in order to be remain stable in the face of changing energy inputs. This is loosely related to the idea that ecosystems organize in order to be not only productive, but resilient to a changing environment.
Cellular automata as models of complexity (Wolfram 1984) | Self-organization happens when complex behavior emerges from a system made of many simple parts. Cellular automata are models made of many simple parts, and complex behavior emerges from them.
Lattice-gas automata for the Navier-Stokes equation (Frisch et al, 1986) | Lattice-gas automata are used to simulate air flow in ReSCAL. The authors of this paper think ahead to the simulation of fluid flows on massively parallel machines.