Problem area Traditionally a helicopter predesign is driven by flight and mission performance requirements, with the helicopter mass being considered the design optimization criterion. Other important requirements, such as costs, are not treated in the same manner. The need for cost-effective operations is becoming increasingly important. The new design goal would be to find the optimum helicopter design which not only fulfils the required performance requirements, but also satisfies customer’s requirements at the lowest possible cost. For that a helicopter Life Cycle Cost (LCC) model is needed which reflects the impact of both the major technical parameters and the major categories of customers and their (multiple) missions. Description of work A rotorcraft pre-design analysis tool from a European aeronautical research institute has been combined with an LCC model from a major European helicopter manufacturer. A rotorcraft design optimization for the evaluation and optimization of the design objectives has been created in an interactive environment. The applied optimization methodology is based on the formulation of a generic optimization problem that allows for singleor multi-objective optimization problems, non-linear constraints and discrete variables. The final objective was to include all enabling processes, models and tools available for use in an Aeronautical Collaborative Design Environment. Insurance 8% DMC 42% Fuel 3% Piloting 35% Spares 1%
[1]
Benjamin C. Rush,et al.
Cost as an Independent Variable: Concepts and Risks
,
1997
.
[2]
A. Land,et al.
An Automatic Method for Solving Discrete Programming Problems
,
1960,
50 Years of Integer Programming.
[3]
J. Clausen,et al.
Branch and Bound Algorithms-Principles and Examples
,
2003
.
[4]
Balbir S. Dhillon.
Life Cycle Costing: Techniques, Models and Applications
,
1989
.
[5]
Kalyanmoy Deb,et al.
A fast and elitist multiobjective genetic algorithm: NSGA-II
,
2002,
IEEE Trans. Evol. Comput..
[6]
David E. Goldberg,et al.
Genetic Algorithms in Search Optimization and Machine Learning
,
1988
.