Overview of CFD Validation Experiments forCirculation Control Applications at NASAG.S. Jones,1 J.C. Lin*, B.G. Allan*, W.E. Milholen,2 C.L. Rumsey,Γ R.C. SwansonΓNASA Langley Research Center, Hampton, Virginia, 23681Circulation control is a viable active flow control approach that can be used to meet the NASASubsonic Fixed Wing project’s Cruise Efficient Short Take Off and Landing goals. Currently,circulation control systems are primarily designed using empirical methods. However, largeuncertainty in our ability to predict circulation control performance has led to the development ofadvanced CFD methods. This paper provides an overview of a systematic approach to developingCFD tools for basic and advanced circulation control applications. This four-step approach includes“Unit”, “Benchmark”, “Subsystem”, and “Complete System” experiments. The paper emphasizesthe ongoing and planned 2-D and 3-D physics orientated experiments with corresponding CFDefforts. Sample data are used to highlight the challenges involved in conducting circulation controlcomputations and experiments.NomenclatureAOAARbCCCpCD,CdCLClCμC, chLETEMACMmNPRPqrReRSTUVW angle of attack (degrees)w slot width (inches)aspect ratioX streamwise tunnel coordinate (inches)span (inches)Y horizontal cross plane tunnel coordinate (inches)circulation controlZ vertical cross plane tunnel coordinate (inches)pressure coefficientdrag coefficientSymbols:section drag coefficientα pitch angle of attack (deg)lift coefficientδ jet angle relative to free stream (deg)section lift coefficientδ* boundary layer displacement thicknessmomentum coefficientγ ratio of specific heatschord, wing, airfoil (inches)ρ densityslot height (inches)φ separation angle relative to model (deg)leading-edgeΓ circulationtrailing-edgeθ separation angle relative to free stream (deg)mean aerodynamic chord (inches)Mach numberSubscriptsmass flow rate (lbm/sec)J jetnozzle pressure ratioo free stream total conditionpressure (psf) free stream static conditiondynamic pressure (psf)radius of Coanda surface or flap (inches)Reynolds numbergas constantplanform area (in2)temperature (oF)velocity component in streamwise direction (ft/sec)velocity component in horizontal cross plane (ft/sec)velocity component in vertical cross plane (ft/sec)1Researcher, Ph.D., Flow Physics and Control BranchResearcher, Ph.D., Configuration Aerodynamics BranchΓResearcher, Ph.D., Computational AeroSciences Branch2-1-
IntroductionRecent interest in circulation control (CC) technology has been prompted by the National Aeronauticand Space Administration (NASA) Cruise Efficient Short Take Off and Landing (CESTOL) initiative andthe Air Force Research Laboratory (AFRL) Advanced Joint Air Combat System program. These initiativeshighlight the need for improved high-lift or powered-lift systems to meet takeoff and landing goals that areintegrated into transonic cruise configurations. Because circulation control technology will requiresignificant modifications to the aircraft design it will need to “buy” its way onto the aircraft. This willrequire designers to optimize the entire wing and propulsion system with particular attention on air deliverysystem efficiency, safety concerns related to an engine out condition, and control issues related to largeaerodynamic moments.The rapid turnaround required by designers to complete trade studies normally limits the use ofComputation Fluid Dynamic (CFD) tools to the improvement of empirical methods. Empirical methodsderived from experiments are traditionally used in the design process. However, the complexities of manyflow control techniques in use today, including circulation control, far exceed the experimental databasesused to develop the empirical methods. By coupling physics-based CFD tools with validation experimentswe can develop parametric studies that characterize the sensitivities of wing geometries and blowingsystems, required to advance empirical techniques necessary to design a CESTOL type aircraft.The application of the state of the art CFD tools to a CC airfoil with an experimental database wasdescribed at the 2004 NASA/ONR Circulation Control workshop.i Results from that workshop showedthat CFD simulations were unable to consistentlyand accurately predict the performance of a CCairfoil. An example of one of the CFD predictionsis shown in Figure 1. The failure to match the jetseparation and the streamline turning for thisexample was linked to turbulence modelsii,iii andCFD grid issues. In addition to identifyinginconsistencies in CFD prediction capability, theworkshop also identified that the experimentaldatabases were inadequate for CFD validation.This paper describes a systematic approach todeveloping experimental and computationaldatabases for improving CC prediction capability. Ingeneral, CFD validation is defined by determiningFigure 1. Comparison of experimental andReynolds Average Navier Stokes (RANS) CFDhow well the CFD model predicts the performanceand flow physics when used for its intendedlift performance data for a typical CC airfoilpurposes.iv The level of CFD validation can bedefined by the complexity of the code and the experiment being used for validation, as described in Figure2.v These levels of validation are being pursued using NASA’s airfoil, semi-span wing, and full-spanHybrid Wing Body geometries that are the focus of this paper.For the purposes of this paper the term circulation control implies using a tangential jet on a highlycurved aerodynamic or Coanda surface. The aerodynamic characteristics of circulation control wings havebeen experimentally and numerically studied since Henri Coanda’s near fatal crash in 1910vi,vii,viii Many ofthese studies have concentrated on the lift performance related to trailing edge shape, slot height, andblowing rates. Despite the large potential performance improvements related to circulation control, onlytwo fixed wing flight demonstrationsix,x,xi have been evaluated. The results of both demonstrator programsshowed large benefits of circulation control applications for short runways. Without requirements for shortrunway access, however, circulation control applications were not pursued and the remaining work waslargely a laboratory effort.The renewed interest in CESTOL aircraft at NASA has been prompted by the escalating airportcongestion and noise abatement criteria.xii,xiii The FAAxiv has projected that air traffic will increase by afactor of 2 or more within the next 20 years. Airport noise has been identified by the FAA as the largestproblem for 50 of the nation’s airports.xv One way to alleviate these problems is to use the shorter runwaysand the infrastructure that are already in existence at under utilized small airports.xvi,xvii It may also benecessary to alter the traffic patterns and glide slopes to keep the noise confined to the airportboundaries.xviii,xix These issues have prompted engineers at NASA to evaluate the improvements in aircrafttake off and landing capabilities that can be integrated into efficient cruise geometries.-2-
NASA has set goalsxx,xxi for a state-of-the-art 100 passenger airliner to be operational by the year 2022and to have the following characteristics: take-off and landing distance of less than 2,000 feet, cruise Mach number 0.8, 1,400 to 2,000 mile range capability, noise containment within an airport footprint, and low speed maneuverabilityImprovement and optimization of advanced flow control systems and wing geometries are required toachieve these goals. Several advanced flow control and propulsion technologies are being considered tomeet these goals, including CC, upper surface blowing, over the wing vectored blowing and/orcombinations of these technologies. To achieve an optimized CESTOL geometry, it is necessary toevaluate the requirements of the aircraft.xxii,xxiii These current CESTOL goals are being continuallyinfluenced and refined by issues such as fuel prices and new air traffic requirements. However, pursuingthese goals using CC may provide a low speed performance capability that exceeds what can be achievedwith conventional high lift systems. In the remainder of the text we will discuss the use and validation ofCFD for advanced CESTOL type of aircraft.Code Validation Process for Circulation ControlCirculation control applications are difficult to compute reliably using state-of-the-art CFD methods asdemonstrated by the inconsistencies in CFD prediction capability described in the 2004 NASA/ONRCirculation Control workshop.1 For Reynolds-averaged Navier-Stokes methods, some turbulence modelssuch as Menter SST k-omegaxxiv and Spalart-Allmaras with rotation and curvature correction (SARC)xxvhave been shown to predict CC flows reasonably well only at certain conditions while other methods havebeen shown to be very sensitive to numerical parameters. There is also a tendency for the predictivecapability of CFD to degrade as the blowing increases. While it is possible that turbulence models bearFigure 2. Four levels of CFD validation used to study circulation control-3-
some responsibility, some of the disagreement may be due to an increasing loss of two-dimensionality innominally 2-D experiments as the blowing rates increase. Unfortunately, it is difficult to determine thesources of the discrepancies because most of the CC experiments have not been adequately documented forCFD validation.One of the parameters that CCperformance is typically characterized by andthat must be carefully documented is thethrust or momentum coefficient, Cμ. Thesensitivity of the airfoil performance to Cμ isdependent on the jet characteristics and theairfoil geometry,xxvi particularly on thesurface near the jet exit. There are twophysical regimes that define circulationcontrol as a function of blowing.xxvii Theseregimes are commonly referred to asseparation control and super-circulationcontrol and exhibit different globalefficiencies as determined by the change inunit lift due to the change in unit blowing asFigure 3. Circulation control flow regimes showingshown by the data in Figure 3.The physical description of the sensitivities and influence of wall blowing (WB)efficiencies of these regimes is demonstrated by the relationship of the jet separation location on the trailingedge surface and the interaction of the jet with the on-coming flow. Nominally the flow separation on theflap or Coanda surface is a function of the pressure gradient along the surface. As the thin jet is applied, theflow separation is moved aft along the surface. This jet also entrains and accelerates the flow in thevicinity of the wall-bounded jet, thus increasing the turning of the streamlines near the trailing edge. Thisalso affects the leading edge stagnation region thus increasing the overall circulation around the airfoil andthe lift.The transition from one regime to another is not always clearly identified and is dependent on thesharpness of the trailing edge. The systematic study of this mechanism should include different trailingedge geometries that include a hinged flapped geometry (i.e. fixed separation located at tip of flap) and acircular geometry (i.e. separation location free to move up to 180o) as shown in Figure 4.NASA’s approach to resolve theissues of CFD inconsistency and theinability to match experiments athigh-blowing conditions involves astrong interaction between CFD andexperimental investigations. ForCFD it is believed that most of thechallenges exist in establishingconsistentrequirementsforboundary conditions, along with(a) Flap Configuration(b) Circular configurationguidelinesfornumericalmethodology, such as the use ofFigure 4. Example of two Coanda surfaces.sufficient grid refinement as well astransition and other parameters that can influence the solution. The planned development of CFD tools forcirculation control applications utilizes RANS and LES codes. The benefits of understanding the physicsof complex flow fields to improve the prediction and modeling techniques in CFD codes should also beused to improve appropriate experimental methods.The goal of this series of investigations is to acquire error bounded experimental results suitable forCFD validation with no attempt to achieve maximum performance on a specific geometry. The approachfor these experiments is to develop a 2-D fundamental circulation control database that will be followed bya 3-D database that captures realistic physics to advance predictive tool development.-4-
An often-overlooked aspect of the quality ofTHRUST 2hbJET ρJET U2JETinformation from CC experiments pertains to the (1)Cμ cbρ U2q Squantification of the blowing parameters. An example of this is quickly shown by considering the methods used to determine Cµ. The momentum coefficient canTHRUST m UJET (2)Cμ be determined by the quantities shown in eitherq Sq SEquation 1 or 2. Equation 1 expresses the momentumWhere,coefficient in terms of basic geometric and flowparameters. For small-scale experiments, theγ 1uncertainty of the slot height (h) dominates Equation 1, γT2γbJEToPbecause the absolute slot height measurement error (e.g.JET UJET 1 P 0.002”) is typically large relative to the total slotγ 1o JET height. This measurement can also be complicated by(3)non-uniformities in the slot along the airfoil span and bysmall changes in slot height when the internal duct isandpressurized. Using Equation 2 simplifies the required measurements for determining Cμ by eliminating them ρJETUJET h w(4)need to measure slot geometry or jet density. Theseparameters are integrated into the mass flow rate, whichcan be measured directly. An additional common error is in the calculation of the jet velocity, UJ. Acommonly accepted method for calculating the jet velocity assumes that the jet flow expands isentropicallyto the freestream static pressure as shown in Equation 3. This introduces an error since the static pressureat the jet exit is less than the freestream static pressure (as demonstrated in surface pressure measurements)at the jet exit. When Equation 1 is used, the error is exacerbated since the UJ value is squared. While themomentum uncertainties are typically smaller when using Equation 2, it is often a CFD validationrequirement that the individual parameters be measured. The uncertainties for these parameters arecumulative and are dependent on the transducers and the instrumentation used for the measurement.xxviii,xxixIt is recommended that multiple measurement techniques be used to quantify these uncertainties.()( )( )Description of Validation CasesIn this section, we will provide a description of the experiments in terms of the systematic approachshown in Figure 2. Example experimental and CFD results will emphasize the critical features related tocirculation control physics.Unit ProblemOne of the main factors influencing the CFD solution is thecharacterization of the wall jet at the jet exit. This can beset with a boundary condition of specified velocity ormomentum either at the jet exit plane, or at an innerplenum wall. In the latter case, the flow (includingturbulence) is allowed to adjust naturally as it is forced outof the high-pressure plenum. An example showing thecomplexity of the results from a computation that includesa plenum is shown in Figure 5. In the former case, it ismore difficult to prescribe realistic profiles at the exitplane. Unfortunately, both methods have traditionallyrelied upon under-resolved details from the experiment.The only information available from experimentsregarding the jet has often been the jet momentumcoefficient, Cμ, as determined by Equation (1). In thisequation, the jet velocity is typically obtained fromconditions inside the plenum, combined with isentropicflow relations. This methodology introduces additionaluncertainty into the CFD simulation. Measurements offlow conditions, especially the velocity at the jet exit, help-5-Figure 5. Flow near the upper-surfaceblowing slot of an airfoil, showingnormalized turbulent viscosity contoursalong with mean flow velocity vectors (theplenum is to the left of the lower channel)
to remove this uncertainty by establishing definitive boundary conditions to match at and near the jet exit.Implementation details of the CFD boundary condition at the plenum inflow are probably not tooimportant, as long as the plenum is of sufficient size and provides the desired momentum flux. For manystudies, the velocity and density (i.e., momentum) are specified at the upstream face inside the plenum, andthe pressure is extrapolated from the interior of the domain. Other internal plenum boundary conditionswere also tried by the authors, such as specifying total pressure along with total temperature (andextrapolated pressure), but it made little difference to the solution.The small size of the jet slot height oftencomplicates measurement of the jet profile. Forsmall scale wind tunnel experiments, the slotheight can range from 0.010” h 0.60”. Anominal slot height of 0.020” is typical ofgeometries described in this paper. Miniaturepitot probes have an outer diameter of 0.010”resulting in a large bias due to integration in thelarge velocity gradients typical of wall boundedjets. The use of hot wire probes does not comewithout measurement difficulties that includeinterpretation of transonic hot wire results. Thehot wire prongs can be as large as 5% to 10% ofthe slot height as shown in figure 6. This isoften complicated by model and probevibration. All of these factors lead toFigure 6. CFD velocity profile at the jet exit withmeasurement uncertainties that must bescaled hot wire probe, Cµ 0.119, NPR 1.2, h 0.020”quantified.Another relevant factor related to the jet exit flow is the oncoming boundary layer above the lip ofthe slot. Because this boundary layer interacts/mixes with the jet, it is important that it be characterizedcorrectly in the CFD solution. Accurate experimental measurements of boundary layer profiles andturbulence properties in this region are needed for validation.Benchmark CasesTwo benchmark cases will be described as part of the systematic CC validation effort. Two of thedesired features for each of the benchmark CFD validation studies are a simple geometry that is capable ofsuper-circulation and measured critical boundary conditions required by modern CFD codes. Even thoughthe geometries are simple, a 2-D lift coefficient of 7 – 8 can be achieved in the super-circulation regime.The 2-D Benchmark caseTwo-dimensional CFD has proven itself to be useful for many aerospace design and trade studies.However, when validating turbulence models for specific new applications, it is extremely important toensure that consistent conditions (geometry and boundary) are being considered for the computation andthe experiment. For CC airfoils, this can be difficult to ascertain because of the sensitivity of the Coandasurface flow separation to jet boundary conditions and other factors that can be problematic to measure, aswell as the inherent difficulty of maintaining two-dimensionality in the experiment at high blowingconditions. In ongoing work at NASA we are attempting to address these issues, with the ultimate goal ofvalidating the capabilities of existing turbulence models for 2-D CC flows over a wide range of blowingconditions.To reduce uncertainty and to optimize the measurement capability, two independent wind-tunnel testswere conducted using the same model. The NASA LaRC Basic Aerodynamic Research Tunnel (BART)and the Georgia Tech Research Institute’s Model Test Facility (MTF) are comparable in size and speed, butemphasize different measurement techniques. The BART test series emphasizes the external flow physicsby identifying the leading edge stagnation point, determining the jet separation location, and determiningthe jet trajectory for selected blowing conditions. The MTF test series characterizes the CC model
developing experimental and computational databases for improving CC prediction capability. In general, CFD validation is defined by determining how well the CFD model predicts the performance and flow physics when used for its intended purposes.iv The level of CFD validation can be
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The CFD software used i s Fluent 5.5. Comparison between the predicted and simulated airﬂow rate is suggested as a validation method of the implemented CFD code, while the common practice is to compare CFD outputs to wind tunnel or full-scale . Both implemented CFD and Network models are brieﬂy explained below. This followed by the .
downstream of the grid. The CFD results and experimental data presented in the paper provide validation of the single-phase flow modeling methodology. Two-phase flow CFD models are being developed to investigate two-phase conditions in PWR fuel assemblies, and these can be presented at a future CFD Workshop. 1. INTRODUCTION
CFD Simulation and Experimental Validation of a 385. Computational Fluid Dynamics (CFD) is a powerful tool for investigating complex fluid flow and heat transfer. It also can greatly reduce the extent and number of experiments required for the . It is capable of modeling compressible flows in a closed volume with a moving boundary using 2D or .
A.2 Initial Interactive CFD Analysis Figure 2: Initial CFD. Our forward trained network provides a spatial CFD analysis prediction within a few seconds and is visualised in our CAD software. A.3 Thresholded and Modiﬁed CFD Analysis Figure 3: Threshold. The CFD is thresholded to localise on
performing CFD for the past 16 years and is familiar with most commercial CFD packages. Sean is the lead author for the tutorial and is responsible for the following sections: General Procedures for CFD Analyses Modeling Turbulence Example 3 - CFD Analysis
CFD Analysis Process 1. Formulate the Flow Problem 2. Model the Geometry 3. Model the Flow (Computational) Domain 4. Generate the Grid 5. Specify the Boundary Conditions 6. Specify the Initial Conditions 7. Set up the CFD Simulation 8. Conduct the CFD Simulation 9. Examine and Process the CFD Results 10. F
Emphasis is on comparing CFD results, not comparison to experiment CFD Solvers: BCFD, CFD , GGNS Grids: JAXA (D), ANSA (E), VGRID (C) Turbulence Models: Spalart-Allmaras (SA), SA-QCR, SA-RC-QCR Principal results: Different CFD codes on same/similar meshes with same turbulence model generate similar results
Converge UGM 2105 2 Agenda 1. Overview 2. Converge CFD Model Setup 3. Experimental Data 4. CFD Results and Validation 5. Future Work
misleading results. The single and 2-phase models in the CFD tool need to be validated with the test data applicable to the PWR fuel design. To support validation, the CFD model results were compared to LDV data from 5x5 rod bundle tests for a spacer grid design. The CFD predictions were then compared to 5x5 rod bundle single phase mixing data
CFD Modeling and Experimental Validation of Combustion in Direct . In the present study the Computational Fluid dynamics (CFD) . Assessment between modeling and experimental data revels that .
Lumped Element –––––––. 1.11 . three test cases selected for the Langley CFD validation workshop to assess the current CFD . The bias estimates were based on experimental geometrical parameters, LDV processor bias, and biases related to the seeding material used. .
Dipl.-Ing. Becker EN ISO 13849-1 validation EN ISO 13849-2: Validation START Design consideration validation-plan validation-principles documents criteria for fault exclusions faults-lists testing is the testing complete? Validation record end 05/28/13 Seite 4 Analysis category 2,3,4 all
validation (Coleman and Stern, 1997) [hereafter referred to as C&S] thereby providing the framework for overall procedures and methodology. The philosophy is strongly influenced by experimental fluid dynamics (EFD) uncertainty analysis (Coleman and Steele, 1999), which has been standardized. Hopefully, CFD verification and validation
Inter-comparison of CFD simulations among partners . 3 PRESLHY Dissemination Conference, 5-6 May 2021 Background / Scope . Need for further development / validation of existing models / simulation methodologies against LH2 release and dispersion experiments
GPU Status Structural Mechanics Fluid Dynamics ANSYS Mechanical AFEA Abaqus/Standard (beta) LS-DYNA implicit Marc RADIOSS implicit PAM-CRASH implicit MD Nastran NX Nastran LS-DYNA Abaqus/Explicit 6 Electromagnetics AcuSolve Moldflow Culises (OpenFOAM) Particleworks CFD-ACE FloEFD Abaqus/CFD FLUENT/CFX STAR-CCM CFD LS-DYNA CFD Nexxim EMPro .
AUTODYN LS-Dyna CFD AcuSolve CFD CGNS Cobalt CONVERGE CFD FAST FIDAP FIRE Flow-3D GASP/GUST KIVA FEA ABAQUS I-DEAS LS-DYNA MP-Salsa MSC.Dytran MSC.Nastran MSC.Marc MSC.PATRAN NX Nastran PERMAS BIF/BOF RADIOSS NASTAR OpenFOAM Overflow PAM-FLOW Plot3D PowerFLOW RADIOSS-CFD
CFD and Process Engineering Conclusions CFD is well established and important for analysis of hydraulic components. There is growing appreciation that CFD can be a powerful tool for analysis of the imp
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