Speakers
Description
Traditional spacecraft thermal modelling processes can be costly and time-consuming due to manual effort required by engineers to build and update thermal models. Recent advancements in AI and machine learning have enabled automation of many thermal engineering tasks including geometry construction, model reduction, mesh creation, etc. In this technical presentation, we will showcase the progress made in automating spacecraft thermal modelling processes through a real-world example: the Copernicus CO2M Ka-Band Downlink Antenna System.
Our presentation will demonstrate how automation has decreased the time and resources required to create thermal models, leading to more optimized thermal control system designs. We will also outline a roadmap towards near-full automation of spacecraft thermal modelling processes. By minimizing manual intervention, we can further optimize the design and performance of spacecraft thermal control systems.
Our work presents a unique application of state-of-the-art AI and machine learning algorithms that specifically target the challenges faced by spacecraft thermal engineers. By utilizing these algorithms, we can automate tedious and repetitive tasks such as geometry construction, model reduction, mesh creation, and more. This enables engineers to focus on more complex and creative problem-solving, leading to more efficient and effective spacecraft designs. Our approach represents a significant departure from traditional spacecraft thermal modelling processes and has the potential to revolutionize the field.
Our presentation will showcase the benefits of using AI and machine learning to automate thermal modelling tasks. Specifically, we will demonstrate the significant reduction in time and resources required to create thermal models, leading to more optimized thermal control system designs. We will also provide a roadmap towards near-full automation of spacecraft thermal modelling processes, highlighting the potential benefits of further minimizing manual intervention.
The work presented in this abstract is in development, with preliminary results achieved through prototypes. Our roadmap towards near-full automation of spacecraft thermal modelling processes is a concept, with ongoing development and testing.
By attending this presentation, participants will learn the latest techniques for automating thermal modelling tasks, as well as best practices for integrating these technologies into their own thermal engineering workflows. The potential benefits of this work include improved efficiency and effectiveness in spacecraft design, as well as a reduction in manual effort required by thermal engineers.
