Multi-Objective Optimization of a Grease Mechanical Filter using modefrontier & STAR-CCM+ Marco Carriglio, Alberto Clarich ESTECO SpA Trieste (Italy)

Transcription

1 Multi-Objective Optimization of a Grease Mechanical Filter using modefrontier & STAR-CCM+ Marco Carriglio, Alberto Clarich ESTECO SpA Trieste (Italy)

2 Summary Analysis of the performance of the mechanical filter for solid suspended substances used in extractor hoods of industrial kitchens: CFD analysis STAR-CCM+ Experimental tests Optimization of the performance Multi-objective optimization modefrontier Results analysis

3 Introducing modefrontier is an integration platform for multi-objective and multi-disciplinary optimization. It provides a seamless coupling with third party engineering tools, enables the automation of the design simulation process, and facilitates analytic decision making.

4 Multi-disciplinary optimization with modefrontier modefrontier integrates with any parametric software (CAD, CAE, FEM, generic, etc.) automating the entire optimization process in which data is transferred from one simulation to the next and the relevant values of outputs and objectives are extracted. This multi-disciplinary approach allows to exploit the interaction between the disciplines and determine the global optimum solution, instead of optimizing each discipline sequentially.

5 What can you do with modefrontier?

6 Where we are The company headquarters are located in Trieste, Italy with offices in North America and India Our technology is distributed throughout the world by a network of qualified channel partners, who provide proficient local service and support to our customers.

7 ESTECO clients Over 300 international clients have relied on our software to design better and more efficient products across a wide spectrum of industrial sectors. Petrobras Whirlpool Embraer Honda Polestar Racing Sony Bombardier Nissan Yamaha Continental Piaggio Mitsubishi Electric Corporation Nokia Siemens Fujitsu Laboratories Kawasaki Mazda Jaguar Land Rover FIAT Toyota Volvo Ferrari Ford Hitachi Shimano BMW

8 The filter Filter of the type widely present on the market of industrial kitchen. The filter consists of a series of sheets of stainless steel folded to form channels for the passage of fluid.

9 CFD analysis Outlet Inlet STAR-CCM+ Mesh 2D Inlet Outlet Turbolence Time Polyhedral + Prism layers = cells Air at T = 23 C and constant density Standard pressure Flows = m 3 /h Static pressure = 0 Pa Reference pressure = Pa k-ω model Unsteady model - Δt = 10 sec (step 1/10 sec) Multiphase Lagrangian model (oil, 800 kg/m 3, diameters 1 25 micron, mass flow 10 g/min)

10 Experimental tests Laser Doppler Velocimetry: 5mm thick transparent plexiglass Centrifugal fan: 600 m 3 /h 0.83 m/s Spray of water and glycerin (10%/volume) 2D measurements (YZ planes) 2D flow symmertry along X-axis Grid of points of measurements

11 Calibration of numerical model Mass flow [m 3 /h] Pressure drop [Pa] CFD Experimental tests

12 CFD Results Experimental data Vector plots of velocity magnitude (m/s) CFD

13 CFD Results CFD air flow streamline 0<t<3 sec : instable 3<t<10 sec : stable

14 Results CFD separation efficiency at different mass flows: Efficiency % m grease m grease inlet m grease inlet outlet 100 Low efficiency Low efficiency for D < 10 micron : 2D Flow Limited time inside the filter

15 Optimization Multi-objective optimization of the geometry: modefrontier Maximize efficiency of particle separation Minimize pressure loss

16 Optimization - modefrontier workflow 6 geometric input CAD node: CATIA CFD node: STAR-CCM+ FAST outputs 2 objectives

17 Geometric inputs 6 Geometric inputs Top view Front cross section

18 Direct interface with STAR-CCM+ Optimate: how it works Input parameters (simulation or geometry modeled within) are automatically introspected Available output results are automatically introspected and can be selected Optimization variables nodes are automatically created in the workflow

19 Direct interface with STAR-CCM+ and external CAD Optimization setup with external CAD and Optimate (STAR-CCM+)

20 Grid Automation in modefrontier for parallel executions 1. The job starts on a grid node (local folder) 2. modefrontier distributes the jobs on available grid machines (remote folder) 3. The controlling grid node gets results back

21 FAST Algorithms (Adaptive RSM) DoE RSM training New FAST exploits an efficient internal adaptive Response Surface method Selection of best fitting RSM functions Virtual Optimization / Exploration Phase Combination of adaptive RSM, Incremental Space Filler DOE and Optimization algorithms for costeffective design optimization Selection of Pareto points Validation of Pareto points Convergence reached? This is an alternative of running a DOE, train RSM and use them for a virtual optimization, since RSM are in this way automatically updated and validated Optimal Design

22 Optimization results Real Virtual

23 Optimization results section A-A section A-A Starting design Optimized design Pressure [Pa] Efficiency [%] Baseline Optimal 450 ( 59%) 95 (+121%)

24 Conclusions The presentation highlights the powerful capabilities of modefrontier coupled with STAR-CCM+ for an efficient multiobjective optimization; As an example it has been shown an optimization of a grease mechanical filter, whose CFD numerical model has been validated by experimental tests; Any problem of industrial relevance can be optimized with modefrontier, as confirmed by many of our customers including many leading companies working with STAR-CCM+ (please check for more details).

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