CD-adapco s vision of STAR-CCM+ in the Chemical Process Industry Ravindra Aglave CPI Sector Manager

Transcription

1 CD-adapco s vision of STAR-CCM+ in the Chemical Process Industry Ravindra Aglave CPI Sector Manager

2 Agenda Success with CFD: Automotive and Chemical Industries STAR-CCM+ in the CPI Demands and Fulfillment CFD Simulation space topology Looking Forward: Future developments

3 Comparison with automobile industries Similarities Value addition in engineering process Experimental validation necessary Long & Expensive experimentation Lower fidelity models can be still useful Commonly cited differences Multiphase modeling Complex Physics and material properties Designs/projects are one-off A step wise approach to address complexity No repetitive/quick changes but only perceived Tank sloshing, spray injection, internal combustion, NOx formation, film on windshields, SCR evaporation, reactions, heat transfer (boiling, condensation), soiling

4 Stay Put Stay Move Civilization and Chemical Industry Ground/Air/Water transport Consumables Structure Fuels Additives Lubricants Components Body Control Steel Rubber Electronics materials Foams, Plastic Interior Furnishings Material Cement, glass Paper, Ink Carpets, Textiles Electronics materials Home, Office, Theater, Hotel Health & personal care Pharma Cleaning People Nutrition Water treatment Food Ceramics Beverage Utensils Environment Pollution control Waste treatment Comfort & Safety Defense Risk Mitigation Explosion Leaks, Dispersions

5 Demands of the Chemical Industry of Future Shortened product-process development cycles Optimization to improve yield and efficiency Efficient design of new products and processes Improvements in health, safety, and environment CFD identified as key enabling technology Major issues/barriers clearly identified How is CD-adapco / STAR-CCM+ conceptualized to address these issues? December 1996 by The American Chemical Society, American Institute of Chemical Engineers, The Chemical Manufacturers Association, The Council for Chemical Research, and The Synthetic Organic Chemical Manufacturers Association

6 Innovation Barriers to CFD in Chemical industry Incorporate complex geometry New Product Excessive time required for set up New Process In-house codes developed by industry are typically usable only by specialists Lack of models describing appropriate physics Efficiency

7 Innovation Simulation Demands Efficiency New Product/ Producing Machine Faster time to market Safer Reduce number of reacting steps New Process Less downtime Safer Increased capacity/yield Innovation Efficiency

8 Simulation Topology Efficiency Dollars/Euros Maturity Level 1 Level 2 Level 3 Level 4 Level 5 Objective Simplest Physics Understand Troubleshoot Predict Explore Optimize Simple Physics Complex More Complex Most Complex Innovation Expertise (Research, Academic)

9 Chemical Process Industry Breakdown Reactor feed systems: Distribution Ducts, pipes, tees, injectors Distributors, collectors, reactor heads Stirred & unstirred reactors, bubble columns Single phase hydrodynamics G-L, S-L phase distributions, Coalescence & Break-up, Interphase transfer, reactions, G-L-S systems Packed bed reactors Hydrodynamics Heat transfer Surface and gas phase chemistry Fluidized bed reactors Minimum fluidization, pressure drop Various fluidization regimes Reactions High Temperature Burner design, Heater design, Heater ducts, Cracking furnaces Waste incinerators Separation Equipment Stripping, Distillation Complex Thermodynamics

10 Stirred Vessel Reactor Roadmap 8 OPTIMIZATION Complete design space, automated if manual Single or multiple objectives Hybrid optimization algorithm Pareto front 7 Gas-Solid-Liquid E.g Catalysed hydrogenation Coalescence-breakup Other methods for solid behaviour > Few weeks 6 Solid-Liquid flows Minimum suspension rpm Suspension height Structural analysis (vibration) 1-2 Weeks 1 3 Hdrodynamics (MRF) Power consumption, pumping capacity, free surface shape Turn-Around: 1-2 Days Gas-Liquid flows Gas injection (single bubble size) Complex geometry Gas holdup 3-4 days 2 Unsteady (RBM) Free surface shape Mixing time Gas-Liquid flows Fixed distribution of bubble size Coalescence breakup 1 week days Gas-Liquid flows Adaptive distribution of bubbles (Adaptive MUSIG) Detailed coalescence-breakup > 1Week 8 Note: Times are estimated on past projects. The times are constantly decreasing, or model is increasing in complexity.

11 Stirred Vessel Reactor Roadmap Optimize Explore Predict Troubleshoot Understand 8 OPTIMIZATION Complete design space, automated if manual Single or multiple objectives Hybrid optimization algorithm Pareto front 7 Gas-Solid-Liquid E.g Catalysed hydrogenation Coalescence-breakup Other methods for solid behaviour > Few weeks 6 Solid-Liquid flows Minimum suspension rpm Suspension height Structural analysis (vibration) 1-2 Weeks 1 3 Hdrodynamics (MRF) Power consumption, pumping capacity, free surface shape Turn-Around: 1-2 Days Gas-Liquid flows Gas injection (single bubble size) Complex geometry Gas holdup 3-4 days 2 Unsteady (RBM) Free surface shape Mixing time Gas-Liquid flows Fixed distribution of bubble size Coalescence breakup 1 week days Gas-Liquid flows Adaptive distribution of bubbles (Adaptive MUSIG) Detailed coalescence-breakup > 1Week 8 Note: Times are estimated on past projects. The times are constantly decreasing, or model is increasing in complexity.

12 Your simulation deployment roadmap Have I solved all level 1 or level 2 problems throughout my company? Is every batch stirred reactor operating optimally in all plants? If not where is the hurdle? Is doing nothing a good option? Is this engineers tool or an experts tool? Is there a reason to confine it to CFD groups Or a research tool? Going far from bread & butter? What is the rest of the industry /world doing? 1-2 simulations for a project versus a set/optimization? Is Simulation looked upon as first choice or last resort?

13 CD-adapco Roadmap for 2014 Liquid Liquid Micromixing Polymerization Framework Adaptive MUSIG Model Alpha-Pressure coupling EMP with Porous Media Rheology Turbulent Non-Newtonian Flow Temperature dependent Viscosity Property averaging of liquid mixtures Emulsion and Suspension Reacting Channel Sauter Mean Diameter

14 STAR-CCM+ Conceptualization Operational Efficiency Easy to use Cost efficient Physics Rapid Expansion Co-simulation Workflow Integrated / Repeatable Parameterized Conserves expertise: Simulation assistant Framework Modern Object Oriented Pipeline Multi-disciplinary

15 In our vision Incorporate complex geometry Lack of models describing appropriate physics Excessive time required for set up In-house codes developed by industry are typically usable only by specialists Operational Efficiency Easy to use Cost efficient Physics Rapid Expansion Co-simulation Workflow Integrated / Repeatable Pipeline Parameterized Framework Modern, Object Oriented Multi-disciplinary Expertise: Simulation Assistant

16 If you think good design is expensive, you should look at the cost of bad design. Dr. Ralph Speth, CEO Jaguar Final thoughts If you think good design is expensive, you should look at the cost of bad design. Dollars/Euros Dr. Ralph Speth, CEO Jaguar Expertise (Research, Academic) because doing nothing was even a bigger risk Dr.-Ing. Norbert Reithofer, CEO BMW AG

17 Thank You!

18 Liquid Liquid Micromixing Eddy Contact Model (based on Froney and Nafia, 2000) Kolmogorov (corresponds to Engulfment type of mixing) Classical Scalar Mixing Numerically Efficient Reasonable Accuracy Easy to use for multiple reactions Steady or Unsteady Case 1: EBU A= 4.0 Case 2: Classical

19 Example A + B = R R + B = S GO BACK

20 Polymerization Method of Moments Users to have options to input kinetic parameters for Initiation Propagation Termination Reactions Each of the Initiator Radical Monomer Solvent Species assigned to appropriate liquid species for transport Choice for Quasi-steady state for radical species In future users can be given a choice of specifying user-defined moments sources. GO BACK

21 Adaptive MUSIG Model At the end of a calculation step Mass and number density are redistributed between neighbour groups Each group has the same mass but new diameters d d

22 Adaptive MUSIG Mode Each size group is treated as a phase, solving standard equations plus a number density equation.

23 Adaptive MUSIG - Droplet breakup through an orifice Sauter Mean Diameter Breakup Rate (log scale) Turbulent-induced breakup Shear-induced breakup GO BACK