用基于实验数据的时空扩散模型生成湍流火焰的合成演化
Generating synthetic evolution of turbulent flames with an experimental data-based spatiotemporal diffusion model
浏览论文内容
中文总结 AI 辅助
该研究开发条件扩散模型，结合 x 预测流匹配框架及时空变压器，生成湍流火焰合成轨迹，能保留关键特征与统计一致性，还进行过渡合成外推任务，为数据稀疏环境下的数据探索提供新途径。
中文摘要 AI 辅助
本研究开发了一种条件扩散模型（一类生成式机器学习模型），以生成基于实验数据的湍流火焰合成轨迹。生成的实验数据对应于旋流燃烧器配置中附着和分离火焰状态的同步场测量，即 OH 平面激光诱导荧光（OH-PLIF）场和多分量粒子图像测速（PIV）场。这是通过结合基于像素的时空变压器的 x 预测流匹配框架完成的，该框架能够在推理时生成包含合成火焰演化的整个时空板，以火焰状态为条件。使用该框架，发现合成火焰在空间和时间上保留了关键火焰特征和统计一致性，特别是在大尺度上——在高时间频率和小空间长度尺度上的偏差取决于生成的时空板的时间跨度。还进行了过渡合成的外推任务，其中条件扩散模型用于合成训练期间模型未见过的时空连贯火焰过渡（火焰升起和重新附着）。这是通过使用一种基于附着和分离去噪速度的时变线性组合的去噪过渡速度模型来实现的，从而产生一种方法，该方法（a）允许控制生成的过渡方向和时间尺度，并且（b）在生成的过渡过程中保留样本间的变异性。总体而言，本研究为利用基于实验数据的生成模型作为数据稀疏环境中数据探索的新手段提供了一条有前景的途径，补充了实验和基于计算流体动力学的方法。
英文摘要
In this study, a conditional diffusion model -- a class of generative machine learning models -- is developed to generate synthetic, experimental data-based trajectories of turbulent flames. Generated experimental data corresponds to simultaneous field measurements, namely OH planar laser-induced fluorescence (OH-PLIF) fields and multi-component particle image velocimetry (PIV) fields, for attached and detached flame states in a swirl combustor configuration. This is done using an x-prediction flow matching framework combined with a pixel-based spatiotemporal transformer, which is capable of generating entire spatiotemporal slabs containing synthetic flame evolution at inference time, conditioned on the flame regime. Using this framework, synthetic flames were found to preserve key flame features and statistical consistency across space and time, particularly at the large scales -- deviations at high temporal frequencies and small spatial length scales were found to depend on the time-span of the generated space-time slabs. An extrapolation task of transition synthesis is also conducted, in which the conditional diffusion model is used to synthesize spatiotemporally coherent flame transitions (flame liftoff and reattachment) unseen by the model during training. This was accomplished using a model for the denoising transition velocity that relies on time-varying linear combinations of attached and detached denoising velocities, leading to an approach that (a) allows for control of the generated transition directions and timescales, and (b) retains sample-to-sample variability in the generated transitions in the process. Overall, this study provides a promising pathway for the utilization of experimental data-based generative models as a new means of data exploration in data-sparse environments, complementing both experiments and computational fluid dynamics-based approaches.
