The digital twin in STAR-CCM+ Automated design

The digital twin in STAR-CCM+

Automated design optimization

of fuel cellsChristoph Heining

Unrestricted © Siemens AG 2017


2017.11.03Page 2 Siemens PLM Software

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

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

TRENDS IMPLICATIONS



From System Simulation to Detailed 3D Analysis

With a Multi-level Approachle

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f d

eta

il =

le

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f co

mp

lexity

Fuel cell system

design and

optimization

Multi-physics

quasi-static models

Model in the Loop Hardware in the Loop

LMS Amesim ControlDevelopment

Objective

Concept phase,

sizing, controls

Modeling Focus

Electrical

static model

Design

Function & Specification

Implementation

Function Test

(Pre) Calibration

Cell design

Full flow, energy,

electrochemistry

and species

STAR-CCM+

Stack design

and integration

Physical, transient

models

Software in the Loop





ve

l o

f d

eta

il =

le

ve

l o

f co

mp

lexity

Development

Objective

Cell design

Concept phase,

sizing, controls

Fuel cell system

design and

optimization

Stack design

and integration

Modeling Focus

Electrical

static model

Multi-physics

quasi-static models

Physical, transient

models

Full flow, energy,

electrochemistry

and species

Compare control

strategies with respect to

fuel consumption





ve

l o

f d

eta

il =

le

ve

l o

f co

mp

lexity

Development

Objective

Cell design

Concept phase,

sizing, controls

Fuel cell system

design and

optimization

Modeling Focus

Electrical

static model

Multi-physics

quasi-static models

Full flow, energy,

electrochemistry

and species

Assess cell size, layout,

materials, …

Stack design

and integration

Physical, transient

models





ve

l o

f d

eta

il =

le

ve

l o

f co

mp

lexity

Fuel cell system

design and

optimization

Multi-physics

quasi-static models

Development

Objective

Concept phase,

sizing, controls

Modeling Focus

Electrical

static model

Cell design

Full flow, energy,

electrochemistry

and species

Stack design

and integration

Physical, transient

models

H2O

H2

Assess & optimize

cell geometry, flow

rates, humidification...



STAR-CCM+

• STAR-CCM+ fully integrated solution

• Leverages HEEDS design optimization technology

Cell Design – Design Optimization

CAD Mesh Solution Analysis

BUILD SIMULATE

ASSESS

Parametric Geometry

Workflow Automation

Intelligent Design Exploration

Speed and Performance

Multiphysics Models

Flexible and Robust Meshing

Powerful Data Analysis

EXPLORE



Parametric Geometry

Bi-directional connectivity to CAD software

Built-in parametric 3D-CAD tool (here)

Fuel (H2, H2O)

Air (O2, N2)

Power IN = 𝒘𝒄 𝒎𝒂𝒊𝒓

SOFC

Power OUT

Fuel OUT

Air OUT

COMPRESSOR

0. Interdigitated 1. Parallel

2. Serpentine-Even 3. Serpentine-Odd

Parameters

• Channel geometry

• Anode, membrane, cathode

iithickness

• Air flow rate

Goals

• Maximize efficiency

• Minimize temperature variation



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 ~ 100-200K

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



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]

Nhalf [quantity] [1 : 1 : 9]

Type [quantity] [0 : 1 : 3]

Air Flowrate [kg/s] 7.57*[106 : 106 : 104]



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



Thank you

Model in the Loop Hardware in the Loop

LMS Amesim Control

Design

Function & Specification

Implementation

Function Test

(Pre) Calibration

STAR-CCM+

Software in the Loop

Documents

The digital twin in STAR-CCM+ Automated design