Revive 1D radiation improvements of PR965

[wandadars]

I was looking through the open pull requests and saw #965 . It seemed like it was something that could be pushed across the finish line given the recent updates to Cantera that allow for easier handling of data in the YAML file.

Potentially allows for the discussions in Cantera/enhancements#72 to be realized.

I added some of the data from the original pull request into the gri30.yaml file (attached), and ran the 1D diffusion flame example

gri30.txt

From the discussions in the #965 it seemed like there was more than one possibility for handling that absorption coefficient calculation, so I added a keyword of fit-type which can be table or polynomial. The polynomial type is what was originally used and the table is the log linear interpolation data that was apparently better. PMAC is Plank Mean Absorption Coefficient. I'm pretty clueless on radiation modeling, so if anyone else knows more about this, please chime in.

As an example this would be the YAML entry for a species C2H6 that used the log-linear interpolation tabulated data.

- name: C2H6
  composition: {C: 2, H: 6}
  thermo:
    model: NASA7
    temperature-ranges: [200.0, 1000.0, 3500.0]
    data:
    - [4.29142492, -5.5015427e-03, 5.99438288e-05, -7.08466285e-08, 2.68685771e-11,
      -1.15222055e+04, 2.66682316]
    - [1.0718815, 0.0216852677, -1.00256067e-05, 2.21412001e-09, -1.9000289e-13,
      -1.14263932e+04, 15.1156107]
    note: L8/88
  transport:
    model: gas
    geometry: nonlinear
    well-depth: 252.3
    diameter: 4.302
    rotational-relaxation: 1.5
  radiation:
    model: PMAC
    fit-type: table
    temperatures: [200.0, 300.0, 400.0, 500.0, 600.0,
            700.0, 800.0, 900.0, 1000.0, 1100.0, 1200.0, 1300.0, 1400.0, 1500.0, 1600.0,
            1700.0, 1800.0, 1900.0, 2000.0, 2100.0, 2200.0, 2300.0, 2400.0, 2500.0, 2600.0,
            2700.0, 2800.0, 2900.0, 3000.0, 3100.0, 3200.0, 3300.0, 3400.0, 3500.0]
    data: [0.38479552555827473, 0.330335973067736, 0.4412642688261878, 0.7234904395389986,
           1.3047333754840624, 2.4582817867678797,
            4.728954047788753, 9.185141033669707, 17.899543021230603, 34.850795560924084,
            67.58893234939407, 130.26844542962587, 249.09122926085138, 471.9318246172849,
            885.130031885148, 1642.3336783887491, 3013.480869817436, 5466.6396914389925,
            9803.371388627636, 17379.457798083575, 30460.7202083096, 52790.33601327037,
            90480.336797945, 153407.43112853833, 257355.273339689, 427303.61580306507,
            702366.9359563864, 1143267.5642510268, 1843354.9491235327, 2944846.812649131,
            4662659.170473707, 7318745.5983123155, 11391692.329199707, 17587602.579628713]

And for the case where the older polynomial fits (I'm not sure how prevalent/standardized this form is) are used, we specify using:

  radiation:
    model: PMAC
    fit-type: polynomial
    data: [-0.23093, -1.12390, 9.41530, -2.99880, 0.51382, -1.86840e-5]

Tagging the original creator of the pull request and the user who opened the enhancement. @lavrenyukiv @BYUignite