Future aircraft concepts and design methods

With an annual growth in travel demand of about 5% globally, managing the environmental impact is a challenge. In 2019, the International Civil Aviation Organisation (ICAO) issued emission reduction targets, including well-to-wake greenhouse gas (GHG) emissions reduced at least 50% from 2005 levels by 2050. This discusses several technologies from an aircraft design perspective that can contribute to achieving these targets. One thing is certain: aircraft will look different in the future. The Transonic Truss-Braced Wing and Flying V configurations are promising significant efficiency improvements over conventional configurations. Electric propulsion, in various architectures, is becoming a feasible option for general aviation and commuter aircraft. It will be a growing field of aviation with zero-emissions flight and opportunities for special missions. Lastly, this paper discusses methods and design processes that include all relevant disciplines to ensure that the aircraft is optimised as a complete system. While empirical methods are essential for initial design, Multidisciplinary Design Optimisation (MDO) incorporates models and simulations integrated in an optimisation environment to capture critical trade-offs. Concurrent design places domain experts in one site to facilitate collaboration, interaction, and joint decision-making, and to ensure all disciplines are equally considered. It is supported by a Collaborative Design Facility (CDF), an information technology facility with connected hardware and software tools for design analysis.

Multidisciplinary design optimisation of a fully electric regional aircraft wing with active flow control technology

The German research Cluster of Excellence SE2A (Sustainable and Energy Efficient Aviation) is investigating different technologies to be implemented in the following decades, to achieve more efficient air transportation. This paper studies the Hybrid Laminar Flow Control (HLFC) using boundary layer suction for drag reduction, combined with other technologies for load and structural weight reduction and a novel full-electric propulsion system. A multidisciplinary design optimisation framework is presented, enabling physics-based analysis and optimisation of a fully electric aircraft wing equipped with HLFC technologies and load alleviation, and new structures and materials. The main focus is on simulation and optimisation of the boundary layer suction and its influence on wing design and optimisation. A quasi three-dimensional aerodynamic analysis is used for drag estimation of the wing. The tool executes the aerofoil analysis using XFOILSUC, which provides accurate drag estimation through boundary layer suction. The optimisation is based on a genetic algorithm for maximum take-off weight (MTOW) minimisation. The optimisation results show that the active flow control applied on the optimised geometry results in more than 45% reduction in aircraft drag coefficient, compared to the same geometry without HLFC technology. The power absorbed for the HLFC suction system implies a battery mass variation lower than 2%, considering the designed range as top-level requirement (TLR).

Multidisciplinary Optimisation of Aircraft Structures with Critical Non-Regular Areas: Current Practice and Challenges

The design optimisation of aerostructures is largely based on Multidisciplinary Design Optimisation (MDO), which is a set of tools used by the aircraft industry to size primary structures: wings, large portions of the fuselage or even an entire aircraft. The procedure is computationally expensive, as it must account for several thousands of loadcases, multiple analyses with hundreds of thousands of degrees of freedom, thousands of design variables and millions of constraints. Because of this, the coarse Global Finite Element Model (GFEM), on which the procedure is based, cannot be further refined. The structures represented in the GFEM contain many components and non-regular areas, which require a detailed modelling to capture their complex mechanical behaviour. Instead, in the GFEM, these components are represented by simplified models with approximated stiffness, whose main role is to contribute to the identification of the load paths over the whole structure. Therefore, these parts are kept fixed and are not constrained during the optimisation, as the description of their internal deformation is not sufficiently accurate. In this paper, we show that it would nevertheless be desirable to size the non-regular areas and the overall structures at once. Firstly, we introduce the concept of non-regular areas in the context of a structural airframe MDO. Secondly, we present a literature survey on MDO with a critical review of several architectures and their current applications to aircraft design optimisation. Then, we analyse and demonstrate with examples the possible consequences of neglecting non-regular areas when MDO is applied. In the conclusion, we analyse the requirements for alternative approaches and why the current ones are not viable solutions. Lastly, we discuss which characteristics of the problem could be exploited to contain the computational cost.

A comparative study of conventional and tri-lobed stratospheric airships

The design and development of Stratospheric Airships for High-Altitude Long-Endurance missions (HALESAs) has generated interest worldwide. Conventional airships usually have a single-lobed axisymmetrical envelope shape. In contrast, several non-axisymmetric envelope configurations have been proposed for the HALESAs, such as flattened single lobed and multi lobed. This paper describes a methodology for carrying out a comparative analysis of a conventional HALESA and the multi-lobed HALESA designed for the same design mission. A sizing methodology which enables the estimation of its design parameters to meet some user-specified requirements has been developed for airships with envelopes of both these shapes. A Multidisciplinary Design Optimisation (MDO) approach has been followed in this methodology, which includes considerations from the disciplines of aerodynamics, energy, environment and structures. The study indicates that the envelope volume, solar array area and total mass of the single-lobed conventional airship are better than those of the tri-lobed HALESA. While the multi-lobed HALESA has the advantage of a flatter upper surface resulting in higher efficiency of the solar panels, the conventional airship has lower drag, which results in superior mission performance.

A framework for enhanced decision-making in aircraft conceptual design optimisation under uncertainty

This paper presents a framework to support decision-making in aircraft conceptual design optimisation under uncertainty. Emphasis is given to graphical visualisation methods capable of providing holistic yet intuitive relationships between design, objectives, feasibility and uncertainty spaces. Two concepts are introduced to allow interactive exploration of the effects of (1) target probability of constraint satisfaction (price of feasibility robustness) and (2) uncertainty reduction through increased state-of-knowledge (cost of uncertainty) on design and objective spaces. These processes are tailored to handle multi-objective optimisation problems and leverage visualisation techniques for dynamic inter-space mapping. An information reuse strategy is presented to enable obtaining multiple robust Pareto sets at an affordable computational cost. A case study demonstrates how the presented framework addresses some of the challenges and opportunities regarding the adoption of Uncertainty-based Multidisciplinary Design Optimisation (UMDO) in the aerospace industry, such as design margins policy, systematic and conscious definition of target robustness and uncertainty reduction experiments selection and prioritisation.

Global Optimisation of Car Front-End Geometry to Minimise Pedestrian Head Injury Levels

The paper presents a multidisciplinary design optimisation strategy for car front-end profile to minimise head injury criteria across pedestrian groups. A hybrid modelling strategy was used to simulate the car- pedestrian impact events, combining parametric modelling of front-car geometry with pedestrian models for the kinematics of crash impact. A space filling response surface modelling strategy was deployed to study the head injury response, with Optimal Latin Hypercube (OLH) Design of Experiments sampling and Kriging technique to fit response models. The study argues that the optimisation of the front-end car geometry for each of the individual pedestrian models, using evolutionary optimisation algorithms is not an effective global optimization strategy as the solutions are not acceptable for other pedestrian groups. Collaborative Optimisation (CO) multidisciplinary design optimisation architecture is introduced instead as a global optimisation strategy, and proven that it can enable simultaneous minimisation of head injury levels for all the pedestrian groups, delivering a global optimum solution which meets the safety requirements across the pedestrian groups.

Turbocharger Axial Turbines for High Transient Response, Part 2: Genetic Algorithm Development for Axial Turbine Optimisation

In a previous paper [1], a preliminary design methodology was proposed for the design of an axial turbine, replacing a conventional radial turbine used in automotive turbochargers, to achieve improved transient response, due to the intrinsically lower moment of inertia. In this second part of the work, the focus is on the optimisation of this preliminary design to improve on the axial turbine efficiency using a genetic algorithm in order to make the axial turbine a more viable proposition for turbocharger turbine application. The implementation of multidisciplinary design optimisation is essential to the aerodynamic shape optimisation of turbocharger turbines, as changes in blade geometry lead to variations in both structural and aerodynamics performance. Due to the necessity to have multiple design objectives and a significant number of variables, genetic algorithms seem to offer significant advantages. However, large generation sizes and simulation run times could result in extensively long periods of time for the optimisation to be completed. This paper proposes a dimensioning of a multi-objective genetic algorithm, to improve on a preliminary blade design in a reasonable amount of time. The results achieved a significant improvement on safety factor of both blades whilst increasing the overall efficiency by 2.55%. This was achieved by testing a total of 399 configurations in just over 4 h using a cluster network, which equated to 2.73 days using a single computer.

Radial Inflow Turbine Design Through Multi-Disciplinary Optimisation Technique

Multidisciplinary design optimisation (MDO) is nowadays widely employed to obtain advanced turbomachines design. The aim of this work is to provide a complete tool for the aeromechanical design of a radial inflow gas turbine. The high rotational speed of such machines, especially if used for micro cogenerative power plants, coupled with high exhaust gas temperature, exposes blades to really high centrifugal and thermal stresses; thus the aerodynamics optimisation has to be necessarily coupled with the mechanical one. Such an approach involves two different computational tools: a fully 3D Reynolds Averaged Navier-Stokes (RANS) solver is used for the aerodynamic optimisation, while an open source Finite Element Analysis (FEA) solver is employed for the mechanical integrity assessment. The geometry parameterization is handled with a commercial tool that employs b-spline advanced curve for blades and vanes definition. The aerodynamic mesh generation is managed via dedicated tools provided by the CFD software and it is a fully structured hexahedral multi-block grid. The FEA mesh is built by means of a harmonic map approach, which is able to provide high quality second order unstructured grid preserving geometrical features starting from boundary surfaces of the fluid domain. The finite element calculation provides stresses, displacements and eigenmodes that are used for mechanical integrity assessments while the CFD solver provides performance parameters and local thermodynamic quantities. Due to the high computational cost of both these two solvers, a metamodel, such as an artificial neural network, is employed to speed up the process. The interaction between two codes, the mesh generation and the post processing of the results is obtained via in-house developed scripting modules. Results obtained are presented and discussed.