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result(s) for
"Snowflakes."
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How come it's snowing?
\"Explains in simple terms the science behind snow and includes a glossary and the water cycle\"-- Provided by publisher.
Book
SOLPS-ITER numerical simulations of ITER-scale Snowflake divertors: low-field-side SF−/SF+ and high-field-side SF−/SF+ configurations
Using the edge plasma code package SOLPS-ITER, we study the four types of Snowflake (SF) divertors for an ITER-size tokamak, with toroidal magnetic field BT∼ 5 T, major radius R ∼ 5 m and plasma current Ip ∼ 10 MA. Our aim is to provide insights into SF divertor design for future devices. In this work, the impacts of magnetic geometry and divertor target geometry in the four types of SF configurations on plasma behavior and power exhaust performance are investigated in detail. Low-recycling regime, high-recycling and detachment in the four types of SF divertors are obtained through an upstream density scan. The secondary X-point positions of SF divertors are systematically varied to examine their impact. For Low-Field-Side (LFS) SF− and High-Field-Side (HFS) SF− divertors the observed power splitting, induced by the secondary X-point, is consistent with experimental observations. The effect of target geometry is studied by comparing the flat target plates with the ITER-like divertor shape. The overall simulation results reveal a notable consequence of the LFS SF− divertor: a closed structure of the inner target with highly inclined plate can compress recycling neutrals originating from the HFS divertor region towards the LFS Scrape-Off Layer (SOL) and Private Flux Region regions. This results in considerable volumetric dissipation through strong ionization and recombination, causing the connected outer target region to detach. This feature can be considered in the design of the LFS SF− divertor for future devices. For the LFS and HFS SF+ divertors, the region between the two X-points exhibits strong ionization and recombination sources which are close to the primary X-point. This feature might be beneficial for the formation of X-Point Radiator (XPR) but would require further impurity seeding simulation study.
Journal Article
100 snowflakes : a winter counting book
\"Snowflakes are everywhere--on mittens, on presents, on windows--and young readers can practice counting up to 100 by ones with this book\"-- Provided by publisher.
Book
Magnetic control of tokamak plasmas through deep reinforcement learning
Nuclear fusion using magnetic confinement, in particular in the tokamak configuration, is a promising path towards sustainable energy. A core challenge is to shape and maintain a high-temperature plasma within the tokamak vessel. This requires high-dimensional, high-frequency, closed-loop control using magnetic actuator coils, further complicated by the diverse requirements across a wide range of plasma configurations. In this work, we introduce a previously undescribed architecture for tokamak magnetic controller design that autonomously learns to command the full set of control coils. This architecture meets control objectives specified at a high level, at the same time satisfying physical and operational constraints. This approach has unprecedented flexibility and generality in problem specification and yields a notable reduction in design effort to produce new plasma configurations. We successfully produce and control a diverse set of plasma configurations on the Tokamak à Configuration Variable
1
,
2
, including elongated, conventional shapes, as well as advanced configurations, such as negative triangularity and ‘snowflake’ configurations. Our approach achieves accurate tracking of the location, current and shape for these configurations. We also demonstrate sustained ‘droplets’ on TCV, in which two separate plasmas are maintained simultaneously within the vessel. This represents a notable advance for tokamak feedback control, showing the potential of reinforcement learning to accelerate research in the fusion domain, and is one of the most challenging real-world systems to which reinforcement learning has been applied.
A newly designed control architecture uses deep reinforcement learning to learn to command the coils of a tokamak, and successfully stabilizes a wide variety of fusion plasma configurations.
Journal Article
If snowflakes tasted like fruitcake
Imagines the results if snowflakes tasted like different things, such as sugar plums, oatmeal, or noodle soup.
Book
A Passive Micromixer with Koch Snowflakes Fractal Obstacle in Microchannel
The passive micromixer is one of the essential devices that can be integrated into the Lab on Chip (LoC) system. Micromixer is needed to increase mixing efficiency. In this paper, two Koch fractal obstacle-based micromixer models of Secondary Snowflakes Fractal Micromixer (SSFM) and Tertiary Snowflakes Fractal Micromixer (TSFM) were designed. The effect of the Koch fractal resistance angles (15o, 30o, 45o, 315o, 330o, 345o) and the influence of the inlet (T and T-vortex) were studied in this paper using COMSOL Multiphysics numerical simulations. The results showed that the TSFM structure with a 30o angle on the T-vortex inlet is optimal. The deflection phenomena generated by the TSFM obstacle enhance the contact area between the two fluids and chaotic convection can be increased at Reynolds Number (Re) 0.05 and Re 100. This paper examines concentration curves along the channel ranging from 1 mol/L to 5 mol/L. This clearly shows that the fluid flow direction changes within the microchannel. This work provides a new design for the micromixer.
Journal Article
I'm scared!
Chilly the snowflake is scared of lots of things, but learns that facing his fears and trying something new can be fun.
Book
Who invented von Koch's Snowflake Curve?
A strange title, might you say: Answer is in the question! However, contrary to popular belief and numerous citations in the literature, the image of the snowflake curve is not present or even mentioned in von Koch's original articles. So, where and when did the first snowflake fall? Unravel the mystery of the snowflake curve with us on a journey through time.
Paper
De novo evolution of macroscopic multicellularity
While early multicellular lineages necessarily started out as relatively simple groups of cells, little is known about how they became Darwinian entities capable of sustained multicellular evolution
1
–
3
. Here we investigate this with a multicellularity long-term evolution experiment, selecting for larger group size in the snowflake yeast (
Saccharomyces cerevisiae
) model system. Given the historical importance of oxygen limitation
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, our ongoing experiment consists of three metabolic treatments
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—anaerobic, obligately aerobic and mixotrophic yeast. After 600 rounds of selection, snowflake yeast in the anaerobic treatment group evolved to be macroscopic, becoming around 2 × 10
4
times larger (approximately mm scale) and about 10
4
-fold more biophysically tough, while retaining a clonal multicellular life cycle. This occurred through biophysical adaptation—evolution of increasingly elongate cells that initially reduced the strain of cellular packing and then facilitated branch entanglements that enabled groups of cells to stay together even after many cellular bonds fracture. By contrast, snowflake yeast competing for low oxygen
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remained microscopic, evolving to be only around sixfold larger, underscoring the critical role of oxygen levels in the evolution of multicellular size. Together, this research provides unique insights into an ongoing evolutionary transition in individuality, showing how simple groups of cells overcome fundamental biophysical limitations through gradual, yet sustained, multicellular evolution.
After 600 rounds of selection, anaerobic snowflake yeast evolved to be macroscopic, becoming around 20,000 times larger (approximately mm scale) and about 10,000-fold more biophysically tough, while retaining a clonal multicellular life cycle.
Journal Article