Technological progress leads to an on-going improvement of products in view of functionality, energy consumption or safety at the cost of increased complexity of tasks to be performed. The aim to achieve optimal performance of a process is getting more and more difficult to achieve as the number of parameters that influence the performance of the process is constantly increasing. The high complexity typically prohibits an optimization by hand and instead requires automatic optimization procedures and efficient optimization software, such as the ESA NLP solver WORHP (We Optimize Really Huge Problems). It is specifically designed to solve large-scale nonlinear optimization problems with several hundred thousands or even millions of optimization parameters and already satisfies many of the Clean Sky requirements. The purpose of this project is to adapt WORHP to aviation objectives and constraints with the aim to obtain an even more robust and efficient European NLP solver.
The project is divided into three parts. Part 1 addresses theoretical foundations and structural definitions of the optimization problems to be considered. This includes a study of problems that are typical for aeronautics applications. Part 2 is concerned with the implementation of extensions towards trajectory optimization (optimal control) in the existing solver WORHP and its companion transcriptor TransWORHP. A detailed test campaign on commonly used testsets like CUTEr and on testcases from aviation industry is performed In part 3. The implemented algorithms will be tested in a systematic way on the previously defined test-sets in order to assess the robustness and efficiency of the algorithm. The results of the test campaign will be used to refine the algorithm. The developed software will be documented, validated and tested. Suitable interfaces to existing software packages will be provided.
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