Approach to improve the Crew Module of the Virtual Habitat Simulation to depict Quantitative and Qualitative Crew Efficiency

Abstract

The Virtual Habitat project (V-HAB) at the Technische Universitat Munchen (TUM) aims to develop a dynamic simulation environment for life support systems (LSS). The tool is implemented using the object orientated programming approach, leading to a modular structure which enables the combination of different LSS technologies and components. The environmentally sensitive dynamic model of the human physiology provides the relevant metabolic inputs and outputs based on internal, environmental, and operational factors. The V-HAB is intended to support the optimization of early LSS architectures with respect to three optimization criteria: the equivalent system mass (ESM), the stability of the LSS, and the crew time required to maintain that LSS. By taking the efficiency of the crew into account, the actual time required for LSS maintenance can be derived. Crew efficiency is influenced by most LSS subsystems as well as the overall mission operations. In turn, it influences the overall mission success and has an impact on the LSS. Hence, crew efficiency is assumed to be a key factor not only for the LSS, but for the overall mission design optimization. It is therefore intended to extend the V-HAB Crew Module to provide a sensor value depicting the crew efficiency quantitatively, in terms of mechanical power provided by the physiology, and qualitatively, in terms of task success probabilities. To achieve this, additional influences on the crew and its performance have to be taken into account. In this paper, an approach towards such an improvement is presented, consisting of three steps: 1) improve the physiological model and interface with other models to depict additional effects like fatigue, 2) include a model covering psychological effects of the crew members and improve V-HAB to provide additional input parameters, 3) create a database to store information about tasks, schedules, boundary conditions, simulation scenarios, and operational influences like operability issues or failure modes.