Applications of PETSc to open questions in planet formation theory

Ellen M. Price

Center for Astrophysics | Harvard & Smithsonian

PETSc '19, 7 June 2019

Coauthors

  • Prof. Karin Öberg (Harvard)
  • Prof. Ilse Cleeves (UVa)
  • Prof. Leslie Rogers (UChicago)

Outline of today's talk

  1. Introduction to exoplanets
  2. Project 1: SQUISHv2 and tidally distorted rocky planets
  3. Introduction to planet formation
  4. Project 2: Coupling accretion and chemistry in protoplanetary disks
  5. Questions

Exoplanets and their properties

Discovering planets

(source: nasa.gov)

Detecting planets

(source: jpl.nasa.gov)

Aside: Kepler selection biases

  • Large planets: deeper, stronger transit signal
  • Close-in planets: more frequent transits, better identification of periodic signals

Planets are diverse!

(source: popularmechanics.com)

Planets are ubiquitous!

Project 1: Structures and compositions of tidally-distorted rocky planets

What are squishy planets?

  • Original idea (Hachisu 1986a,b): Use potential theory and relaxation method to solve for the shapes of stars in multiple systems
  • Stars modeled as polytropes, density $\propto$ pressure to some power $\gamma$
  • A rocky planet will act as a fluid on geological timescales
  • Use a “modified polytrope” equation of state (Seager+ 2007) $\rho = \rho_0 + A p^\gamma$

What are squishy planets?

Methodology

  1. Initialize with a guess density distribution
  2. Compute the potential everywhere
  3. Compute the enthalpy from the potential
  4. Compute the density from the enthalpy
  5. Repeat until convergence

PySquish implementation

  • Uses no internal parallelization — jobs are submitted to slurm as array jobs
  • Python frontend, Cython bridge to C++ backend
  • Compiled-in grid resolution
  • Truncate the spherical harmonics series at a fixed number

Results from PySquish

Results from PySquish

SQUISHv2 implementation

  • Uses some pure MPI for task management
  • Uses PETSc for memory management (DMDA) and ODE solve (TS)
  • Compiled-in grid resolution
  • Truncate the spherical harmonics series at a fixed number

Volume render of a squishy planet

Planet formation theory

Where do planets form?

(inspired by Henning & Semenov 2013)

Forming planets from dust

  • Dust grains collide and stick, bounce, or fragment
  • Grains are stickier if they have ices
  • Still have barriers to formation, so not a perfect theory

ALMA observes disks

(source: theverge.com)

ALMA observes disks

(source: eso.org)

Even more substructure in disks

(source: almascience.eso.org, see Andrews, Huang+ 2018 and associated papers)

Observed early planets

(source: nature.com, Haffert+ 2019)

Formation environment is important

(inspired by Henning & Semenov 2013)

Disk chemistry

Formaldehyde

Methanol

Hydrogen cyanide

Disk chemistry

(courtesy of Karin Öberg)

Project 2: Coupling chemistry and accretion in a protoplanetary disk

Problem statement

We want to know how accretion affects the chemistry that occurs in disks to gain insight into the compositions of planets that can form from the disk material.

Global solution

  • Can do a single, “global” simulation of disk chemistry
  • Pros:
    • Can incorporate as much physics as needed
  • Cons:
    • Very computationally expensive!
    • If we have $M$ species and $N$ computational cells and cells are not independent, would need a $M N \times M N$ (sparse) matrix to solve for all chemistry at a single timestep
  • Examples given in Haworth+ (2016)

Alternative: reduce chemical network

  • Can choose a subset of “important” reactions to reduce number of reactions and species being considered
  • Pros:
    • Can incorporate as much physics as needed
  • Cons:
    • Lose chemical insight
    • Chemical network reduction is a science/art itself!

Alternative: reduce physics

  • Can reduce the physical processes considered
  • Common approach is to assume that disk material is not moving radially or vertically
  • Pros:
    • No loss of chemical complexity
  • Cons:
    • Missing (possibly) important effects of dynamic processes

Alternative: local simulation

  • Solve for accretion tracks self-consistently and follow chemistry along tracks
  • Pros:
    • Under some assumptions, no compromise between chemistry and physics
    • Approximately equal computational load to a static model
    • Can be built upon in future work
  • Example: Heinzeller, Nomura, & Walsh (2011)

Surface density

$$ \frac{\partial \Sigma}{\partial t} - \frac{3}{R} \frac{\partial}{\partial R} \left[R^{1/2} \frac{\partial}{\partial R} \left(\nu \Sigma R^{1/2}\right)\right] = 0 $$
  • From Lynden-Bell & Pringle (1974) and Pringle (1981)
  • By combining the Navier-Stokes and mass continuity equation, we find a diffusion-type equation in surface density $\Sigma$

Chemistry post-processing

Consider chemistry in a “box”

$$ n_i + n_j \rightarrow \cdots $$ $$ n_{j1} + n_{j2} \rightarrow n_i + \cdots $$

Chemical network: reactions with rates $P_j$ and $R_j$

$$ \frac{\mathrm{d} n_i}{\mathrm{d} t}\bigg|_V = \sum\limits_j P_j n_{j1} n_{j2} - n_i \sum\limits_j R_j n_j $$

Tracks through the disk

(source: Price, Cleeves, & Öberg, 2019, in prep.)

Does accretion affect chemistry?

(source: Price, Cleeves, & Öberg, 2019, in prep.)

Acknowledgements