PERFORMANCE OF OPTIMIZATION ALGORITHMS

FOR DERIVING MATERIAL DATA FROM BENCH SCALE TESTS

Patrick Lauer
University of Wuppertal

Content

Introduction

  • Aim: Find good performing optimization algorithm for material parameter estimation to simulate pyrolysis

  • Way: Compare best known algorithm for material parameter estimation with two not yet evaluated algorithms utilizing synthetic data and bench scale tests

Method (Flow Chart)

proc

Method

gif

Bench Scale Tests

  • Thermogravimetric Analysis (TGA)
  • Mass Loss Cone Calorimeter (MLC)

TGA

tga

  • Sample size: few mg
  • Defined heating rate
  • Defined atmosphere
  • Capturing mass loss and mass loss rate

MLC

  • Sample size: g…kg
  • Defined heat flux
  • Capturing mass loss and mass loss rate

MLC Video

Setups

  • TGA model
    • Synthetic data
    • TGA experiment with PU
  • MLC model
    • Material: PMMA
    • Isolating and conducting background layer
    • Two experiments:
      • Single heat flux (50 kW/m2)
      • Five heat fluxes parallel (20…75 kW/m2)

Optimization Process

opt

Algorithms

  • Shuffled Complex Evolution (SCE)
  • Artificial Bee Colony (ABC)
  • Fitness Scaled Artificial Bee Colony (FSCABC)

SCE

  • Introduced for hydrologic model calibration
  • Evolutionary algorithm
  • State of the technology for material parameter estimation
  • Divides a population into complexes
  • Two phases after initialization:
    1. Local search per complex
    2. Global evolution between complexes

ABC I

  • Swarm intelligence optimization algorithm
  • Mimics foraging behavior of a honey bee swarm
  • Combines local, global and random search
  • Outperformes standard benchmark tests for optimization algorithms
  • Quite simple
  • Three phases after initialization:
    • Employed bee phase
    • Onlooker bee phase
    • Scout bee phase

bee

ABC II

  • Initialization
    • Find random food source for half oft the bees
  • Employed bees
    • Find food source in neighborhood of each bees known food source

ABC III

  • Onlooker bee phase
    • Find food source based on food sources of all employed bees.
    • Assignment probability is based on quality of employed bees food source
  • Scout bee phase
    • New random food source if no improvement

FSCABC I

  • Modified version of ABC
  • Introduced for path planning of unmanned combat air vehicles
  • Outperformed ABC in this application
  • Changes two parts:
    • Fitness function for assigning in onlooker bee phase
    • Random number generator in scout bee phase

FSCABC II

  • Fitness function is replaced by a fitness power scaling function
    • Sorted ascending by rank
    • Best solution is weighted to the power of k
  • RNG replaced with a chaotic random number generator
    • Pseudorandom
    • Travels ergodically over [0,1]
      chaos

Results

  • Synthetic data
  • TGA
  • MLC50
  • MLCall

Synthetic Data I

  • TGA setup
  • Two reactions
  • Input parameters
    • Density
    • Conductivity
    • Specific Heat
    • Reference Temperature
    • Reference Rate
  • Target: normalized mass loss

Synthetic Data II

syn_01

Synthetic Data III

syn_02

TGA I

  • TGA setup
  • Material: PU
  • Three reactions
  • Input parameters
    • Reference temperature
    • Pyrolysis range
  • Target: normalized mass loss

TGA II

tga_01

TGA III

tga_02

MLC50 I

  • MLC setup
    • Heat flux: 50 kW/m2
  • Material: PMMA
  • Input parameters
    • Density
    • Conductivity
    • Specific Heat
    • Reference Temperature
    • Pyrolysis range
  • Target: normalized mass loss

MLC50 II

mlc50_01

MLC50 III

mlc50_02

MLCall I

  • MLC setup
    • Heat flux: 20, 30, 40, 50, 75 kW/m2
  • Material: PMMA
  • Input parameters
    • Density
    • Conductivity
    • Specific Heat
    • Reference Temperature
    • Pyrolysis range
  • Target: normalized mass loss

MLCall II

mlcall_01

Conclusion

  • Comparsion of three algorithms with synthetic and bench scale data
  • All three generate similar accurate solutions
  • SCE most efficient, but FSCABC often not significant inferior
  • Future tasks:
    • Tune FSCABC parameters
    • Apply on other models