Tailor-Made Turbulence for All Your Modeling Needs

Turbulence — the culprit behind bumpy airplane rides, breaking waves, and billowing clouds — happens throughout the universe. Modeling realistic turbulent plasmas is difficult and computationally expensive, but new methods make simulating turbulence easier, faster, and more flexible than ever before.

A Chaotic Challenge

In the accretion disks around supermassive black holes, the clouds of molecular gas where stars are born, and even the atmosphere of the Sun, turbulence plays an important role in transferring energy and mixing plasmas. Because turbulent plasmas are so common, researchers across astronomical fields must wrestle with the challenge of modeling these chaotic systems.

The traditional way to model a turbulent system is to do so numerically: churning through sets of equations to track the behavior of a plasma across tiny time increments. But as models become more complex, this comes at a higher computational cost, requiring an increasingly large number of hours for computers to handle the math.

Synthetic Magnetic Fields

Synthetic models that use relatively simple algorithms to generate realistic plasmas provide an alternative. Recently, Daniela Maci (KU Leuven) and collaborators introduced a new synthetic model of turbulent magnetic fields. The team’s model builds on an existing synthetic model called BxC, a Python-based code that speedily produces three-dimensional turbulent magnetic fields.

To generate realistic turbulence, BxC starts with a random field of vectors based on white noise. From there, this vector field is tweaked and transformed to adjust its statistical properties. In practice, this means that not only do BxC’s synthetic fields look turbulent, they also reproduce the expected statistical properties of a turbulent field, setting BxC apart from other models. This makes BxC an excellent jumping-off platform for creating realistic turbulent magnetic fields, and Maci’s team showed how the statistical properties of the modeled field depend on the model’s inputs.

Paths Forward

To make the synthetic fields more applicable to astrophysical plasmas, Maci’s team expanded upon the original code in a couple of key ways. First, they recognized that turbulence often develops on top of a background magnetic field, and they developed a way for turbulence to be applied while maintaining the underlying structure of the field. This applies to magnetic field structures like loops or flux tubes, both of which are seen in the atmosphere of the Sun. Second, they developed a way to incorporate anisotropy, or a difference in the strength of the turbulence with regard to direction.

This work demonstrates the power and flexibility of synthetic models for representing turbulent magnetic fields — and all of these features are available orders of magnitude faster than they would be from a traditional numerical model. The team anticipates making future additions to their model, all of which would give the user more customization options and make the model applicable to more astrophysical scenarios.

Citation

“BxC Toolkit: Generating Tailored Turbulent 3D Magnetic Fields,” Daniela Maci et al 2024 ApJS 273 11. doi:10.3847/1538-4365/ad4bdf