用少量实验数据生成高质量冷却图像，减少航天推进测试次数。
Reducing Experimental Testing in Space Propulsion Film Cooling Analyses by Pixelwise Generative Image Interpolation
- 用位置编码的轻量神经网络，从稀疏数据生成冷却图像。
- 图像误差低于8%，相似度超93%，测试量减少30%仍保精度。
- 可适配局部细节，适合需高效优化的工程设计场景。
我们提出一种基于机器学习的图像回归方法，从稀疏实验测量中生成图像。该方法应用于推进系统中的气膜冷却研究，旨在减少对大量物理测试的依赖。采用带有位置编码的轻量级前馈神经网络，根据输入参数生成条件图像。在真实与合成数据上验证，生成图像的均方根误差低于8%，结构相似性超过93%，同时实现30%的测量点减少而保持高精度。我们还提出了知识引导的扩展方法，增强生成图像的局部适应能力。该方法显著降低测试需求，同时保留高质量数据，支持冷却喷嘴构型的高效优化，应用范围不限于航空航天领域。
原文摘要 · Abstract (English)
We propose a machine learning approach for image regression from sparse experimental measurements. We show the application of the proposed method on film cooling studies in propulsion system development, aiming to reduce the need for extensive physical testing. Our method employs a lightweight feed-forward neural network with positional encoding to generate images conditioned by input parameters. Validated on real and synthetic data, it achieves high image similarity (RMSE < 8 %, SSIM > 93 %) while maintaining accuracy with a 30 \% reduction of measurements. We further propose a knowledge-informed extension for local adaptability of the generated images. This approach significantly reduces required tests while preserving high-quality data, enabling efficient optimization of coolant injector configurations with applications beyond aerospace.
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