Revolutionary Computational Aerosciences (RCA) - NASA

1y ago
19 Views
3 Downloads
5.18 MB
37 Pages
Last View : 21d ago
Last Download : 2m ago
Upload by : Laura Ramon
Transcription

Transformational Tools and Technologies ProjectRevolutionary Computational Aerosciences (RCA)Research Portfolio and CFD 2030Mujeeb R. MalikTechnical Lead, RCANASA Langley Research CenterCetin C. KirisHead, Computational Aerosciences BranchNASA Ames Research CenterHiFi-TURB Project Kick-Off MeetingBrussels, BelgiumJuly 2nd, 20191

Outline NASA Aeronautics Organization Structure̶ARMD à TACP à TTT à RCA CFD Vision 2030 Revolutionary Computational Aerosciences (RCA) Research Portfolio Example Highlights Foundational Research in RCA à ARMD and other Mission Directorates Summary2

NASA Aeronautics Programs and ProjectsNASA Mission DirectoratesAeronautics Research (ARMD)Dr. Jaiwon Shinn, AAHuman Exploration and Operations(HEOMD)Science(SMD)Space Technology(STMD)ARMD PROGRAMSDr. James Kenyon: DirectorAkbar Sultan: DirectorDr. Ed Waggoner: DirectorDr. John Cavolowsky: DirectorAdvancedAir VehiclesAirspaceOperations and SafetyIntegratedAviation SystemsTransformativeAeronautics ConceptsProjects Advanced Air TransportTechnology Revolutionary Vertical LiftTechnology Commercial SupersonicTechnology Hypersonic TechnologyProjects Airspace TechnologyDemonstrations UAS Traffic Management System-Wide Safety ATM-XProjects Unmanned AircraftSystems Integration in theNational Airspace System Flight Demonstrationsand Capabilities Low Boom FlightDemonstratorProjects Convergent AeronauticsSolutions Transformational Toolsand Technologies University Innovation X-planes/test environment Critical cross-cutting tool developmentAerosciences Evaluation & Test Capability Officewww.nasa.gov3

Transformational Tools and Technologies (T3) ProjectT3 project performs deep-discipline research and engages indevelopment of first-of-a-kind capabilities to analyze, understand,predict, and measure performance of aviation systems; research anddevelopment of “tall-pole” technologies; all of which enable design ofadvanced aeronautics systems.§ Revolutionary Computational Aerosciences (RCA)§ Innovative measurements§ Multi-disciplinary analysis and optimization (MDAO)§ Combustion modeling and technologies§ Propulsion and flight controls§ Materials and structures for next generation aerospace systems§ Autonomous systemsRCA develops high-fidelity, physics-based computational analysiscapability informed by CFD Vision 2030 study recommendations4

RCA Research Portfolio Guided by:CFD 2030 Technology Development RoadmapTRLLOWMEDIUMHIGHTechnology Milestone2015CFD on Massively Parallel SystemsPETASCALERANSImproved RST modelsin CFD codesAlgorithmsLESIntegrated transitionpredictionCombustionChemical kineticscalculation speedupTighter CAD couplingAdaptive GridWMLES/WRLES for complex 3D flows at appropriate ReChemical kineticsin LESGrid convergence for acomplete configurationScalable optimal solversReliable error estimates in CFD codesLarge scale parallelmesh generationUnsteady, 3D geometry, separated flow(e.g., rotating turbomachinery with ion modelProduction scalableentropy-stable solversLarge scale stochastic capabilities in CFDUncertainty propagationcapabilities in CFDAutomated in-situ meshwith adaptive controlProduction AMR in CFD codesIntegrated DatabasesKnowledge ExtractionEXASCALEYESAutomated robust solversCharacterization of UQ in aerospaceFixed GridNONOUncertainty Quantification (UQ)Geometry and GridGeneration30 exaFLOPS, unsteady,maneuvering flight, full enginesimulation (with combustion)Unsteady, complex geometry, separated flow atflight Reynolds number (e.g., high lift)NOConvergence/Robustness2030Highly accurate RST models for flow separationHybrid RANS/LESPhysical Modeling2025Demonstrate efficiently scaledCFD simulation capability on anexascale systemDemonstrate solution of arepresentative model problemYESCFD on Revolutionary Systems(Quantum, Bio, etc.)Decision Gate2020Demonstrate implementation of CFDalgorithms for extreme parallelism inNASA CFD codes (e.g., FUN3D)HPCMDAOTechnology DemonstrationCreation of real-time multi-fidelity database: 1000 unsteady CFDsimulations plus test data with complete UQ of all data sourcesSimplified datarepresentationVisualizationOn demand analysis/visualization of a10B point unsteady CFD simulationDefine standard for couplingto other disciplinesHigh fidelity couplingtechniques/frameworksOn demand analysis/visualization of a100B point unsteady CFD simulationIncorporation of UQ for MDAORobust CFD forcomplex MDAsMDAO simulation of an entireaircraft (e.g., aero-acoustics)UQ-Enabled MDAOCFD Vision 2030 Study report published in 20145

Progress Towards CFD 2030 VisionSpecial Session at AIAA Aviation 2019§ John Cavolowsky (NASA): NASA Aeronautics and CFD 2030§ Jeffrey Slotnick (Boeing): Progress Towards CFD Vision 2030 – AnIndustrial Perspective for Air and Space Vehicle Applications§ Gorazd Medic (UTRC): Impact of Vison 2030 on CFD Practices inPropulsion Industry§ Eric Nielsen (NASA): High Performance Computing Towards CFDVision 2030§ Dimitri Mavriplis (U. Wyoming): Progress in CFD Discretizations,Algorithms and Solvers for Aerodynamic Flows§ John Chawner (Pointwise): Progress in Geometry Modeling andMesh Generation Toward the CFD Vision 2030§ Philippe Spalart (Boeing): Turbulence Prediction in AerospaceCFD: Reality and the Vision 2030 RoadmapSession Sponsored by AIAA CFD Vision 2030 Integration Committee (IC)The newly formed IC was approved by AIAA in 20186

“Three Pillars” of RCA ResearchRevolutionary Computational Aerosciences (RCA) Robustness/ReliabilityAbility to generate results with error bounds on every try, by a nonexpert userØ Robust solver technology/nonlinear stabilityØ Uncertainty quantification̶ Cost/EfficiencyAbility to compute faster by orders of magnitude compared to the currentpractice̶Ø Exploit emerging HPC hardware capabilityØ Numerical algorithms (e.g., solvers, adaptive grids) ACCURACY̶Ability to accurately compute complex turbulent flows (e.g., transition, flowseparation, free shear flows, shock/boundary-layer interaction)Ø Numerical methods (e.g., HOMs), grids, boundary/initial conditions, etc.Ø Improved physical modeling and simulations§ CFD validation experiments (including physics experiment for modeldevelopment) a critical needCFD technology with above attributes will enable “Simulation-Based Engineering”:Ø Application to novel configurations, with confidence, for all NASA missions– Airplanes (fixed-wing , vertical lift, manned/unmanned)– Launch vehicles, airbreathing propulsion– Entry, Descent, LandingØ Aircraft certification by analysis7

RCA Research PortfolioTechnical Areas and Approaches Physical Modeling and Simulations§ LES/WMLES, hybrid RANS/LES and Lattice-Boltzmann Method§ Laminar-turbulent transition modeling§ Data driven modeling HPC Tools and Methods§ Effective utilization of emerging HPC hardware§ Accurate, efficient, and robust computational methods§ Grid adaptationEXP§ Uncertainty quantification CFD Validation Experiments§ Juncture flowCFD§ Flow separation (wall bump)Outcome: Accurate, fully§ Turbulent heat fluxvalidated CFD capability§ Shock/boundary-layer interactionFoundational research aimed at§ Supersonic jet flowsolving technical challenges§ High-speed mixing layer§ TDT aeroelastic experiment8

RCA’s Completed Technical ChallengeIdentify and downselect critical turbulence, transition, andnumerical method technologies for 40% reduction inpredictive error against standard test cases for turbulentseparated flows, evolution of free shear flows and shockboundary layer interactions on state-of-the-art highperformance computing hardware.Technical Challenge (TC) completed in 20189

RCA’s New Technical Challenge (TC) - 1Efficient High-Fidelity Computational Tools for Predicting MaximumLift on Transport AircraftDevelop and demonstratecomputationally-efficient, eddyresolving modeling tools that predictmaximum lift coefficient (CLmax) fortransport aircraft with equal or betteraccuracy than certification flight tests.Eddy-resolving methods could enable reliable predictionthroughout the flight envelope, but will be validatedspecifically for CLmax prediction10

RCA’s New Technical Challenge (TC) - 2Propulsion-related challengesinclude inlet/distortion-tolerant fanaerodynamics and aeromechanics Scale resolving simulations of inlet-fan interaction Enhanced CFD fan aeromechanics11

Research Products Advancements in numerical methods and modeling implemented inNASA CFD codes§FUN3D§OVERFLOW§LAVA (NS LBM)§EDDY (high-order spectral element)§GFR (flux reconstruction scheme)Multiple NASA projects fund capability developments inCFD codes12

RCA Research Execution Strategy Foundational Research in Computational Fluid Dynamics (CFD)̶ Breakthroughs cannot be predicted§ Requires innovative thinking and a lot of trial and error§ Find and challenge the best people available, let them learn fromfailures/false startsØ Success will follow, but it cannot be scheduled Cross Centers Research Effort̶ NASA Ames Research Center (ARC)̶ NASA Glenn Research Center (GRC)̶ NASA Langley Research Center (LaRC)§ Additional Postdoctoral Fellows/Research Associates, whereneeded Leverage Expertise Available at Universities and Industry throughNASA Research Announcements (NRAs)̶ Critical for training future workforce13

Currently Funded RCA NRAsNASA Research Announcements (NRAs) provide a mechanism to collaboratewith academia and industry, a recommendation of the CFD Vision 2030 StudyPhysical Modeling & Simulations: Improving the accuracy and efficiency of scale resolving simulations for favorable and adversepressure gradient flows; U Colorado (Kenneth Jansen) Adaptivity in wall-modeled large eddy simulations of complex three-dimensional flows; U Maryland(Johan Larsson). Assessment of wall-modeled LES in nonequilibrium flows with emphasis on grid independency; UPennsylvania (George Park) Scale-resolving turbulent simulations through adaptive high-order discretizations and data-enabledmodel refinements; U Michigan (Krzysztof Fidkowski) Validation of wall models for LES with application to the NASA Common Research Model; Stanford(Parviz Moin)HPC Tools & Methods: Scalable hierarchical CFD solvers for future exascale architectures; Stanford (Juan Alonso)Efficient and robust CFD solvers for exascale architectures; U Wyoming (Dimitri Mavriplis)A stochastic framework for computation of sensitivities in chaotic flows; U Pittsburg (Hessam Babaee)Parallel geometry for design and analysis; Syracuse U (John Dannehoffer)CFD Validation Experiments: Smooth wall separation over bumps: Benchmark experiments for CFD validation; VA Tech (KevinLowe). Benchmark experimental measurements of turbulent, compressible mixing layers for CFD validation;U-Illinois (Craig Dutton)Certification by Analysis: Requirements for aircraft certification by analysis; Boeing (Dinesh Naik)14

Some Technical Highlights15

CFD Validation ExperimentsA critical element of the RCA research portfolio Recommendation of CFD Vision 2030 Study: NASA should leadefforts to develop and execute integrated experimental testing andcomputational validation campaigns Experiments to provide data for development of advanced turbulencemodels/prediction capability is a critical need A CFD validation experiment should include the measurement of allinformation, including boundary conditions, geometry information,and quantification of experimental uncertainties, necessary for athorough and unambiguous comparison to CFD predictions All data sets made available to interested parties for analysis16

Juncture Flow Experiment Unique on-board Laser-Doppler Velocimetry (LDV)system specifically designed for measuring thenear-wall juncture region flow field through windows̶CFDEXP̶Off-body velocities and moments (LDV and somePIV)Model surface pressures (steady/unsteady) Experiment performed in NASA 14x22 ft wind tunnel̶With careful attention to measuring flow-field BC Comparisons made using OVERFLOW and FUN3D̶̶Nonlinear turbulence modeling (e.g., quadraticconstitutive relations or a full Reynolds stress model)necessary to predict size of corner separationSome disagreement between the RANS models andthe measured Reynolds stresses in the corner regionLDV system in use: (Right) near wing LE;(Left) near TEPOC: Chris Rumsey (LaRC), Mike Kegerise (LaRC), and Dan Neuhart (LaRC).17

Juncture Flow flowNormalstressesLDVProfilesat1168.4mmfromnose,a 5LDV measurement tkineticenergyKineticenergyTriple productsu’u’u’ triple productsWealth of detailed flow physics data acquired in the juncture flow experimentat multiple locationsPOC: Chris Rumsey (LaRC), Mike Kegerise (LaRC), and Dan Neuhart (LaRC).18

Turbulent Heat Flux (THX) ExperimentsData for improving models for turbulent heat flux and coolinghole boundary conditions. High-quality flow field data includes:Mean velocities and temperatures, turbulence/thermal statistics –using PIV and Raman; surface temperatures using TCs and IRimagery. Well documented inflow conditions for CFD modeling.Experimentalists and CFD modelers involved in all aspects ofexperiments from test planning through experimentation.TESTING Phase 1 experiments: low speed/temperature cooling flow Phase 2 round jet experiments: Maximum Tt 1000 K and jet Mach 0.9 - NASA GRC Acoustic reference nozzle (ARN) Phase 3 square nozzle with plate having single cooling injector(similar flow conditions as step 2, multiple blowing ratios) Phase 4 same nozzle as Phase 3, 3 arrays of 45 cooling holesTHX Phase 3 nozzle and plate (single injectorhole) installed in NASA GRC AAPL facility.PIV measurements ofmean velocity.Raman measurementsof mean temperature.Computational analysis is underway Mainstream flow(30 ft/sec, room temp)Hot-wire (T’)PIV (u’, v’, w’)ThermocouplesPitot probesRaman spectroscopyTHX Phase 4 plate concept (3 arrays of cooling holes).Flat plate Heated film injectionThru single hole and three holes(DT 75 deg F)POC: Nick Georgiadis (GRC), Mark Wernet (GRC), and Randy Locke (GRC/VPL).19

Smooth Wall Separation Experiments over Bumps(NRA to Virginia Tech)Bump planform308207610504-103-20-30-30Z, [in]Pressure gradients imposed with wall-mountedhump̶Attached flow, incipient separation and massive separation̶Rotation provides parameters for data-driven modeling̶Geometric symmetries provide key UQ information aboutgeometry uncertainties and flow non-uniformities̶Instrumentation: Optical (LDV, PIV), pressure rake, hotwire, temperature, skin-friction (indirect)2-20-1001020130()!"# %& 1.83 m *!,#-. !"# %& 1.83 m Document uncertainties̶̶Wall-mounted hump in VATech Stability TunnelRotated bump, same location in the tunnel (quantifies geometric nonuniformity)Rotated bump mounted on other side of the tunnel (quantifies flowfieldnonuniformity)POC: Todd Lowe et al. (Virginia Tech)20

Compressible Mixing Layer Experiments(NRA to U-Illinois) CFD validation-quality measurements of compressiblemixing layer to document effects on growth rate,Reynolds stress field, turbulent large-scale structure,mixing (including thermal mixing)̶ Convective Mach numbers, Mc 0.19, 0.37, 0.54, 0.74,0.86 and 1.0̶ Schlieren and planar laser-sheet visualizations, static andpitot pressure measurements, with emphasis on stereo PIVmeasurements of mean and turbulent velocity fields̶ Complete documentation of the inflow conditions/boundaryconditions for each case, especially the boundary layers onall incoming walls to the mixing layer̶ Complete uncertainty analysis of entire spatial fields ofpressure and SPIV mean and turbulence velocitymeasurementsPOCs: Craig Dutton, Greg Elliott (UIUC)Self-similarity of meanvelocity profilesMc 0.19 mixing layer recordedat 60,000 FPS21

Wall-resolved LES for Axisymmetric Transonic Bump Experimental set up̶ Bachalo and Johnson, AIAA J. 24(3), 1986.̶ M 0.875̶ Rec 2.763 x 106̶ Reθ 6600 Flow simulation̶ 4th-order compact scheme, with 10th-order filter̶ 24 billion grid points to cover 120 degree azimuth̶ Free air assumption (tunnel wall effect ignored)̶ Reasonable agreement with measured wallpressure and separation length-1-0.75Mach number 0.875POCs: Ali Uzun (NIA), Mujeeb Malik(LaRC)2 2 ft tunnel experiment126 6 ft tunnel data from Horstman and Johnson6 6 ft tunnel data from Johnson3Spalart et al. hybrid DNS-IDDES on 15-deg slice (8.5B points)baseline grid 30-deg slice (3B points)refined grid 120-deg slice (24B points)Cp-0.5-0.2500.250.40.60.81x/c1.21.41.622

LAVA: NASA Wall-Mounted Hump LAVA curvilinear Navier-Stokes as well as Lattice-Boltzmann Method has been successfullyapplied to NASA’s wall-mounted hump. Improvement of 96% in reattachment location.Reattachment Locationx/c [-]Error [%]Greenblatt1.105-RANS from TMR1.2614.02DDES1.3421.26ZDES SEM1.110.45LBM SEM1.120.5496 % improvementcompared to RANSPOC: Cetin Kiris et al. (ARC)23

Lesson Learned from RCA’s CompletedTCHigh lift JSM geometrysuction side view§ Reynolds-Averaged Navier-Stokes (RANS) failed toaccurately predict complex flow physics (e.g., RCAstandard test cases; CLmax)§ Wall-modeled large eddy simulation (WMLES),emerged as a promising approach for prediction ofCLmax, which is critical for aircraft certification byanalysis.OutboardseparationWMLES w/ CharLES : surface flow velocityJAXA Standard ModelslatfuselageSlat supportmainflapflap support/track fairingsGeometry houses fuselage, slat, wing, flap and relevant support structures (no nWing aspect ratio of 9.4; 85% slat span, 75% flap span (Yokokawa et al. 2008)WMLESStanford NRASlat support wakesRANSEddy resolving methods development and validation will beMc 0.38 mixing layerthe main thrust of the RCA’s new TC24

FUN3D Node-Level Performance Relative to Broadwell (BWL)Hardware6.04.0CoresBaseline Code6.8BWL: 2 Xeon E5-2680v428MPI FortranSKY: 2 Xeon Gold 614840MPI FortranKNL: Intel Xeon Phi 723064OpenMP Fortran (coloring)P100: NVIDIA Pascal--CUDA C (atomics)V100: NVIDIA Volta--CUDA C (atomics)Meanflow Relative PerformanceMeanflow Relative Performance/ Meanflow Relative Performance/Watt3.02.32.05.41.4BWL0.8 1.10.7 1.2 0.72.21.5BWL0.0SKYPOC: Eric Nielsen (LaRC)KNLP100V10025

Each node solves 12.8M gridpoints̶̶5 nodes: 60M pts/267M elms1,024 nodes: 13.2B pts/58B elms CPU curve is MPI OpenMP with3 ranks/socket (total of 6 per node)with 168 total OpenMP threads pernode (smt4)60M Points267M Elems 25% ofSummit Plot captures weak scaling on newORNL Summit system(IBM Power9 V100) PleiadesFUN3D Performance on Summit13.2B Points58B Elems GPU curve is MPI CUDA:3 ranks/socket shepherding 1 GPU each (total of 6 per node); all MPI via GPU Direct Nearly linear performance for both GPU performance is 23x-37x faster, depending on data point (generally around 25x)A potential 25-100x speedup in sight for CFD codes, from both hardware and algorithmsPOC: Eric Nielsen (LaRC)26

Foundational research in RCA impacts focused applicationsin ARMD and other mission directorates27

LAVA Hybrid RANS/LES : Jet-Noise Predictions Demonstrated jet noise prediction capabilities within LAVA curvilinear solver utilizing ZDES onunheated axisymmetric round jet. Excellent Comparison between near-field CFD and experiments achieved Far-field predictions utilizing the permeable Ffowcs-Williams Hawkins (FWH) agree verywell with experiments. Improvements to previous simulations demonstrated. Demonstrated improvement in predictionof length of potential core by 90%(RCA requirement 40%)Results published in AIAA/CEASAeroacoustics Conference [AIAA 2019-2475] (a) TKE centerline(a) PSD at 100D from nozzle exitPOC: Cetin Kiris et al. (ARC)28

LAVA Hybrid RANS/LES : Jet-Surface Interaction Noise LAVA has demonstrated capabilities to capture noise shielding effects of an inserted plate inclose proximity to a jet. This is an important step towards predicting full airframe jet noise.(a) Lipline velocity(b) PSD at 100D from nozzle exittrailing edge noisePOC: Cetin Kiris et al. (ARC)29

LAVA Lattice Boltzmann Simulations of PropellerNoiseAPPROACH:Isocontours of Q-criterion colored by streamwisemomentum from the simulated flow around thepropeller blade in hover The Lattice Boltzmann Method Solver extended with robust in-house boundary treatmentfor moving blades which do not violate strict realizability ofthe density distribution functions (The algorithmdevelopment was supported by the TTT project and thesystematic validation using far-field noise measurementsfrom NASA Langley Research Center was supported by theRVLT project.)RESULTS: Predicted both the performance and the aerodynamic noisegenerated by the propeller with unprecedented accuracyand turnaround timesSIGNIFICANCE: First step towards predicting Urban Air Mobility Noise fromfirst principles using the Lattice Boltzmann solver Completely automated workflow without labor intensivemesh generation Quick turnaround time - approximately 4000 core hours for25 revolutions of isolated propeller (10% chord resolution) General formulation – Valid for arbitrarily moving (anddeforming) geometryPOC: Cetin Kiris et al. (ARC)Far-field noise prediction compared tomeasurements conducted at LSAWT and SALTfacilities in NASA Langley30

LAVA Lattice-Boltzmann: Landing Gear Demonstrated the LBMapproach on the AIAA BANC IIIWorkshop Landing Gearproblem IV. Computed results comparewell with the experimentaldata 12-15 times speed-up wasobserved between LBMand NS calculations. LBM has better memory accessand significantly lower floatingpoint operations relative toWENO RK4 LBM has minimal numericaldissipation Cartesian methods are verysuccessful for the right problemsPOC: Cetin Kiris et al. (ARC)31

LAVA Lattice-Boltzmann: Landing Gear AccuracySurface Pressure Spectra at Sensor LocationsGrid Sensitivity for PSD:Channel 5: Upper Drag Link10-410710510610-710713P SD (psi2 /Hz)PSD(psi2/Hz)15431012584108109Near FieldPSDChannel 5LB: 90 MillionLB: 260 MillionLB: 1.6 2-1313101010141021029103103 F requency (Hz)104104Frequency (Hz)POC: Cetin Kiris et al. (ARC)3232

Orion Launch Abort Acoustics Simulations CFD Vision 2030 Study recommends developmentof “capability” computing and necessary algorithmimprovements to reach exa-scale Added a layer of fine-grain parallelism to theLaunch, Ascent, and Vehicle AerodynamicsCartesian solver to scale to 16,000 cores Demonstrated excellent agreement withexperimental validation data from wind tunnel,ground and flight tests Short enough turnaround time to affect design ofOrion Launch Abort System: completed 400,000time steps on 630 million cell AMR mesh withaccelerating LAV in 30 days on 16,000 coresPOC: Cetin Kiris et al. (ARC)Ignition overpressure New capability used successfully for spacevehicles (HEOMD) to predict surface fluctuatingpressures over Orion launch abort vehicle (LAV)for a range of abort scenariosQM-1 CFDSound Pressure Level (dB) Ongoing efforts target many-core machines withan eye towards GPUsQM-1 EXP- QM1 Measurements- LAVA QM1v1 SimulationTime (s)Heat Shield AcousticsShaded gray areaindicates /- 1 dBFrequency (Hz)34

Orion Launch Abort Acoustics SimulationsSound Pressure Level (dB)Sound Pressure Level (dB)-- Wind Tunnel Measurements- LAVA PredictionsSound Pressure Level (dB)Transonic ascent abort at moderate angle of attack and side slipShaded gray area is /- 2dB because of uncertaintyin simulation results due toshort integration time (0.02s) vs experiment (5.00 s)Frequency (Hz)Frequency (Hz)Volume rendering of pressurefluctuationsFrequency (Hz)LAVA CPOC: Cetin Kiris et al. (ARC)35

36Orion Launch Abort Acoustics SimulationsQuestions?Pressure on the vertical plane (white is high, black is low) for LAV transonic ascent abort at high angle of attackPOC: Cetin Kiris et al. (ARC)36

Summary RCA research portfolio is aimed at making progresstowards CFD Vison 2030 and meeting the challengeof predicting aircraft CLmax̶̶Physical modelingHPCNumerical algorithmsGrid adaptationCFD validation experiments̶̶̶ Explore potential collaborations with HiFi-TURBproject̶Leverage available expertise and data, for acceleratedprogress toward CFD 2030 goalsDevelopment of Accurate and Efficient Computational Tools will EnableAircraft Certification by Analysis and Design of New Aerospace Configurations37

38

Unsteady, complex geometry, separated flow at flight Reynolds number (e.g., high lift) 2015 2020 2025 2030 HPC CFD on Massively Parallel Systems CFD on Revolutionary Systems (Quantum, Bio, etc.) TRL LOW MEDIUM HIGH PETASCALE Demonstrate implementation of CFD algorithms for extreme parallelism in NASA CFD codes (e.g., FUN3D) EXASCALE Technology .

Related Documents:

CC Headset LOCAL 4-Pin XLR M 4-Pin XLR F ClearCom Base Station XLR-M XLR-F LOCAL XLR-M XLR-F Wall Plate 1 HOUSE Cordor 1 Military F Military M Military M Military F Mic 24 House Snake XLR-M XLR F AD24 Remote Out Denon 1800F RCA F RCA M LOCAL RCA M RCA F 2TK1 L RCA F RCA M LOCAL&

1) AC power cord 17) EQ/MIX input RCA jack 2) Ground 18) Preamp output RCA jack 3) Speaker outputs 19) Tape output RCA jack 4) Manual muting terminals 20) Booster output RCA jack 5) MIC-6 input terminals (Balanced) 21) AUX-3 input RCA jacks

2016 nasa 0 29 nasa-std-8739.4 rev a cha workmanship standard for crimping, interconnecting cables, harnesses, and wiring 2016 nasa 0 30 nasa-hdbk-4008 w/chg 1 programmable logic devices (pld) handbook 2016 nasa 0 31 nasa-std-6016 rev a standard materials and processes requirements for spacecraft 2016 nasa 0 32

RADIO SHACK 0031 0004 0048 0049 0053 0170 0009 0227 0342 0000 0012 0041 0042 0095. 9 TV (including LCD, LED and Plasma), cont. RCA 0031 0004 0048 0054 0094 0100 . PHILIPS DSHD800R 0632 PIONEER SH-D505 0604 PROSCAN PSHD105 0636 RCA HD65W20 0658 RCA DTC-100 0636 RCA DTC-210 0636

Ideal OmniCONN 6 & 59 (F and RCA) Ideal Uniseal 6 & 59 (F) Liberty ConnecTec 6 & 59 (F and RCA) Liberty ConnecTec 6 & 59 (BNC) Paladin Seal Tight 6 & 59 (F and RCA) Paladin Seal Tight 6 & 59 (BNC) Phoenix (PCT) DRS 6 & 59 (F and RCA) Phoenix (PCT) Mini 59 (F) Phoenix (PCT) DRS 6 & 59 (BNC) Phoenix (PCT) DRS 7 & 11 (F)

Ideal OmniCONN 6 & 59 (F and RCA) Ideal Uniseal 6 & 59 (F) Liberty ConnecTec 6 & 59 (F and RCA) Liberty ConnecTec 6 & 59 (BNC) Paladin Seal Tight 6 & 59 (F and RCA) Paladin Seal Tight 6 & 59 (BNC) Phoenix (PCT) DRS 6 & 59 (F and RCA) Phoenix (PCT) Mini 59 (F) Phoenix (PCT) DRS 6 & 59 (BNC) Phoenix (PCT) DRS 7 & 11 (F)

This manual covers all integrated amplifiers. It begins with some general installation notes. Product specific information begins in Section 4. Introduction 1 Connections It is important for both safety and performance that the standard cables supplied are not modified. . RCA RCA RCA RCA

A.R. Paterson, A First Course in Fluid Dynamics, Cambridge University Press. (The recommended text to complement this course - costs ˇ 50 from Amazon; there are 6 copies in Queen’s building Library and 3 copies in the Physics Library) 2. D.J. Acheson, Elementary Fluid Dynamics. Oxford University Press 3. L.D. Landau and E.M. Lifshitz, Fluid Mechanics. Butterworth Heinemann Films There is a .