Heat Flux Optimization for Laser-Heated Surfaces

When laser systems heat a surface, the resulting heat flux distribution often creates localized hot spots that can exceed material temperature limits or cause thermal stress. This is particularly critical in high-power laser applications, where peak heat flux densities can reach dangerous levels.

Our solution addresses this challenge by optimizing the surface geometry through strategic tilting. By carefully selecting surface angles (α and β) and positioning a split point, we can redistribute the heat flux more evenly across the surface. This geometric optimization reduces maximum temperatures when under a given laser, effectively improving thermal management without requiring changes to the laser system itself.

𝐑𝐞𝐬𝐮𝐥𝐭:
The optimization algorithm successfully reduced the maximum heat flux 𝐟𝐫𝐨𝐦 𝟗𝟐.𝟕𝟒 𝐖/𝐦𝐦² 𝐭𝐨 𝟔𝟓.𝟓𝟖 𝐖/𝐦𝐦², 𝐫𝐞𝐩𝐫𝐞𝐬𝐞𝐧𝐭𝐢𝐧𝐠 𝐚 𝟐𝟗.𝟑% 𝐫𝐞𝐝𝐮𝐜𝐭𝐢𝐨𝐧 𝐢𝐧 𝐩𝐞𝐚𝐤 𝐡𝐞𝐚𝐭 𝐟𝐥𝐮𝐱. This significant improvement was achieved using:
- Alpha angle (α): 63.3° for the right surface section
- Beta angle (β): 45.0° for the left surface section

Optimized split position: Strategically placed to balance heat distribution. This temperature reduction translates directly to improved thermal management, reduced material stress, and enhanced system reliability. The dual-angle approach allows for more sophisticated heat distribution control compared to single-angle solutions, particularly effective for asymmetric heat flux patterns common in laser applications.

The Heat Flux Optimizer provides a streamlined workflow through five main steps:
𝐒𝐭𝐞𝐩 𝟏: 𝐃𝐚𝐭𝐚 𝐋𝐨𝐚𝐝𝐢𝐧𝐠
- Import CSV files containing a heat flux data file (X, Y coordinates and flux values)
- Configure the appropriate data separator (semicolon, comma, tab, etc.)
- Preview your data to ensure correct formatting
𝐒𝐭𝐞𝐩 𝟐: 𝐃𝐚𝐭𝐚 𝐀𝐧𝐚𝐥𝐲𝐬𝐢𝐬
- Select the appropriate columns for X, Y coordinates and heat flux values
- Analyze the data range and calculate total power
- Visualize the original heat flux distribution with contour plots
𝐒𝐭𝐞𝐩 𝟑: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐞𝐝 𝐎𝐩𝐭𝐢𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧
- Set boundary conditions for surface angles α and β (varying from 0° to 90°)
- Define geometric constraints of length of tilted surface through maximum Z-span
- Run the optimization algorithm to find optimal angles and split position
- View 3D visualization of the optimized tilted surface
𝐒𝐭𝐞𝐩 𝟒: 𝐌𝐚𝐧𝐮𝐚𝐥 𝐏𝐚𝐫𝐚𝐦𝐞𝐭𝐞𝐫 𝐓𝐞𝐬𝐭𝐢𝐧𝐠
- Fine-tune parameters manually for specific requirements
- Test different α angles, split positions, and Z-span constraints
- Calculate second tilted surface β automatically based on geometric constraints
- Visualization to have a preview of the optimized geometry
𝐒𝐭𝐞𝐩 𝟓: 𝐑𝐞𝐬𝐮𝐥𝐭𝐬 𝐄𝐱𝐩𝐨𝐫𝐭 𝐚𝐧𝐝 𝐀𝐥𝐢𝐠𝐧𝐦𝐞𝐧𝐭
- Export optimized surface data with interpolated high-resolution grids
- Align coordinate systems with the split point as origin
- Generate production-ready CSV files with surface coordinates and scaled heat flux values
- The software handles all complex calculations including area scaling corrections and geometric constraint validation automatically.

𝐒𝐨𝐟𝐭𝐰𝐚𝐫𝐞 𝐛𝐚𝐜𝐤𝐠𝐫𝐨𝐮𝐧𝐝:
This solution leverages proven scientific computing libraries including NumPy for numerical operations, SciPy for optimization algorithms, Matplotlib for visualization, and Pandas for data management. The user interface is built with Tkinter for cross-platform compatibility.

The software architecture is designed for easy adaptation to various thermal management scenarios. The modular optimization engine can be readily configured for different:
- Geometric constraints and boundary conditions
- Heat source patterns and distributions
- Material properties and temperature limits
- Manufacturing constraints and tolerances

The flexible CSV input/output format ensures compatibility with existing simulation workflows and CAD systems, making integration into current design processes straightforward and efficient.