Rapidly Accelerated CFD-based Optimisation of Reactive Systems

Transcription

1 Rapidly Accelerated CFD-based Optimisation of Reactive Systems Hier könnte eine kurze Erläuterung gegeben werden. Philip Rößger, Konrad Uebel, Korcan Kirkici, Andreas Richter, Bernd Meyer 8 th International Freiberg Conference on IGCC & XtL Technologies June 13, 2016

2 CFD-based Optimization Uebel et al., Fuel Proc. T. 149, 2016 Quench concept Optimization setup Steam from nozzle Results Pareto optimization Height Wall cooling T (K) Diameter Approx design points (=CFD simulation) using MOGA-II genetic algorithm 2

3 Motivation Proven concept (see Uebel et al., Fuel Proc. T. 149, 2016) For complex systems (multiphase flows, 3d geometries, ) still too time-consuming Solution: Reduce computational effort for a single calculation Reduce number of designs USE OF META MODELS 3

4 Content Challenges for reactive systems Meta-models FAST algorithm Conclusion and Outlook 4

5 Challenges for reactive systems Highly nonlinear systems Large number of design parameters Reactor geometry (height, length, shape, angle) Burner geometry (nozzle diameter, angle, swirl) Process parameter (mass flow, temperature, cooling) OPEX 10,000 design combinations possible META-MODELS 5

6 Meta-models Meta-models or Response Surface Models (RSM) approximate a response variable by mathematical functions using a given database Design base approximated calculation (< 1ms) Meta-model CFD model time-consuming (1-10 h) Objective function(s) Accelerated calculation of objectives & optimization process Accuracy important 6

7 Meta-models Investigated types of meta-models for nonlinear problems Interpolating Approximating Polynomial (Poly) Anisotropic Kriging (AKR) Shepard K-nearest (KN) Smoothing Spline Analysis of Variance (ANOVA) Radial Basis Function (RBF) Neuronal Networks (NN) Database: optimization results using MOGA algorithm (1000 Designs) 7

8 Mean relative error (%) Mean relative error (%) Comparison of meta-models: Mean relative error H2/CO Temperature RBF AKR 1 RBF AKR 1 ANOVA KN 0.5 ANOVA KN Number of basis designs N Mean relative Error < 4% N RBF & ANOVA: Error Number of basis designs N (Highest error: KN, lowest error: AKR) (AKR inconsistent dependency) 8

9 Maximum relative error (%) Maximum relative error (%) Comparison of meta-models: Maximum rel. error H2/CO Temperature RBF AKR ANOVA KN 4 2 RBF AKR ANOVA KN Number of basis designs N Number of basis designs N Lowest maximum error (H2/CO + temperature) for RBF RBF best suitable meta-model 9

10 Optimization using RBF Comparison of Pareto designs for different number of basis designs N = 60 N = 200 MOGA optimization (N = 1000) RBF optimization MOGA optimization (N = 1000) RBF optimization using N CFD designs using N CFD designs Valid meta-model for N = 60 of basis designs Accelerated calculation (60 instead of 1000 CFD calculations) 10

11 FAST optimization Now: Optimization using CFD AND meta-model Design base Meta-model training Virtual exploration Virtual optimization Validation using CFD Meta-model evaluation Loop per generation Developed to accelerate the convergence to Pareto front 11

12 H2/CO at outlet H2/CO at outlet FAST optimization: Validation N = 60 N = MOGA 0.5 MOGA 0.45 FAST 0.45 FAST MOGA1000 MOGA Temperature at outlet (K) Temperature at outlet (K) FAST algorithm performance better for less design evaluations 12

13 Conclusion The optimization of a new quench design was used to test different metamodels. It could be shown, that meta-models enable a rapidly accelerated optimization (60 instead of 1000 CFD calc. AND improved Pareto front). Outlook Optimization of burners to minimize CAPEX and OPEX is in progress. 13

14 Acknowledgements The authors acknowledge the financial support by the European Social Fund (ESF) and the Free State of Saxony in the framework of ProVirt (project no ). 14