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Driver assistance features have been introduced to the market focusing on basic, independent functional scenarios. The trend is showing that these kinds of products are facing more and more complex scenarios and we are transitioning from single independent functions to a strongly networked system. Some of the drivers for future autonomous vehicles are 360° monitoring by active safety technology and V2X (vehicle to vehicle or vehicle to infrastructure) communication. In the past vehicles were strictly operated by the driver. Advanced driver assistance products added so called feedback features like lane departure warning, forward collision warning, and blind spot monitoring. First steps towards semi-autonomous driving started with the development of active support functions like adaptive cruise control or lane keeping support. Collision mitigation with various authority levels is the next milestone towards automation followed by other, even more advanced, features.

A three phase catalytic mathematical model was developed for analysis and optimization of the volatile reactor assembly (VRA) used on International Space Station (ISS) Water Processor. The Langmuir-Hinshelwood Hougen-Watson (L-H) expression was used to describe the surface reaction rate. Small column experiments were used to determine the L-H rate parameters. The test components used in the experiments were acetic acid, acetone, ethanol, 1-propanol, 2-propanol and propionic acid. These compounds are the most prevalent ones found in the influent to the VRA reactor. The VRA model was able to predict performance of small column data and experimental data from the VRA flight experiment.

Future lunar missions will utilize a Lunar DC microgrid (LDCMG) to construct the infrastructure for distributing, storing, and utilizing electrical energy. The LDCMG’s energy management, of which energy storage systems (ESS) are crucial components, will be essential to the success of the missions. Standard system design currently employs a rule-of-thumb approach in which design methodologies rely on heuristics that may only evaluate local power balancing requirements. The Hamiltonian surface shaping and power flow control (HSSPFC) method can also be utilized to analyze and design the lunar LDCMG power distribution network and ESS. In this research, the HSSPFC method will be utilized to determine the ideal energy storage requirements for ESS and the optimally distributed control architecture.