AI for New Space Engineering

Supercharge your engineering capabilities.

Here are some engineering workflows you can dramatically accelerate with generative AI.

FYI: I’m not affiliated with any of the tools mentioned. I'm also not claiming they're any good — some of them might be rubbish. Others might be gone by the time you read this.

But I figured real examples would be more useful than vague theory.

Let’s dive in.

1. Design Optimization

   AI tools can suggest design improvements to enhance performance or manufacturability. For example, you get suggestions for structural or thermal tweaks for mass savings or space readiness.

   
   

   Execution Example: Use a tool like nTop to create mass-reducing lattice structures for a CubeSat housing.

   
   

2. Simulation Acceleration

   AI tools can replace or accelerate traditional simulations using learned models. For example, AI can speed up thermal, structural, or orbital analysis for subsystem iterations.

   
   

   Execution Example: Use a tool like Altair AI for surrogate modeling to approximate FEA results 10x faster.

   
   

3. Fault Prediction

   AI tools can identify patterns that could lead to system or component failures. For example, AI can spots risks in onboard electronics or mechanical designs using test data.

   
   

   Execution Example: Use a tool like Avathon to flag risk of component failure based on vibration patterns in telemetry data.

   
   

4. Version Control

   AI tools can help manage code or CAD changes and dependencies. For example, AI can flag risky updates to firmware, GNC code, or design files.

   
   

   Execution Example: Use a tool like GitHub Copilot to flag legacy code use in satellite control firmware.

   
   

5. Automated Documentation

   AI tools can create test reports or logs from structured input. For example, use AI to write subsystem test summaries, integration logs, or traceability matrices.

   
   

   Execution Example: Use a tool like ChatGPT to auto-generate test logs after sensor integration using Jira and Notion.

   
   

6. Component Matching

   AI tools can suggest compatible parts based on design constraints. For example, use AI to find drop-in replacements or alternative suppliers for aerospace components.

   
   

   Execution Example: Use a tool like Octopart AI to get recommendations for in-stock equivalents for a discontinued microcontroller.

   
   

7. Workload Balancing

   AI tools can help allocate tasks based on skills and availability. For example, use AI to balance workloads during peak integration or testing phases.

   
   

   Execution Example: Use a tool like Asana AI to get suggestions for reallocating tasks from overburdened avionics engineers.

   
   

8. Root Cause Analysis

   AI can be used to correlate system issues to likely causes using structured data. For example, an AI tool can find links between hardware faults and environmental test parameters. Execution Example: Use a tool like Uptake AI to link repeated actuator failures to an improper soldering temperature range.

   
   

9. Technical Debt Flagging

   AI can identify risky or outdated code and design elements. For example, an AI tool can highlight legacy firmware, untested logic, or unsupported libraries. Execution Example: Use a tool like CodeClimate AI to flag under-documented legacy code in propulsion system software.

   
   

10. Compliance Checks

    AI can review documentation or designs against known standards. For example, an AI tool flags design issues violating ECSS, MIL-STD, or other spaceflight specs.
    

    Execution Example: Use a tool like Compliance.ai to scan ECSS standards and provide an alert if a design change violates material specs.