cfd prediction for high lift aerodynamics · •cfd has been calibrated only in relatively small...

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BCA Engineering Flight Sciences

CFD Prediction for High Lift AerodynamicsRecent Progress and Emerging Opportunities

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

15 October 2019


BCA Engineering | Flight Sciences

Outline

▪ Introduction

▪Flow Modeling Challenges

▪Recent Progress

▪Emerging Opportunities



Modeling and Simulation Digital Transformation

• 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

▪ 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

• Obtaining reasonably accurate simulations with full configuration geometry and complex flow physics is now commonplace.



CFD is used for virtually every airplane configuration component

Much CFD penetration.Accurate simulation analysis

capabilities for validated applications

Some CFD penetration.Opportunities exist for large increases in

design process speed and application

High-Speed WingDesignCab Design

Engine/Airframe Integration

Inlet Design

Inlet Cross-Flow

ExhaustSystem Design

CabinNoise

Community Noise

Wing-BodyFairing Design

Vertical Tail Design

Design ForStability &

Control

High-Lift Wing Design

APU Inlet

And Ducting

ECS Inlet Design

APU and PropulsionFire Suppression

Nacelle Design

Thrust ReverserDesign

Design for FOD

Prevention

Aeroelastics

Icing

Air Data System

Location

AntennaRadome

Vortex Generator Placement

PlanformDesign

Buffet Boundary

Wake Vortex PredictionReynolds Number Corrections for Loads

and S&C

Flutter

Control Failure Analysis

Wind Tunnel Corrections

Tail Design For Loads

Wing Tip Design

Wing Controls

Avionics Cooling

Interior Air

Quality

Engine Bay Thermal Analysis

Aft Body DesignGear Effects

Inlet Cert

Edge Loads

CLmax

CFD Penetration OpportunityFundamental improvements to physical

modeling and solver efficiency required

before trusted application is possible

Takeoff with cross wind

Improvement (from 2014) or new

VMU Cert

Certification



Full Virtualization Requires Accurate Simulation in the Full Flight Envelope

• 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.”

— CFD Vision 2030 Report, 2014

Slotnick, J. and Heller, G., “Emerging Opportunities for Predictive

CFD for Off-Design Commercial Airplane Flight Characteristics”,

54th 3AF Conference, Paris 2019



High Lift Flow Modeling is Complex and Challenging

Airbus

Airbus

Boeing

• 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 required to close gaps between the

virtual and real worlds



Current Status: Reynolds Averaged Navier-Stokes (RANS) Results

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

▪ Considerable time spent evaluating fixed grid RANS on simplified to complex airplane geometries

– Gridding sensitivity

– Turbulence modeling

– Geometric considerations

– Solver execution (numerics, settings, best practices)

▪ Using best options, we can get absolute levels of maximum lift(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

-neg+pos

CL

Alpha

DR0153 Run 11

C014 Baseline Geometry

C019 All Geom (SARC)

Alp

ha

CM

Experimental Data

CFD (Best Comparison)

CFD (Baseline)



Recent Progress: Turbulence Resolving Methods

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

▪ 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 (DDES) demonstrated some capability of correcting this deficiency, but limitations aren’t well understood.

Experiment CFD (RANS ‒ SA-QCR)

CFD (DDES)

▪ 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

Ito, Y., et al., “JAXA’s and KHI’s Contribution

to the Third High Lift Prediction Workshop”,

https://doi.org/10.2514/1.C035131

https://doi.org/10.2514/1.C035131



Recent Progress: Wall-Modeled Large Eddy Simulation (WMLES)

Some promise with different approaches and emerging toolsets

▪ Evaluating Simulia PowerFLOW solver:

▪ Lattice-Boltzmann formulation (models fluid with particle dynamics)

▪ Includes a proprietary WMLES method to include effects of turbulence

▪ Inherently unsteady, time-accurate

▪ Features a refined process flow and is computationally tractable

▪ 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

8 10 12 14 16 18 20 22

Coeff

icie

nt

of Lift

(CL)

Angle of Attack [deg]

QinetiQ, Classic LE (DR0153 Run 22)

PowerFLOW Rounded

PowerFLOW Sharp

WT Test DataWT Test Data



Significant progress in productionizing WMLES methods

▪ Evaluating Cascade Technologies CharLESsolver:

▪ 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

Recent Progress: Wall-Modeled Large Eddy Simulation (WMLES)



How can we accelerate progress?

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

• 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



High-Lift Common Research Model (CRM-HL) Ecosystem

▪ 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 across borders to raise the water level together by benchmarking and advancing predictive methods.

▪ 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

Lacy, D. and Sclafani, A, “Development of the High

Lift Common Research Model (HL-CRM): A

Representative High Lift Configuration for Transonic

Transports” AIAA-2016-0308,

https://doi.org/10.2514/6.2016-0308.

https://doi.org/10.2514/6.2016-0308



Data Requirements – Categories

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



Data Requirements – Types and Locations

▪ 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.

https://doi.org/10.2514/6.2019-3727



▪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

Cryogenic

Tunnels

NASA NTF,

ETW

Small

Atmospheric

Tunnels

Smaller

University

Tunnels

Mid-size

Atmospheric

Tunnels

Larger

University

Tunnels

Large

Atmospheric

Tunnels

NASA LaRC

14’x22’

Large

Pressurized

Tunnels

QinetiQ 5m,

ONERA F1

Increasing Re # (and testing cost)

Wind Tunnel Testing Options



Wind Tunnel Data Capabilities Maturity/availability of

measurement technology

High Low

▪ 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

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

Surface Pressures

(static, dynamic)smal ler model smal ler model

smal l model ,

cryo materia l

l imitations

smal l model ,

cryo materia l

l imitations

cryo materia l

l imitations

Surface Flow

Visualization

tunnel

dependent

china clay,

UV oi ltufts , UV oi l tufts , UV oi l tufts , UV oi l tbd

Poss ibly TSP -

Requires

veri fication

Poss ibly TSP -

Requires

veri fication

Poss ibly TSP -

Requires

veri fication

Off-Body Velocity

(very near body)

tunnel

dependentrakes only? rakes only?

rakes only at

present

rakes only at

presenttbd

low l ikel ihood

w/high power

laser

low l ikel ihood

w/high power

laser

low l ikel ihood

w/high power

laser

Off-Body Velocity

(near body)

tunnel

dependent

PIV in

development

PIV in

development

PIV in

development

PIV in

developmenttbd

low l ikel ihood

for at a l l

des i red

low l ikel ihood

for at a l l

des i red

low l ikel ihood

for at a l l

des i red

Off-Body Velocity

(away from body)

tunnel

dependentQWSS QWSS? QWSS QWSS tbd

requires

further

development

requires

further

development

requires

further

development

Model Deformationtunnel

dependent

tunnel

dependent

tunnel

dependent

tunnel

dependent

tunnel

dependent

tunnel

dependent

tunnel

dependent

tunnel

dependent

tunnel

dependent



Development Plan

CY

CRM = Common Research Model

HL = High Lift

HiLiftPW = High Lift Prediction Workshop

SS = Semi-SpanFS = Full Span

atm = Atmosphere

2019 2020 2021 2022 2023

NTF

ETW

Q5m

14x22

TDT

1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q 1Q 2Q 3Q 4Q

Design/Fab

Test Objective

NASA 2.7% SS Cryo

Boeing/UK 1.2m SS 1atm

ONERA F1

Boeing 3.5m FS 3atm

NASA 2.7% FS Cryo

ONERA 3.0m FS 3atm

DNW-NSB

NASA 5.2% SS Cryo

Proposed

DRAFT 8 August 2019

NSS C1

NASA 10% SS 1atm NSS P1NSS A1 NSS A2

MODEL

NFS C1

BFS P1

UKSS A1

1. Reference Configuration

2. Optimization/Sensitivity Data

3. Reynolds Number Effects

University

4. WT Modeling Effects

5. Flow Physics CFD Validation Data

14x22 / Q 10% NASA half model

Confirm CRM-HL design features

Establish reference configurations

NASA research (AFC, noise)

Tie in to NTF-derived half model Re # trend data

Q 6.0% 3atm full model

Configuration variation data

Half-full model issues


Mounting system effects (T&I)

Wall effects (collaboration with ONERA)

Configuration-level PIV data

Q 6.0% 3atm half model


Half-full model issues

ONERA 5.1% 3.85atm full model

Wall effects (collaboration with UK/Boeing)

Exploit unique data collection opportunities

NASA 5.2% cryo half model

Primary model for Re # trends

NASA 2.7% cryo full and half models

Half-full model issues deemed Re # dependent

*

*



NASA 10% Scale Half-Model

▪ 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 pressuresLin, 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.

https://doi.org/10.2514/6.2019-3723



NASA 10% Scale Half-Model

▪ 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)



Summary

▪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

cfd prediction for high lift aerodynamics · •cfd has been calibrated only in relatively small...

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