Abstract:Modal analysis is a popular approach used in structural dynamic and aeroelastic problems due to its efficiency. The response of a structure is composed of the sum of orthogonal eigenvectors or modeshapes and corresponding modal frequencies. This paper investigates the importance of modeshapes on the aeroelastic response of the Goland wing subject to structural uncertainties. The wing undergoes limit cycle oscillations (LCO) as a result of the inclusion of polynomial stiffness nonlinearities. The LCO computatio… Show more
“…Further refinement from adding more velocities restricts the posterior to a narrow range of store location and quite a wide range of spar thickness. As a result, it is concluded that the store location is the more important factor for model performance and this is concurrent with results shown previously by the authors in Hayes et al (35) .…”
Section: Goland Wing With Normal Modes Variabilitysupporting
confidence: 90%
“…Further refinement from adding more velocities restricts the posterior to a narrow range of store location and quite a wide range of spar thickness. As a result, it is concluded that the store location is the more important factor for model performance and this is concurrent with results shown previously by the authors in Hayes et al (35) . Figure 16. Goland wing with changing mode shapes – Posterior and parameter distributions using data from two velocities. Figure 17 Goland wing with changing mode shapes – Posterior and parameter distributions using data from three velocities. Figure 18. Goland wing with changing mode shapes – Posterior and parameter distributions using data from five velocities.…”
Section: Goland Wing With Normal Modes Variabilitysupporting
confidence: 89%
“…The store CG location in particular has a highly non-linear relationship with stability due to a significant change in the mode shapes of the structure; changing the mode shapes alters the way the structure interacts with the flow and this is the source of the peculiar posterior shape. Evidence of the mode shape variability induced by changes in these structural parameters has been shown by previous work of Hayes et al (35) . It is possible to see an underlying trend amongst the distinctive shape of the posterior; most of the regions of medium to high probability show a positive correlation between the two parameters.…”
Section: Goland Wing With Normal Modes Variabilitysupporting
The assimilation of discrete data points with model predictions can be used to achieve a reduction in the uncertainty of the model input parameters, which generate accurate predictions. The problem investigated here involves the prediction of limit-cycle oscillations using a High-Dimensional Harmonic Balance (HDHB) method. The efficiency of the HDHB method is exploited to enable calibration of structural input parameters using a Bayesian inference technique. Markov-chain Monte Carlo is employed to sample the posterior distributions. Parameter estimation is carried out on a pitch/plunge aerofoil and two Goland wing configurations. In all cases, significant refinement was achieved in the distribution of possible structural parameters allowing better predictions of their true deterministic values. Additionally, a comparison of two approaches to extract the true values from the posterior distributions is presented.
“…Further refinement from adding more velocities restricts the posterior to a narrow range of store location and quite a wide range of spar thickness. As a result, it is concluded that the store location is the more important factor for model performance and this is concurrent with results shown previously by the authors in Hayes et al (35) .…”
Section: Goland Wing With Normal Modes Variabilitysupporting
confidence: 90%
“…Further refinement from adding more velocities restricts the posterior to a narrow range of store location and quite a wide range of spar thickness. As a result, it is concluded that the store location is the more important factor for model performance and this is concurrent with results shown previously by the authors in Hayes et al (35) . Figure 16. Goland wing with changing mode shapes – Posterior and parameter distributions using data from two velocities. Figure 17 Goland wing with changing mode shapes – Posterior and parameter distributions using data from three velocities. Figure 18. Goland wing with changing mode shapes – Posterior and parameter distributions using data from five velocities.…”
Section: Goland Wing With Normal Modes Variabilitysupporting
confidence: 89%
“…The store CG location in particular has a highly non-linear relationship with stability due to a significant change in the mode shapes of the structure; changing the mode shapes alters the way the structure interacts with the flow and this is the source of the peculiar posterior shape. Evidence of the mode shape variability induced by changes in these structural parameters has been shown by previous work of Hayes et al (35) . It is possible to see an underlying trend amongst the distinctive shape of the posterior; most of the regions of medium to high probability show a positive correlation between the two parameters.…”
Section: Goland Wing With Normal Modes Variabilitysupporting
The assimilation of discrete data points with model predictions can be used to achieve a reduction in the uncertainty of the model input parameters, which generate accurate predictions. The problem investigated here involves the prediction of limit-cycle oscillations using a High-Dimensional Harmonic Balance (HDHB) method. The efficiency of the HDHB method is exploited to enable calibration of structural input parameters using a Bayesian inference technique. Markov-chain Monte Carlo is employed to sample the posterior distributions. Parameter estimation is carried out on a pitch/plunge aerofoil and two Goland wing configurations. In all cases, significant refinement was achieved in the distribution of possible structural parameters allowing better predictions of their true deterministic values. Additionally, a comparison of two approaches to extract the true values from the posterior distributions is presented.
“…During flight, the wing mass variation due to fuel burn, the wing structural model should be updated and recalculated. In CFD-based ROMs aeroelastic analysis, the influence of variation in the structural will also affect the solution of fluid model (Hayes et al, 2014), thus, it is required to construct a new CFD-based ROM (Xie et al, 2019). Therefore, for every wing fuel mass variation of the structural model configuration, CFD-based ROM should be reconstructed, which destroy the increases rapidly and it requires a great number of computational cost.…”
Purpose
This paper aims to develop an efficient evaluation method to more intuitively and effectively investigate the influence of the wing fuel mass variations because of fuel burn on transonic aeroelasticity.
Design/methodology/approach
The proposed efficient aeroelastic evaluation method is developed by extending the standard computational fluid dynamics (CFD)-based proper orthogonal decomposition (POD)/reduced order model (ROM).
Findings
The results of this paper show that the proposed aeroelastic efficient evaluation method can accurately and efficiently predict the aeroelastic response and flutter boundary when the wing fuel mass vary because of fuel burn. It also shows that the wing fuel mass variations have a significant effect on transonic aeroelasticity; the flutter speed increases as the wing fuel mass decreases. Without rebuilding an expensive, time-consuming CFD-based POD/ROM for each wing fuel mass variation, the computational cost of the proposed method is reduced obviously. It also shows that the computational efficiency improvement grows linearly with the number of model cases.
Practical implications
The paper presents a potentially powerful tool to more intuitively and effectively investigate the influence of the wing fuel mass variation on transonic aeroelasticity, and the results form a theoretical and methodological basis for further research.
Originality/value
The proposed evaluation method makes it a reality to apply the efficient standard CFD-based POD/ROM to investigate the influence of the wing fuel mass variation because of fuel burn on transonic aeroelasticity. The proposed efficient aeroelastic evaluation method, therefore, is ideally suited to deal with the investigation of the influence of wing fuel mass variations on transonic aeroelasticity and may have the potential to reduce the overall cost of aircraft design.
“…As the aircraft design process proceeds, with the outer shape being frozen at the early stages, the structural model undergoes multiple changes to guarantee the design target loads are met. Structural modeshapes and associated frequencies are dependent upon the mass and stiffness distribution, and this should be correctly included in an aeroelastic analysis, Hayes [32]. When a structural modification is made, the structural model need to be updated and the new modeshapes and frequencies recalculated.…”
Time domain aeroelastic analysis has high computing costs when using computational fluid dynamics. These costs become prohibitive when the structural model undergoes large changes from the baseline design, as within an aircraft design process. To overcome this realistic challenge, we have developed, implemented, and demonstrated an efficient method that is robust in the presence of global modifications of the structure. The method consists of: a) a reduced order model of the linearized Navier-Stokes equations generated around an aeroelastic equilibrium that depends, in turn, on the structural model; b) an approximate structural dynamic reanalysis method valid for global modifications of the structure; and c) a mechanism to exchange information between fluid and structural solvers without need for calculating at each iteration of the structural design an eigenvalue problem of the modified structure. The resulting aeroelastic reduced order model is demonstrated for the AGARD 445.6 wing, and material properties are varied up to 100% from their original values. It is found that: a) predictions of the time domain aeroelastic response and of the flutter speed are accurate for all modifications of the structure; and b) the computational efficiency of the proposed aeroelastic reduced order model is linearly proportional to the number of structural configurations considered. The method, therefore, is ideally suited for optimization and uncertainty studies.
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