Creating a Test Validated Structural Dynamic Finite Element Model of the X 56A Aircraft

Pak, Chan-gi; Truong, Samson

doi:10.2514/6.2014-3157

Cited by 11 publications

(6 citation statements)

References 8 publications

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“…The model at each grid point has 247 states, including 56 states corresponding to the 2 nd -order sensors (28 sensors in total), 12 rigid body states, 25 elastic structural modes and 25 derivatives (modal velocity), 93 aerodynamic lag states, and 36 states for the third order actuators (10 control surfaces and 2 engine throttles). According to the V-g and V-f plots of the baseline model [35], the normalized flutter frequencies for SBFF (symmetric body freedom flutter), SWBTF (symmetric wing bending torsion flutter), and AWBTF (anti-symmetric wing bending torsion flutter) modes are, respectively, at 1, 3.68, and 3.912 (all the flutter frequencies are normalized by the one for SBFF due to ITAR requirement). The target normalized frequency range  is determined to be 0.01 <  < 5.37 to ensure full coverage of the instability of interest and system response.…”

Section: Resultsmentioning

confidence: 99%

Greedy Sampling and Incremental Surrogate Model-based Tailoring of Aeroservoelastic Model Database for Flexible Aircraft

Wang

Pant

Brenner

et al. 2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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This paper presents a data analysis and modeling framework to tailor and develop linear parameter-varying (LPV) aeroservoelastic (ASE) model database for flexible aircrafts in broad 2D flight parameter space. The Kriging surrogate model is constructed using ASE models at a fraction of grid points within the original model database, and then the ASE model at any flight condition can be obtained simply through surrogate model interpolation. The greedy sampling algorithm is developed to select the next sample point that carries the worst relative error between the surrogate model prediction and the benchmark model in the frequency domain among all input-output channels. The process is iterated to incrementally improve surrogate model accuracy till a pre-determined tolerance or iteration budget is met. The methodology is applied to the ASE model database of a flexible aircraft currently being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies indicate that the proposed method can reduce the number of models in the original database by 67%. Even so the ASE models obtained through Kriging interpolation match the model in the original database constructed directly from the physics-based tool with the worst relative error far below 1%. The interpolated ASE model exhibits continuouslyvarying gains along a set of prescribed flight conditions. More importantly, the selected grid points are distributed non-uniformly in the parameter space, a) capturing the distinctly different dynamic behavior and its dependence on flight parameters, and b) reiterating the need and utility for adaptive space sampling techniques for ASE model database compaction. The present framework is directly extendible to high-dimensional flight parameter space, and can be used to guide the ASE model development, model order reduction, robust control synthesis and novel vehicle design of flexible aircraft.

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Section: Resultsmentioning

confidence: 99%

Greedy Sampling and Incremental Surrogate Model-based Tailoring of Aeroservoelastic Model Database for Flexible Aircraft

Wang

Pant

Brenner

et al. 2018

2018 AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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“…Usually, a reduced order model (ROM) is needed for a structural dynamics FE model tuning. [1] Once the GVT data are collected, the test data from each accelerometer should be expanded to the intermediate ROM to compare the modal data obtained from the ROM and the GVT. Optimum selection of accelerometer and shaker locations improves the accuracy of the test and of analysis correlation.…”

Section: Imentioning

confidence: 99%

“…Frequencies and mode shapes from the GVT will become the target frequencies and mode shapes when a structural dynamic model tuning is performed. [1] The off-diagonal terms of the orthonormalized mass and cross-orthogonality matrices between the FOM and the ROM should be as small as possible because the military standard [2] and the NASA standard [3] for comparing the analytical and experimental modal data will be applied to the correlation study between the analytical (based on the ROM) and the GVT data.…”

Section: A Number Of Degrees Of Freedom For Reduced Order Modelmentioning

confidence: 99%

Multiple Shaker Placement for Ground Vibration Test of X-59 Aircraft using Topology Optimization

Pak

2020

AIAA Scitech 2020 Forum

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A multiple shaker placement methodology is developed and tested using a topology optimization technique. Current multiple shaker placement methodology requires optimum accelerometer placement and optimum single-shaker placement techniques. The proposed methodology is tested using a finite element model of the X-59 Low Boom Flight Demonstrator aircraft. The effective independence and the driving point acceleration transfer function (DPATF) methods are used for the accelerometer placement study. In this study, four shakers are used to excite each mode more effectively during the ground vibration test; all the modes of interest thus are separated into four groups. Each shaker takes care of a separate group of modes. Grouping the modes of interest is performed utilizing topology optimization. The number of modes for each group therefore will be automatically decided during grouping. For each group of modes, perform the following two steps to determine optimal location of four shakers: 1) At each accelerometer location, compare the magnitude of DPATF values at natural frequencies, select the minimum value, and make a vector with these minimum values of the DPATF magnitudes for each group; and 2) Select the degrees of freedom corresponding to the maximum value of this vector. The objective function value is the maximum value of the vector with minimum value of the magnitude of the superposed acceleration transfer function. This objective function value is maximized by changing the modes for each group. Forty accelerometers are enough to have good correlation between mode shapes obtained from the reduced order model and the simulated ground vibration test. * ̅ ( ) = vector in Laplace domain (*) T = transpose of a matrix (*) -1 = inverse of a matrix II.

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“…The models have 44 states corresponding to the 2 nd -order sensors (22 in total), 12 rigid body states, 14 elastic structural modes and 14 derivatives (modal velocity), 60 aerodynamic lag states, and 36 states for the third order actuators (12 control surfaces). According to the V-g and V-f plots of the X-56A baseline model at M = 0.16 [12] the normalized flutter frequencies for SBFF (symmetric body freedom flutter), SWBTF (symmetric wing bending torsion flutter), and AWBTF (anti-symmetric wing bending torsion flutter) modes are, respectively, at 1, 3.68, and 3.912 (that is, all the flutter frequencies are normalized by the one for SBFF). The target normalized frequency range  for X-56A model reduction is determined to be 0.01 <  < 5.36 to ensure full coverage of the interesting flutter behavior and system response.…”

Section: Figure 3 Sensors and Actuators Deployment In The X-56a Muttmentioning

confidence: 99%

Model Order Reduction of Aeroservoelastic Model of Flexible Aircraft

Wang

Song

Pant

et al. 2016

57th AIAA/ASCE/AHS/ASC Structures, Structural Dynamics, and Materials Conference

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This paper presents a holistic model order reduction (MOR) methodology and framework that integrates key technological elements of sequential model reduction, consistent model representation, and model interpolation for constructing high-quality linear parameter-varying (LPV) aeroservoelastic (ASE) reduced order models (ROMs) of flexible aircraft. The sequential MOR encapsulates a suite of reduction techniques, such as truncation and residualization, modal reduction, and balanced realization and truncation to achieve optimal ROMs at grid points across the flight envelope. The consistence in state representation among local ROMs is obtained by the novel method of common subspace reprojection. Model interpolation is then exploited to stitch ROMs at grid points to build a global LPV ASE ROM feasible to arbitrary flight condition. The MOR method is applied to the X-56A MUTT vehicle with flexible wing being tested at NASA/AFRC for flutter suppression and gust load alleviation. Our studies demonstrated that relative to the fullorder model, our X-56A ROM can accurately and reliably capture vehicles dynamics at various flight conditions in the target frequency regime while the number of states in ROM can be reduced by 10X (from 180 to 19), and hence, holds great promise for robust ASE controller synthesis and novel vehicle design.

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Creating a Test Validated Structural Dynamic Finite Element Model of the X 56A Aircraft

Cited by 11 publications

References 8 publications

Greedy Sampling and Incremental Surrogate Model-based Tailoring of Aeroservoelastic Model Database for Flexible Aircraft

Greedy Sampling and Incremental Surrogate Model-based Tailoring of Aeroservoelastic Model Database for Flexible Aircraft

Multiple Shaker Placement for Ground Vibration Test of X-59 Aircraft using Topology Optimization

Model Order Reduction of Aeroservoelastic Model of Flexible Aircraft

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