Unlocking the Potential of Astrodynamics with Kernel Systems

Table of Contents

1. Introduction
2. What is Distributed Computing?
3. Why is Distributed Computing Useful for Astrodynamics?
4. The Challenges of Astrodynamics in Cislunar Space
5. The Kernel System: A Distributed Computing Platform for Astrodynamics
6. Using the Kernel System for Trajectory Design
7. Using the Kernel System for Spacecraft Search and Rescue
8. Using the Kernel System with Meridian
9. Recommendations for Building a Distributed Computing System
10. Conclusion

The Kernel System: A Distributed Computing Platform for Astrodynamics

Astrodynamics is a complex field that requires a significant amount of computational power to perform simulations and calculations. In recent years, distributed computing platforms have become increasingly popular for handling large-Scale data processing and analysis. In this article, we will explore the kernel system, a distributed computing platform designed specifically for astrodynamics.

What is Distributed Computing?

Distributed computing refers to a system in which computational processes are run across multiple computers. This approach is useful when You don't want to be limited by a single computer and you don't want to have to manage the coordination of running lots of things across disparate computers. Companies like Microsoft and Amazon have stood up systems like AWS and Azure and are really sort of the front runners in the industry at doing distributed computing. They offer a whole ecosystem of tools to allow you to do this on their cloud provider, but you don't need to be on a cloud ecosystem to be able to do distributed computing. You can do it on the local side too on an on-premise cluster or something like that.

Why is Distributed Computing Useful for Astrodynamics?

Astrodynamics is a field that requires a significant amount of computational power to perform simulations and calculations. Distributed computing becomes really useful when you need to run a large number of simulations or calculations. For example, let's say you're in an operations center and you have a script or a free-flyer mission plan that needs to be run 50 or 100 times. Rather than running that script 50 or 100 times on your laptop that has limited resources or taking that script and giving it to some co-workers and having us each run batches of that job and then having to coordinate and Collect the results, you can send that script to a distributed computing platform that's going to handle running all of those jobs across a larger set of computers on the back end and then automatically collect and process those results.

The Challenges of Astrodynamics in Cislunar Space

Cislunar space is a challenging environment for astrodynamics. Any minor error in a spacecraft's trajectory can result in a wildly different result. Small initial errors along the path lead to significant deviations downstream. To handle this, analysts need to significantly increase the amount of predictive calculations that they're running. Instead of just running a single or a handful of simulations, they need to run these simulations in the hundreds or the thousands or sometimes even more to account for every single possibility. Not only are there more calculations to be done in cislunar space, but each calculation is far more complex. It takes more computational resources and more time to compute.

The Kernel System: A Distributed Computing Platform for Astrodynamics

The kernel system is a distributed computing platform designed specifically for astrodynamics. It contains all of the cislunar algorithms and handles the heavy lifting of computational processing and data management. The system is built to handle large-scale data as well. The kernel system uses intelligent networking to handle the coordination of resources, processes, environment, and data exchange. In the industry, this is called an orchestration framework, and it's what allows us to scale up processes and resources.

Using the Kernel System for Trajectory Design

One of the major challenges in cislunar space is trajectory design. The kernel system can be used to design complicated trajectories in real-time, enabling a fast iterative workflow. For example, we can use the kernel system to run a massive set of numerical integration to populate the list of candidate transfer trajectories. Using that tailored in orbit as input, our system is able to come up with a list of all the possible ways we could get our spacecraft to that destination. This is a process that could take hours or days on a single laptop, but because we can run it all in Parallel, the process can happen in minutes.

Using the Kernel System for Spacecraft Search and Rescue

Another major challenge in cislunar space is reconnecting with a lost spacecraft. We can use the kernel system to run a simulation of every possible trajectory, essentially turning our platform into an effective spacecraft search engine. This is something that we can't do just using a limited set of hardware and resources or manual analysis.

Using the Kernel System with Meridian

Meridian is another product that we develop at AI Solutions. It's our flight dynamics ground system, which is interfacing with our kernel system to perform a system interstation keeping analysis. Meridian can run large-scale sets of processes and can Interact with the system in the same way that DSD and FreeFlyer can.

Recommendations for Building a Distributed Computing System

Building a distributed computing system can be a complex process. It's important to start small and build up. Familiarity with the installation of flight dynamics systems, containers, and orchestration platforms like Kubernetes is essential. Learning these technologies takes time, but it's worth the investment.

Conclusion

The kernel system is a distributed computing platform designed specifically for astrodynamics. It's a powerful tool that can be used to handle large-scale data processing and analysis. By using the kernel system, analysts can design complicated trajectories in real-time, run simulations of every possible trajectory, and perform system interstation keeping analysis. Building a distributed computing system takes time and effort, but the benefits are well worth it.