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Some RMG Basics . William H. Green MIT Dept. of Chemical Engineering RMG Study Group Sept. 27, 2013. Key Components of RMG the Design Tool. Mechanism Generation Algorithm Estimation Procedures for all the Numbers Known numbers in Libraries / Databases

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some rmg basics

Some RMG Basics

William H. Green

MIT Dept. of Chemical Engineering

RMG Study Group

Sept. 27, 2013

key components of rmg the design tool
Key Components of RMG the Design Tool
  • Mechanism Generation Algorithm
  • Estimation Procedures for all the Numbers
    • Known numbers in Libraries / Databases
    • Successive refinement of sensitive numbers
  • Numerical Solvers (and Sensitivity analysis)
  • User GUI for variety of use cases
slide3

Simulation Predictions

Numerical

Diff. Eq. Solver

Very

Long

List

of

Reactions

with

Rate

Parameters

Rxn Mechanism

Generator

Where RMG fits in…

Chemistry Knowledge (some data, mostly generalizations)

Unambiguous

Documentation

of Assumptions

about how

molecules react

Simulation

Eqns

dY/dt = …

Interpreter

Current paradigm: CHEMKIN

(Cantera, KIVA, etc.)

slide4

Rate-Based algorithm: Faster pathways are explored further, growing the model

Before:

First rate-based algorithm paper:

Susnow et al. JPCA (1997)

B

E

“Current Model” inside.

RMG decides whether

or not to add edge species

to this model. Currently:

rE(tn) > tol*Rchar(tn) ?

This algorithm always

converges in finite steps,

but not always efficient.

A

D

C

F

After:

H

B

E

Open-Source RMG software developed with funding from

DOE Basic Energy Sciences.

Download from rmg.sourceforge.net

A

D

C

G

F

accuracy depends on both mechanism completeness and accurate numbers
Accuracy depends on both mechanism completeness and accurate numbers

Current RAM limit

Completeness

of Reaction

Network

(tightness of

Rtol)

Current Human

+ CPU time limit

Avg of Avgs

Group Est

CBS-QB3

RRHO TST

CCSD(T)-F12

Careful rotors

Definitive

Experiment

Accuracy of sensitive k’s, DH’s, DS’s, Cp’s

Often assume simulation solver errors & approximations are negligible

and that we have perfect knowledge of boundary conditions; sometimes these errors

are larger than errors from reaction network incompleteness and errors in k’s etc.

Model can also be incomplete due to missing reaction family or forbidden species.

slide7

Numerical Accuracy of DH’s & k’s affects reaction network!

Rate-based algorithm is selecting species based on estimated

formation rate. If rate is wildly underestimated, (e.g. too-low k,

possibly from too-high H) the edge species will never be

considered important at any practical Rtol, and so it will

never make it into the kinetic model.

Edge Species omitted from the model are currently GONE FOREVER.

No easy way to catch a mistake like this (except sometimes by

Experimental validation).

recommended model construction procedure with
Recommended Model-Construction Procedure with
  • RMG assembles large kinetic model for particular conditions using rough estimates of rate coefficients k to decide which species to include.
  • If sensitive to k derived from rough estimate, recompute that k using quantum chemistry.
    • Unfortunately, quantum calcs for rates not fully automated.
    • Generalize from quantum to improve rate estimation rules, and ensure they get incorporated into RMG database.
  • Iterate until predictions you care about are not sensitive to any rough estimates.
  • Repeat for different conditions (Co,T,P,Dt) using current model as seed.
  • Compute prediction& compare with experiments.
    • Called “validation”. Predictive Mode: no tweaking of k’s to force agreement with experiment.
what do we expect from model vs data comparisons
What do we Expect from Model vs. Data Comparisons?
  • At present, Thermo rarely known better than 1 kcal/mole, Ea’s uncertain by ~ 2 kcal/mole, and A’s often uncertain by factor of 2. So….
    • we don’t expect perfect agreement!
    • Precise agreement means model parameters were fitted to match experiment, not predictions. Or lucky.
  • However, we think our estimates are reasonable, and our software is pretty good.
  • So… we expect discrepancies to be less than an order of magnitude for both overall reaction timescale and product distribution.