Platelets are strongly involved in endothelial repair, playing a key role in hemostasis and thrombosis. Their rapid activation is fundamental to ensure correct hemostatic plug formation and regulate thrombus growth. Any disorder in platelet function could result in the onset of pathological cardiovascular events. Indeed, platelet function tests (PFT) are strongly relied upon for early diagnosis of cardiovascular diseases [1], the global leading cause of mortality, representing 32\% of deaths in 2019. Despite their relevance, current PFTs still lack predictive power when it comes to quantifying platelet deposition levels and distinguishing physiological aggregation rates from pathological ones [2]. In this work we present a dynamical numerical model to predict platelet deposition in a flow chamber. The present approach differs from [1] by the fact that here, we consider an open system, with a continuous flow of fresh blood. Early results indicate a different behavior than in a closed system. The determination of adhesion rate, aggregation rate, and deposition patterns are fundamental to identify pathological platelet function. By comparing the properties of in-silico aggregates (their size and number distribution, as well as their shape) with aggregates observed in-vitro of healthy or pathological blood, we expect to calibrate the bio-physical platelet properties and determine which ones may be impaired. The model is defined as a set of stochastic processes or equations that simulate the transport of platelets in the flow chamber, platelet-platelet aggregation and platelet-substrate adhesion dynamics. We apply this model to a micro channel geometries so as to provide in-silico testing of a new generation of aggregometers suited for platelet function tests.