Characterization of dust is a key aspect in recent space missions to Mars. Dust has a huge influence on the planet's global climate and it is always present in its atmosphere. MicroMED is an optical particle counter that will be part of the "Dust Complex" suite led by IKI in the ExoMars 2020 mission and it will determine size distribution and concentration of mineral grains suspended in martian atmosphere. A Computational Fluid Dynamic (CFD) analysis was performed aimed at the optimization of the instrument's sampling efficiency in the 0.4-20 µm diameter range of the dust particles. The analysis allowed to understand which conditions are optimum for operations on Mars and to consequently optimize the instrument's fluid dynamic design.

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The purpose of this paper is to present optimization method of an inducer blade shape to improve its suction performance and clarify the relationship between pump performance and design parameters. In order to conduct the optimization process a response surface based optimization framework was established. Baseline was designed in previous research [1]. The inducers were 3Dprinted in ABS plastic and their wetted and cavitating characteristics were measured. It was confirmed that the optimized inducer can maintain its wetted performance at lower cavitation numbers. A response surface is a mathematical model that approximates the relationship between the input parameters and the objective function from a finite number of learning points within the design space. The design space was defined by four parameters: sweep angle, sweep radius, incidence angle and blade solidity at the tip that controlled the blade shape. The performance of each design was evaluated with a CFD simulation established in a commercial solver. The optimization goal was to minimize the critical cavitation number that corresponds to a 5% drop of pressure increase through the pump due to cavitation. A starting point of the optimization was the industrial pump designed by a Japanese company Teral [1]. The results of the numerical optimization show that the critical cavitation number was decreased by 17.6% with respect to the baseline design. In the experimental results, an average improvement of 15.4% was achieved.