Multilevel Monte-Carlo methods applied to the stochastic analysis of aerodynamic problems

This paper demonstrates the capabilities of the Multi-Level Monte Carlo Methods (MLMC) for the stochastic analysis of CFD aeronautical problems with uncertainties. These capabilities are compared with the classical Monte Carlo Methods in terms of accuracy and computational cost through a set of benchmark test cases. The real possibilities of solving CFD aeronautical analysis with uncertainties by using MLMC methods with a reasonable computational cost are demonstrated.Postprint (published version

Description of the test cases

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.Peer ReviewedPostprint (author's final draft

Aerodynamic shape optimization using adaptive remeshing

Adaptive mesh refinement is one of the most important tools in Computational Fluid Dynamics (CFD) for solving complex engineering design problems. The paper investigates two practical transonic aerofoil design optimization problems using a genetic algorithm coupled with an Euler aerodynamic analysis tool. The first problem consists in the minimization of transonic drag whereas the second is a reconstruction transonic problem solved by minimizing the pressure error. In both cases, the solutions obtained with and without adaptive mesh refinement are compared. Numerical results obtained by both drag minimization and reconstruction design clearly show that the use of adaptive mesh refinement reduces the computational cost and also produces a better solution.Postprint (published version

Lift maximization with uncertainties for high lift devices optimization

In this paper, the aerodynamic shape optimization problems with uncertain operating conditions has been addressed. After a review of robust control theory and the possible approaches to take into account uncertainties, the use of Taguchi robust design methods in order to overcome single point design problems in Aerodynamics is proposed. Under the Taguchi concept, a design with uncertainties is converted into an optimization problem with two objectives which are the mean performance and its variance, so that the solutions are as less sensitive to the uncertainty of the input parameters as possible. Furthermore, the Multi-Criterion Evolutionary Algorithms (MCEAs) are used to capture a set of compromised solutions (Pareto front) between these two objectives. The flow field is analyzed by Navier-Stokes computation using an unstructured mesh. The proposed approach drives to the solution of a multi-objective optimization problem that is solved using a modification of a Nondominated Sorting Genetic Algorithm (NSGA). In order to reduce the number of expensive evaluations of the fitness function a Response Surface Modeling (RSM) is employed to estimate the fitness value using the polynomial approximation model. During the solution of the optimization problem a Semi-torsional Spring Analogy is used for the adaption of the computational mesh to all the obtained geometrical configurations. The proposed approach is applied to the robust optimization of the 2D high lift devices of a business aircraft by maximizing the mean and minimizing the variance of the lift coefficients with uncertain free-stream angle of attack at landing and takeoff flight conditions, respectively.Preprin

On the need for the use of error-controlled finite element analyses in structural shape optimization processes

This is the peer reviewed version of the following article: Ródenas, J. J., Bugeda, G., Albelda, J. and Oñate, E. (2011), On the need for the use of error-controlled finite element analyses in structural shape optimization processes. Int. J. Numer. Meth. Engng., 87: 1105–1126, which has been published in final form at http://dx.doi.org/10.1002/nme.3155. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.This work analyzes the influence of the discretization error associated with the finite element (FE) analyses of each design configuration proposed by the structural shape optimization algorithms over the behavior of the algorithm. The paper clearly shows that if FE analyses are not accurate enough, the final solution provided by the optimization algorithm will neither be optimal nor satisfy the constraints. The need for the use of adaptive FE analysis techniques in shape optimum design will be shown. The paper proposes the combination of two strategies to reduce the computational cost related to the use of mesh adaptivity in evolutionary optimization algorithms: (a) the use of an algorithm for the mesh generation by projection of the discretization error, which reduces the computational cost associated with the adaptive FE analysis of each geometrical configuration and (b) the successive increase of the required accuracy of the FE analyses in order to obtain a considerable reduction of the computational cost in the early stages of the optimization process.The second author is grateful for the financial support received for the development of this paper through the research project DPI2008-05250 of the Ministerio de Ciencia e Innovacion (Spain).Ródenas, J.; Bugeda Castelltort, G.; Albelda Vitoria, J.; Oñate Ibáñez De Navarra, E. (2011). On the need for the use of error-controlled finite element analyses in structural shape optimization processes. International Journal for Numerical Methods in Engineering. 87(11):1105-1126. https://doi.org/10.1002/nme.3155S11051126871

Multidisciplinary optimization of aircraft aerodynamics for distributed propulsion configurations

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.Peer ReviewedPostprint (published version

Robust active shock control bump design optimisation using parallel hybrid-MOGA

The paper investigates a robust optimisation for detail design of active shock control bump on a transonic Natural Laminar Flow (NLF) aerofoil using a Multi-Objective Evolutionary Algorithm (MOEA) coupled to Computational Fluid Dynamics (CFD) software. For MOEA, Robust Multi-objective Optimisation Platform (RMOP) developed in CIMNE is used. For the active shock control bump design, two different optimisation methods are considered; the first method is a Pareto- Game based Genetic Algorithm in RMOP (denoted as RMOGA). The second method uses a Hybridised RMOGA with Game-Strategies and a parallel computation for high performance computation. The paper not only shows how a shock control bump approach coupled to CFD improves aerodynamic performance of original transonic aerofoil but also it shows how high performance computation with applying Hybrid- Game and parallel computation increase the efficiency of optimisation in terms of computational cost and result accuracy.Postprint (published version

Optimization of the experimental set-up for a turbulent separated shear flow control by plasma actuator using genetic algorithms

Since 1947, when Schubauer and Skramstad 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). 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 flowanalysis 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.Peer ReviewedPostprint (author's final draft

Desarrollo de un sistema integrado para tratamiento de geometría, generación de malla y datos para el análisis por el método de los elementos finitos

En este trabajo se describe el desarrollo e implementación de un sistema informático para el tratamiento de toda la información necesaria para un análisis por el Método de los Elementos Finitos o por otros métodos numéricos basados en la discretización de ecuaciones diferenciales (diferencias finitas, volúmenes finitos, métodos de contorno, métodos de puntos, etc.). Algunas de sus partes se refieren principalmente al diseño y organización de un sistema de estas características. En otras, se describen los nuevos algoritmos que ha sido necesario desarrollar para cumplir los objetivos propuestos. Las diversas disciplinas que se describen a lo largo de la monografía se pueden clasificar en: - Organización del sistema. Consiste en definir el tratamiento de todos los datos de un análisis genérico de manera uniforme. También se dan criterios sobre el ordenamiento interno de los datos. - Modelación geométrica. Se presenta una serie de algoritmos que se han desarrollado para tratar y modificar la geometría del modelo. - Generación de malla. Se describen diferentes técnicas y algoritmos para poder realizar la generación de la malla. - Adaptabilidad del sistema a diferentes análisis. Se describe como se ha diseñado y como se realiza la adaptación del sistema a un código de análisis cualquiera. La implementación del conjunto de criterios y algoritmos descritos a lo largo de esta monografía ha permitido la creación de un sistema que da soporte al proceso de análisis mediante métodos numéricos de modelos, tanto a nivel académico como industrial.Postprint (published version

Robust Design Optimization applied to aeronautics combining stochastic calculus with evolutionary algoritms

Las incertidumbres son un problema cotidiano en la ingeniería aeroespacial y en sus aplicaciones. Los métodos de optimización robusta utilizan, normalmente, y para asegurar la robustez de las soluciones, la generación aleatoria de los valores con incertidumbres así como criterios de selección multi-punto para la determinación del óptimo. Desde un punto de vista computacional, la aplicación a problemas de fluido-dinámica (CFD) o interacción fluido-estructura (FSI) puede ser extremadamente cara. Este trabajo presenta el acoplamiento entre el cálculo estocástico y los algoritmos evolutivos para la definición de un procedimiento de optimización robusta. Se propone, en primer lugar, una metodología para el cálculo estocástico, que a continuación se aplica a la solución de problemas de optimización. Estos métodos propuestos se han aplicado a dos tipos de problemas; un problema de CFD y otro de FSI orientados a la reducción de la resistencia aerodinámica y del fenómeno de estabilidad estructural conocido por «flutter», respectivamente.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, respectively.Peer Reviewe