Monitoring and optimising waste heat recovery for diesel-electric vessel propulsion. Eric Link LMS Imagine.Lab Amesim

Transcription

1 Monitoring and optimising waste heat recovery for diesel-electric vessel propulsion Eric Link LMS Imagine.Lab Amesim Realize innovation.

2 Table of content 1. Industry Challenges 2. WHR Potential 3. WHR Decission Support Application 4. Summary Page 2

3 Table of content 1. Industry Challenges 2. WHR Potential 3. WHR Decission Support Application 4. Summary Page 3

4 1. Industry Challenges Environmental laws Slow Steaming Emission Regulations High Efficiency 20% CO2 reduction by % CO2 reduction by 2050 Technology agendas How to do trade-off between various power drive architectures? How to optimize the efficiency of internal combustion engines? How to save time and cost in sub-systems specification and design? How to rapidly assess the performance of fluid, electric and mechanical systems? Page 4

5 Table of content 1. Industry Challenges 2. WHR Potential 3. WHR Decission Support Application 4. Summary Page 5

6 2. WHR Potential Scavenge Air Cooler 16 % kw Fuel (LHV) 100 % kw Oil Cooler 3.8 % kw TC Oil Cooler 0.9 % kw Jacket Water Cooler 6.7 % Exhaust Gas kw 24 % kw Shaft Power 48 % kw Radiation & Convection 0.6 % 776 kw Page 6

7 2. WHR Potential Scavenge Air Cooler 16 % kw Jacket Water Cooler 6.7 % kw 46,7 % kw Theoretical Usable Energy Exhaust Gas 24 % kw Page 7

8 2. WHR Potential Ship Diesel Engines with hybrid diesel-electric propulsion Waste Heat Recovery (WHR) can increase efficiency up to 13 % WHR-Systems use the thermal energy of the exhaust gas To maximize efficiency a combined system of exhaust gas turbine and Rankine Cycle is used Page 8

9 2. WHR Potential Page 9

10 2. WHR Potential 1 Main Engine 12 Power-Turbine 2 TC-Turbine 13 HP Steam Turbine 3 TC-Compressor 14 LP Steam Turbine 4 Feed water pre-heater 15 Generator 5 6 Sea water heat exchanger Exhaust gas heat exchanger 16 LP Service Steam 17 HP Service Steam 7 HP Superheater 18 Vacuum Condenser 8 HP Evaporator 19 Deaerator Tank 9 LP Evaporator HP Steam Drum 21 Condensate pre heater Shaft-Generator- Motor LP Steam HP Steam Exhaust gas Water 11 LP Steam Drum 22 Propeller Air Shaft Page 10

11 Table of content 1. Industry Challenges 2. WHR Potential 3. WHR Decission Support Application 4. Summary Page 11

12 3. WHR Decission Support Application Siemens EcoMain is a communication platform for ships Data Backbone for several applications WHR-Application monitors the running system on board Comparison of actual state and ideal state Discover malfunctions by the help of exergy analysis Page 12

13 3. WHR Decission Support Application Waste Heat Recovery Decission Support Application Project tasks Working thermodynamical model of WHR Ensure accuracy for a wide operational range Integrate exergy analysis in simulation model Connect WHR-model to live measurement data on board Feed simulation results back to monitoring system on board Page 13

14 3. WHR Decission Support Application LMS Imagine.Lab Amesim Simulation platform for mechatronic system simulation Physical modeling approach Dedicated libraries and components Extensive analysis tools Easy-to-use solver technology Page 14

15 3. WHR Decission Support Application Application Solutions Oil, Cooling circuits Air Condition Fuel System IC Engine, Exhaust Aftertreatment Powertrain, Gearbox Actuation systems: Electric, Mechanic Pneumatic Hydraulic Energy Systems: Fuel Cell Power Electronic Battery Vehicle Energy Management Thermal Management Vehicle Dynamics Controls Braking Steering, Anti-roll Physical Libraries Fluids Thermal Mechanical Electrical Authoring Platform Imagine.Lab Amesim Unrivalled Numerical Core Model Creation, Analysis & Optimization Library Extension Collaboration & IP Protection Productivity & Interfaces Page 15

16 3. WHR Decission Support Application Control Electrical Fluids Mechanical Thermo Main Engine LP Steam Exhaust gas HP Steam Water Air Shaft Page 16

17 LMS Amesim Demo Page 17

18 3. WHR Decission Support Application Physical modeling of WHR components Thermodynamical model System Simulation with LMS Imagine.Lab Amesim Multiphysical approach to combine all physical domains of WHR system Validation with measurement and design data Flexible model with ability to change components and system layout Generic interfaces in LMS Amesim can couple the parameters and results of the simulation model to any other data process Interface to connect to EcoMAIN API Page 18

19 3. WHR Decission Support Application Physical modeling of WHR components Thermodynamical model System Simulation with LMS Imagine.Lab Amesim Multiphysical approach to combine all physical domains of WHR system Validation with measurement and design data Flexible model with ability to change components and system layout Page 19

20 3. WHR Decission Support Application Live Data Connection Python script to set parameters from measurement data and starts simulation Python script connects to EcoMAIN API Python scripts can customize simulation models The python script is build to ensure no user interaction is required A control mechanism ensures that no invalid measurement data is set into the model Page 20

21 3. WHR Decission Support Application Exergy Analysis Exergy destruction rate of a component can indicate its efficiency Reference values for the exergy destruction rates are compared to current values Derivatives are introduced to distinguish between normal operational behavior and anomalies Thermoeconomic model for failure prediction Exergy Destruction Rate: E V = T 0 S irr E V = E ph.i+1 E ph.i E ph = h i h 0 T 0 (s i s 0 ) Mass flow sensor Enthalpy sensor Entropy sensor Low Pressure Steam Turbine Page 21

22 Agenda 1. Industry Challenges 2. WHR Potential 3. WHR Decission Support Application 4. Summary Page 22

23 4. Summary Thermodynamical model Live Data Connection Exergy Analysis No required user interaction Page 23

24 4. Summary Drive innovation Explore a wider range of ideas Early validation of technical feasibility Test and compare new ideas via simulation Streamline process Reduce time with virtual system integration Increase reusability through knowledge capitalization Improve quality Explore a wider range of ideas Early validation of technical feasibility Test and compare new ideas via simulation Reduce costs Reduce costs with fewer prototypes Minimize risk on test beds Reduce products breaks and failures Page 24

25 MAN Diesel & Turbo speeds fuel injection system development by a factor of five Business challenges: Remain a leader in marine engine innovation Meet shipping companies needs for lower emissions and reduced fuel costs Keys to success: Adapting HFO fuel systems to gas injection constraints Simulating the behavior of different fuel injection systems using LMS Imagine.Lab Amesim Rapidly making design modifications by changing parameters of off-the-shelf components To work with our in-house code, it is necessary to be an expert in hydraulics mathematics and programming. Using LMS Imagine.Lab Amesim, the training process for new colleagues is much simpler. With the upgrade of our existing PLM system, we have seen significant improvements in data management and migration, BOM control, and collaboration. All of these improvements have helped us reduce costs and speed time-to-market. Our company has become more competitive and stronger in the global market, and we Mikkel will Thamsborg be sure R&D to Project further Manager, enhance R&D our Injection innovation and Hydraulics process Department based on the needs of the users in out next stage of PLM expansion Moon Kim Team Leader LS Page 25

26 Simulation of a waste heat recovery (WHR) system on a large scale container ship engine Business challenges: Predict the system s behavior under changing conditions Connect simulation model to live ship data Increase flexibility to adapt the model to system changes Keys to success: Fully working thermodynamic model of the WHR-System Reusable components for upcoming projects Possibility to use the model for future system design This project is a customized solution where a former model, written in commercial code, is transferred into a state-of-the-art simulation platform for improved quality of the results and easier maintenance of the model. Kay Tigges: Application Management Siemens Marine Page 26 Quelle:

27 Thank you Page 27

28 Contact Data Eric Link LMS Imagine.Lab Amesim Presales Solutions Consultant +49 (89) (173) Page 28