The digital twin in STAR-CCM+ Automated design optimization of fuel cells

Transcription

1 The digital twin in STAR-CCM+ Automated design optimization of fuel cells Christoph Heining

2 Why Simulate Fuel Cells Environmental Sustainability Transportation Electrification Reduce emissions Improve performance Increase energy efficiency Energy Grid Storage Load balancing On-site conversion and storage Innovation New technology New materials TRENDS IMPLICATIONS Increase Efficiency Energy density Lifetime and durability Decrease Thermal variations Material costs Development costs Maintenance costs Avoid abuse conditions Poisoning, degradation, hot spots, flooding, dry-out Page 2

3 level of detail = level of complexity From System Simulation to Detailed 3D Analysis With a Multi-level Approach Development Objective Modeling Focus LMS Amesim Control Design (Pre) Calibration Concept phase, sizing, controls Electrical static model Fuel cell system design and optimization Multi-physics quasi-static models Function & Specification Model in the Loop Function Test Hardware in the Loop Stack design and integration Physical, transient models Implementation Cell design Full flow, energy, electrochemistry and species Software in the Loop Page 3 STAR-CCM+

4 level of detail = level of complexity From System Simulation to Detailed 3D Analysis With a Multi-level Approach Development Objective Modeling Focus Concept phase, sizing, controls Electrical static model Fuel cell system design and optimization Stack design and integration Multi-physics quasi-static models Physical, transient models Compare control strategies with respect to fuel consumption Cell design Full flow, energy, electrochemistry and species Page 4

5 level of detail = level of complexity From System Simulation to Detailed 3D Analysis With a Multi-level Approach Development Objective Modeling Focus Concept phase, sizing, controls Electrical static model Fuel cell system design and optimization Multi-physics quasi-static models Assess cell size, layout, materials, Stack design and integration Physical, transient models Cell design Full flow, energy, electrochemistry and species Page 5

6 level of detail = level of complexity From System Simulation to Detailed 3D Analysis With a Multi-level Approach Development Objective Modeling Focus Concept phase, sizing, controls Electrical static model Fuel cell system design and optimization Multi-physics quasi-static models Assess & optimize cell geometry, flow rates, humidification... H 2 O Stack design and integration Physical, transient models Cell design Full flow, energy, electrochemistry and species H 2 Page 6

7 Cell Design Design Optimization Parametric Geometry BUILD SIMULATE STAR-CCM+ Flexible and Robust Meshing CAD Mesh Solution Analysis Multiphysics Models Speed and Performance EXPLORE Powerful Data Analysis ASSESS Workflow Automation Intelligent Design Exploration STAR-CCM+ fully integrated solution Leverages HEEDS design optimization technology Page 7

8 Parametric Geometry Bi-directional connectivity to CAD software Built-in parametric 3D-CAD tool (here) 0. Interdigitated 1. Parallel Parameters Channel geometry Anode, membrane, cathode iithickness Air flow rate Goals Maximize efficiency Minimize temperature variation 2. Serpentine-Even 3. Serpentine-Odd Fuel (H 2, H 2 O) COMPRESSOR Air (O 2, N 2 ) SOFC Fuel OUT Air OUT Power IN = w c m air Power OUT Page 8

9 Mesh & Multiphysics Mesh Automated polyhedral mesh for bipolar plate and channels Prism layers along flow channel walls Directed mesh for membrane and electrodes Total mesh elements ~ K Physics Laminar Flow, Energy and Species in channels and porous anode and cathode Electrodynamic Potential with Ohmic Heating in membrane, anode, cathode and bipolar plate Electrochemical Reactions (Butler-Volmer type) at anode and cathode catalyst layers Boundary Conditions Electric Potential at bipolar plate surfaces Gas Velocity Inlet at 900C and Pressure Outlet Page 9

10 Design Manager State-of-the-art optimization search combines Genetic algorithm Simulated annealing Response surface Many more Multi-Objective optimization simultaneously explores trade-offs between design variables Property Range Height Anode [um] [50 : 10 : 500] Height Membrane [um] [50 : 10 : 500] Height Cathode [um] [50 : 10 : 500] Channel Fraction Height [0.2 : 0.01 : 0.8] Channel Fraction Width [0.2 : 0.01 : 0.8] N half [quantity] [1 : 1 : 9] Type [quantity] [0 : 1 : 3] Air Flowrate [kg/s] 7.57*[10 6 : 10 6 : 10 4 ] Page 10

11 Optimization Results Explore trade-offs between SOFC efficiency and temperature variation to discover optimal designs Channel type and number Few serpentine-odd channels is best Non optimal Optimal Page 11

12 Thank you LMS Amesim Control Design (Pre) Calibration Function & Specification Function Test Model in the Loop Hardware in the Loop Implementation Software in the Loop STAR-CCM+ Page 12