Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation

2026-05-28 · Source: Machine Learning · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Robotics & Autonomous Systems · Depth: Advanced, quick

Summary

A machine learning approach is proposed for image regression from sparse experimental measurements, specifically applied to film cooling analyses in space propulsion system development. This method aims to reduce the need for extensive physical testing by employing a lightweight feed-forward neural network with positional encoding. The network generates images conditioned by input parameters. Validated on both real and synthetic data, the approach demonstrates high image similarity, achieving a Root Mean Square Error (RMSE) below 8 % and a Structural Similarity Index (SSIM) above 93 %. Crucially, it maintains accuracy while enabling a 30 % reduction in necessary measurements. The authors also introduce a knowledge-informed extension to enhance local adaptability of the generated images. This technique promises to streamline the optimization of coolant injector configurations and has potential applications beyond the aerospace sector.

Key takeaway

For aerospace engineers developing propulsion systems, this method offers a pathway to drastically cut down on expensive and time-consuming physical film cooling tests. You can achieve accurate data with a 30 % reduction in measurements by utilizing generative image interpolation. Consider integrating this machine learning approach to accelerate design iterations and optimize coolant injector configurations more efficiently.

Key insights

A machine learning method interpolates images from sparse data, reducing experimental testing in propulsion film cooling.

Principles

• Image regression can reduce physical testing.
• Positional encoding enhances image generation.
• Knowledge-informed extensions improve adaptability.

Method

A lightweight feed-forward neural network with positional encoding generates images conditioned by input parameters, with a knowledge-informed extension for local adaptability.

In practice

• Optimize coolant injector configurations.
• Reduce physical tests by 30%.
• Apply to other engineering image regression.

Topics

• Space Propulsion
• Film Cooling
• Generative Image Interpolation
• Machine Learning
• Neural Networks
• Experimental Design Reduction

Best for: AI Scientist, Machine Learning Engineer, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by Machine Learning.