Gareth A. Vio

Optimization of Aeroelastic Composite Structures using Evolutionary Algorithms

The flutter/divergence speed of a simple rectangular composite wing is maximized through the use of different ply orientations. Four different biologically inspired optimization algorithms (binary genetic algorithm, continuous genetic algorithm, particle swarm optimization, and ant colony optimization) and a simple meta-modeling approach are employed statistically on the same problem set. In terms of the best flutter speed, it was found that similar results were obtained using all of the methods, although the continuous methods gave better answers than the discrete methods. When the results were considered in terms of the statistical variation between different solutions, ant colony optimization gave estimates with much less scatter.

Aeroelastic analysis through linear and non-linear methods: a summary of flutter prediction in the PUMA DARP

This paper presents a comparison of linear and non-linear methods for the analysis of aeroelastic behaviour and flutter boundary prediction. The methods in question include NASTRAN and ZAERO, based on linear aerodynamics, and the non-linear coupled CFD-CSD methods RANSMB and PMB, developed at the Universities of Bristol and Glasgow respectively. The test cases used for this comparison are the MDO and AGARD 445.6 weakened wing. In general, it was found that the non-linear methods demonstrate excellent agreement with respect to pressure distributions, deflections, dynamic behaviour, and flutter boundary locations for both cases. This is in contrast to previous studies involving similar methods, where notable differences across the MDO span were found, and is taken to imply good performance of the CFD-CSD interpolation schemes employed here. While the linear methods produce similar flutter boundaries to the coupled codes for the aerodynamically simple AGARD 445.6 wing, results for the transonic ‘rooftop’ MDO wing design did not agree as well.

Optimization of a Scaled Sensorcraft Model with Passive Gust Alleviation

High altitude long endurance UAVs such as sensorcraft are extremely flexible and consequently are susceptible to excessive gust loads; therefore it is desirable to incorporate some form of gust load alleviation system into the air vehicle design. If successful, this could result in a significant weight reduction. This paper describes part of an EOARD / AFOSR and ESF supported research project aiming to design, manufacture and test a wind tunnel model of a sensorcraft structure incorporating a passive gust alleviation device. Various elements of the project are described, including initial simulated validation of the device, design and test of a concept prototype and aeroelastic scaling of the sensorcraft wind tunnel model. Conclusions are made as to the effectiveness and feasibility of incorporating the gust alleviation device on an aeroelastically scaled wind tunnel model.

Flight-Regime Dependent Reduced Order Models of CFD/FE aeroelastic systems in transonic flow

This paper is part of a study investigating the prediction of the aeroelastic behavior of aircraft subjected to transonic aerodynamic forces. The main objective of the work is the creation of Reduced Order Models from coupled Computational Fluid Dynamic and Finite Element calculations. The novelty of the approach lies in the identification of different types of Reduced Order Model in different flight regimes. Linear modal models are used in the Mach range range where the full CFD/FE system is linear and nonlinear modal models in the transonic flight regime where the CFD/FE system undergoes Limit Cycle Oscillations. Static solutions of the CFD/FE system are used in order to determine the extent of the nonlinear Mach number range. The model treated in this work is a three-dimensional wing in a transonic flowfield.

On the Use of Adaptive Internal Structures For Wing Shape Control

This paper describes part of a research programme investigating the dev elopment of “adap tive intern al structures” concepts to enable active aeroelastic control of aerospace structures. A number of different concepts have been considered as part of the EU funded Active Aeroelastic Aircraft Structures (3AS) project that allow the bending and torsional stiffness of aircraft wings to be controlled through changes in the internal aircraft structure. The aeroelastic behaviour, in particular static bending and twist deflections, can be controlled through changes in the posit ion, orientation and stiffness of the spars. In this paper, finite element models ar e used to explore the use of rotating spars to vary structural stiffness, thus adjust ing the static aeroelastic wing twist and bending shape, and thus altering the lift an d drag properties . The effect on the flutter characteristics is also explored. A number of experimental studies of the concepts are also described.

Topology optimisation of micro fluidic mixers considering fluid-structure interactions with a coupled Lattice Boltzmann algorithm

Abstract Recently, the study of micro fluidic devices has gained much interest in various fields from biology to engineering. In the constant development cycle, the need to optimise the topology of the interior of these devices, where there are two or more optimality criteria, is always present. In this work, twin physical situations, whereby optimal fluid mixing in the form of vorticity maximisation is accompanied by the requirement that the casing in which the mixing takes place has the best structural performance in terms of the greatest specific stiffness, are considered. In the steady state of mixing this also means that the stresses in the casing are as uniform as possible, thus giving a desired operating life with minimum weight. The ultimate aim of this research is to couple two key disciplines, fluids and structures, into a topology optimisation framework, which shows fast convergence for multidisciplinary optimisation problems. This is achieved by developing a bi-directional evolutionary structural optimisation algorithm that is directly coupled to the Lattice Boltzmann method, used for simulating the flow in the micro fluidic device, for the objectives of minimum compliance and maximum vorticity. The needs for the exploration of larger design spaces and to produce innovative designs make meta-heuristic algorithms, such as genetic algorithms, particle swarms and Tabu Searches, less efficient for this task. The multidisciplinary topology optimisation framework presented in this article is shown to increase the stiffness of the structure from the datum case and produce physically acceptable designs. Furthermore, the topology optimisation method outperforms a Tabu Search algorithm in designing the baffle to maximise the mixing of the two fluids.

A Non-Intrusive Polynomial Chaos Method to Efficiently Quantify Uncertainty in an Aircraft T-Tail

The problem of developing robust methods for uncertainty quantification (UQ) is of major interest in the engineering and scientific community. To quantify uncertainty, probabilistic models have been developed where traditionally Monte Carlo (MC) methods were used to capture uncertainty bounds. In the engineering context, UQ methods can be practically implemented to limit the amount of prototype redesigns. However MC methods are computationally inefficient due to the large number of samples required to obtain an accurate solution. Polynomial Chaos (PC) methods have recently emerged as an efficient method of probabilistic quantification in lower dimensions compared to MC. This paper will show the ability of a non-intrusive PC method to efficiently quantify uncertainty through first and second order statistics. This approach will lend itself to the treatment of a finite element T-Tail model, using Nastran as a black box around which PC curves can be fit based on its outputs.

Experimental Validation of Polynomial Chaos Theory on an Aircraft T-Tail

Uncertainty quantification (UQ) is a notion which has received much interest over the past decade. It involves the extraction of statistical information from a problem with inherent variability, where this variability may stem from a lack of model knowledge or through observational uncertainty. Traditionally, UQ has been a challenging pursuit owing to the lack of efficient methods available. The archetypal UQ method is Monte Carlo theory, however this method possesses a slow convergence rate and is therefore a computational burden. In contrast to Monte Carlo theory, polynomial chaos theory aims to spectrally expand the modelled uncertainty via polynomials of random variables which have deterministic coefficients. Once the spectral expansion has been fully defined, it is possible to obtain statistical properties using simple integration procedures. Although literature has proven polynomial chaos theory to be more efficient than Monte Carlo theory in several contexts, there has been very little effort to experimentally validate polynomial chaos theory. Hence, it is the aim of this paper to perform an experimental validation on an in-house physical T-Tail structure by analysing the first six vibrational modes of this structure, and comparing these against the predicted uncertainty bounds of polynomial chaos theory.

Design of Composite Structures to Improve the Aeroelastic Performance

Despite the benefits of composite structures, it is only recently that the main load bearing structures in large aircrafts, such as Boeing 787 and Airbus A350, have started to be manufactured using carbon fibre composites. Even then, the unique directional properties of composite laminates and design possibilities have yet to be exploited to improve the aircraft performance. In this study, the weight of a typical composite commercial aircraft wing structure is optimised using evolutionary algorithms with the aim of improving the aeroelastic response, concentrating on flutter instabilities and gust loads. Conclusions are drawn based on the performance gains that can be achieved using aeroelastic tailoring and new design ideas, whilst taking into account performance constraints regarding the flutter speed and the root bending moment of the wing.

Improved Implementation of the Harmonic Balance Method

Harmonic Balance (HB) methods have been applied to non-linear aeroelastic problems since the 1980s. As the computational power available to researchers has increased, so has the order of calculated HB solutions. However, the computational cost of a HB solution increases with the square of the order. Additionally, the traditional Newton-Raphson, Broyden, Toeplitz Jacobian and other techniques used for the solution of the non-linear algebraic problem at the heart of the HB methodology rely on a good initial guess for the unknown coefficients. If there are many such coefficients the proba bility that a good guess will be available is very low and the HB scheme may well fail. In this paper a search procedure using Genetic Algorithms (GA) is introduced to evaluate the coefficients of a harmonic balance solution. It is shown that the GA can provide high quality initial guesses for the HB coefficients. The method is applied to an aeroelastic galloping-type problem.