Efficiency increase is seen as one of the main goals in any energy converting device. In this direction, the present study aims to demonstrate that large-scale wind turbines can still be improved in order to generate larger amounts of energy. The research presented in this manuscript consists of two main blocks. Initially, it analyzes via Computational Fluid Dynamics (CFD) the boundary layer dynamics on a set of pre-determined airfoils cut along the DTU-10MW reference blade by using the 2D URANS k omega SST turbulence model. The aim of this initial stage is to identify the boundary layer separation point, its associated frequency, and peak to peak amplitude for each airfoil cut along the blade, evaluating as well their respective aerodynamic characteristics. The second main goal of the present research consists of implementing the Active Flow Control (AFC) technology and, when employing synthetic jets, to reattach the boundary layer in all airfoils where it is separated. To accomplish this second goal efficiently, the five parameters associated to the AFC implementation performed to each airfoil will be obtained through respective genetic algorithm optimizations. An energy assessment is finally undertaken at each airfoil to validate the respective energy gain obtained. When comparing the net power gain, before and after AFC implementation, generated by each of the airfoils evaluated, net power gains between 23 and 36KW are obtained in all airfoils analyzed, clarifying that the proposed technology is capable of improving the performance of the wind turbines, very likely at any operating condition.

A multi-fidelity optimisation strategy has been developed in the present work, and its performance is illustrated through a series of test cases. The strategy is based on hybrid methods such that two genetic optimisation algorithms are employed, each coupled to a different fidelity level with transfer of information between them. The aim is that the low fidelity model, being less accurate but with a lower computational cost, performs a comprehensive search along the design space guiding the high fidelity model to the optimum region. This strategy has been shown to reduce the computational time of an optimisation through analytical test cases as well as numerical cases. The analytical cases have been used to tune the parameters that define the multi-fidelity strategy, while the numerical cases are employed to apply the method to engineering problems, focusing on the aerodynamic performance of an airfoil. The speed-up shows a certain dependency to the models relation, both regarding their similarity level as well as the relative computational cost. For cases exhibiting a significant dissimilarity between models, wherein the low fidelity model is notably inaccurate, the attained speed-up diminishes, and numerous instances demonstrate an absence of speed-up. However, for most cases, even with poor model similarity the optimisations are accelerated by an order of 2, while values up to 3–5 were found for higher similarity levels. Hence, the developed strategy shows a relevant decrease of computational cost of an optimisation procedure although its performance is affected by the models relative accuracy.

The combination of different aerodynamic configurations and propulsion systems, namely, aero-propulsion, affects flight performance differently. These effects are closely related to multidisciplinary collaborative aspects (aerodynamic configuration, propulsion, energy, control systems, etc.) and determine the overall energy consumption of an aircraft. The potential benefits of distributed propulsion (DP) involve propulsive efficiency, energy-saving, and emissions reduction. In particular, wake filling is maximized when the trailing edge of a blended wing body (BWB) is fully covered by propulsion systems that employ boundary layer ingestion (BLI). Nonetheless, the thrust–drag imbalance that frequently arises at the trailing edge, excessive energy consumption, and flow distortions during propulsion remain unsolved challenges. These after-effects imply the complexity of DP systems in multidisciplinary optimization (MDO). To coordinate the different functions of the aero-propulsive configuration, the application of MDO is essential for intellectualized modulate layout, thrust manipulation, and energy efficiency. This paper presents the research challenges of ultra-high-dimensional optimization objectives and design variables in the current literature in aerodynamic configuration integrated DP. The benefits and defects of various coupled conditions and feasible proposals have been listed. Contemporary advanced energy systems, propulsion control, and influential technologies that are energy-saving are discussed. Based on the proposed technical benchmarks and the algorithm of MDO, the propulsive configuration that might affect energy efficiency is summarized. Moreover, suggestions are drawn for forthcoming exploitation and studies.

The Boundary Layer (BL) dynamic performance greatly affects the forces acting on any Bluff body [...]

The use of composite materials has grown exponentially in transport structures due to their weight reduction advantages, added to their capability to adapt the material properties and internal micro-structure to the requirements of the application. This flexibility allows the design of highly efficient composite structures that can reduce the environmental impact of transport, especially if the used composites are bio-based. In order to design highly efficient structures, the numerical models and tools used to predict the structural and material performance are of great importance. In the present paper, the authors propose a multi-objective, multi-scale optimization procedure aimed to obtain the best possible structure and material design for a given application. The procedure developed is applied to an aircraft secondary structure, an overhead locker, made with a sandwich laminate in which both, the skins and the core, are bio-materials. The structural multi-scale numerical model has been coupled with a Genetic Algorithm to perform the optimization of the structure design. Two optimization cases are presented. The first one consists of a single-objective optimization problem of the fibre alignment to improve the structural stiffness of the structure. The second optimization shows the advantages of using a multi-objective and multi-scale optimization approach. In this last case, the first objective function corresponds to the shelf stiffness, and the second objective function consists of minimizing the number of fibres placed in one of the woven directions, looking for a reduction in the material cost and weight. The obtained results with both optimization cases have proved the capability of the software developed to obtain an optimal design of composite structures, and the need to consider both, the macro-structural and the micro-structural configuration of the composite, in order to obtain the best possible solution. The presented approach allows to perform the optimisation of both the macro-structural and the micro-structural configurations.

Genetic Algorithms (GA) are useful optimization methods for exploration of the search space, but they usually have slowness problems to exploit and converge to the minimum. On the other hand, gradient based methods converge faster to local minimums, although are not so robust (e.g., flat areas and discontinuities can cause problems) and they lack exploration capabilities. This article presents a hybrid optimization method trying to combine the virtues of genetic and gradient based algorithms, and to overcome their corresponding drawbacks. The performance of the Hybrid Method is compared against a gradient based method and a Genetic Algorithm, both used alone. The rate of convergence of the methods is used to compare their performance. To take into account the robustness of the methods, each one has been executed more than once, with different starting points for the gradient based method and different random seeds for the Genetic Algorithm and the Hybrid Method. The performance of the different methods is tested against an optimization Active Flow Control (AFC) problem over a 2D Selig–Donovan 7003 (SD7003) airfoil at Reynolds number 6×104 and a 14 degree angle of attack. Five design variables are considered: jet position, jet width, momentum coefficient, forcing frequency and jet inclination angle. The objective function is defined as minus the lift coefficient (−Cl), so it is defined as a minimization problem. The proposed Hybrid Method enables working with N optimization algorithms, multiple objective functions and design variables per optimization algorithm.

This book contains the results of a European-Chinese collaborative research project—MARS—and presents recent advances of the understanding of the relationship between Reynolds stress, turbulent coherent structure, and flow control effectiveness, and provides novel optimisation techniques

The high-level objective of MARS project was to understand the formation and behaviour of turbulent structures which affects the Reynolds stress and skin friction. The aim was, once understood, to apply flow control techniques in order to control these structures and reduce the overall drag derived from the Reynolds stress and mainly from the skin friction. Active flow control devices were the main interest; DBD plasma, Synthetic jets, Micro Blowing and Suction, Moving Surfaces were included on the list. To test all these devices, two test cases were defined, and a database and file repository were established in the project webserver. The present chapter is aimed to describe the test cases, including the set-up of the flow control devices, as well as to describe the file repository were all the data was stored.

Since 1947, when Schubauer and Skramstad (J Res: 69–78, 1947 [1]) established the basis of the technology with its revolutionary work about steady state tools and mechanisms for the flow management, the progress of the flow control technology and the development of devices have progressed constantly. Anyway, the applicability of such devices is limited, and only few of them have arrived to the assembly workshop. The problem is that the range of actuation is still limited. Despite their operability limitations, flow control devices are of great interest for the aeronautical industry. The number of projects investigating this technology demonstrates the relevance of in the Fluid Dynamic field. The scientific interest focus not only on the industrial applications and the improvement of the technology, but also on the deep understanding of the physical phenomena associated to the flow separation, turbulence formation associated to the final drag reduction aim. A clear example of what has been mentioned is the EC MARS research project (MARS project, FP7 project number 266326, [2]). Its objectives are aimed to a better understanding of the Reynolds Stress and turbulent flow related to both drag reduction and flow control. The research was carried out through the analysis of several flow control devices and the optimization of the parameters for some of them was an important element of the research. When solving a traditional fluid dynamics optimisation problem numerical flow analysis are used instead of experimental ones due to their lower cost and shorter needed time for evaluation of candidate solutions. Nevertheless, in the particular case of the selected flow control plasma devices the experimental measurement of the performance of each candidate configuration has been much quicker than a numerical analysis. For this reason, the corresponding optimisation problem has been solved by coupling an evolutionary optimization algorithm with an experimental device. This paper discusses the design quality and efficiency gained by this innovative coupling.

Today, mainly man-made materials such as carbon and glass fibres are used to produce composite parts in aviation. Renewable materials such as natural fibres or bio-sourced resin systems have not found their way into aviation, yet. The project ECO-COMPASS aims to evaluate the potential applications of ecologically improved composite materials in the aviation sector in an international collaboration of Chinese and European partners. Natural fibres such as flax and ramie will be used for different types of reinforcements and sandwich cores. Furthermore, the bio-based epoxy resins to substitute bisphenol-A based epoxy resins in secondary structures are under investigation. Adapted material protection technologies to reduce environmental influence and to improve fire resistance are needed to fulfil the demanding safety requirements in aviation. Modelling and simulation of chosen eco-composites aims for an optimized use of materials while a life cycle assessment aims to prove the ecological advantages compared to synthetic state-of-the-art materials. In this paper, the status of selected ecologically improved materials will be presented with an outlook for potential application in interior and secondary structures.

Uncertainty quantification (UQ) is becoming a strategic step in the design phase. Robust Design Optimization (RDO) is the following step. The Technological Readiness Level (TRL) of intrusive and non-intrusive methodologies is increasing rapidly, although several limitations remain. Nowadays, UQ is a major trend in research, because there is a lot of room for improvement.

The present section will focus on the applicability issues of Monte Carlo-based methods, as well as those methods based on sampling techniques. Special focus will be put on the Multi-Level Monte Carlo method and the two implementations developed during the UMRIDA project, namely the Continuous MLMC and MLMC. All named methods have been described in the above sections of this book.

Design optimization has already become an important tool in industry. The benefits are clear, but several drawbacks are still present, being the main one the computational cost. The numerical simulation involved in the solution of each evaluation is usually costly, but time and computational resources are limited. Time is key in industry. The present communication focuses on the methodology applied to optimize the installation and design of a Tuned Mass Damper. It is a structural device installed within the tower of a wind turbine aimed to stabilize the oscillations and reduce the tensions and the fatigue loads. The paper describes the decision process to define the optimization problem, as well as the issues and solutions applied to deal with a huge computational cost.

In this chapter, we present a general introduction to Monte Carlo (MC)-based methods, sampling methodologies, stratification methods, and variance reduction techniques. In the first part, we will discuss the theoretical basis and the convergence proprieties of MC methods. The next part is devoted to pseudorandom and quasi-random number generation, the generation of random variables and the application of stratification. It is followed by techniques for correlation and discrepancy control. The third part presents the concept of Latin Hypercube Sampling (LHS). The last part introduces the concept of Multi-Level Monte Carlo (MLMC).

Turbulent boundary layer control (TBLC) for skin-friction drag reduction is a relatively new technology made possible through the advances in computational-simulation capabilities, which have improved the understanding of the flow structures of turbulence. Advances in micro-electronic technology have enabled the fabrication of active device systems able to manipulating these structures. The combination of simulation, understanding and micro-actuation technologies offers new opportunities to significantly decrease drag, and by doing so, to increase fuel efficiency of future aircraft. The literature review that follows shows that the application of active control turbulent skin-friction drag reduction is considered of prime importance by industry, even though it is still at a low technology readiness level (TRL). This review presents the state of the art of different technologies oriented to the active and passive control for turbulent skin-friction drag reduction and contributes to the improvement of these technologies.

Aircraft emission targets worldwide and their climatic effects have put pressure in government agencies, aircraft manufacturers and airlines to reduce water vapour, carbon dioxide (CO2) and oxides of nitrogen (NOx) resulting from aircraft emissions. The difficulty of reducing emissions including water vapor, carbon dioxide (CO2) and oxides of nitrogen (NOx) is mainly due to the fact that a commercial aircraft is usually designed for a particular optimal cruise altitude but may be requested or required to operate and deviate at different altitudes and speeds to archive a desired or commanded flight plan, resulting in increased emissions. This is a multi-disciplinary problem with multiple trade-offs such as optimizing engine efficiency, minimizing fuel burnt and emissions while maintaining prescribed aircraft trajectories, altitude profiles and air safety. There are possible attempts to solve such problems by designing new wing/aircraft shape, new efficient engine, ATM technology, or modifying the aircraft flight plan. Based on the rough data provided by an air carrier company, who was willing to assess the methodology, this paper will present the coupling of an advanced optimization technique with mathematical models and algorithms for aircraft emission, and fuel burnt reduction through flight plan optimization. Two different approaches are presented; the first one describes a deterministic optimization of the flight plan and altitude profile in order to reduce the fuel consumption while reducing time and distance. The second approach presents the robust design optimization of the previous case considering uncertainties on several parameters. Numerical results will show that the methods are able to capture a set of useful trade-offs solutions between aircraft range and fuel consumption, as well as fuel consumption and flight time.

Uncertainties are a daily issue to deal with in aerospace engineering and applications. Robust optimization methods commonly use a random generation of the inputs and take advantage of multi-point criteria to look for robust solutions accounting with uncertainty definition. From the computational point of view, the application to coupled problems, like fluid-dynamics (CFD) or fluid-structure interaction (FSI), can be extremely expensive. This work presents a coupling between stochastic analysis techniques and evolutionary optimization algorithms for the definition of a stochastic robust optimization procedure. At first, a stochastic procedure is proposed to be applied into optimization problems. The proposed method has been applied to both CFD and FSI problems for the reduction of drag and flutter,