BIOSMHARS: WP4 Model development and calibration - Requirements of the model: Deliverable 4.1

Microorganisms are constant ecological partners of humans during manned space flight in hermetically sealed environments. Although the microbes are rarely a health risk to the crew members during relatively short duration flights, increased immuno-suppression on the one hand and microgravity induced effects like enhanced pathogenicity of microbes on the other hand may threaten the crew members' health during long distance flights. In addition, there has been observed biodegradation of materials on the spacecrafts. Hence, in view of future long-duration spaceflights for exploration, it is mandatory to better understand the underlying mechanisms of biocontamination in confined environments in relation with human activities to prevent or mitigate the possible associated risks for the crew and the overall mission. This effort should rely on optimized predictive models rather than solely on empirical information. Micro-organisms can be transmitted to humans in a variety of ways, including person-to-person transmission (e.g. sexual transmission and fecal-oral contact), exposure to food-borne, water-borne, vector-borne and air-borne pathogens, and contact with contaminated objects. Of these transmission modes airborne aerosols are believed to be an important one. Therefore it would be important to be able to accurately simulate the generation, dispersion and deposition airborne microbes under conditions encountered during manned space missions. The rapid development of computational fluid dynamics (CFD) and ever increasing computer power during the past few decades has facilitated numerical methods for solving the indoor air movements and contamination transport. CFD is potentially a promising and often the only method for detailed predictions, but the accuracy of the results is affected by several factors, like the simplifications used in setting up the case, uncertainties in the initial and boundary conditions, and shortcomings of the used models. For high quality predictions the possible error sources should be recognised and the results validated.