Materialmodellierung & -simulation, eine wichtige Voraussetzung für die Entwicklung effizienter und umweltfreundlicher Energiewandlungstechnik

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1 Corporate Technology DGM-Strategieworkshop Modellierung und Simulation 22. November 2010, Aachen Materialmodellierung & -simulation, eine wichtige Voraussetzung für die Entwicklung effizienter und umweltfreundlicher Energiewandlungstechnik Stefan Lampenscherf, Siemens Corporate Technology / Copyright Siemens AG Alle Rechte vorbehalten.

2 Healthcare Energy Industry Sectors and Divisions Drive Technologies Industry Automation Building Technologies Mobility Lighting (OSRAM) Industry Solutions Fossil Power Generation Renewable Energy Oil & Gas Energy Service Power Transmission Power Distribution Corporate Department Corporate Finance Corporate Personnel Corporate Technology Corporate Development Corporate Centers Imaging & IT Workflow & Solutions Diagnostics Corporate Communications Corporate Information Office Corporate Supply Chain and Procurement Global Government Affairs Global Shared Services Management Consulting Personnel Seite 2 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

3 Corporate Technology Technology Segmentation - Cluster Materials, Hardware Design & Manufacturing Technologies Electronics, Energy & Environment Processes & Production Information & Automation Technologies Ceramic Materials & Devices Polymeric Materials High-Perform. Metals & Alloys Substitutional Materials Coatings Microsystems Micro-Mechatronics Organic Electronics Electronic & Photonic Assembly ASIC and PCB Technologies Embedded Hardware Laser & Joining Technologies High-end Material Analytics Sustainable Lifecycle Engineering Power Electronics & Energy Management Actuator & Drive Systems Energy Conversion & Environment Power Components & Thermodynamic Processes Decentralized Renewable Energy Technologies RF Technology Sensor Systems & Applications Non Destructive Evaluation LP: Smart Grid Enterprise Processes Product and Service Innovation Project and Risk Management Modeling, Simulation & Optimization Production Networks and Factory Planning Production Engineering Project Engineering Knowledge Management Self Organization and Model- Predictive Control Autonomous Systems Medical Informatics Automation Engineering & Runtime & Systems Condition Monitoring Medical Imaging Technologies Real-time Vision & Industrial Imaging LP: High Performance Building Software & Systems Engineering Technologies System Development Technologies System Architecture and Platforms Requirements Engineering User Interface Design IT Security CERT *) LP: Mechatronic Design *) Computer Emergency Response Team Oil, Gas & Nuclear Oil & Gas Field Development Nuclear Technologies Special Projects LP: ecar Nuclear Energy Configuration Technologies Constraint-based Configurators Sustainable Infrastructure Technologies for Emerging Markets LP: Rural Healthcare Solutions for India Communication Technologies Wireless Communications Communication Systems & Control Networks SMART and Cost Innovation LP: Rural Healthcare in China LP: Lighthouse Projects Seite 3 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

4 Global Technology Field Cluster Materials, Hardware Design & Manufacturing Technology Ceramic Materials & Devices Polymeric Materials High Performance Metals & Alloys Substitutional Materials Coatings Microsystems Micro-Mechatronics GTF Cluster CT T MHM Organic Electronics Electronic & Photonic Assembly ASIC & PCB Technologies Embedded Hardware Laser & Joining Technologies High-End Material Analytics Sustainable Lifecycle Engineering Seite 4 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

5 Status and Challenge for Materials Engineering Yesterday: Materials have paced Technology Evolution - Stone, Bronze, Iron, Silicon,... - Trial and Error Methods Today: Time + Costs are critical for Success - Material Development for demanding Applications: Time: ~ Years, Costs: ~ Mio$ - But! Typical Product Cycle: 18 months (Cars, Electronics) Tomorrow: Need Combined Approach: Simulation Experiments Seite 5 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

6 Status and Challenge for Materials Engineering Structure-Property Relations Structure Properties Process Simulation Computational Material Engineering Processing Performance - Reduce Cost and Time for Material Development - Identify relevant Material Candidates for Future Applications (IP) - Allow more efficient System Design and Product Development Seite 6 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

7 Fossile Power Generation - Gas Turbine Seite 7 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

8 Thermal Barrier Coatings in Power Generation Gas Turbine Power output: 340/530MW (cc) Efficiency: >39/60% (cc) SGT5-8000H TBCs: Heat Shields, Burner Inserts, 1st & 2nd Stage Blades/Vanes Seite 9 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

9 Temperature / C TBCs are today the Materials technology with the highest pay-off allowable Hot Gas Temperature Film Cooling Closed-loop Cooling CMC 1100 TBC IN738 IN939 2 nd Gen. SX 1 st Gen. SX IN792 DS 800 U Year Allowable Material temperature Seite 10 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

10 Cooling air Hot gas TBC Function und Material Function 150mm ~300mm Material: Yttria-stabilized Zirconia (YSZ) DT~1 K/mm Air Plasma Spray Super alloy Bond Coat TGO TBC Cross Section Thermal Conductivity 8YSZ: 3W/mK (Bulk) < 1W/mK (sprayed) Microstructure Seite 11 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

11 TBC Thermal Conductivity Seite 13 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

12 TBC Thermal Conductivity Need TBC with higher temperature capability and improved thermal insulation to achieve long term efficiency goals: Composition Microstructure Thermal transport in TBCs under engine operation conditions has some characteristic features that should be taken into account: - Effect of TBC Sintering & Temperature Gradient - Effect of Hot Gas Pressure - Radiative Heat Transport Seite 14 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

13 TBC Thermal Conductivity - Measurement Method The most common and standardized method to determine the thermal conductivity of TBCs for the relevant temperature range is to calculate it from 3 other material properties using the relationship: l(t) = r(t). c p (T). a(t) Density Specific Heat Thermal Diffusivity Differential Scanning Calorimetry Laser Flash Analysis Seite 15 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

14 Time Thermal Conductivity - Multi Scale Approach Develop simulation methods on all relevant scales to predict thermal conductivity as function of Composition and Microstructure (BMBF Project: VEKTRA) Simulation Scales Simulation Methods Electronic Atomistic Micro Component Length Continuum Scale: FEM Microstructure Scale: EMT, RVE Atomistic Scale: Ab initio/tb/classical MD Electronic Scale: DFT Macroscopic thermal Conductivity Bulk thermal Conductivity interatomic potentials, forces Seite 16 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

15 Thermal Conductivity - Atomistic Scale Direkte Methode Green-Kubo Methode - lokale Energiezu-/abfuhr erzeugt Wärmestrom PBC - Simulation im Gleichgewicht - Messung der spontanen Fluktuationen des Wärmestroms J(t) - Zeitliche Mittelung: Temperaturgradient τ τ t 0 t 0 +τ -Bestimmung der Wärmeleitfähigkeit mit Fourierschem Gesetz: dq dt A dt dx - Zusammenhang zwischen spontanten Fluktuationen von J(t) und Wärmeleitfähigkeit - Auswertung mittel Green-Kubo Formel: 1 3Vk B T J( t) J(0) 2 0 Seite 18 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology dt

16 Thermal Conductivity - Microstructure Scale Effektive Medien Theorie Representatives Volumen Element - Bestimmung der effektiven Materialeigenschaften mittels Näherungsverfahren - Definition der effektiven Materialeigenschaften stochastische Gleichungen - Vorteil: wenig Aufwand effektive Gleichungen - Nachteil: eingeschränkte Gefügeinformationen (Volumenanteile, Orientierung, Textur, Topologie, Eigenschaften der einzelnen Phasen) - Numerische Lösung der Feldgleichungen und Mittelung über repräsentatives Volumen (RVE) RVE - Vorteil: berücksichtigt komplizierte Gefüge - Nachteil: aufwändig T 2 T 1 Dz l eff 1 A A Dz j( x, y) dx dy T T 2 1 Matrix: Maxwell-Garnett Approximation Aggregat: Effective Medium Approximation Künstliche Mikrostruktur Realistische Mikrostruktur Seite 20 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

17 Thermal Conductivity - Microstructure Scale Beispiel für RVE-Methode: Metallschaum Computer Tomographie Image Processing FE-Vernetzung Stahlschaum (Fraunhofer IFAM, Dr. Stephani) m-ct Siemens AG (Dr. Stephan) Simpleware, COMSOL (Dr. Farag, IfWW TU Dresden) Wärmeleitfähigkeit (FEM): 0.3W/mK (Messung: 0.4W/mK) Ausblick - Anwendung auf komplizierte Gefüge (z.b. WDS) - Gefügeoptimierung -> Composite Simpleware, <nur für internen Dr. Farag, Gebrauch IfWW TU Dresden) / vertraulich> Seite 21 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

18 Beyond TBC High Perfomance Composites for Power Generation Energy Harvesting Wind Power CMCs Seite 22 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

19 Summary Multi Scale Simulation - need simulation methods on all relevant scales - need user friendly tools Multi Physics Simulation - coupled field simulations Material Design Tools Seite 23 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

20 TBC Reliability Seite 24 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

21 TBC Reliability Prediction Life Prediction Methodology for TBCunder Extended Engine Operation Conditions Temperature Difference DT = T S - T BC Engine Operating Conditions New TBC System Limit (to be established) Current TBC System Limit Bond Coat Temperature T BC Seite 25 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

22 TBC Reliability Prediction - Flowchart TBC Reliability Prediction Rig Tests Failure Map Failure Model Material Tests Simulations Prediction Perform critical tests and sample analysis to explore TBC failure and establish test database. Extract relevant failure mechanisms and key drivers; establish link between load conditions and failure scenario Establish fracture mechanics based model, failure criterion and identify necessary material data Determine all necessary material data for numerical load analysis and measure fracture resistance Perform load analysis and fracture mechanical simulations (FEM) to evaluate defect driving force Based on simulation results predict surface temperature limit by applying failure criterion Failure Phenomena Mechanisms Failure Model (Fracture Mech.) Stiffness, CTE Load Analysis Failure Kinetics Key Drivers List of necessary Material Data Thermal Cond. Fracture Mechanical Simulation Test Database Preliminary Failure Map Failure Map Fracture Resistance Seite 26 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

23 TBC Surface Strain Compression Tension Temperature TBC Life Prediction - Load Analysis (HHF Test) HT phase cooling phase heating phase RT phase Time surface temperature bond coat temperature HT high temperature RT room temperature high cooling rates, thick TBC high surface + moderate bond coat temperature, cylinder geometry Time moderate surface+high bond coat temperatures, very high heating rates, thick TBC button geometry Seite 27 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

24 TBC Life Prediction Failure Map Surface Temperature Limit Stationary: no transients! Temperature Difference DT = T S - T BC Delamination Limit (rapid tensile failure) Vertical Crack Limit Fail Safe Region BC Oxidation Limit Spallation (Buckling) Limit (rapid compressive failure) TBC YSZ Current T BC Limit (cyclic furnace tests) Bond Coat Temperature T BC Seite 28 < > <Stefan Lampenscherf>, <CT MM 2> Siemens AG, Corporate Technology

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