Use of Python in LS-DYNA Post-Processing

🔹 Introduction

Hello friends! 👋
In this post, I’ll show you how to use Python as a powerful tool in LS-DYNA post-processing for Computer-Aided Engineering (CAE).
In today’s world of data-driven design, we all know that —

More data = More clarity = Better decisions.

With Python, we can automate post-processing, extract meaningful patterns, and make decisions faster during optimization and product development.

🔹 LS-DYNA Python Automation

When we work in crash and safety analysis, LS-DYNA produces output files like matsum, which contain critical data such as energy absorbed by components during impact.

Typically, we open this file in HyperView or META Post to visualize internal energy vs. time graphs.
But what if we want to take it one step further — automate the process, extract insights instantly, and even build custom tools for engineers?

That’s exactly what we’ll start learning here.

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🔹 Step 1: Reading a Matsum File with Python

Here’s a simple starter code that reads the matsum file and prints all the Part IDs (PIDs) it contains:

📘 Note:

1. Open your matsum file in Notepad.

2. Save it as a .txt file.

3. Paste it in your Python IDE (like PyCharm).

4. Run this script to list all the Part IDs contained in the file.

This may look simple now — but it’s your first automation step toward building a CAE data pipeline.

🔹 Next Steps

In upcoming posts, I’ll show you how to:
✅ Use Python libraries like NumPy, Matplotlib, and Pandas for data exploration.
✅ Plot energy vs. time graphs for each part directly from the Matsum file.
✅ Compare two Matsum files in one click — generating all graphs automatically.
✅ Build your own Tkinter-based application for LS-DYNA post-processing.

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🔹 Why This Matters

Mechanical engineers today need to think beyond modeling — toward data science, automation, and AI-driven engineering.
Python bridges that gap beautifully.

By learning to handle LS-DYNA outputs with Python, you can:

• Save hours of repetitive work

• Generate insights faster

• Build your own engineering tools

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🔹 Conclusion

We explored how Python can be used in LS-DYNA post-processing to enhance understanding, efficiency, and automation.
This is just the beginning — data science can truly empower the mechanical community to reach new heights of innovation.

“Automation is not just coding — it’s the art of freeing your mind for innovation.” 💡

Stay tuned for upcoming tutorials on plotting Matsum graphs, automation scripts, and AI integration in CAE.