1 / 30

MODELING METEORITE IMPACTS WHAT WE KNOW AND WHAT WE WOULD LIKE TO KNOW

MODELING METEORITE IMPACTS WHAT WE KNOW AND WHAT WE WOULD LIKE TO KNOW. H. J. Melosh (Lunar and Planetary Lab, University of Arizona, Tucson AZ 85721. jmelosh@lpl.arizona.edu). Why Create Computer Models?. Expand (contract) size scale from experimentally feasible studies

onan
Download Presentation

MODELING METEORITE IMPACTS WHAT WE KNOW AND WHAT WE WOULD LIKE TO KNOW

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MODELING METEORITE IMPACTS WHAT WE KNOW AND WHAT WE WOULD LIKE TO KNOW H. J. Melosh (Lunar and Planetary Lab, University of Arizona, Tucson AZ 85721. jmelosh@lpl.arizona.edu).

  2. Why Create Computer Models? • Expand (contract) size scale from experimentally feasible studies • Study conditions beyond the reach of experiment (eg. velocity) • Verify the physics

  3. Models must be tested! • Models of experiments are important • Models must be compared with observations • Lessons from DoD code verification program--Pacific Craters debacle not all bad!

  4. BEWARE! Just because a computer image looks good, doesn’t mean it represents reality!

  5. Decide what you want to know Are we modeling a Planet? Or a Rock? You must decide on a scale, L, before you can start a modeling task

  6. Resolution, r All models work by discretizing a real object into a large number of smaller elements (cells) whose properties and interactions with neighbors are represented by averages

  7. Imagine a complex geologic system

  8. Divide it into smaller elements

  9. The number of elements depends on the desired resolution and the number of space dimensions

  10. The number of cells translates into the amount of memory a computer must have to do the simulation: • For a 1-D simulation, storage ~ N • For a 2-D simulation, storage ~N2 • For a 3-D simulation, storage ~N3

  11. For example, assuming a small problem in which 10 double-precision numbers are stored for each cell (80 Bytes/cell) and N = 1000, • For 1-D, need 80 kBytes storage (trivial!) • For 2-D, need 80 MBytes storage (This labtop can do that easily!) • For 3-D, need 80 GBytes storage (now we are up to supercomputers).

  12. The amount of computer storage needed depends on the desired resolution--you cannot simulate a planet and a rock in the same calculation!

  13. The runtime required for a computation depends on the model duration, T, and the resolution r: Stability requires that the time step Dt be a fraction (usually about 1/5) of the time for sound to traverse the smallest cell: Dt = r/soundspeed The number of timesteps is T/ Dt So the total runtime is proportional to N times the number of cells in the model

  14. For the same example as before, assuming the computation takes 1 ms/cell, to get to the time for sound to traverse the entire mesh • For 1-D, need 5 million operations, or 5 sec of runtime • For 2-D, need 5 billion operations, or 1 hour of runtime • For 3-D, need 5 trillion operations, or 1 month of runtime

  15. The first 2-D simulation of an impact (Bjork et al 1967) proudly displayed the resolution

  16. Most modern simulations don’t

  17. But it is there, and resolution tests for accuracy should be made for every simulation

  18. What “test” means… Is that the result important to you (whether it be mass of rock melted, maximum shock pressure, speed of ejecta, etc. Must NOT depend on the resolution, r!

  19. There are two basic types of hydrocode simulations, each with its own advantages and drawbacks:

  20. Lagrangian • The cells follow the material--the mesh itself moves • Free surfaces and interfaces are well defined • But mesh distortion can end the simulation too soon

  21. Eulerian • Material flows through a static mesh • Material interfaces are blurred • Cells contain mixtures of material • Mesh must be large enough to contain entire time evolution

  22. Hydrocode modeling stands on two main pillars:

  23. Equations of State: • Perfect Gas • Stiffened Gas • Grüneisen • Tillotson • ANEOS • SESAME • ???

  24. Constitutive Relations: • Elasticity • Viscosity • Strength • Fracture mechanics, tensional and compressional • Porosity/dilatency • How to treat mixed materials in Eulerian simulations?

  25. The Pacific Craters “Problem” A thrilling tale of Simulation vs. Observation, Courtesy of DoD turf wars

  26. Nuclear testing on Enewetak Atoll in 1958

  27. Produced some remarkable craters

  28. Broad and Shallow, no simulation succeeded in modeling them!

  29. The Moral: Observation, Experiments and Modeling cannot be successful by themselves: Communication between all three disciplines is essential!

  30. Happy Birthday, Zibbi!

More Related