Comparative study of transition models for high-angle-of-attack behavior

This paper considers transition modeling for the flow over small unmanned aerial vehicles with a span of around 1 m. Such flows are characterized by very low values of turbulence intensity, and the main cause for transition corresponds to flow separation. Four different turbulence models for low-Reynolds-number flow are compared with the experimental data for a NACA 0018 airfoil over a range of two-dimensional as well as three-dimensional (3-D) conditions. The turbulence models under consideration are the k-omega shear-stress transport (SST) model with low-Reynolds-number modification, (k-omega SST) gamma-Re theta model along with its simplified version in the form of the (k-omega SST) gamma model, and k-kl-omega model. The NACA 0018 profile is rotated in a flow with a chord-based Reynolds number of 3x105 at three different rotational speeds between an angle of attack of 0 and 25 deg. Using a curve fitting methodology, an estimate of the results at an infinitesimally slow rotation can be made. Both clockwise and counterclockwise rotations are considered to allow an assessment of the model for predicting steady hysteresis. Furthermore, 3-D computations for an infinite wing are performed to examine the appearance of coherent structures at high angle of attack, namely, stall cells or low-frequency fluctuations

Multi-objective optimization of a wing fence on an unmanned aerial vehicle using surrogate-derived gradients

In this paper, the multi-objective, multifidelity optimization of a wing fence on an unmanned aerial vehicle (UAV) near stall is presented. The UAV under consideration is characterized by a blended wing body (BWB), which increases its efficiency, and a tailless design, which leads to a swept wing to ensure longitudinal static stability. The consequence is a possible appearance of a nose-up moment, loss of lift initiating at the tips, and reduced controllability during landing, commonly referred to as tip stall. A possible solution to counter this phenomenon is wing fences: planes placed on top of the wing aligned with the flow and developed from the idea of stopping the transverse component of the boundary layer flow. These are optimized to obtain the design that would fence off the appearance of a pitch-up moment at high angles of attack, without a significant loss of lift and controllability. This brings forth a constrained multi-objective optimization problem. The evaluations are performed through unsteady Reynolds-Averaged Navier-Stokes (URANS) simulations. However, since controllability cannot be directly assessed through computational fluid dynamics (CFD), surrogate-derived gradients are used. An efficient global optimization framework is developed employing surrogate modeling, namely regressive co-Kriging, updated using a multi-objective formulation of the expected improvement. The result is a wing fence design that extends the flight envelope of the aircraft, obtained with a feasible computational budget

ERGO : a new robust design optimization technique combining multi-objective Bayesian optimization with analytical uncertainty quantification

In this work, robust design optimization (RDO) is treated, motivated by the increasing desire to account for variability in the design phase. The problem is formulated in a multi-objective setting with the objective of simultaneously minimizing the mean of the objective and its variance due to variability of design variables and/or parameters. This allows the designer to choose its robustness level without the need to repeat the optimization as typically encountered when formulated as a single objective. To account for the computational cost that is often encountered in RDO problems, the problem is fitted in a Bayesian optimization framework. The use of surrogate modeling techniques to efficiently solve problems under uncertainty has effectively found its way in the optimization community leading to surrogate-assisted optimization-under-uncertainty schemes. The Gaussian processes, the surrogates on which Bayesian optimization builds, are often considered cheap-to-sample black-boxes and are sampled to obtain the desired quantities of interest. However, since the analytical formulation of these surrogates is known, an analytical treatment of the problem is available. To obtain the quantities of interest without sampling an analytical uncertainty, propagation through the surrogate is presented. The multi-objective Bayesian optimization framework and the analytical uncertainty quantification are linked together through the formulation of the robust expected improvement, obtaining the novel efficient robust global optimization scheme. The method is tested on a series of test cases to examine its behavior for varying difficulties and validated on an aerodynamic test function which proves the effectiveness of the novel scheme

Design optimization-under-uncertainty of a forward swept wing unmanned aerial vehicle using SAMURAI

In this paper the design optimization-under-uncertainty of a forward swept wing (FSW) blended wing body (BWB) unmanned aerial vehicle (UAV) is examined. Conventional BWBs are often tailless, which leads to a backward swept wing to ensure longitudinal static stability. This in turn can induce flow separation at the tip, leading to a loss of lift, controllability and the appearance of a nose-up pitching moment. A possible solution to this problem is a conceptual redesign by introducing a forward swept wing, which is inherently free of tip-stall, but needs a careful design in order to be controllable. However, fixed wing UAVs are often produced by means of direct injection expanded foam moulding, which is characterized by not negligible production tolerances. This lead to a reliability-based robust design optimization problem, for which a novel framework is employed: SAMURAI. Firstly, the method accounts for computational cost by means of surrogate modelling, an analytical treatment of the problem and an asynchronous updating scheme that balances design space exploration and objective exploitation. Secondly, the method treats the problem as a multi-objective problem, which leads to a Pareto front of robust and reliable designs. The result is a novel series of UAV designs that are inherently free of tip stall, perform robustly and meet the stability requirements with the target reliability obtained with a computationally feasible budget

Surrogate-assisted parametric study of a wing fence for unmanned aerial vehicles

In this paper the application of a wing fence on an unmanned aerial vehicle (UAV) is examined. The UAV under consideration is characterized by flow separation initiating at the tip, leading to a loss of lift and controllability and the appearance of a nose-up pitching moment. A possible solution to this problem is the use of wing fences: plates placed on top of the wing aligned with the flow and developed from the idea of stopping the transverse component of the boundary-layer flow. Firstly, existing theories in regard to the working of wing fences are brought together. Secondly, the sensitivity of stall speed and controllability to the design variables of the wing fence are laid bare. Finally, the aerodynamic and stability characteristics of the UAV as a function of the design variables are assessed. To accomplish the aforementioned three objectives in both an affordable and accurate manner, computational fluid dynamics simulations using the gamma -Re theta model to correctly model the low Reynolds effects that characterize the flow over a UAV and surrogate modeling in the form of regressive universal cokriging are brought together

ESLA : a new surrogate-assisted single-loop reliability-based design optimization technique

In this paper, we address the formulation of a novel scheme for reliability-based design optimization, in which the design optimization problem is characterized by constraints that must be met with a certain probability. Assessment of the aforementioned is typically referred to as reliability analysis. Conventional methods rely on sampling approaches or by reformulating the problem as a two-level optimization that requires gradient or Hessian information of the constraints to obtain a trustworthy solution. However, the computational cost of such methods makes them often impractical. To overcome the aforementioned, a surrogate-assisted asymptotic reliability analysis (SARA) is presented that makes use of surrogate-derived gradient and Hessian information. The sub-optimization problem is reformulated as a set of constraints using the Karush-Kuhn-Tucker conditions and fitted in an efficient global optimization-like setting through the formulation of the reliability-based expected improvement (RBEI), obtaining the novel efficient single-loop approach (ESLA). The method is tested on a series of test cases which prove the effectiveness of the novel scheme