Overview
In the past, aerodynamic loads on an aircraft were primarily determined by using empirical data, analogies, and wind tunnel experiments. Nowadays it is required to design a secure aircraft structure as lightweight as possible in order to come up with environmentally friendly vehicles. This necessitates the reduction of safety factors as best as possible, which can only be achieved by precise prediction of aerodynamic loads over the entire flight envelope, including fringes and areas beyond, since load limiting cases can no longer be foreseen. In addition, more detailed information for aerodynamic loads is requested, i.e. not only global loads but also local load distributions (i.e. pressure distributions) are to be delivered for better optimization of single components as well as the overall aircraft.
The project will address the extremes of the flight envelope, which feature complex flows that are characterised by non-linear/unsteady aerodynamic phenomena.
The ALEF (Aerodynamic Loads Estimation at Extremes of the Flight Envelope) objective is to enable the European aeronautical industry to create complete aerodynamic models of their aircraft based on numerical simulation approaches within the respective development processes. The project aims at precise prediction of aircraft loads for entire flight envelope.
Numerical methods will be developed to determine local load distributions (i.e. pressure distributions) allowing to optimise single components as well as the overall aircraft.
ALEF will kick-off a paradigm shift from greater confidence in experimentally measured loads data to greater confidence in computational results. Beyond the scope of ALEF this paradigm shift will essentially influence the overall aerodynamic development process.
ALEF strives to comprehensively predict aerodynamic forces, moments and their derivatives in time for any point of the flight regime. This is done by two complementary approaches: the high fidelity CFD-based approach not only limited to the inner region of the flight envelope but also at the extremes of the flight envelope dominated by complex flow phenomena and the multi-fidelity approach using numerical tools to efficiently generate the full set of aerodynamic data based on state-of-the art and emerging CFD techniques.
ALEF intends to provide means to efficiently compute the entire aero data space within time frames dictated by industrial design processes at given costs. This will be done by means of surrogate models for both steady and unsteady flows. Also planning techniques for efficient simulation campaigns are addressed.
ALEF will tackle two main challenges: it will "certify" CFD for aerodynamic data for loads processes and it will provide tools to deal with the quantity and quality of data associated with the complete flight envelope and various data sources.
The ultimate scope of using simulation tools in aero data generation is to cover all flight conditions and configurations by means of a numerical toolbox. This would ensure an up-to-date and fast estimation of most recent statuses of aircraft with every data consistent. ALEF will essentially contribute to a substantial wind tunnel testing cost reduction by 2020, which will inherently cut the aerodynamic development effort.
Funding
Results
- Definition of requirements on aerodynamic data for loads and handling qualities
- Identification and selection of three reference test cases: the DLR F-12, HiReTT(EC FP5 project High Reynolds number Tools and Techniques) and X-31 models
- Development of Variable Fidelity Method to combine data (i.e.CFD, wind tunnel, flight test, ...) into continuous multi-dimensional aerodynamic data model for rapid prediction of loads
- High-fidelity (RANS) simulations carried out and results obtained on ALEF test cases
Innovation aspects
The project shall ensure accuracy and physical correctness of each flow simulation result used for aerodynamic data prediction as well as a high coherence of aerodynamic data integrated over the complete flight envelope using tools of varying fidelity. It is improved by considering the impact of physical modelling as well as novel quality control means to achieve a highly coherent data space representation.
Strategy targets
Innovating for the future: technology and behaviour