High-fidelity fluid-structure interaction applied to static aeroelasticity in transonic flows

Implicit large Eddy simulation of the NASA CRM high-lift configuration near stall

SummaryIn this paper, a new take on the concept of an active subspace for reducing the dimension of the design parameter space in a multidisciplinary analysis and optimization (MDAO) problem is proposed. The new approach is intertwined with the concepts of adaptive parameter sampling, projection‐based model order reduction, and a database of linear, projection‐based reduced‐order models equipped with interpolation on matrix manifolds, in order to construct an efficient computational framework for MDAO. The framework is fully developed for MDAO problems with linearized fluid‐structure interaction constraints. It is applied to the aeroelastic tailoring, under flutter constraints, of two different flight systems: a flexible configuration of NASA's Common Research Model; and NASA's Aeroelastic Research Wing #2 (ARW‐2). The obtained results illustrate the feasibility of the computational framework for realistic MDAO problems and highlight the benefits of the new approach for constructing an active subspace in both terms of solution optimality and wall‐clock time reduction.

The NASA Common Research Model (CRM) has enabled many formal and informal international cooperative activities and has enabled aeronautical researchers and engineers in industry, government, and academia to work together across organizational and international borders sharing results on relevant problems for the benefit of all. The NASA Common Research Model (CRM) was conceived in 2007 and its aerodynamic design completed in 2008 responding to needs broadly expressed both within the US and international aeronautics communities for modern/industry-relevant and open/public geometries coupled with advanced experimental data for applied computational fluid dynamic validation studies. This paper provides a brief history of the development of the CRM, along with a summary of wind tunnel model data that has been obtained over the past 10 years. This paper presents data obtained from the NASA Langley National Transonic Facility, the Ames 11-ft Transonic Wind Tunnel and the European Transonic Windtunnel. Sample comparisons are given between the three wind tunnels for lift, drag and pitching moment. Several CRM-derivatives that have been developed are also summarized in this paper.

The flow in the near-field wake region of a generic transport aircraft configuration has been studied using both unsteady RANS and delayed detached-eddy simulations. The NASA Common Research Model has been used for this purpose, as experimental data obtained with this model in the cryogenic European Transonic Wind Tunnel during the ESWIRP test campaign were available for comparison and validation. Focusing on high angles of attack of \(\alpha = 16^\circ\) and \(\alpha = 18^\circ\), the results form the basis for the study of flow phenomena occurring at stall conditions. Such flight conditions are characterized by massive flow separation at the wing and highly unsteady flow in the wake. In a first step, the numerical results are compared with available test data using global force coefficients and pressure distributions on the wing. Both show a good agreement of numerical and experimental results, indicating slight deviations of the pressure variable at the inboard wing sections. In a second step, the turbulent structures of the near-field wake are examined using the proper orthogonal decomposition method based on snapshots (two-dimensional instantaneous flow fields). Frequencies of the first mode-pair match the dominant frequencies of the lift and velocity wake spectra very well.

Since its introduction, the NASA Common Research Model has proved a useful aerodynamic benchmark for predicting computational-fluid-dynamics-based drag and aerodynamic design optimization. The model was originally conceived as a purely aerodynamic benchmark, and as such the wing geometry corresponds to the deflected shape at its nominal flight condition. However, interest has been growing to extend this model to aeroelastic studies. Unfortunately, because of its predefined deflection, the model is not suitable for aeroelastic analysis and design. To address this issue, an undeflected Common Research Model is defined through an inverse design procedure that includes the outer mold line geometry of the undeflected wing and the corresponding internal wingbox structure. Additionally, because modern transport aircraft are trending toward higher-aspect-ratio wing designs, a higher-aspect-ratio variant of the Common Research Model wing is developed to assess next-generation wing designs. This variant has an aspect ratio of 13.5 and is designed by using buffet-constrained multipoint aerostructural optimization. The purpose of these models is to provide publicly available benchmarks for aeroelastic wing analysis and design optimization.

The application of reduced-order modeling (ROM) techniques in the context of aerodynamic nonlinear system identification of realistic aircraft configurations gained increasing attention in recent years. Therefore, in the present study the application of a recurrent neuro-fuzzy model (NFM) that is serial connected with a multilayer perceptron (MLP) neural network is introduced concerning the computation of transonic buffet aerodynamics. In particular, the intention of the ROM is the prediction of coefficient time-series trends in contrast to a precise resolution of detailed flow effects. Further, a reduction of computational time compared to a full-order reference Computational Fluid Dynamics (CFD) solution is pursued. The training of the ROM is accomplished based on a data set computed by means of unsteady Reynolds-averaged Navier-Stokes (URANS) simulations. The performance of the trained ROM is demonstrated by predicting the buffet flow characteristics of the NASA Common Research Model (CRM) investigated at transonic flow conditions. Therefore, the wing of the configuration is excited by an external pitching motion beyond buffet onset. By comparing the ROM result with a reference URANS solution, a precise prediction capability of the aerodynamic characteristics as well as a reduction in computational time is demonstrated.KeywordsBuffet aerodynamicsReduced-order modelingMultilayer perceptron neural networkNeuro-fuzzy modelsNonlinear system identification

Some recent developments in the Lamb-vector-based aerodynamic force breakdown used the concept of vortex force to define the lift and to decompose the drag into lift-induced drag and profile drag. However, the Lamb vector formulation involves moment transformations, and the associated force breakdown may depend on the reference point adopted for their computation. Yet, the force acting on an airplane cannot be dependent on this point. Thus, a systematic method based on the far-field flow symmetries is here proposed to eliminate this dependence. Those symmetries also allow for a better understanding of the progressive invariance of the drag decomposition with respect to the size of the integration domain. Finally, a reference-point-invariant formulation is defined and tested for a 2-D transonic flow around an OAT15A airfoil and a 3-D flow around the NASA Common Research Model under cruise flight conditions.

Mesh deformation on 3D complex configurations using multistep radial basis functions interpolation

An experimental investigation of the NASA Common Research Model was conducted in the NASA Langley National Transonic Facility and NASA Ames 11-foot Transonic Wind Tunnel Facility for use in the Drag Prediction Workshop. As data from the experimental investigations was collected, a large difference in moment values was seen between the experiment and computational data from the 4th Drag Prediction Workshop. This difference led to a computational assessment to investigate model support system interference effects on the Common Research Model. The results from this investigation showed that the addition of the support system to the computational cases did increase the pitching moment so that it more closely matched the experimental results, but there was still a large discrepancy in pitching moment. This large discrepancy led to an investigation into the shape of the as-built model, which in turn led to a change in the computational grids and re-running of all the previous support system cases. The results of these cases are the focus of this paper.

Computational studies were conducted in order to gain an understanding and to quantify the impact of the model support system and aeroelastic wing deformation on the Common Research Model at subsonic inflow conditions at . Comparisons between results obtained in the European Transonic Windtunnel and from simulations using TAU reveal the extent of the modeling error introduced by neglecting the support structure. RANS simulations with and without the support system show that it has a near-field effect on the model’s rear fuselage, significantly altering the pressure field and thereby impacting the force coefficients, most significantly drag and pitching moment. Aeroelastic wing deformation was also included in the investigation, showing non-negligible impact on the forces. The inclusion of the support system and wing deformation, as well as the use of advanced turbulence models, was shown to promise considerable error reduction potential in comparative studies between wind tunnel and simulation data.

Following the completion of the European Strategic Wind Tunnel Improved Research Potential test campaign in the European Transonic Windtunnel, a large amount of data exists for comparison and validation of computational results at high Reynolds numbers. Unsteady simulations were performed using the NASA Common Research Model at a low-speed stall condition. The flowfield is characterized by large scale separation and a highly unsteady wake. An embedded high-resolution hexahedron block in the wake region of the grid has been used to improve resolution of the turbulence in unsteady Reynolds-Averaged Navier–Stokes and delayed detached eddy simulations. Model deformation occurring in the wind tunnel was incorporated into the computational setup. The computational fluid dynamics predictions match the force and wing pressure measurements from the wind tunnel very well. The mean and fluctuation data from the time-resolved particle image velocimetry measurements in the wake is matched by the delayed detached eddy simulations result, with the Reynolds-Averaged Navier–Stokes results showing stronger wake dissipation. Wake characteristics in delayed detached eddy simulations and Reynolds-Averaged Navier–Stokes have been compared using pressure spectra. The validity of the computational models has been evaluated as well.

The inverse design method of aerodynamic configuration is hard to give a reasonable pressure distribution, and strongly rely on experience of designers. The method has been difficult to adapt to the needs of modern aircraft design. Aiming at the shortcoming of the method, an efficient and robust aerodynamic configuration inverse design method is developed, employing knowledge of machine learning methods and optimization methods. The present method establishes the mapping between the high dimensional data obtained from aerodynamic shape and pressure distribution and the variables in the latent space. Then, the global optimization is carried out in the latent space by using the genetic algorithm. The optimum pressure distribution and the corresponding shape can be obtained. Through the the GTM model with high precision, there is not necessary for the flow solver in the whole iterative process, thus the design efficiency can be enhanced. Besides, by taking the advantage of optimization method, the target pressure distribution can be given in a very flexible way, and does not need to be physically meaningful. This feature can reduce reliance on the design experience. Airfoils in low speed and transonic flow and a three-dimensional laminar nacelle design cases are carried out. It is shown that the method robustly and efficiently converges to the target pressure, and has good engineering application potential.

The present study introduces a Gauss-Seidel fluid-structure interaction (FSI) method including the flow solver, structural statics solver and a fast data transfer technique, for the research of structural deformation and flow field variation of rotor blades under the combined influence of steady aerodynamic and centrifugal forces. The FSI method is illustrated and validated by the static aeroelasticity analysis of a transonic compressor rotor blade, NASA Rotor 37. An improved local interpolation with data reduction (LIWDR) algorithm is introduced for fast data transfer on the fluid-solid interface of blade. The results of FSI calculation of NASA Rotor 37 show that when compared with the radial basis function (RBF) based interpolation algorithm, LIWDR meets the interpolation accuracy requirements, while the calculation cost can be greatly improved. The data transmission time is only about 1% of that of RBF. Moreover, the iteration step of steady flow computation within one single FSI has little impact on the converged aerodynamic and structural results. The aerodynamic load-caused deformation accounts for nearly 50% of the total. The effects of blade deformation on the variations of aerodynamic performance are given, demonstrating that when static aeroelasticity is taken into account, the choke mass flow rate increases and the peak adiabatic efficiency slightly decreases. The impact mechanisms on performance variations are presented in detail.

In order to accurately investigate the Reynolds number influence of supercritical airfoil with miniature trailing-edge devices (mini-TED) under transonic conditions. The supercritical airfoil pressure measurement test of high Reynolds number was conducted in the European transonic wind tunnel. The reliability of the high Reynolds number numerical calculation method was validated by the wind tunnel test results. The calculation results are in good agreement with the wind tunnel test results, indicating that the high Reynolds number calculation accuracy could meets the investigation requirement. The transonic flow of supercritical airfoil with mini-TED in the range of Reynolds number 3×106∼50×106 and Mach number 0.76 was simulated using the validated calculation method. Due to the existence of mini-TED, the Reynolds number influence self-adjustment range of the supercritical airfoil disappeared, while the influence magnitude and sensitivity reduced. At the same time, the supercritical airfoil’s linear influence of Reynolds number effects has been changed, and the Reynolds number prediction method based on pressure distribution is no longer applicable. After considering the influence of Reynolds number, the detailed design of supercritical wing with mini-TED could be conducted to improve its reliability and economy in the application of advanced aircraft.

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