Aerodynamic Shape Optimization of Natural-Laminar-Flow Wing Using Surrogate-Based Approach

Abstract

Aerodynamic shape optimization of a swept natural-laminar-flow wing in the transonic regime is still challenging. The difficulty is associated with reliable prediction of laminar-turbulence transition and reasonable compromise of viscous and wave drags. This paper proposes to use efficient global optimization based on surrogate models to address this problem. The Reynolds-averaged Navier-Stokes flow solver features automatic transition prediction via a full e N method, in which dual N factors are used for Tollmien-Schlichting and crossflow instabilities, respectively. The optimizer is based on the kriging surrogate model and parallel infill-sampling method. The baseline natural-laminarflow wing for short-and medium-range transport aircraft is designed at a cruise Mach number 0.75. Then, drag minimization with up to 42 design variables is carried out, and significant drag reduction (8.79%) has been achieved. A close examination of the optimal wing shows that the drag reduction mainly comes from shock-wave weakening on the upper surface and laminar flow extending via suppression of crossflow instability on the lower surface. Robustness of the optimal wing is investigated, and multipoint optimization is further exercised to improve the robustness to the Mach number variation. It is demonstrated that surrogate-based optimization is feasible and effective for aerodynamic shape optimization of transonic natural-laminar-flow wings.

Design Optimization of a Multi-Megawatt Wind Turbine Blade with the NPU-MWA Airfoil Family

“…The surrogate-based optimization refers to a method which uses surrogate models to replace time-consuming flow solvers, and refines the surrogate models by adding new sample points according to certain infill-sampling criteria, until the resulting "sequence of sample points" converges to the optimal solution. This method is proven to be efficient and robust, and more details about this method are presented in References [44][45][46][47][48][49][50][51][52][53][54][55][56][57][58]. The optimization process was as follows:…”

“…Step 2, "DoE" in Figure 14. The Latin Hypercube Sampling (LHS) method [52] was used to generate 40 or more initial sample points in the design space, which was called design of experiment (DoE). This method can avoid sample points clustering and make the sampling points more uniform.…”

Aerodynamic Design Optimization and Shape Exploration using Generative Adversarial Networks

“…In each iteration, the sampling method samples a point in the design space for evaluation of the QoI, and then that point and its QoI update the surrogate model. Compared to methods like genetic algorithms, surrogate-based optimization reduces the number of expensive CFD evaluations needed in aerodynamic shape optimization [7,9,12,14,29]. However, for a high-dimensional design space, the number of evaluations will still be inevitably high due to the curse of dimensionality [6,30].…”

“…Usually during design optimization, parameters are sampled to generate design candidates [29]. There are two main issues when optimizing these parameters from conventional parameterization: (1) one has to guess the limits of the parameters to form a bounding-box within which the optimization operates, and (2) the design space dimensionality is usually higher than the underlying dimensionality for representing sufficient shape variability [50] -i.e., to capture sufficient shape variation, manually designed shape parameterizations require higher dimensions than are strictly necessary.…”

Surrogate-based optimization with adaptive sampling for microfluidic concentration gradient generator design

“…Fig. 1 illustrates the SBO process with adaptive sampling, 19,23,26,27 specically for designing inlet operating parameters of mCGGs that allow generating user-desired/prescribed CGs. It includes initial sampling, model selection, surrogate modeling, surrogate model optimization, adaptive sampling (or inll), and iterative surrogate model update to gradually identify the global optimum parameters within the design space.…”