Accurate and Efficient Turbomachinery Simulation. Chad Custer, PhD Turbomachinery Technical Specialist

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1 Accurate and Efficient Turbomachinery Simulation Chad Custer, PhD Turbomachinery Technical Specialist

2 Outline Turbomachinery simulation advantages Axial fan optimization Description of design objectives Optimization algorithm Model setup Design space Results

3 STAR-CCM+ Integrated multi-physics simulation environment Single environment for the complete simulation process CAD Geometry Modifications Mesh Physics Post-processing

4 Geometry Handling Direct of native and neutral CAD formats Defeaturing Fluid extraction

5 Mesh Generation Key Capabilities Automatic mesh generation Polyhedral cells Pipelined meshing Simple global size settings Local refinement control Automatic solution interpolation Fewer cells required

6 Mesh Generation Critical for blade cooling and turbocharger analyses Key Capabilities Polyhedral cells Accurately capture complex geometry Conformal interfaces One-to-one communication between the fluid and solid Prism layer generation Body-fitted boundary layer mesh on all fluid walls

7 Mesh Generation Key Benefits Geometry capturing High quality mesh Smooth cell growth Low skewness Boundary layer capturing Accurate heat transfer

8 Physics Coupled Flow & Energy Model Solves for conservation of mass, momentum, and energy simultaneously Uses a time-marching (or pseudo-time-marching) approach Preconditioned form allows for rapid convergence independent of Mach number Segregated Flow & Energy Model Also Available Often used for low-mach simulations Solves flow & energy equations in an uncoupled manner Linkage between the momentum and continuity equation achieved with a predictor-corrector approach

9 Physics Grid Sequencing Initialization Modeling Time Steady solver Frozen rotor Mixing plane Implicit unsteady Full wheel Periodic sector Harmonic balance

10 Steady Simulation Frozen rotor Solving for a particular instant in time Achieved with in-place interfaces Solution can vary from one blade to the next The entire machine must be meshed Common for cases where there are large difference in the flow solution from one blade to the next (radial machines with volute)

11 Steady Simulation Mixing Plane Capable of using a non-reflecting treatment Non-reflecting option should always be used with explicit interfaces Eigen-mode analysis performed on interface Pressure allowed to vary within each bin such that reflections are eliminated

12 Steady Simulation Non-reflecting treatment critical for cut-on cases Reflecting Non-reflecting

13 Unsteady Simulation Model equal sectors of each row

14 Harmonic Balance Basics The harmonic balance method allows for the rapid computation of periodic, unsteady flows By recasting the governing equations in the frequency domain, it is possible to converge to the unsteady solution The HB method can be applied to many applications including: Turbomachinery Aeroelasticity (flutter and limit cycle oscillations) Aeroacoustics

15 Outline Axial fan optimization Description of design objectives Optimization algorithm Model setup Design space Results

16 Model Overview Fan is installed in a duct with a diameter of 59 cm Fan inner diameter and shape fixed with a radius of 19 cm Current straight bladed fan produces 2.5 kg/s using 542 W power (aerodynamic only)

17 Analysis Objectives Optimization study seeks to achieve two objectives: 1. Improve an existing fan design so that the same mass flow rate is achieved with a lower power requirement? Baseline Design Mass flow rate = 2.5 kg/s Power = 542 W

18 Analysis Objectives Optimization study seeks to achieve two objectives: 2. Obtain a set of fan designs that require the least power for any given mass flow rate Best Possible Wasteful Possible Design x Lower Power Design Unfeasible

19 Optimization Algorithm The optimization of two competing factors (mass flow and power) is Pareto optimization All points on the Pareto Front are the best possible designs Pareto Front Wasteful Unfeasible

20 Typical Optimization Process Standard Procedure Build Baseline Model Define Optimization Problem Select Optimization Algorithm and Set Tuning Parameters Proposed Solution Satisfied? Optimized Solution

21 Modern Optimization Process HEEDS Procedure Build Baseline Model Define Optimization Problem Select Optimization Algorithm and Set Tuning Parameters Proposed Solution SHERPA Satisfied? Hybrid Adaptive No Tuning Parameters No Optimization Expertise Required Optimized Solution

22 Optimization Workflow HEEDS Procedure Build Baseline Model Define Optimization Problem Setup Complete. SHERPA takes care of the rest. SHERPA Optimized Solution

23 Model Setup Mesh Fan generated in STAR-CCM+ 3D CAD Single blade passage meshed using periodic interfaces Polyhedral mesh with body-fitted prism layers Baseline design contained 690,000 cells

24 Model Setup Physics Steady MRF simulation Coupled solver (CFL = 20) Grid sequencing initialization Realizable k-ϵ turbulence model

25 Design Space Blade generated from three section profiles Fan defined with 17 design parameters: Number of blades Rotation rate Chord, camber, angle, x-translation and y-translation of each section With these parameters it is possible to define blade shape, twist, sweep, and stacking Section 0 Section 1 Section 2

26 Visual Representation of Design Space

27 Examples of Possible Designs Previous movie shows design parameters varied one-at-a-time Optimate is able explore all combinations of design parameters

28 Computational Setup Total of 300 trials Each trial run on 16 cores Ten trial run concurrently

29 Trial Process Assign new design parameters Mesh new configuration Run grid sequencing initialization Converge solution Report results to SHERPA Controlled by Optimate. No user intervention needed. Process takes ~1 hour for each trial.

30 Pareto Optimization Optimate will run cycles of cases used to explore the design space Using the results from previous cycles, the optimization algorithm is able to find better fan designs

31 Results of Objective #1 Improve the existing fan design so that the same mass flow rate is achieved with lower power Baseline Design Optimized Design m = 2.5 kg/s p = 542 W m = 2.6 kg/s p = 242 W

32 Investigation of Optimized Fan Baseline Design Optimized Design

33 Investigation of Optimized Fan Baseline Design Optimized Design

34 Investigation of Optimized Fan Baseline Design Optimized Design

35 Investigation of Optimized Fan Baseline Design Optimized Design 10% Span

36 Investigation of Optimized Fan Baseline Design Optimized Design 50% Span

37 Investigation of Optimized Fan Baseline Design Optimized Design Separation at the tip results in low mass flow near the outer diameter Optimized design results in uniform exit profile

38 Review of Objective #1 Reduced power required for 2.5 kg/s flow rate by 55% Design parameters and number of runs were the only inputs to the optimization algorithm Algorithm produced a case that resulted in: Attached flow at all span-wise stations of the blade High camber High mass flow & Low power

39 Results of Objective #2 Obtain a set of fan designs that require the least power for any given mass flow rate

40 Pareto Convergence Converged Pareto optimization gives geometry requiring the lowest power for a given mass flow rate For each rank one response, there is no design that produces a higher mass flow at a lower power

41 Results of Objective #2

42 Review of Objective #2 20 optimal fan designs produced Mass flow rates up to 17 kg/s

43 Conclusions Fan optimization study achieved two objectives: 1. Improve an existing fan design so that the same mass flow rate is achieved with lower power Baseline Design Mass flow rate = 2.5 kg/s Power = 542 W Optimized Design Mass flow rate = 2.6 kg/s Power = 242 W

44 Conclusions Fan optimization study achieved two objectives: 2. Found a set of fan designs that require the least power for any given mass flow rate from 0 kg/s to 17 kg/s

45 Outline Turbomachinery simulation advantages Axial fan optimization Description of design objectives Optimization algorithm Model setup Design space Results

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