State-of-the-art in aerodynamic shape optimisation methods

Aerodynamic optimisation has become an indispensable component for any aerodynamic design over the past 60 years, with applications to aircraft, cars, trains, bridges, wind turbines, internal pipe flows, and cavities, among others, and is thus relevant in many facets of technology. With advancements in computational power, automated design optimisation procedures have become more competent, however, there is an ambiguity and bias throughout the literature with regards to relative performance of optimisation architectures and employed algorithms. This paper provides a well-balanced critical review of the dominant optimisation approaches that have been integrated with aerodynamic theory for the purpose of shape optimisation. A total of 229 papers, published in more than 120 journals and conference proceedings, have been classified into 6 different optimisation algorithm approaches. The material cited includes some of the most well-established authors and publications in the field of aerodynamic optimisation. This paper aims to eliminate bias toward certain algorithms by analysing the limitations, drawbacks, and the benefits of the most utilised optimisation approaches. This review provides comprehensive but straightforward insight for non-specialists and reference detailing the current state for specialist practitioners

Exploring the fitness landscape of a realistic turbofan rotor blade optimization

Aerodynamic shape optimization has established itself as a valuable tool in the engineering design process to achieve highly efficient results. A central aspect for such approaches is the mapping from the design parameters which encode the geometry of the shape to be improved to the quality criteria which describe its performance. The choices to be made in the setup of the optimization process strongly influence this mapping and thus are expected to have a profound influence on the achievable result. In this work we explore the influence of such choices on the effects on the shape optimization of a turbofan rotor blade as it can be realized within an aircraft engine design process. The blade quality is assessed by realistic three dimensional computational fluid dynamics (CFD) simulations. We investigate the outcomes of several optimization runs which differ in various configuration options, such as optimization algorithm, initialization, number of degrees of freedom for the parametrization. For all such variations, we generally find that the achievable improvement of the blade quality is comparable for most settings and thus rather insensitive to the details of the setup. On the other hand, even supposedly minor changes in the settings, such as using a different random seed for the initialization of the optimizer algorithm, lead to very different shapes. Optimized shapes which show comparable performance usually differ quite strongly in their geometries over the complete blade. Our analyses indicate that the fitness landscape for such a realistic turbofan rotor blade optimization is highly multi-modal with many local optima, where very different shapes show similar performance

Investigation into the Aerodynamic Performance of a Concept Sports Car

Transport aerodynamic optimisation has become an increasingly important field of study in response to emerging factors, such as new human needs and market demands. This paper provides a concept in-house built sports-car aerodynamic and shape optimisation. Wind tunnel tests and numerical simulations have been set-up and conducted to understand the concept vehicle aerodynamic structure and needs for performance improvement. A computer-aided design model has been developed and implemented into the computational fluid dynamics (CFD) software of StarCCM+ for detailed analysis. A 1/4th full-scale fibreglass model has been manufactured for validation. The combined experimental and CFD analyses show that the original aesthetic design exhibits high rear-end lift-force. Modifications have been assessed to improve the drag and lift forces for the front, middle and rear regions. Several geometrical changes are introduced, including new rear-wing design. Also, the front end, roof profile and various ducting modifications have been considered. The introduced design changes lead to optimised downforce of -560.18 N with negligible increase to the accumulated drag effects with C_D≤0.3

Applications of Polynomial Chaos-Based Cokriging to Aerodynamic Design Optimization Benchmark Problems

In this work, the polynomial chaos-based Cokriging (PC-Cokriging) is applied to a benchmark aerodynamic design optimization problem. The aim is to perform fast design optimization using this multifidelity metamodel. Multifidelity metamodels use information at multiple levels of fidelity to make accurate and fast predictions. Higher amount of lower fidelity data can provide important information on the trends to a limited amount of high-fidelity (HF) data. The PC-Cokriging metamodel is a multivariate version of the polynomial chaos-based Kriging (PC-Kriging) metamodel and its construction is similar to Cokriging. It combines the advantages of the interpolation-based Kriging metamodel and the regression-based polynomial chaos expansions (PCE). In the work the PC-Cokriging model is compared to other metamodels namely PCE, Kriging, PC-Kriging and Cokriging. These metamodel are first compared in terms of global accuracy, measured by root mean squared error (RMSE) and normalized RMSE (NRMSE) for different sample sets, each with an increasing number of HF samples. These metamodels are then used to find the optimum. Once the optimum design is found computational fluid dynamics (CFD) simulations are rerun and the results are compared to each other. In this study a drag reduction of 73.1 counts was achieved. The multifidelity metamodels required 19 HF samples along with 1,055 low-fidelity to converge to the optimum drag value of 129 counts, while the single fidelity models required 155 HF samples to do the same

Machine Learning for Fluid Mechanics

The field of fluid mechanics is rapidly advancing, driven by unprecedented volumes of data from field measurements, experiments and large-scale simulations at multiple spatiotemporal scales. Machine learning offers a wealth of techniques to extract information from data that could be translated into knowledge about the underlying fluid mechanics. Moreover, machine learning algorithms can augment domain knowledge and automate tasks related to flow control and optimization. This article presents an overview of past history, current developments, and emerging opportunities of machine learning for fluid mechanics. It outlines fundamental machine learning methodologies and discusses their uses for understanding, modeling, optimizing, and controlling fluid flows. The strengths and limitations of these methods are addressed from the perspective of scientific inquiry that considers data as an inherent part of modeling, experimentation, and simulation. Machine learning provides a powerful information processing framework that can enrich, and possibly even transform, current lines of fluid mechanics research and industrial applications.Comment: To appear in the Annual Reviews of Fluid Mechanics, 202

Transonic nacelle design for future medium range aero-engines

It is expected that future civil aero-engines will operate at low specific thrust and high-bypass ratios to improve propulsive efficiency. This may result in an increment in fan diameter and associated weight and nacelle drag penalties. For this reason, these new architectures may use compact nacelles to meet the benefits of the new engine cycles. The aim of the current work is to evaluate the aerodynamic design and performance of compact nacelles for medium range, single-aisle aircraft with a cruise Mach number of M = 0.80. This work encompasses the 3D multi-point, multi-objective optimisation of nacelles by considering cruise conditions as well as a range of off-design requirements such as an increased cruise Mach number, a windmilling engineout diversion scenario and a windmilling end-of-runway case at high-incidence. This paper also explores the robustness and sensitivity of selected designs to flight Mach number (M), massflow capture ratio (MFCR) and angle of attack (AoA). The limits of the feasible design space for this new design challenge are identified. It is concluded that relative to a conventional aero-engine nacelle, the nacelle length (Lnac/rhi) can be reduced by approximately 13% with a mid-cruise drag reduction of 5.8%, whilst maintaining an acceptable aerodynamic performance at off-design conditions