⚡ electrical engineering

A parametric modeling approach tailored for multiperforated propellant extrusion and its comparative numerical simulation

This paper presents a novel Python-driven parametric modeling approach using Gmsh and OpenCASCADE to automate the CAD and CFD preprocessing of complex multiperforated propellant extrusion dies, demonstrating through cross-validation with Fluent and OpenFOAM that the method achieves high accuracy and efficiency while eliminating human-induced deviations compared to conventional manual modeling.

Original authors: Dong Chen, Youcai Xiao, Ziqiang Ning, Pengyun Liu

Published 2026-07-01

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CC BY 4.0

Original authors: Dong Chen, Youcai Xiao, Ziqiang Ning, Pengyun Liu

Original paper licensed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). ✨ This is an AI-generated explanation of the paper below. It is not written or endorsed by the authors. For technical accuracy, refer to the original paper. Read full disclaimer

Imagine you are a master chef trying to bake a very specific, complex cake. This isn't just any cake; it's a "gun propellant" cake, which is a highly energetic material used in weapons. To make it, you have to squeeze a thick, gooey dough through a special metal mold (a die) that has many tiny holes, like a giant, high-tech cookie cutter.

The problem is that designing this "cookie cutter" is incredibly difficult. If the holes aren't perfectly shaped or positioned, the cake comes out wrong, or the machine breaks. Traditionally, engineers have had to design these molds by hand, using trial and error. It's like trying to sculpt a statue with a hammer and chisel while wearing thick gloves—you can't see exactly what you're doing, it takes forever, and if you want to change the shape, you have to start almost from scratch.

The "Magic Recipe" (The New Method)
This paper introduces a new way to design these molds using a "magic recipe" written in computer code (Python). Instead of an engineer manually drawing every line and curve, they write a set of instructions that tells the computer: "Here is the size of the cake, here is how many holes you need, and here is how thick the walls should be."

The computer then uses a free, open-source tool called Gmsh (think of it as a super-smart 3D printer for digital models) to instantly build the mold, slice it up into tiny puzzle pieces (mesh), and prepare it for a simulation.

The "Digital Twin" Analogy
To test if this new method works, the researchers created a "digital twin" of a seven-hole mold. They ran two different experiments:

The Old Way: An engineer manually built the mold in standard software, slice by slice.
The New Way: The computer code built the mold automatically based on the parameters.

They then poured "digital dough" through both molds using two different simulation engines (Fluent and OpenFOAM). Think of these engines as two different weather forecasters trying to predict how the wind (the dough) will flow.

The Results: Speed and Accuracy
The paper found three main things:

It's Faster: The automated method built the model and prepared the simulation much quicker than the manual method. It's like using a 3D printer instead of carving wood by hand.
It's Just as Accurate: Even though the computer built the model differently (using a different "logic" for how the puzzle pieces fit together), the results were almost identical to the manual method. The "digital dough" flowed exactly the same way in both versions.
It's Robust: When they tested the same computer-generated model with two different simulation engines (Fluent and OpenFOAM), the results were still very close (within 10% of each other). This proves the method is reliable, no matter which "weather forecaster" you ask.

The Bottom Line
This paper doesn't claim to have invented a new type of gun or a new chemical. Instead, it offers a better way to design the tools used to make the propellant. By automating the design process, engineers can quickly test different shapes and sizes without the risk of human error or the long wait times of manual modeling. It turns a slow, error-prone craft into a fast, repeatable, and precise digital process.

Technical Summary: A Parametric Modeling Approach for Multiperforated Propellant Extrusion

Problem Statement
The extrusion molding of multi-perforated gun propellants is a critical process in energetic material manufacturing, where the design of the extrusion die directly dictates the quality of the propellant grains and production efficiency. Current design methodologies rely heavily on empirical trial-and-error, leading to extended development cycles, high costs, and safety risks. While Computational Fluid Dynamics (CFD) has emerged as a tool to optimize these processes, the preprocessing workflow remains a bottleneck. Traditional approaches involve manual CAD modeling, cross-platform format conversion, and manual mesh generation. These steps are labor-intensive, prone to human error, difficult to reproduce, and inefficient for iterative optimization, particularly when dealing with complex topologies like multi-hole propellant dies where geometric parameters frequently change.

Methodology
To address these limitations, the authors propose a fully automated, Python-driven parametric modeling and automated CFD preprocessing framework. The methodology is built upon the open-source software Gmsh and its underlying OpenCASCADE (OCC) geometric kernel.

Hybrid Modeling Strategy: The approach integrates Constructive Solid Geometry (CSG) for macroscopic topological generation with Boundary Representation (B-Rep) for precise local feature manipulation. Analytical surface definitions are used to mathematically define geometric primitives (cylinders, cones, sectors) based on global parameters.
Automation Workflow: A Python script controls the entire process, including:
1. Parametric Geometry Construction: Generating complex die geometries (e.g., single-hole, seven-hole, nineteen-hole) by defining key parameters such as mandrel radius, hole count, contraction angles, and die-forming section lengths.
2. Boolean Operations: Utilizing CSG trees to perform Boolean unions and differences to create the fluid domain by subtracting mandrels and needle holders from the sleeve cavity.
3. Automated Boundary Identification: A topological query mechanism automatically identifies and labels boundary surfaces (inlet, outlet, and walls) based on spatial coordinates, eliminating manual selection.
4. Adaptive Meshing: A distance-field-based adaptive mesh generation algorithm is employed. It defines a spatially varying mesh size function $h(x)$ that refines elements near the mold pins (where flow gradients are high) and coarsens them in the far field, ensuring computational efficiency without sacrificing accuracy near critical features.
Validation Strategy: The study validates the method using a seven-hole propellant die. It compares:
- Modeling Methods: Parametric modeling vs. conventional manual modeling.
- Solvers: Results from the Fluent and OpenFOAM solvers using identical parametric meshes.
- Convergence: Grid Convergence Index (GCI) analysis using three grid densities (0.067 mm, 0.1 mm, 0.15 mm).

Key Contributions

End-to-End Automation: The paper establishes a closed-loop workflow that automates geometric reconstruction, mesh generation, and boundary condition assignment, removing the need for manual intervention in the preprocessing stage.
Parametric Template Library: A specialized library for propellant extrusion dies is developed, allowing for rapid adaptation to different configurations (e.g., varying hole counts and mandrel arrangements) through parameter updates rather than rebuilding models from scratch.
Robustness Verification: The study provides a dual-validation framework demonstrating that the automated method produces results consistent with manual modeling and is robust across different solver platforms (Fluent and OpenFOAM).

Results

Grid Independence: GCI analysis confirmed that the numerical solutions achieved monotonic convergence. For the parametric model, a mesh size of 0.15 mm was found to be sufficient, with results stabilizing as the grid was refined further.
Modeling Consistency: When comparing parametric and manual modeling using the Fluent solver, the results showed high consistency in pressure and velocity distributions. Despite generating fewer elements than the manual model under the same size settings, the parametric approach achieved equivalent solution accuracy, effectively eliminating systematic human-induced deviations.
Solver Consistency: Cross-validation between Fluent and OpenFOAM using the same parametric mesh (even at a relatively coarse 0.15 mm resolution) yielded relative errors in pressure and velocity below 10% at characteristic cross-sections. Specifically, errors were 5.9% for pressure and 6.4% for velocity within the flow field, and 6.3–6.5% for outlet velocity components.
Efficiency: The automated method significantly reduced the time required for model iteration and mesh generation compared to traditional workflows.

Significance
The paper claims that this automated, parametric approach provides a reliable pathway for the precision parametric design and rapid iterative optimization of complex propellant extrusion dies. By eliminating the reproducibility issues and inefficiencies of manual preprocessing, the method supports the accelerated development of next-generation propellants. The high consistency observed between different modeling strategies and solver platforms confirms the method's accuracy and stability, offering a scalable technical foundation for the digital design of advanced energetic material manufacturing processes.

Technical Summary: A Parametric Modeling Approach for Multiperforated Propellant Extrusion

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