Simu mulati lating ng Syst stems ems in Ground und Vehicl cle e Design ign Frederick J. Ross Director, Ground Transportation
Agenda da: : Simu imulatin lating g Syst stems ems Matur urit ity y of Simulat ation ion – Growing from validation to virtual simulation Simulat ating ng Syst stem ems – Driving virtual prototype to look at early design behaviors
Syst stem em Simulation mulation Maturi rity ty Model l Increased Optimize ROI Automate As simulat ation on matures es, greater er ret eturn n of investm estment nt is seen. n. Predict – Each analysis goes through phase. Troubleshoot – First, the process needs to be validated. Critical inversion point (from reactive to – Once validated, engineers start taking Validate (software) proactive engineering) advantage of the tool to troubleshoot existing designs. – Key turning point in simulation is where the use becomes more predictive • Replace test by virtual simulation – Next key turning point is automation • Automation leads directly into optimization. To build the best design in the shortest period. • Ease-of-Use to run design modifications Ultimate Goal: Find best design in shortest time
Power ertr train ain Simulation mulation Roadma dmap Optimize Automate Predict Troubleshoot Increased ROI Validate Environment Evaluation • Dynamometer Testing 4 • Engine in Vehicle • Drive Cycle Simulation System Analysis • Coolant Filling • Crank Case Ventilation 3 • Oil Circuits Component Analysis • Turbo Charger • Port Flow • Aftertreatment 1 • Coolant Flow • Intake/Exhaust Manifold flow Transient Behavior • Couple Simulation to 1D Code 2 • Look at EGR mixing • Exhaust manifold temperatures System Analysis Component Analysis Complexity
Autom omat ation ion: : Inta take e Port t Flow Analysis lysis Virtual Vi tual Port t Flow Tool Challenge Combustion efficiency depends a lot on the intake air flow, tumble, and swirl to get complete, and fast burn. CFD has proven to be a valuable tool to optimize port flow. Engineer needs quick design studies to evaluate flow efficiency at different valve lifts. Solution tion • Automated tool has been built and designed. • Port Flow optimization. • Follows work from established best practices. • Pass data to other software/databases without manual interactions. Impact act • Reduce errors in simulation. • Leverage product expertise without needing software expertise. • Leverage the expertise of analysis to the experts. Return to a focus on Design as opposed to Analysis!
Autom omat ation ion: : The SCR R Simulation mulation Assistant istant STAR AR-CC CCM+ M+ envi vironme onment nt promo motes es aut utomat mation ion – Tools from CAD to Results The e Simulat ation ion As Assist stant ant helps ps gui uide de us user for specif cific c applicat ations ions – New for 2013 – User can define steps needed to define the workflow
Coolant olant Jac acket ket Sim imulat lation ion Assis sistan ant Guidi iding ng the e user er thro rough ugh set up and d post proc oces essin ing g of a Cylin inder der Bloc ock / Head ad Coola lant nt Jacket et.
Optimiz imizat ation: ion: Coolant lant Flow Challen llenge ge: : – Minimize pressure drop across water jacket • Modifying 24 gasket hole – Subject to constraints: • Specified peak head and liner temperatures • Cylinder to cylinder variation in peak liner & dome temperatures < 10 ° C • Peak coolant temperature specified • Peak velocity of coolant in head/block water jackets < 10 m/s Optimat imate+ + Result ults: s: Opt ptimal imal Desig ign – 1/3 less design evaluations compared to DOE – 10% reduction in pressure drop relative to DOE- optimized design • 7% reduction in max head temperature – 16 feasible designs in highly constrained design space Improvement t in Cooling ng Jacket t Temperatu ture Variatio ion Opti timized Baselin ine Opt ptimiz Baseli Opt imizatio ptimal eline imal Desig ion e Design n Proc ocess ign ign Coolant t Inlet Gasket t Holes 4.38M Cell Polyhedral al Mesh 8
Simulating mulating Syst stems: ems: Power ertr train ain Challenge During development process, test are design to look at engine for early design testing. But critical tests need to consider installation of the powertrain in the vehicle. Solution tion Use existing geometry of the engine in dynamometer and place engine in vehicle. Includes: • Cooling Air Flow Air Induction System • • Coolant Flow Network • Oil Flow Impact act Reduce prototype of engine/vehicle • construction. • Reduce time to find out thermal failures. • Reduce cost • Reduce time to production. Improve information on failure cause. • 9
Syst stem em Simulations: mulations: Exhaust haust After ertr treatme eatment nt Simulat ation ion Fea eature tures • NOx reduction in the catalyst • Lagrangian multiphase with pulsed spray injection • Multi-component droplets (water/urea mixture) • CHT (multi-phase fluid + solid pipe walls and mixers) • Liquid film + droplet/film wall interaction • Droplet/film evaporation + gas mixing (air, Urea gas, NH3, H2O…) • Chemical reactions (Thermolysis/Hydrolysis) • Porous Media Spray ay Injector tor DPF (Discrete ete Partic icle e Filter er) Flow Flow inlet et outlet et DOC Mixer ers (Diesel el Oxidat ation ion Catalys lyst) t) SCR
SCR R Simulation mulation Roadma dmap Optimize Automate Predict Increased ROI Troubleshoot Validate Crystallization Prediction 3 • Full Chemistry • Solidification prediction Uniformity Test • Urea Injection 1 • Wall Modeling NOx Prediction • Urea/Gas Mixing • Surface Chemistry 2 Optimization demo exists • Detail Chemistry Using DARS Clients have validate results Complexity
Vehic icle le Therm rmal l Managem gement nt Roadma dmap GUM: Grand Unified Model • Complete vehicle simulation • 4000+ Solid Components • Cabin Thermal Comfort 8 • Vehicle Aerodynamics • HVAC Simulation • Electronics Cooling • Co-Simulation STAR-CCM+ to STAR-CCM+ Full Vehicle Thermal Management • Co-Simulation from STAR- 7 CCM+ to STAR-CCM+ Increased ROI • 4000 Solid Components • Includes Drive Cycle Simulation via Ports Full Vehicle Thermal Management • Conduction/Radiation using 6 Radtherm • Includes Drive Cycle Simulation Underbody Temperature Power Train Cooling • ~ 100 Solids 3 • Full Engine CHT model • Includes Exhaust System, hangers, 5 • Induction System engine mounts, heat shields • Exhaust System • Oil Flow Front End Air Flow • Top Tank Temperature 1 Prediction Total Vehicle Simulation 4 • Turn-Around: 1 Day • Using existing sub-models Local Component Temperature 2 • 30-60 Solids • Local to a component 1 2 3 4 5 6 7 8 Complexity
Simulation mulation using ng the Digital ital Prototyp type Aerodynamics Heat Protection Digit ital Prot ototy type pe becomes comes enabler for advan ance ce simulat ation on HVAC/ NVH – Simulation for more advance Thermal Comfort analysis then just component design – Simulation includes multi-physics. Manufacturing Climate Control Transmission Powertrain – Simulation can involve motion as needed as well. Whatever best helps engineer design their product efficiently. Durability Durability Ride/Handling Crash Chassis (BiW) – In the past, these would not have been possible until hardware of the vehicle has been produced.
Generati eration on of a Digit ital al Prototyp type Data Freeze define nes s digital l prototy otype pe As with a real prototype, design teams work – Damping force F together to meet a goal for the design freeze. 3000 2500 Grade 1 Review board checks, to make sure all – 2000 1500 Grade 2 1000 components are fitted together and data pool is 500 0 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 complete. Deflection speed v Data Filter: Filters data for simulation Geometrical Data Functional Data – Data needed for simulation is filtered from the overall data pool, and provided for the virtual simulation. • Key component for data transfer – Example of data filters: • Red Cedars Heeds • Custom tool designed to pull data together. – OpenRoad • CAD plugin can help provide data filter – PLM (product lifecycle management) tools enable communication between different tools. Analysis Response – Feeds back into the data pool for design improvement.
Aut utom omation ation: : Front nt End nd Cooli ling/ ng/Aerodynamics odynamics 1 Challenge: Data Filtering Large CAD database needs to be quickly moved from 1000’s of CAD Damping force F parts to few boundaries needed for 3000 2500 Grade 1 CFD. 2000 1500 Grade 2 1000 500 0 0 0,1 0,2 0,3 0,4 0,5 0,6 0,7 0,8 Solution: Deflection speed v OpenRoad Geometrical Data Functional Data • Provides part filtering with link to boundary setup for the simulation. • Forms template for the full simulation process including dual stream heat exchangers. Impact: • Enables users to quickly predict drag and/or front end air flow. • Enabler for more complex studies such as component temperature prediction, soiling, aero-acoustics • Runs fully in batch: good for optimization with Heeds
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