System Modeling of Vented Space Systems

Discover how system simulation with Simcenter Amesim empowers your team to understand and optimize new design concepts for honeycomb structures and cold gas thrusters in space. View the recording, plus other tips, tricks, and free resources: https://www.ata-e.com/resources/syste... Commercial use of space is experiencing a renaissance, with individual satellites and entire constellations of new satellites taking advantage of increasingly affordable commercial launch services. Many of these space systems are made of strong, light components that need to be vented during launch and/or use cold gas thrusters for positioning once in orbit. The design and analysis of these vented systems and these thrusters can be greatly accelerated through careful modeling and simulation at the system level. System simulation, by enabling design trade studies even at very early conceptual design stages, allows designers to minimize weight, cost, complexity, and risk when designing systems that will be out of reach once deployed. Siemens technology for this type of model-based systems engineering (MBSE) includes Amesim, which enables the physics-based system simulation of all necessary components and their interactions to predict and optimize performance for a given set of duty cycles. This webinar will describe the use of Siemens Simcenter Amesim for MBSE modeling of a vented volume for space flight and of a cold gas thruster. The power, energy, and activity tools in Amesim will be demonstrated on the vented system analysis to show critical temperature, pressure, and flow parameters of the system during operation and predict overall system performance during launch and while on orbit. The webinar will wrap up by discussing how this modeling can be applied to other aspects of satellite, thruster, rocket engine, and launch vehicle design optimization. In this webinar, we will discuss: Seeing the big picture: What is system modeling and why does it matter? Understanding where system modeling belongs in the development cycle Employing multiple levels of fidelity simultaneously Integrating subsystem models to handle even very large and complex systems Evaluating performance over a defined duty cycle or set of duty cycles Extending 1D models in Amesim with reduced-order modeling or co-simulation