【提出用】

Solid oxide fuel cell, Heat engines

Dw204

S hSHIKAZONO
i k a z o n o LAB.
LAB.
※研究 室 名Energyの み ⼊Conversion
⼒ください
Efficient
Department of Mechanical and Biofunctional Systems
Research Center for Sustainable Material Energy Integration
Energy System Integration Social Cooperation Program
Thermal Energy Engineering
Department of Mechanical Engineering, Graduate School of Engineering

http://www.feslab.iis.u-tokyo.ac.jp/index-e.html

Electrode Microstructures of Solid Oxide Fuel Cell (SOFC)& Electrolysis Cell (SOEC)Electrode microstructures strongly affects the performance and reliability of solid oxide fuel cells(SOFCs) and electrolysis cells (SOECs). Machine learning, e.g. CNN, CGAN, UNIT, LSTM, PINN etc.,
as well as numerical simulations such as lattice Boltzmann, phase field, kinetic Monte Carlo anddiscrete element methods are developed to optimize the characteristics of the electrodes frominitial powder to long time operation. Three dimensional microstructures reconstruction by FIBSEM and operando observations play inevitable role for understanding the phenomena and modelvalidation.
Optimization

Machine learning in SOFC research

ML-supported microstructure analysis

SOFC Experiment& Evaluation

Multiscale
Reconstructions

Infiltration
Patch-CNN net

FIB-SEM

5 µmSegmentation
Patch-CNN net

5 µm

Microstructure segmentation

NiYSZ

Laser microscope

CGAN

SEM microscope

CGAN super resolution

Degradation prediction

Optimization of electrodemicrostructure by adjoint method

Reaction prediction

Microstructure
Characterization
Quantitative characterization of electrodemicrostructures using focused ion beam scanningelectron microscope (FIB-SEM)

Resin infiltration-less FIB-SEM measurement
Small0.4
0.3

Long short term memory (LSTM)
LargeLong-time degradation prediction
0.7
After operation
After operation
Initial t=00.5(Phase field)(Residual U-net)

Particle size
Ni / (Ni+GDC)

Before reduction
After reduction
Real NiO-YSZ UNIT Ni-YSZ
Real Ni-YSZ
850℃, VA-C=0.5 V
H2:N2:H2O = 10:87:3 %, O2 = 100%

High temperature chamber

3D synthetic microstructure
generation by CGAN

Ni
GDC

Unsupervised image-toimage translation (UNIT)
prediction of NiO→Nireduction.

R&D on Heat Pumps & Heat Engines

Operando observation of patterned Ni-GDC
electrodes under real operation.

Efficient utilization of thermal energy is becoming even more important in the energy demandside. In order to reduce exergy loss, heat cycles which operate at small temperature difference,and component technologies such as efficient heat exchangers and gas-liquid separators aredeveloped under collaboration with industry partners.
• Novel steam/refrigerant cycles (2 phase expansion/compression, Lorenz cycle)• 3D measurement of frost microstructure• Heat exchangers and gas-liquid separators, etc.
Refrigeration cycle

100PropaneTemperature (oC)806040200-20

Twall = -15℃, Tair = 7℃, RH = 45 %, Vair = 4.0 m/s

0Temperature (oC)V i b rat i o n - f re e t w op h a s e ex p a n d e r

wallairair

400
600
Specific Enthalpy (kJ/kg)

800

Lorenz cycle

100

Propane/N-pentane

80

Highp e r fo r m a n c ea nt i - s ca l e
P l a n a r f ro st g row i n gV-fins
T = -15℃, T = 7℃, RH = 85 %, V = 6.0 m/so n A g I p a tte r n
Re co n st r u c t i o n o f f ro st 3 D m i c ro st r u c t u re2006040200-20-400100

200
300
400500Specific Enthalpy (kJ/kg)

600

700

L a rge te m p e ra t u re g l i d eL o re n z h e at p u m p