HEEDS/ DARS-Basic Global Mechanism Optimization

HEEDS/ DARS-Basic

Global Mechanism Optimization

Megan Karalus, PhD Application Engineer

CD-adapco

November 2014

Why do I need a global mechanism?

Simple

Chemistry

STAR-CCM+

Predict

CO Emissions

Flame Behavior

Global

Mechanism

What is a global mechanism?

Level of

Description

Reactions Notes

Single Step CH4 + 2O2 CO2 + 2H2O • Complete Combustion

Three Step

CH4 + 1.5O2 CO + 2H2O

CO + 0.5O2 CO2

CO2 CO + 0.5O2

• Includes Some Intermediate

Species

• Rates are fitted

• Valid for a narrow range of

conditions

Detailed

Kinetic

Mechanism

Hundreds……

Species: OH, O, H, CH,

CH2O, C2H6, etc

• Includes “all” intermediate

species

• Rates are measured

• Valid for a wide range of

conditions

Take Methane as an Example

What are the rates of the global mechanism????

Process

Surrogate

Model Analysis

Software

Optimization Software/

Algorithm

Surrogate Model and Analysis Software

• Compare against results using detailed mechanism

• Can’t run detailed in CFD.

Need Rates for Global Mechanism

• Allows us to focus on kinetics

• Can handle detailed mechanism to generate target values

Need Surrogate Model • Freely propagating

laminar flame.

DARS-Basic

DARS-Basic Simulation

• Freely propagating flame (fuel and air are premixed)

Fuel/Air Premix Hot Products

Reaction Mechanism

describes this part

Process

Surrogate

Model

Optimization Software/

Algorithm

HEEDS MDO HEEDS MDO is a multi-disciplinary

optimization tool from Red Cedar Technology.

There are two components to HEEDS MDO:

Process Automation

Automate the Virtual Prototype Build Process

Enable Scalable Computation across platforms

Design Exploration

Efficient Exploration (Optimization, Sweeps, DOE)

Sensitivity & Robustness Assessment

These two components combined , coupled with its leading hybrid adaptive search algorithm SHERPA, makes HEEDS MDO the most technologically advanced parametric optimization tool in the world

Process Automation

Design Exploration

9

Standard Optimization Process

Define Optimization Problem

Optimized Solution

Build Baseline Model

Proposed Solution

Yes

No Satisfied?

Select Optimization Algorithm and Set Tuning Parameters

10



Optimized Solution


Proposed Solution

Yes

No Satisfied?

Characteristics of the design space are unknown


11



Optimized Solution


Proposed Solution

Yes

No Satisfied?

Gradient-based methods

Linear programming

Simplex methods

Genetic algorithm

Simulated annealing

Particle swarm method

Ant colony method

Response surface methods

Etc.


12



Optimized Solution


Proposed Solution

Yes

No Satisfied?

Genetic algorithm (GA)

Population size

Number of generations

Cross-over type

Mutation type

Selection type

Cross-over rate

Mutation rate

Selection parameters

Etc.


SHERPA

Hybrid, Adaptive Optimization Algorithm

No Tuning Parameters

No Opt Expertise Required

13

Modern Optimization Process


Optimized Solution


Proposed Solution

Yes

No Satisfied?



Optimized Solution


HEEDS Procedure Standard Procedure

14

Hybrid

Blend of search strategies used simultaneously

Global and local search performed together

Leverages the best of all methods

Adaptive

Adapts itself to the design space

Efficiently searches simple and

very complicated spaces

Very cost effective for complex problems!

SHERPA Search Algorithm The SHERPA Search Algorithm

Process

Surrogate

Model

Now we look at our

study….

Global Mechanism

1) JetA + 2O2 -> 4C2H4 + 4CO + 3.5H2

2) C2H4 + O2 -> 2CO + 2H2

3) CO+ H2O = CO2 + H2

4) CO2 -> CO + 0.5 H2O

5) H2 + 0.5O2 -> H2O

Pressure = 3.5 bar,

Temperature = 450K,

Equivalence Ratio = 0.4 – 4.8

F. Xu, V. Nori, J. Basani. “CO Prediction for Aircraft

Gas Turbine Combustors.” Proceedings of the ASME

Gas Turbo Expo 2013. GT2013-94282.

Honeywell

What do we need for Optimization Study?

• Variables

– What are they?

– Range to vary?

– Initial guess (Baseline)

• Responses

– How do we evaluate results?

• Objectives

– How do we measure improvement?

• Constraints

– Do we need to constrain?

10 Variables

𝜔 = 𝑨𝑒−𝐸𝐴/𝑅𝑇 𝐶 𝒏 𝐷 𝒎

𝐶 + 𝐷 → 𝐸 + 𝐹 Sample Reaction:

Reaction Rate:

Variables we can vary

• A : Pre-exponential Factor

• n, m : FORD (forward reaction rate exponents)

4 Responses (Curve Fits)

CO vs. T (K)

Phi = 0.6

CO vs. T (K)

Phi = 1.0

CO vs. T (K)

Phi = 1.4 Flame Speed (cm/s)

vs.

Phi

Blue = Target (Dagaut)

Red = Baseline

Objectives and Constraints

Objectives Weight

Curve Fit: Flame Speed 1

Curve Fit: CO vs. T, Phi = 0.6 20



Constraints

Flame Speed Error at Phi=1.0 +/- 10%

Max CO Error at Phi = 0.6 +/- 10%

SHERPA Benchmark Example

HEEDS Optimization

SHERPA

21 | Design Exploration

Change design variables

Responses

Target Curve

Design Curve

Note that only the CO (0.6 value for phi) objective history plot is shown

SHERPA Benchmark Example

HEEDS Optimization

SHERPA

22 | Design Exploration

Change design variables

Responses

Target Curve

Design Curve

Note that only the CO (0.6 value for phi) objective history plot is shown

OPTIMIZED DESIGN

HEEDS Results

1000 Evaluations

5.5 hours

Results: Parallel Plots

Percent Change from Baseline

A_1 1E+12 3.41E+11 -66%

A_2 1E+12 1E+12 0%

A_3 5E+12 7.49E+12 50%

A_4 2.00E-08 5.12E-08 156%

A_5 1E+14 1.01E+14 1%

_1_FORD_JetA 0.5 0.544 9%

_1_FORD_O2 0.6 0.59 -2%

_2_FORD_C2H4 0.8 0.788 -2%

_2_FORD_O2 0.8 0.82 2%

_4_FORD_H2 0.5 0.552 10%

_4_FORD_O2 1.2 1.2 0%

1) JetA + 2O2 -> 4C2H4 + 4CO + 3.5H2

2) C2H4 + O2 -> 2CO + 2H2

3) CO+ H2O = CO2 + H2

4) CO2 -> CO + 0.5 H2O

5) H2 + 0.5O2 -> H2O

HEEDS Results

CO vs. T (K)

Phi = 0.6

CO vs. T (K)

Phi = 1.0

CO vs. T (K)

Phi = 1.4 Flame Speed (cm/s)

vs.

Phi

Blue = Target (Dagaut)

Red = Baseline Green = Best Design

Purple = Honeywell

Testing this mechanism across full range….

Summary of Multi-Objective Study

• Large range explored for each variable (non-error designs)

• Many feasible designs found.

• Best designs are mostly clustered around same solution.

• Focusing on smaller equivalence ratio range sped overall

computations with little cost to the final optimized

mechanism.

• Adequately captured best result from manually tuned global

mechanism in ASME paper.

Can we do “better”?

• Multi-objective optimization showed significant

improvements over baseline.

• We know (from experience and the paper) that there is a

trade-off in predicting CO vs. Flame Speed. Specifying how

much we’re willing to compromise on one or another can be

difficult -> Trade-off Study to find Pareto Front.

• Trade-off Study also helps illuminate the underlying

limitation of the global mechanism chosen for optimization.

• Competing Objectives:

– Flame Speed Curve Fit

– Unified CO Curve Fit

Pareto: Trade-off Study – Best Compromise

Pareto: Trade-off Study – Better CO

Pareto: Trade-off Study – Better Flame Speed

Pareto Front Conclusions

• Confirms trade-off in predicting Flame Speed and CO.

• Provides additional information on limitations of chosen

global mechanism.

• Gives engineer multiple options, depending on goal of CFD

simulation.

Thank you!

Questions?

Efficient Exploration

HEEDS Software

Function : f x( ) = - xi sin xi( )i=1

n

å

- 500 £ xi £ 500

Minimum : f = -418.9829n

Graph showing function for 2 variables

Average values for 25 optimizations from random baselines

Results for n = 20

Benchmark

x1

x2

36 | Copyright 2014 - Red Cedar Technology: All Rights Reserved

HEEDS/ DARS-Basic Global Mechanism Optimization

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