The role of simulation in contemporary Industrial Research Sharing experiences Rajendra Naik, PhD Senior Principal, GE Global Research May 15, 2018
This is General Electric (GE) POWER HEALTHCARE RENEWABLES AVIATION TRANSPORTATION GLOBAL RESEARCH BAKER HUGHES A GE COMPANY
Intersection of Big data and physics High Performance Computing High Fidelity Models Hybrid Models Industrial Big Data Physics Based + Data Adapted Artificial Intelligence, Learning Model 3
Digital Twin Engineering models with defined outcome Data source Asset Business outcome Fuel consumption Sensors Operational Data Digital Twin Performance Power output System efficiency Inspection Maintenance Actions Learning/AI Performance Models Control Model Lifing Models Reliability Integrated Optimization Remote Monitoring & Diagnostics Mission Optimization Asset assignment Domain knowledge Predict failure of components Condition based maintenance Life Optimizing controls 4
Digital Twin Aviation Objective : Maximum availability of an aircraft engine by intelligent workscoping Environmental conditions; Per-Flight data; Prior damage; Engine Operating Mode Inspection time; Optimized shop time Increased availability, reduce unnecessary service overhauls Reduce maintenance cost using turbine blade cumulative damage models updated per flight 5
Digital Twin Transportation 1 Per asset model 2 3 Business outcomes Continuously tuned new data / insights 4 Scalable MMs assets 5 Adaptable new Locomotive Trip Optimizer Locomotive data; Track database; Operating condition Objective : Minimize fuel consumption & emissions generated per trip Real time optimization : Optimal speed & horse power 3-17% fuel savings Enabled by system modeling, real time optimization & controls Operator Cab 6
Wind Farm Layout Optimization Objective : Determine optimal wind turbine positions to maximize Annual Energy Production (AEP) and reduce Balance of Plant costs Constraints of geography, turbine loads, acoustic noise and mix of turbines Heuristics + Best in class MINLP algorithms + multi threaded optimization code Novel approach to modeling, algorithms, software architecture 7
Wind Farm Operational Optimization Objective : Maximize Annual Energy production (AEP) of a wind farm Solution : Minimize inter turbine wake losses with coordinated controls Networked Controls Digital Twin of the wind farm Co-ordinated turbine control Communications Farm-wide awareness 0.5-2% AEP improvement 1% AEP improvement $ 2MM value per year for 100 MW farm 8
Model Based Design Cycle Model of a plant Plant : Process / system to be controlled Data driven and/or physics based Iterate on different designs, sensors, actuators Controller development Simulations Controller synthesis & analysis Choose appropriate type of controller Offline or real time simulation Model in the loop/ Hardware in the loop Deployment on actual system Autocode generation Reference : http://en.wikipedia.org/wiki/modelbased_design 9
6 Num_Activ e_cy l_right Number of active cylinders on the right bank 6 Num_Activ e_cy l_lef t Number of active cylinders on the left bank Ground5 5 Act_Inj 6 Tfuel 7 1 4 2 Pright_exh_man 3 Pleft_exh_man Ground6 6 Qdot_water 1/2 7 Qdot_oil Pright_exh_man Pexh_man Plef t_exh_man <Mf uel> <Adv _Angl> Tf uel <> <> <> <Pright_exh_man> <Mf uel> <Adv _Angl> <> <> <> <Pright_exh_man> <Mf uel> <Adv _Angl> <> <> <> <Pexh_man> <> <> <> <Pexh_man> <> <> <> <Pright_exh_man> <Mf uel> <Adv _Angl> <> <> <> <Pright_exh_man> <Mf uel> <Adv _Angl> Pexh_man Mf uel Adv _Angl Pexh_man Volumetric Efficiency Ind_Ef f Right Bank - Indicated Efficiency Pexh_man Mf uel Adv _Angl Pexh_man Mf uel Adv _Angl Ind_Ef f Left Bank - Indicated Efficiency Pexh_man Vol_Ef f Exh_Enrgy _Frac Right Bank - Exhaust Energy Fraction Pexh_man Mf uel Adv _Angl Friction Torque Friction_ Torque Exh_Enrgy _Frac Left Bank - Exhaust Energy Fraction <Mf uel> <Num_Activ e_cy l_right> <Num_Activ e_cy l_lef t> <> <> <> <Mf uel> <Num_Activ e_cy l_right> <Num_Activ e_cy l_lef t> <> <Tf uel> <Num_Activ e_cy l_right> <Num_Activ e_cy l_lef t> Right_Ind_Ef f Mf uel Num_Activ e_cy l_right Num_Activ e_cy l_lef t Lef t_ind_ef f Friction_Torque Mf uel Num_Activ e_cy l_right Num_Activ e_cy l_lef t Vol_Ef f Mdot_cy l_out Mdot_cy l_in Calculate Mass Flowrates Mdot_cy l_out Mdot_cy l_in Mdot_f uel Right_Enrgy _Frac Tf uel Calculate Brake Torque Num_Activ e_cy l_right Num_Activ e_cy l_lef t Lef t_enrgy _Frac Brake_Torque Mdot_f uel Tright_exh_man Tlef t_exh_man Calculate Exhaust Temperatures 5 Brake_Torque 2 Mdot_cyl_out 1 Mdot_cyl_in 3 Tright_exh_man 4 Tleft_exh_man Model Based Design Example IC Engine Design a controller for an IC engine to meet speed and power requirements across the operating range Engine Plant to be controlled Engine These models are taskes for GRC. These should be models extracted from GT Power. Model : Physics based and data driven (Grey box approach) Controller design Multi-loop PID Actual engine testing Control Unit Simulation studies Desktop/ hardware 10
Marine Engine Turbocharger - Model Based Design Four Turbo Configuration Three Turbo Configuration Trade off matrix Three turbo Four turbo Cost, packaging & system complexity Lower Higher Transient response Slower but well within CTQ Faster & well within CTQ Results from Simulink Mean Value Model Model based design helped business take a decision to go for 3-Turbo configuration 11
Transmission and Distribution Voltage Stability RT-LAB Command Station TCP/IP RT-LAB Target PC Voltage/ Current Analog output Control Command N60 Phasor Measurement Unit Phasor Data Communication Latencies XPC Real Time Target Machine Voltage stability monitoring and control algorithms Voltage Stability Indices Visualization 12
I find out what the world needs, then I proceed to invent it. - Thomas Alva Edison And he didn t have the tools that we have today.