A novel hybrid method for aerodynamic noise prediction of high-lift devices

Aerodynamic noise of High-Lift Devices(HLDs)is one of the main sources of airframe noise,and has immediate impacts on the airworthiness certification,environmental protection and security of commercial aircraft.In this study,a novel hybrid method is proposed for the aerodynamic noise prediction of HLD.A negative Spalart-Allmaras(S-A)turbulence model based Improved Delayed Detached Eddy Simulation(IDDES)method coupling with AFT-2017b transition model is developed,in order to elaborately simulate the complex flow field around the HLD and thus obtain the information of acoustic sources.A Farassat-Kirchhoff hybrid method is developed to filter the spurious noise sources caused by the vortex motions in solving the Ffowcs Williams-Hawkings(FW-H)equation with permeable integral surfaces,and accurately predict the far-field noise radiation of the HLD.The results of the 30P30N HLD indicate that,the computational Sound Pressure Levels(SPLs)obtained by the Farassat-Kirchhoff hybrid method conform well with the experimental ones in the spectrum for the given observation point,and are more accurate than those obtained by the Farassat 1A method.Based on the hybrid method,the acoustic directivity of the HLD of a commercial aircraft is obtained,and the variation of the SPLs in the spectrum with the deflection angle of the slat is analyzed.

Application of a PCA-DBN-based surrogate model to robust aerodynamic design optimization

An efficient method employing a Principal Component Analysis(PCA)-Deep Belief Network(DBN)-based surrogate model is developed for robust aerodynamic design optimization in this study.In order to reduce the number of design variables for aerodynamic optimizations,the PCA technique is implemented to the geometric parameters obtained by parameterization method.For the purpose of predicting aerodynamic parameters,the DBN model is established with the reduced design variables as input and the aerodynamic parameters as output,and it is trained using the k-step contrastive divergence algorithm.The established PCA-DBN-based surrogate model is validated through predicting lift-to-drag ratios of a set of airfoils,and the results indicate that the PCA-DBN-based surrogate model is reliable and obtains more accurate predictions than three other surrogate models.Then the efficient optimization method is established by embedding the PCA-DBN-based surrogate model into an improved Particle Swarm Optimization(PSO)framework,and applied to the robust aerodynamic design optimizations of Natural Laminar Flow(NLF)airfoil and transonic wing.The optimization results indicate that the PCA-DBN-based surrogate model works very well as a prediction model in the robust optimization processes of both NLF airfoil and transonic wing.By employing the PCA-DBN-based surrogate model,the developed efficient method improves the optimization efficiency obviously.

A Stochastic Collocation Approach to Bayesian Inference in Inverse Problems

We present an efficient numerical strategy for the Bayesian solution of inverse problems.Stochastic collocation methods,based on generalized polynomial chaos(gPC),are used to construct a polynomial approximation of the forward solution over the support of the prior distribution.This approximation then defines a surrogate posterior probability density that can be evaluated repeatedly at minimal computational cost.The ability to simulate a large number of samples from the posterior distribution results in very accurate estimates of the inverse solution and its associated uncertainty.Combined with high accuracy of the gPC-based forward solver,the new algorithm can provide great efficiency in practical applications.A rigorous error analysis of the algorithm is conducted,where we establish convergence of the approximate posterior to the true posterior and obtain an estimate of the convergence rate.It is proved that fast(exponential)convergence of the gPC forward solution yields similarly fast(exponential)convergence of the posterior.The numerical strategy and the predicted convergence rates are then demonstrated on nonlinear inverse problems of varying smoothness and dimension.

A boundary surrogate model for micro/nano grooved surface structure applied in turbulence flow control over airfoil

The application of grooved surface structure is an emerging and effective means in turbulence flow control.However,for a realistic configuration,the global flow field described directly by simple application of massive grids makes it unfeasible to simulate.In this paper,a boundary surrogate model reproducing the effect of microscopic near-wall region is proposed to improve computational efficiency.The surrogate model trained with Lattice Boltzmann Method(LBM)considering the rarefied effect based on real micro/nanoflow field is new among literature,which accurately shows flow characteristics of the micro/nano structure.With this approach,numerical simulations via Reynolds-averaged Navier Stokes equations with modified wall boundary condition are performed in subsonic and transonic flow.The results show that micro/nano grooved surface structure has the effect of delaying transition from laminar to turbulence,thus reducing the skin friction significantly.Analysis of turbulence intensity and turbulence kinetic energy shows that the near-wall flow field of grooved airfoil is more stable compared with that of the smooth airfoil.The reducing rate of maximum turbulent intensity reaches 13.39%.The paper shows a perspective for further application of micro/nano groove structure to turbulence flow control in aircraft design by providing an accurate and efficient simulation method.