Multi-Physics Integration Framework MuPIF design, operation and optimization of a photovoltaic cell

Transcription

1 Multi-Physics Integration Framework MuPIF design, operation and optimization of a photovoltaic cell B. Patzák*, V. Šmilauer*, M. Appelª, R. Altenfeldª, L. Thielen, A. Lankhorst *Czech Technical University in Prague, ªACCESS Materials&Processes, CelSian Glass & Solar B.V. mech.fsv.cvut.cz/mupif

2 Outline Introducton to MuPIF platform History Key features Case study: Selenization (CFD+thermodynamics) Macro-scale (furnace) models RVE miscro-structure evolution Conclusions

3 MuPIF platform Initially developed under the national project funded by Czech Grant Agency (Project No. P105/10/1402.), Currently developed in FP7 project - Multiscale Modelling Platform: Smart design of nano-enabled products in green technologies, , 8 partners from 5 different countries, 2 industrial endusers. Industrial application for photovoltaic devices and LEDs Selenization (CFD+thermodynamics) Light conversion (opto+thermal)

4 MuPIF platform: Key features Focus on combining existing tools to build a customized multi-physics and multiscale simulation chains provides an underlying infrastructure enabling high-level data exchange and steering of individual applications. From discovery pf problem domain individual objects identified and grouped. Courtesy: G. Schmitz Models Models For each group, a base class introduced defining abstract interface (set of methods) Interfaces allow to communicate with any object using generic interface, hiding the particular implementation details Interfaces allow for a plug-and-play architecture Data Properties Spatial Fields Discretizations Time steps Units Microstructures

5 MuPIF Platform API design pattern Models (applications) Interfaces allow for a plug&play architecture Different ways how to implement interface direct approach for apps with API or source code indirect approach (file exchange) for closed source apps scale Initial/boundary conditions as well as effective properties from lower scales imposed via set methods Internal state of the model updated via solveyourself, finishstep, etc. methods Effective properties for upper scales extracted using get methods set boundary conditions provide effective values Initial conds output provide boundary conditions set effective values time/process

6 MuPIF Platform API design pattern Data components Platform abstracts not only applications, but also high level data (Properties, Fields, Discretizations, Units, etc.) These entities represented by objects encapsulating raw data, metadata, and operations on the data Individual applications do not have to interpret raw data, they just use the interface Abstracts different data formats (hidden behind the interface) Redundancy avoided (operations on data are part of data itself) Advantage: the models receive (and provide) data in a self consistent package: raw data with operations on the data Component-based simulation scenario Model #1 Model #2 Model #3 Models/applications with generic API High level data with API

7 MuPIF platform: Key features Based on interacting, distributed objects Transparent, distributed object system fully integrated using socalled proxies; SSL based encryption for security Integrates with existing grid middleware (Condor) or provides own resource manager Support new business models, such as software as a service Local scenario App1 App2 Distributed scenario Pyro App1 App2 Proxy Name server Internet App2

8 CIGS for solar cells: selenisation of a Cu-Ga-In precursor film furnace (~500 C) with Se containing atmosphere Mo film Se gas Cu-In-Ga precursor film glas ~ 10 cm SEM picture of the CIGS microstructure (by Abengoa) ~ 2 µm Mo film crystalline CIGS (photoactive) glas

9 Simulation model portfolio multi-physics CFD code XStream (thermal fields & Se gas flow) [Se](x), =f(cig S) T( x ), [ S e] (x ) CALPHAD based thermodynamics (driving force & phase composition) Phase field software MICRESS (phase transformation & microstructure)

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11 CFD Simulation results t0 Δ[Se], ε Δ[Se], ε texchange T, [Se] RVE PF T, [Se] CFD tend

12 Phase-field simulation results boundary conditions for RVE CIGS formation vs. time CIGS volume [a.u.] Se consumption T, [Se] update every texchange RVE at location mid Prototypic simulation of CIGS growth (no coupling to thermodynamics) [Se] update every texchange Time [s]

13 Functional for process optimisation (quality factor) RVE1 QF1 RVE2 QF2 Quality factor map as a functional for process optimisation QF interpolation via Mupif Quality factor defined as a product of individual QF (Cu/(Ga+In) ratio, rel. concentration of binary phases, thickness of MoSe layer, etc)

14 Conclusions MuPIF enables to link different simulation models operating on different scales and distributed resources MuPIF provides services for data high level data exchange, interpolation / extrapolation, and I/O services The capabilities of MuPIF demonstrated on a simulation chain for CIGS thin film processing The developed simulation chain allows to asses the quality of the process (QF) and subsequent optimization of the process.