Optimisation - Revision history

Seltz at 09:54, 4 April 2017

2017-04-04T09:54:38Z

Seltz at 09:53, 4 April 2017

2017-04-04T09:53:09Z

Seltz: Created page with "Because of the lost of intermediates species, the reduced mechanisms obtained after the DRGEP and QSS simplifications do not predict well the trajectories of the detailed sche..."

2017-04-04T09:30:08Z

Created page with "Because of the lost of intermediates species, the reduced mechanisms obtained after the DRGEP and QSS simplifications do not predict well the trajectories of the detailed sche..."

New page

Because of the lost of intermediates species, the reduced mechanisms obtained after the DRGEP and QSS simplifications do not predict well the trajectories of the detailed scheme. An optimisation step is then needed.

The optimisation algorithm performed in ORCh is the genetic algorithm <ref> J.H. Holland, Adaptation in natural and artificial systems, MIT press, Cambridge, MA, USA, 1992. ISBN 0-262-58111-6 </ref> which apply the principles of natural selection and evolution, in order to look for the optimal solution of a problem initialised by a population of potential solutions.

In this case, we want to optimise the parameters of the Arrhenius law, describing the reaction rates. For example, the forward Arrhenius law for the reaction rate of the jth reaction reads :

<math> \dot{Q}_j = A_j T^{\beta_j} exp(-\frac{E_{aj}}{R T}) [A]^{n_{Aj}}[B]^{n_{Bj}} </math>

with <math>A_j</math> the pre-exponential factor, <math>E_{aj}</math> the activation energy, <math>\beta_j</math> the temperature exponent, <math>[A]</math> and <math>[B]</math> the concentrations of the reactants involved and <math>n_{Aj}</math> and <math>n_{Bj} </math> the exponents of concentrations.

Given te high values of <math>A_j</math>, we prefer to work with <math>a_j = log(A_j)</math>.

The parameters values to be optimised are then put in the form of a triplet (<math>a_j</math>,<math>\beta_j</math>,<math>E_{aj}</math>) considered as a chromosome. A population will be composed of a set of chromosomes which will evolve and mutate until converging towards the best solution, i.e. the best chromosome.

===Bibliography ===

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 The optimisation directory is organized as follows :
-* The CreateAnalyticDirectory file is for the coupling with Cantera : After the QSS step, the reduced scheme is composed of 2 files : a xml file which contain the transported species and an analytic routine which recalculate the reaction variables of the scheme. The CreateAnalyticDirectory file contains the method which calculate the analytic part of the scheme during the optimisation step.
+* CreateAnalyticDirectory :
-* The OptimScenario method contains all the inputs necessary to define the optimisation step like the population size, the crossover and mutation rates or the allowed variations of the parameters for example.
+* OptimScenario :
-* The optimisation file contains the main methods of the optimisation step.
+* Optimisation :
 ===Bibliography ===

← Older revision		Revision as of 09:53, 4 April 2017
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	The parameters values to be optimised are then put in the form of a triplet (<math>a_j</math>,<math>\beta_j</math>,<math>E_{aj}</math>) considered as a chromosome. A population will be composed of a set of chromosomes which will evolve and mutate until converging towards the best solution, i.e. the best chromosome.		The parameters values to be optimised are then put in the form of a triplet (<math>a_j</math>,<math>\beta_j</math>,<math>E_{aj}</math>) considered as a chromosome. A population will be composed of a set of chromosomes which will evolve and mutate until converging towards the best solution, i.e. the best chromosome.
		+
		+
		+	----
		+
		+	The optimisation directory is organized as follows :
		+
		+	* The CreateAnalyticDirectory file is for the coupling with Cantera : After the QSS step, the reduced scheme is composed of 2 files : a xml file which contain the transported species and an analytic routine which recalculate the reaction variables of the scheme. The CreateAnalyticDirectory file contains the method which calculate the analytic part of the scheme during the optimisation step.
		+
		+	* The OptimScenario method contains all the inputs necessary to define the optimisation step like the population size, the crossover and mutation rates or the allowed variations of the parameters for example.
		+
		+	* The optimisation file contains the main methods of the optimisation step.
		+

	===Bibliography ===		===Bibliography ===