An efficient aerodynamic shape optimization of blended wing body UAV using multi-fidelity models

This paper presents a novel optimization technique for an efficient multi-fidelity model building approach to reduce computational costs for handling aerodynamic shape optimization based on high-fidelity simulation models. The wing aerodynamic shape optimization problem is solved by dividing optimization into three steps—modeling 3D（high-fidelity） and 2D（lowfidelity） models, building global meta-models from prominent instead of all variables, and determining robust optimizing shape associated with tuning local meta-models. The adaptive robust design optimization aims to modify the shape optimization process. The sufficient infilling strategy—known as adaptive uniform infilling strategy—determines search space dimensions based on the last optimization results or initial point. Following this, 3D model simulations are used to tune local meta-models. Finally, the global optimization gradient-based method—Adaptive Filter Sequential Quadratic Programing（AFSQP） is utilized to search the neighborhood for a probable optimum point. The effectiveness of the proposed method is investigated by applying it, along with conventional optimization approach-based meta-models, to a Blended Wing Body（BWB） Unmanned Aerial Vehicle（UAV）. The drag coefficient is defined as the objective function, which is subjected to minimum lift coefficient bounds and stability constraints. The simulation results indicate improvement in meta-model accuracy and reduction in computational time of the method introduced in this paper.

Machine learning for adjoint vector in aerodynamic shape optimization

Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.

Solution Remapping Method with Lower Bound Preservation for Navier-Stokes Equations in Aerodynamic Shape Optimization

It is found that the solution remapping technique proposed in[Numer.Math.Theor.Meth.Appl.,2020,13(4)]and[J.Sci.Comput.,2021,87(3):1-26]does not work out for the Navier-Stokes equations with a high Reynolds number.The shape deformations usually reach several boundary layer mesh sizes for viscous flow,which far exceed one-layer mesh that the original method can tolerate.The direct application to Navier-Stokes equations can result in the unphysical pressures in remapped solutions,even though the conservative variables are within the reasonable range.In this work,a new solution remapping technique with lower bound preservation is proposed to construct initial values for the new shapes,and the global minimum density and pressure of the current shape which serve as lower bounds of the corresponding variables are used to constrain the remapped solutions.The solution distribution provided by the present method is proven to be acceptable as an initial value for the new shape.Several numerical experiments show that the present technique can substantially accelerate the flow convergence for large deformation problemswith 70%-80%CPU time reduction in the viscous airfoil drag minimization.

Global aerodynamic design optimization based on data dimensionality reduction

In aerodynamic optimization, global optimization methods such as genetic algorithms are preferred in many cases because of their advantage on reaching global optimum. However,for complex problems in which large number of design variables are needed, the computational cost becomes prohibitive, and thus original global optimization strategies are required. To address this need, data dimensionality reduction method is combined with global optimization methods, thus forming a new global optimization system, aiming to improve the efficiency of conventional global optimization. The new optimization system involves applying Proper Orthogonal Decomposition（POD） in dimensionality reduction of design space while maintaining the generality of original design space. Besides, an acceleration approach for samples calculation in surrogate modeling is applied to reduce the computational time while providing sufficient accuracy. The optimizations of a transonic airfoil RAE2822 and the transonic wing ONERA M6 are performed to demonstrate the effectiveness of the proposed new optimization system. In both cases, we manage to reduce the number of design variables from 20 to 10 and from 42 to 20 respectively. The new design optimization system converges faster and it takes 1/3 of the total time of traditional optimization to converge to a better design, thus significantly reducing the overall optimization time and improving the efficiency of conventional global design optimization method.

Study on Adjoint-Based Optimization Method For Multi-Stage Turbomachinery

Adjoint-based optimization method is a hotspot in turbomachinery.First,this paper presents principles of adjoint method from Lagrange multiplier viewpoint.Second,combining a continuous route with thin layer RANS equations,we formulate adjoint equations and anti-physical boundary conditions.Due to the multi-stage environment in turbomachinery,an adjoint interrow mixing method is introduced.Numerical techniques of solving flow equations and adjoint equations are almost the same,and once they are converged respectively,the gradients of an objective function to design variables can be calculated using complex method efficiently.Third,integrating a shape perturbation parameterization and a simple steepest descent method,a frame of adjoint-based aerodynamic shape optimization for multi-stage turbomachinery is constructed.At last,an inverse design of an annular cascade is employed to validate the above approach,and adjoint field of an Aachen 1.5 stage turbine demonstrates the conservation and areflexia of the adjoint interrow mixing method.Then a direct redesign of a 1+1 counter-rotating turbine aiming to increase efficiency and apply constraints to mass flow rate and pressure ratio is taken.

Shock control bump optimization for a low sweep supercritical wing

Shock control bumps are a promising technique in reducing wave drag of civil transport aircraft flying at transonic speeds.This paper investigates the optimization of 3D shock control bumps on a supercritical wing with a sweep angle of 16°at the1/4 chord.A similar supercritical wing with a higher sweep angle of 24.5°at the 1/4 chord has been adopted as a baseline for the study.Numerical results show that the drag coefficient of the low sweep wing with the optimized 3D shock control bumps is reduced below that for the high sweep wing,indicating shock control bumps can be used as an effective means to reduce the wave drag caused by reducing the wing sweep angle.From the point of view of the wing structure design,lower sweep angle will also bring the benefits of weight reduction,resulting in further fuel reduction.

Performance improvement of optimization solutions by POD-based data mining

The performance of an optimized aerodynamic shape is further improved by a second-step optimization using the design knowledge discovered by a data mining technique based on Proper Orthogonal Decomposition(POD) in the present study. Data generated in the first-step optimization by using evolution algorithms is saved as the source data, among which the superior data with improved objectives and maintained constraints is chosen. Only the geometry components of the superior data are picked out and used for constructing the snapshots of POD. Geometry characteristics of the superior data illustrated by POD bases are the design knowledge, by which the second-step optimization can be rapidly achieved. The optimization methods are demonstrated by redesigning a transonic compressor rotor blade, NASA Rotor 37, in the study to maximize the peak adiabatic efficiency, while maintaining the total pressure ratio and mass flow rate.Firstly, the blade is redesigned by using a particle swarm optimization method, and the adiabatic efficiency is increased by 1.29%. Then, the second-step optimization is performed by using the design knowledge, and a 0.25% gain on the adiabatic efficiency is obtained. The results are presented and addressed in detail, demonstrating that geometry variations significantly change the pattern and strength of the shock wave in the blade passage. The former reduces the separation loss,while the latter reduces the shock loss, and both favor an increase of the adiabatic efficiency.

A novel surface mesh deformation method for handling wing-fuselage intersections

This paper describes a method for mesh adaptation in the presence of intersections, such as wing-fuselage. Automatic optimization tools, using Computational Fluid Dynamics（CFD） simulations, face the problem to adapt the computational grid upon deformations of the boundary surface. When mesh regeneration is not feasible, due to the high cost to build up the computational grid, mesh deformation techniques are considered a cheap approach to adapt the mesh to changes on the geometry. Mesh adaptation is a well-known subject in the literature; however, there is very little work which deals with moving intersections. Without a proper treatment of the intersections,the use of automatic optimization methods for aircraft design is limited to individual components.The proposed method takes advantage of the CAD description, which usually comes in the form of Non-Uniform Rational B-Splines（NURBS） patches. This paper describes an algorithm to recalculate the intersection line between two parametric surfaces. Then, the surface mesh is adapted to the moving intersection in parametric coordinates. Finally, the deformation is propagated through the volumetric mesh. The proposed method is tested with the DLR F6 wing-body configuration.