Evolutionary optimisation methods with uncertainty for modern multidisciplinary design in aeronautical engineering

One of the new challenges in aeronautics is combining and accounting for multiple disciplines while considering uncertainties or variability in the design parameters or operating conditions. This paper describes a methodology for robust multidisciplinary design optimisation when there is uncertainty in the operating conditions. The methodology, which is based on canonical evolution algorithms, is enhanced by its coupling with an uncertainty analysis technique. The paper illustrates the use of this methodology on two practical test cases related to Unmanned Aerial Systems (UAS). These are the ideal candidates due to the multi-physics involved and the variability of missions to be performed. Results obtained from the optimisation show that the method is effective to find useful Pareto non-dominated solutions and demonstrate the use of robust design techniques.

Optimisation methods for coupled thermodynamic and 1D design of radial-inflow turbines

Optimisation is a fundamental step in the turbine design process, especially in the development of non-classical designs of radial-inflow turbines working with high-density fluids in low-temperature Organic Rankine Cycles (ORCs). The present work discusses the simultaneous optimisation of the thermodynamic cycle and the one-dimensional design of radial-inflow turbines. In particular, the work describes the integration between a 1D meanline preliminary design code adapted to real gases and the performance estimation approach for radial-inflow turbines in an established ORC cycle analysis procedure. The optimisation approach is split in two distinct loops; the inner operates on the 1D design based on the parameters received from the outer loop, which optimises the thermodynamic cycle. The method uses parameters including brine flow rate, temperature and working fluid, shifting assumptions such as head and flow coefficients into the optimisation routine. The discussed design and optimisation method is then validated against published benchmark cases. Finally, using the same conditions, the coupled optimisation procedure is extended to the preliminary design of a radial-inflow turbine with R143a as working fluid in realistic geothermal conditions and compared against results from commercially-available software RITAL from Concepts-NREC.

Design optimisation using advanced artificial intelligent system coupled to hybrid-game strategies

One of the main aims in artificial intelligent system is to develop robust and efficient optimisation methods for Multi-Objective (MO) and Multidisciplinary Design (MDO) design problems. The paper investigates two different optimisation techniques for multi-objective design optimisation problems. The first optimisation method is a Non-Dominated Sorting Genetic Algorithm II (NSGA-II). The second method combines the concepts of Nash-equilibrium and Pareto optimality with Multi-Objective Evolutionary Algorithms (MOEAs) which is denoted as Hybrid-Game. Numerical results from the two approaches are compared in terms of the quality of model and computational expense. The benefit of using the distributed hybrid game methodology for multi-objective design problems is demonstrated.

Design Optimisation of UAVs Systems achieved on a Framework Environment via Evolution and Game Theory

Over recent years, Unmanned Air Vehicles or UAVs have become a powerful tool for reconnaissance and surveillance tasks. These vehicles are now available in a broad size and capability range and are intended to fly in regions where the presence of onboard human pilots is either too risky or unnecessary. This paper describes the formulation and application of a design framework that supports the complex task of multidisciplinary design optimisation of UAVs systems via evolutionary computation. The framework includes a Graphical User Interface (GUI), a robust Evolutionary Algorithm optimiser named HAPEA, several design modules, mesh generators and post-processing capabilities in an integrated platform. These population –based algorithms such as EAs are good for cases problems where the search space can be multi-modal, non-convex or discontinuous, with multiple local minima and with noise, and also problems where we look for multiple solutions via Game Theory, namely a Nash equilibrium point or a Pareto set of non-dominated solutions. The application of the methodology is illustrated on conceptual and detailed multi-criteria and multidisciplinary shape design problems. Results indicate the practicality and robustness of the framework to find optimal shapes and trade—offs between the disciplinary analyses and to produce a set of non dominated solutions of an optimal Pareto front to the designer.

On the effects of turbulence modelling in design optimisation for high-lift devices

Aerodynamic shape optimisation is being increasingly utilised as a design tool in the aerospace industry. In order to provide accurate results, design optimisation methods rely on the accuracy of the underlying CFD methods applied to obtain aerodynamic forces for a given configuration. Previous studies of the authors have highlighted that the variation of the order of accuracy of the CFD solver with a fixed turbulence model affects the resulting optimised airfoil shape for a single element airfoil. The accuracy of the underlying CFD model is even more relevant in the context of high-lift configurations where an accurate prediction of flow is challenging due to the complex flow physics involving transition and flow separation phenomena. This paper explores the effect of the fidelity of CFD results for a range of turbulence models within the context of the computational design of aircraft configurations. The NLR7301 multi-element airfoil (main wing and flap) is selected as the baseline configuration, because of the wealth of experimental an computational results available for this configuration. An initial validation study is conducted in order to establish optimal mesh parameters. A bi-objective shape optimisation problem is then formulated, by trying to reveal the trade-off between lift and drag coefficients at high angles of attack. Optimisation of the airfoil shape is performed with Spalart-Allmaras, k - ω SST and k - o realisable models. The results indicate that there is consistent and complementary impact to the optimum level achieved from all the three different turbulence models considered in the presented case study. Without identifying particular superiority of any of the turbu- lence models, we can say though that each of them expressed favourable influence towards different optimality routes. These observations lead to the exploration of new avenues for future research. © 2012 AIAA.

On the effects of turbulence modelling in design optimisation for high-lift devices

Aerodynamic shape optimisation is being increasingly utilised as a design tool in the aerospace industry. In order to provide accurate results, design optimisation methods rely on the accuracy of the underlying CFD methods applied to obtain aerodynamic forces for a given configuration. Previous studies of the authors have highlighted that the variation of the order of accuracy of the CFD solver with a fixed turbulence model affects the resulting optimised airfoil shape for a single element airfoil. The accuracy of the underlying CFD model is even more relevant in the context of high-lift configurations where an accurate prediction of flow is challenging due to the complex flow physics involving transition and flow separation phenomena. This paper explores the effect of the fidelity of CFD results for a range of turbulence models within the context of the computational design of aircraft configurations. The NLR7301 multi-element airfoil (main wing and flap) is selected as the baseline configuration, because of the wealth of experimental an computational results available for this configuration. An initial validation study is conducted in order to establish optimal mesh parameters. A bi-objective shape optimisation problem is then formulated, by trying to reveal the trade-off between lift and drag coefficients at high angles of attack. Optimisation of the airfoil shape is performed with Spalart-Allmaras, k - ω SST and k - ε realisable models. The results indicate that there is consistent and complementary impact to the optimum level achieved from all the three different turbulence models considered in the presented case study. Without identifying particular superiority of any of the turbu- lence models, we can say though that each of them expressed favourable influence towards different optimality routes. These observations lead to the exploration of new avenues for future research. © 2012 by the authors.

Computational scheduling methods for integrating plug-in electric vehicles with power systems: a review

Traditional internal combustion engine vehicles are a major contributor to global greenhouse gas emissions and other air pollutants, such as particulate matter and nitrogen oxides. If the tail pipe point emissions could be managed centrally without reducing the commercial and personal user functionalities, then one of the most attractive solutions for achieving a significant reduction of emissions in the transport sector would be the mass deployment of electric vehicles. Though electric vehicle sales are still hindered by battery performance, cost and a few other technological bottlenecks, focused commercialisation and support from government policies are encouraging large scale electric vehicle adoptions. The mass proliferation of plug-in electric vehicles is likely to bring a significant additional electric load onto the grid creating a highly complex operational problem for power system operators. Electric vehicle batteries also have the ability to act as energy storage points on the distribution system. This double charge and storage impact of many uncontrollable small kW loads, as consumers will want maximum flexibility, on a distribution system which was originally not designed for such operations has the potential to be detrimental to grid balancing. Intelligent scheduling methods if established correctly could smoothly integrate electric vehicles onto the grid. Intelligent scheduling methods will help to avoid cycling of large combustion plants, using expensive fossil fuel peaking plant, match renewable generation to electric vehicle charging and not overload the distribution system causing a reduction in power quality. In this paper, a state-of-the-art review of scheduling methods to integrate plug-in electric vehicles are reviewed, examined and categorised based on their computational techniques. Thus, in addition to various existing approaches covering analytical scheduling, conventional optimisation methods (e.g. linear, non-linear mixed integer programming and dynamic programming), and game theory, meta-heuristic algorithms including genetic algorithm and particle swarm optimisation, are all comprehensively surveyed, offering a systematic reference for grid scheduling considering intelligent electric vehicle integration.

Methods of construction of OWA operators from data

This paper investigates the problem of obtaining the weights of the ordered weighted aggregation (OWA) operators from observations. The problem is formulated as a restricted least squares and uniform approximation problems. We take full advantage of the linearity of the problem. In the former case, a well known technique of non-negative least squares is used. In a case of uniform approximation, we employ a recently developed cutting angle method of global optimisation. Both presented methods give results superior to earlier approaches, and do not require complicated nonlinear constructions. Additional restrictions, such as degree of orness of the operator, can be easily introduced

Extending robust industrial optimisation using simulation and reinforcement learning

Traditional optimisation methods are incapable of capturing the complexity of today's dynamic manufacturing systems. A new methodology, integrating simulation models and intelligent learning agents, was successfully applied to identify solutions to a fundamental scheduling problem. The robustness of this approach was then demonstrated through a series of real-world industrial applications.

A systems approach to shape and topology optimisation of mechanical structures

Optimisation techniques have become more and more important as the possibility of simulating complex mechanical structures has become a reality. A common tool in the layout design of structural parts is the topology optimisation method, which finds an optimum material distribution within a given geometrical design space to best meet loading conditions and constraints. Another important method is shape optimisation, which optimises weight given parametric geometric constraints. In the case of complex shaped parts or elaborate assemblies, for example automobile body structures, shape optimisation is still hard to do; mainly due to the difficulty in translating shape design parameters into meaningful analysis models. Tools like the parametric geometry package SFE CONCEPT are designed to mitigate these issues. Nevertheless, shape methods usually cannot suggest new load path configurations, while topology methods are often confined to single parts. To overcome these limitations the authors have developed a method that combines both approaches into an Integral Shape/Topology Method (IST) that is capable of finding new optimal solutions. This is achieved by an automated optimisation loop and can be applied for both thin walled structures as well as solid 3D geometries. When optimising structures by applying IST, global optimum solutions can be determined that may not be obtained with isolated shape- or topology-optimisation methods.

An integrated approach to shape and topology optimisation of mechanical structures

In mechanical engineering, simulation and optimisation methods have become indispensable. The thesis looks into a novel way to combine shape and topology optimisation approaches. The proposed method - named IST for Integrated Shape And Topology Optimisation - proves to be beneficial for many application in the automotive and aerospace industry.

Mathematical optimisation techniques applied to power system operation and planning

Nowadays, power system operation becomes more complex because of the critical operating conditions resulting from the requirements of a market-driven operation. In this context, efficient methods for optimisation of power system operation and planning become critical to satisfy the operational (technical), financial and economic demands. Therefore, the detailed analysis of modern optimisation techniques as well as their application to the power system problems represent a relevant issue from the scientific and technological points of view. This paper presents a brief overview of the developments on modern mathematical optimisation methods applied to power system operation and planning. Copyright © 2007 Inderscience Enterprises Ltd.

A review of multi-criteria decision-making methods for bioenergy systems

Bioenergy schemes are multi-faceted and complex by nature, with many available raw material supplies and technical options and a diverse set of stakeholders holding a raft of conflicting opinions. To develop and operate a successful scheme there are many requirements that should be considered and satisfied. This paper provides a review of those academic works attempting to deal with problems arising within the bioenergy sector using multi-criteria decision-making (MCDM) methods. These methods are particularly suitable to bioenergy given its multi-faceted nature but could be equally relevant to other energy conversion technologies. Related articles appearing in the international journals from 2000 to 2010 are gathered and analysed so that the following two questions can be answered. (i) Which methods are the most popular? (ii) Which problems attract the most attention? The review finds that optimisation methods are most popular with methods choosing between few alternatives being used in 44% of reviewed papers and methods choosing between many alternatives being used in 28%. The most popular application area was to technology selection with 27% of reviewed papers followed by policy decisions with 18%. © 2012 Elsevier Ltd.