Methods for the handling with fuzzy knowledge andscaling problems in the modeling of technical systems using the example of large industrial brakes

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The central aim of the project is the development and investigation of methods for the generation, the transformation and the transfer of knowledge from small experiments to the modelling of complex real systems. The example is the brake system of commercial vehicles. The reason for the use of this example is:- The direct use of design parameters from small experiments for modelling the complex reals system is not always possible because of differences between measured values from small test and reals brake systems - Many parameters specifications are fuzzy For reaching the research aim three test facilities should be used. The differences between the test plants are the level of details. The test facilities are a test bed for parts of brake pads, a hydraulic road simulator and a real vehicle. The project starts with the modelling as the basis for calculation methods for transforming the experimental results on the real system. A comparison between the measured results and the models will be used for optimising the scaling modelling methods. The tests of this project are the basis of the real investigation of the dynamic of knowledge in technical sciences using a concrete technical problem. The main task in this project will be the systematic investigation of the whole process of designing, modelling and examination brakes and the relevant modelling and scaling laws. Another aim is the search for of answers to the following questions: 1. In which way do we acquire the used knowledge? 2. What is the influence of the system environment on the acquisition of knowledge? 3. Which parameters will not be used for the modelling? What is the reason? 4. How accurately do describe the developed models the experiments? 5. Is there not measurable expert knowledge which is not used for the development of the test facility and the real brake? 6. What is the influence of the not measurable expert knowledge on the quality of the model? How it is possible to model this not measurable expert knowledge?