Revive 1D radiation improvements of PR965 #1799
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We should integrate RadLib into Cantera. The library is written in C++ and has Python and Fortran interfaces. It is well-documented, and published. I can provide details, and would like to be involved.
Repository:
https://github.com/BYUignite/radlib
Publication:
V.B. Stephens, S. Jensen, I. Wheeler, D.O. Lignell, “RadLib: a radiative heat transfer model library for CFD,” Computer Physics Communications, 272:108227<https://doi.org/10.1016/j.cpc.2021.108227>, (2022).
Documentation:
<https://ignite.byu.edu/radlib_documentation>
RadLib: RadLib<https://ignite.byu.edu/radlib_documentation>
ignite.byu.edu<https://ignite.byu.edu/radlib_documentation>
[X]<https://ignite.byu.edu/radlib_documentation>
David O. Lignell
Professor, Chemical Engineering
Brigham Young University
801-422-1772 | http://ignite.byu.edu
On Oct 21, 2024, at 9:10 PM, Chris Neal ***@***.***> wrote:
I was looking through the open pull requests and saw #965<#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<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<https://cantera.org/examples/python/onedim/diffusion_flame.py.html>
gri30.txt<https://github.com/user-attachments/files/17469322/gri30.txt>
From the discussions in the #965<#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<https://github.com/lavrenyukiv> @BYUignite<https://github.com/BYUignite>
…________________________________
You can view, comment on, or merge this pull request online at:
#1799
Commit Summary
* a9bf1e5<a9bf1e5> [oneD] reproducing PR965 with yaml file addition
File Changes
(3 files<https://github.com/Cantera/cantera/pull/1799/files>)
* M include/cantera/oneD/Flow1D.h<https://github.com/Cantera/cantera/pull/1799/files#diff-33e21ca309e69ad7952e02cc9817dd9a4ce057e21c59a8276888d732a08bef6d> (34)
* M src/oneD/Flow1D.cpp<https://github.com/Cantera/cantera/pull/1799/files#diff-14affd62b2c5b0e4c4d0001f3d72e5f0d3044785ac027e39455a5ebbcde5121b> (115)
* A test/data/air_radiation.yaml<https://github.com/Cantera/cantera/pull/1799/files#diff-89aaafb4bfefb80fc680b8d43085d4181f8a8d8819c75755c1085bfb5306095b> (274)
Patch Links:
* https://github.com/Cantera/cantera/pull/1799.patch
* https://github.com/Cantera/cantera/pull/1799.diff
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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-typewhich can betableorpolynomial. Thepolynomialtype is what was originally used and thetableis the log linear interpolation data that was apparently better. PMAC isPlank 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.
And for the case where the older polynomial fits (I'm not sure how prevalent/standardized this form is) are used, we specify using:
Tagging the original creator of the pull request and the user who opened the enhancement. @lavrenyukiv @BYUignite