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The increasingly complex products and intelligent environment in automotive and transportation technology need to employ the intelligent tools called robots. To be able to solve a difficult and complex problem robot needs to cooperate with other robots. In this process experts from different disciplines utilize different form of knowledge that requires knowledge integration. In order to facilitate knowledge integration one of the forms of visualization can be used. Visual thinking is strictly connected with understanding of the visual forms. The aim of this research is to investigate the visual thinking capabilities of the intelligent systems in different aspects of the problem solving and communication abilities. This research is continuation of the authors previous work focused on investigating understanding capabilities of the intelligent systems based on the shape understanding system (SUS).

The aim of this study was to assess the crash injury benefits of an Automatic Emergency Braking System (AEBS), for the passenger vehicle population in France. These benefits were examined in regards to the number/proportion of fatalities and serious injured crashes that could be saved per annum. The two crash types investigated included pedestrian crashes and rear-end collisions. AEBS was expected to intervene 0.6sec prior to the crash and at published levels of force according to whether braking previously occurred and the road condition/surface adherence. The analysis involved national crash data, BAAC, collected by the French Ministry of transport, in-depth crash data made available from the European EACS (European Accident Causation Survey) database by LAB, as well as findings from the U.S. NASS/CDS and NHTSA PCDS in-depth databases. A step-wise methodology was used to calculate the crash injury benefits of AEBS across the two crash types.

Modeling the combustion process of a diesel-biodiesel fuel spray in a 3-dimensional (3D) computational fluid dynamics (CFD) domain remains challenging and time-consuming despite the recent advancement in computing technologies. Accurate representation of the in-cylinder processes is essential for CFD studies to provide invaluable insights into these events, which are typically limited when using conventional experimental measurement techniques. This is especially true for emerging new fuels such as biodiesels since fundamental understanding of these fuels under combusting environment is still largely unknown. The reported work here is dedicated to evaluating the Adaptive Local Mesh Refinement (ALMR) approach in OpenFOAM® for improved simulation of reacting biodiesel fuel spray. An in-house model for thermo-physical and transport properties is integrated to the code, along with a chemical mechanism comprising 113 species and 399 reactions.