Within the aerospace design, analysis and optimization community, there is an increasing demand to finalize the preliminary design phase of the wing as quickly as possible without losing much on accuracy. This includes rapid generation of designs, an early adaption of higher fidelity models and automation in structural analysis of the internal structure of the wing. To perform the structural analysis, the aerodynamic load can be transferred to the wing using many different methods. Generally, for preliminary analysis, indirect load transfer method is used and for detailed analysis, direct load transfer method is used. For the indirect load transfer method, load is discretized using shear-moment-torque (SMT) curve and applied to ribs of the wing. For the direct load transfer method, the load is distributed using one-way Fluid-Structure Interaction (FSI) and applied to the skin of the wing. In this research, structural analysis is performed using both methods and the nodal displacement is compared. Further, to optimize the internal structure, iterative changes are made in the number of structural members. To accommodate these changes in geometry as quickly as possible, the parametric design method is used through Engineering SketchPad (ESP). ESP can also provide attributions the geometric feature and generate multi-fidelity models consistently. ESP can generate the Nastran mesh file (.bdf) with the nodes and the elements grouped according to their geometric attributes. In this research, utilizing the attributions and consistency in multi-fidelity models an API is created between ESP and Nastran to automatize the multi-fidelity structural optimization. This API generates the design with appropriate parameters and mesh file using ESP. Through the attribution in the mesh file, the API works as a pre-processor to apply material properties, boundary condition, and optimization parameters. The API sends the mesh file to Nastran and reads the results file to iterate the number of the structural member in design. The result file is also used to transfer the nodal deformation from lower-order fidelity structural models onto the higher-order ones to have multi-fidelity optimization. Here, static structural optimization on the whole wing serves as lower fidelity model and buckling optimization on each stiffened panel serves as higher fidelity model. To further extend this idea, a parametric model of the whole aircraft is also created.<br>

<p>Uncertainty analysis is of great use both for calculating outputs that are more akin to real<br> flight, and for optimization to more robust shapes. However, implementation of uncertainty<br> has been a longstanding challenge in the field of aerodynamics due to the computational cost<br> of simulations. Geometric uncertainty in particular is often left unexplored in favor of uncer-<br> tainties in freestream parameters, turbulence models, or computational error. Therefore, this<br> work proposes a method of geometric uncertainty analysis for aerodynamic shapes that miti-<br> gates the barriers to its feasible computation. The process takes a two- or three-dimensional<br> shape and utilizes a combination of multifidelity meshes and Gaussian process regression<br> (GPR) surrogates in a multifidelity Monte Carlo (MFMC) algorithm. Multifidelity meshes<br> allow for finer sampling with a given budget, making the surrogates more accurate. GPR<br> surrogates are made practical to use by parameterizing major factors in geometric uncer-<br> tainty with only four variables in 2-D and five in 3-D. In both cases, two parameters control<br> the heights of steps that occur on the top and bottom of airfoils where leading and trailing<br> edge devices are attached. Two more parameters control the height and length of waves<br> that can occur in an ideally smooth shape during manufacturing. A fifth parameter controls<br> the depth of span-wise skin buckling waves along a 3-D wing. Parameters are defined to<br> be uniformly distributed with a maximum size of 0.4 mm and 0.15 mm for steps and waves<br> to remain within common manufacturing tolerances. The analysis chain is demonstrated<br> with two test cases. The first, the RAE2822 airfoil, uses transonic freestream parameters<br> set by the ADODG Benchmark Case 2. The results show a mean drag of nearly 10 counts<br> above the deterministic case with fixed lift, and a 2 count increase for a fixed angle of attack<br> version of the case. Each case also has small variations in lift and angle of attack of about<br> 0.5 counts and 0.08◦, respectively. Variances for each of the three tracked outputs show that<br> more variability is possible, and even likely. The ONERA M6 transonic wing, popular due<br> to the extensive experimental data available for computational validation, is the second test<br> case. Variation is found to be less substantial here, with a mean drag increase of 0.5 counts,<br> and a mean lift increase of 0.1 counts. Furthermore, the MFMC algorithm enables accurate<br> results with only a few hours of wall time in addition to GPR training. </p>