Aircraft Optimisation based on Mission Requirements.

SOFTWARE

The performance of an aircraft can be predicted in reasonable detail if information such as its weight, its aerodynamic characteristics, the engine properties, flight load limits, etc. are specified.

However, a much more useful requirement is to be able to reverse this process, so that if a desired mission profile is specified, is it possible to predict the design of the vehicle that can meet these requirements. This can be done using iterative techniques where a baseline design is specified and analysed against the mission requirements. By varying many of the baseline parameters and re-analysing the performance it should be possible to optimise the design for the mission requirements specified.

This performance reverse-engineering process is not a simple one. Since it is a multivariable optimisation problem it is likely that the process will be highly non-linear and may produce many local optima over the complete search space. Finding one global optimum design will require a reasonable amount of computational effort.

Some typical design methods,

1. Start with a good baseline design and use gradient based optimisation methods to extend this a small amount to suit the current requirement. This is the successful approach used by Boeing and Airbus. A family of aircraft develop over several generations with successive improvements in design and performance.

2. Start with a block of flexible material and try every possible combination to find the best one. This is the Monte Carlo approach, randomly try every possible combination. The search space is enormous, the number of combinations to test is almost infinite but the final design will be the best.

3. A combination of gradient design with random perturbations. Start with a baseline design (ie. If its going to fly it probably needs wings) but analyse random perturbations of this design to look for new areas which may lead to new optima. Genetic algorithms and neural network algorithms are methods employed to minimise the search space and speed up the process of heading toward a final optimum design.

Tools to help with Mission Performance based design.

The FLight OPtimisation System (FLOPS) written by Arnie McCullers of Swales Aerospace is an aircraft performance and gradient based optimisation program. It is an excellent analytically based Multi-disciplinary Design Optimisation tool. It has been continually developed over many years and has a large inbuilt knowledge base covering a wide range of aircraft configurations. It covers preliminary and conceptual design with modules for evaluating

1. weights,
   
2. aerodynamics,
   
3. engine cycle analysis,
   
4. propulsion data scaling and interpolation,
   
5. mission performance,
   
6. takeoff and landing,
   
7. noise footprint,
   
8. cost analysis, and
   
9. program control.

The software, user manuals and examples can be obtained by contacting Arnie McCullers (l.a.mccullers@larc.nasa.gov).

Extended MDO

For extended techniques to try more exotic design extrapolation see the web site for the Evolutionary Optimisation group. By using Evolutionary Algorithms a random perturbation is introduced that may lead the design in new directions with a better global optimum.

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