CFD Prediction for High Lift Aerodynamics Recent Progress and Emerging Opportunities

BCA Engineering Flight Sciences

Jeffrey Slotnick, Technical Fellow, Boeing Commercial Airplanes RAeS Conference on Aerodynamics Tools and Methods in Aircraft Design

15 October 2019

Outline

▪Introduction ▪Flow Modeling Challenges ▪Recent Progress ▪Emerging Opportunities

• Physics-based numerical simulation continues to expand into all development phases of the aerospace vehicle/system lifecycle. ▪ Drive to reduce non-recurring product development costs and risk ▪ Drive to create products that are environmentally cleaner, more fuel efficient, safer, etc. ▪ Drive to attain designs close to the optimum • Obtaining reasonably accurate simulations with full configuration geometry and complex flow physics is now commonplace. ▪ Enabled by ever evolving High Performance Computing (HPC) to solve on larger and larger models within an acceptable amount of time ▪ Providing deeper physical insight into more realistic flow physics ▪ Creating higher-fidelity aerodynamic databases to support product design, development, and certification ▪ Pushing into aerodynamic optimization

Much CFD penetration. Some CFD penetration. CFD Penetration Opportunity Accurate simulation analysis Opportunities exist for large increases in Fundamental improvements to physical capabilities for validated applications design process speed and application modeling and solver efficiency required before trusted application is possible Improvement (from 2014) or new Certification Takeoff with Design For cross wind Wind Tunnel Stability & Control Failure Interior Control Air Antenna Corrections Buffet Analysis Cabin Boundary Noise Quality Radome High-Lift Wing VMU Cert Vertical Tail Design ECS Inlet Design Wing Tip Design Design High-Speed Wing Cab Design Wing-Body Design CLmax Fairing Design Wing Icing Controls Tail Design Edge Loads For Loads Air Data System Location Inlet Design Aft Body Inlet Cross-Flow Gear Effects Planform Design Inlet Cert Nacelle Design Design Avionics Engine/Airframe APU Inlet Exhaust Integration Cooling Engine Bay Thermal Analysis System Design Flutter And Ducting Aeroelastics Reynolds Number Thrust Reverser Wake Vortex Prediction Corrections for Loads Design APU and Propulsion and S&C Design for FOD Vortex Generator Community Noise Fire Suppression Placement Prevention

• CFD has been calibrated only in relatively small regions of the operating envelope where the external flow is well modeled by current RANS methods ▪ High-speed cruise (aero design) ▪ Low-speed at nominal attitude with moderate flap settings

“…In spite of considerable successes, reliable use of CFD has remained confined to a small but important region of the operating design space due to the inability of current methods to reliably predict turbulent-separated flows.” Slotnick, J. and Heller, G., “Emerging Opportunities for Predictive CFD for Off-Design Commercial Airplane Flight Characteristics”, — CFD Vision 2030 Report, 2014 54th 3AF Conference, Paris 2019

Airbus • Computing flow around high lift wings is complicated due to multiple, interfering, and unsteady flow features, such as turbulent boundary layers, vortices, and wakes • Geometric complexity drives mesh resolution, which creates demanding computing requirements • Adequate mesh resolution is needed for robust propagation of flow features • Accurate physical modeling (e.g. turbulence) is required to make high-lift flow modeling tractable

Modeling improvements are Airbus required to close gaps between the virtual and real worlds Boeing

▪ Considerable time spent evaluating fixed grid RANS on ExperimentalDR0153 Run Data11 CFDC014 (Best Baseline Comparison) Geometry simplified to complex airplane geometries C019CFD (Baseline) All Geom (SARC) – Gridding sensitivity

– Turbulence modeling CL – Geometric considerations – Solver execution (numerics, settings, best practices) ▪ Using best options, we can get absolute levels of maximum lift Alpha (CLmax ) relatively close to experimental data

▪ Separation locations and pitching moment (CM) at stall and post-stall are not predicted accurately ▪ Ongoing evaluation of adaptive grid RANS has not yet improved modeling of flow at maximum lift Alpha

CM +pos -neg

Current RANS methodologies are inadequate for predictions at the edges of the envelope

Turbulence Resolving Methods Experiment CFD (RANS ‒ SA-QCR)

▪ RANS simulation results on the JAXA Standard Model (used for AIAA HLPW3) frequently showed spurious separation behind slat brackets when the test data did not.

▪ Simulations using hybrid RANS/LES methods Ito, Y., et al., “JAXA’s and KHI’s Contribution CFD (DDES) (DDES) demonstrated some capability of to the Third High Lift Prediction Workshop”, correcting this deficiency, but limitations aren’t well https://doi.org/10.2514/1.C035131 understood.

▪ Initial attempts to use DDES methods to predict CLmax on production configuration geometry show mixed results: ▪ Likely due to grid sensitivities, and development of proper gridding procedures ▪ Comprehensive assessment is not currently computationally feasible due to long solution times ▪ Development of best practices may take years

Turbulence Resolving methods may help capture flow physics, but much work remains to apply to real world problems

Copyright © 2019 Boeing. All rights reserved. BCA Engineering | Flight Sciences Recent Progress: Wall-Modeled Large Eddy Simulation (WMLES) QinetiQ,WT Test ClassicData LE (DR0153 Run 22) PowerFLOW Rounded ▪ Evaluating Simulia PowerFLOW solver: PowerFLOW Sharp ▪ Lattice-Boltzmann formulation (models fluid with particle dynamics) ▪ Includes a proprietary WMLES method to include effects of turbulence

▪ Inherently unsteady, time-accurate Coefficient Lift of(CL) ▪ Features a refined process flow and is computationally 8 10 12 14 16 18 20 22 tractable Angle of Attack [deg] ▪ On configurations investigated, PowerFLOW has demonstrated significantly improved correlations at stall: ▪ Generally lower lift levels, but ▪ Evidence that the flow breakdown mechanism may be correctly captured ▪ More work must be done to establish best practices

Some promise with different approaches and emerging toolsets

Copyright © 2019 Boeing. All rights reserved. BCA Engineering | Flight Sciences Recent Progress: Wall-Modeled Large Eddy Simulation (WMLES) ▪ Evaluating Cascade Technologies CharLES solver: ▪ Unstructured grid, finite-volume formulation ▪ Includes refined WMLES methods to include effects of turbulence ▪ Features an efficient grid generation scheme and is computationally tractable ▪ Increasing validation on aerospace cases of interest ▪ Assessment on production high-lift configurations is underway ▪ Very promising correlation to forces and moments near and at stall ▪ Like PowerFLOW, appears to be predicting flow breakdown consistent with experience ▪ More work must be done to establish best practices

Significant progress in productionizing WMLES methods

How can we accelerate progress?

• Acquire high quality validation data on fundamental physics through wind tunnel testing of relevant high-lift configurations (open, or potentially proprietary – e.g. Juncture Flow, CRM-HL) at a range of Reynolds numbers. • Improve flow physics computational modeling (transition, turbulence, chemistry, etc.) and solver numerics (higher-order methods, grid meshing/adaptation) to enable more accurate and reliable flow predictions at edges of flight envelope (CLmax, buffet, integrated power effects, etc.) • Develop robust wind tunnel data corrections to free-air • Develop tools/methods to create integrated databases merging computational/analysis data with ground and/or flight test data • Energize the international CFD/Aero community to collaborate and coordinate efforts

The Challenge is to predict aerodynamics using the right physics and reliable/effective computational modeling

▪ Purpose: – Drive CFD technology development and validation of advanced computational capabilities for low-speed, high-lift aerodynamic analysis, design, and certification. ▪ Approach: – Develop WT models and collect data via international collaboration through pre-competitive, open R&D

– Engage industry, government, and academic expertise Lacy, D. and Sclafani, A, “Development of the High Lift Common Research Model (HL-CRM): A across borders to raise the water level together by Representative High Lift Configuration for Transonic benchmarking and advancing predictive methods. Transports” AIAA-2016-0308, https://doi.org/10.2514/6.2016-0308. ▪ Benefits: ‒ Open, community-driven validation data acquisition and prediction workshops are key to developing broad confidence in CFD capabilities and best practices. ‒ Utilization of advanced WT test and measurement techniques verifies that airplane characteristics are predicted for the right physical reasons ‒ Supports research activities across the entire low-speed aerodynamics spectrum: configuration design, performance enhancement, icing, noise reduction, high lift system simplification, certification, etc.. ‒ Provides baseline and enduring test-bed for advanced CFD technology and tool/method R&D

1. Reference configurations – establish focus points to link ecosystem together; provide conventional HL system performance “yardstick” 2. Configuration variation data – ability to provide meaningful data to support configuration decisions 3. Reynolds number effects – inform how answer changes with airplane size; drive wind tunnel testing strategy 4. WT modeling effects – half/full models; mounting effects; guide data interpretation and model sizing; drive testing strategy 5. Flow physics CFD validation data – all of the above plus detailed localized data around key aerodynamic drivers

▪ Forces and moments ▪ Surface pressures (static, dynamic, paint) ▪ Surface flow visualization (oil, tufts) ▪ Off body velocity measurements (probes, PIV, LDV) ▪ Very near surface (e.g. boundary layer) ▪ Near surface (e.g. bracket wakes over wing, nacelle wake, etc.) ▪ Away from surface (e.g. wakes behind wing)

Koklu, M, et al., “Surface Flow Visualization of the High Lift Common Research Model”, AIAA 2019-3727, https://doi.org/10.2514/6.2019-3727.

Small Mid-size Large Large Cryogenic Atmospheric Atmospheric Atmospheric Pressurized Tunnels Tunnels Tunnels Tunnels Tunnels

Smaller Larger NASA LaRC QinetiQ 5m, NASA NTF, University University 14’x22’ ONERA F1 ETW Tunnels Tunnels

Increasing Re # (and testing cost)

▪ Higher Re # provides better representation of aerodynamic characteristics at flight scale ▪ WT testing of half-span models present tradeoffs: ▪ Larger scale provides higher Re # ▪ Physically larger model parts potentially provide better geometric fidelity, and the ability to measure flow quantities in critical, hard-to-reach areas ▪ Reduced part count provides fabrication and model change efficiencies ▪ Potential flow physics differences with full-span (e.g. tunnel wall effects at body centerline) ▪ Limit on some aerodynamic characteristics (e.g. yawing capability for stability and control) ▪ Differences (potential limitations) in optical access

High Low Maturity/availability of Wind Tunnel Data Capabilities measurement technology

Model 1.2m Univ 2.5m UWAL 10% 14x22 1.75m Q 3.5m Q 3.0m F1 2.7% NTF 2.7% NTF 5.2% NTF Scale 0.041 0.043 0.100 0.060 0.060 0.051 0.027 0.027 0.052 Model type Half Full Half Half Full Full Half Full Half Tunnel medium Air Air Air Air Air Air Cryo Cryo Cryo Design Pressure 1 atmo 1 atmo 1 atmo 3 atmo 3 atmo 3.84 atmo 6 atmo 6 atmo 6 atmo Approx. Re # 1.3 1.4 3.3 5.8 5.8 6.4 16.1 16.1 31.1 Forces & Moments small model, small model, Surface Pressures cryo material smaller model smaller model cryo material cryo material limitations (static, dynamic) limitations limitations Possibly TSP - Possibly TSP - Possibly TSP - Surface Flow tunnel china clay, tufts, UV oil tufts, UV oil tufts, UV oil tbd Requires Requires Requires dependent UV oil Visualization verification verification verification low likelihood low likelihood low likelihood Off-Body Velocity tunnel rakes only at rakes only at rakes only? rakes only? tbd w/high power w/high power w/high power dependent present present (very near body) laser laser laser low likelihood low likelihood low likelihood Off-Body Velocity tunnel PIV in PIV in PIV in PIV in tbd for at all for at all for at all dependent development development development development (near body) desired desired desired requires requires requires Off-Body Velocity tunnel QWSS QWSS? QWSS QWSS tbd further further further dependent (away from body) development development development tunnel tunnel tunnel tunnel tunnel tunnel tunnel tunnel tunnel Model Deformation dependent dependent dependent dependent dependent dependent dependent dependent dependent ▪ Identifies measurement techniques that are likely available and desired ▪ Identifies longer term data needs to provide focus for flow measurement development community to mature low TRL capabilities

14x22 / Q 10% NASA half model MODEL CY 2019 2020 2021 2022 2023 Confirm CRM-HL design features 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q CRM = Common Research Model Establish reference configurations NASA 10% SS 1atm NSS A1 NSS P1 NSS A2 HL = High Lift NASA research (AFC, noise) NASA 5.2% SS Cryo NSS C1 HiLiftPW = High Lift Tie in to NTF-derived half model Re # trend data NASA 2.7% FS Cryo NFS C1 Prediction Workshop NASA 2.7% SS Cryo SS = Semi-Span Q 6.0% 3atm full model FS = Full Span Configuration variation data * atm = Atmosphere Half-full model issues Boeing 3.5m FS 3atm BFS P1 NTF Tie in to NTF-derived half model Re # trend data Boeing/UK 1.2m SS 1atm UKSS A1 ETW Mounting system effects (T&I) ONERA 3.0m FS 3atm Q5m Wall effects (collaboration with ONERA) 14x22 Configuration-level PIV data TDT Q 6.0% 3atm half model ONERA F1 1. Reference Configuration DNW-NSB Tie in to NTF-derived half model Re # trend data University Half-full model issues 2. Optimization/Sensitivity Data

Design/Fab ONERA 5.1% 3.85atm full model 3. Reynolds Number Effects Wall effects (collaboration with UK/Boeing) Test Objective Exploit unique data collection opportunities 4. WT Modeling Effects

Proposed NASA 5.2% cryo half model 5. Flow Physics CFD Validation Data Primary model for Re # trends

NASA 2.7% cryo full and half models Half-full model issues deemed Re # dependent *

▪ Tested in the NASA LaRC 14x22- Foot Subsonic Tunnel (2018) ▪ Main focus was on Active Flow Control (AFC) ▪ Single conventional high-lift system data was collected to provide baseline

▪ Landing configuration (dslat=30, dflap=37) ▪ Nacelle pylon on/off, chine on/off ▪ No positioning sensitivity data ▪ Forces/moments and surface pressures

Lin, J. et al., “Wind Tunnel Testing of Active Flow Control on High-Lift Common Research Model”, AIAA-2019-3723, https://doi.org/10.2514/6.2019-3723.

▪ Testing is underway in the QinetiQ (Q) 5-metre facility ▪ Builds on conventional HL data collected in 14x22 ▪ Objective is to establish an enduring set of reference configurations ▪ Explore flow sensitivities/optimize about nominal landing and take-off configurations ▪ Nacelle pylon on/off, chine on/off ▪ Collect configuration build-up data (e.g. Flaps-up) ▪ Forces/moments, surface pressures, and initial PIV (if successful)

▪ Use of CFD has been largely successful in the core of the flight envelope, but less successful at the edges where much certification takes place ▪ The current state of RANS CFD technology is not accurate enough to model turbulent separated high-lift flows ▪ Boeing continues to assess new CFD technologies for applicability to certification by analysis – the nature and scale of the problems we face are relatively unique in the industry ▪ A key focus for the future is understanding which technologies are capable of robustly predicting flow separation on typical aircraft geometries, and incorporating them into efficient and repeatable processes ▪ A mix of experimental data and computational analysis will yield better predictions and understanding of the flow physics ▪ Boeing is leading the drive to obtain high quality “open” data on relevant geometries to drive R&D to develop predictive capabilities and to validate tools ready for use by Industry