Turns out the partial wall slip amplifies the slip per unit kn but also at the same time flattens the velocity profile’s slope as it grows
The underlying degeneracy of the problem still persists
Gonna check if using curvature based higher order slip model can separate kn and σv
Welcome to the sequel of the 3 am rambling, not at 3 am (hopefully)
Picking up from the earlier endpoint, I kept the σv fixed and swept over 100 evenly spaced kn values from 0.001 to 0.2 to build a better dataset.
Hoping that we could recover the kn from a velocity profile.
Onto the next attempt:
Instead of inverting one profile, I’m gonna try sweeping kn at a fixed σv, and see if that brings enough information for the MLP to predict the params.
Thank you for attending my 3 am rambling!
My expectation was that when the σv=0.5, the kn would be predicted 3x more precisely than the fully accommodating case (σv=1.0).
Based on the conversion factor math but at noise=0.5% it was actually only 1.6x, this piqued my interest.
Onto the next attempt:
Instead of inverting one profile, I’m gonna try sweeping kn at a fixed σv, and see if that brings enough information for the MLP to predict the params.
Thank you for attending my 3 am rambling!
I was studying and trying to understand couette flow with slip condition,
then as a fun experiment I wanted to see if the knudsen number (kn) and the tangential momentum accommodation coefficient (σv)could be predicted given a velocity profile
A nice inverse problem for ML
1/n
Both σv and kn don’t determine the velocity profile independently but are related by the maxwell slip length relation.
There just isn’t enough information to distinguish σv and kn.