System Modeling of Deployable Space Systems
Discover how system simulation with Simcenter Amesim empowers your team to understand and optimize new design concepts at any point in your design cycle.
Commercial use of space is experiencing a renaissance with the development of individual and entire constellations of new satellites that are able to take advantage of increasingly affordable commercial launch services. To fit in the available space in the launch vehicle, these space systems often need to fold compactly and then deploy once in orbit. The design of these systems to unfold and refold reliably and safely can be greatly accelerated through careful modeling and simulation at the system level. By enabling design trade studies even at very early conceptual design stages, this type of system simulation allows designers to minimize weight, cost, complexity, and risk when designing systems that will be out of reach once deployed. Model-based systems engineering (MBSE) enables the modeling of all necessary components and their interactions to predict and optimize performance for a given set of duty cycles.
This live webinar will describe the use of Siemens Simcenter Amesim for MBSE modeling of a deployable space system: a foldable solar array. We will demonstrate the use of Amesim’s power, energy, and activity tools on the array in defining components critical to the array operation and predicting overall system performance during the unfolding process, and the webinar will wrap up with discussion of how MBSE 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