A patient-specific airflow simulation was developed to help address the pressing need for an expansion of the ventilator capacity in response to the COVID-19 pandemic. The computational model provides guidance regarding how to split a ventilator between two or more patients with differing respiratory physiologies. To address the need for fast deployment and identification of optimal patient-specific tuning, there was a need to simulate hundreds of millions of different clinically relevant parameter combinations in a short time. This task, driven by the dire circumstances, presented unique computational and research challenges. We present here the guiding principles and lessons learned as to how a large-scale and robust cloud instance was designed and deployed within 24 hours and 800 000 compute hours were utilized in a 72-hour period. We discuss the design choices to enable a quick turnaround of the model, execute the simulation, and create an intuitive and interactive interface.

In this paper, we present a formal definition of the integration of the requirements modeling language Behavior Trees (BTs). We first provide the semantic integration of two interrelated BTs using an extended version of Communicating Sequential Processes. We then use a Semantic Network Model to capture a set of interrelated BTs, and develop algorithm to integrate them all into one BT. This formalisation facilitates developing (semi-)automated tools for modeling the requirements of large-scale software intensive systems.

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