Generative prediction of laser-induced rocket ignition with dynamic latent space representations

TL;DR

This work proposes a data-driven surrogate modeling approach that combines convolutional autoencoders (cAEs) with neural ordinary differential equations (neural ODEs) and marks a significant step toward real-time digital twins for laser-ignited rocket combustors.

Abstract

Accurate and predictive scale-resolving simulations of laser-ignited rocket engines are highly time-consuming because the problem includes turbulent fuel-oxidizer mixing dynamics, laser-induced energy deposition, and high-speed flame growth. This is conflated with the large design space primarily corresponding to the laser operating conditions and target location. To enable rapid exploration and uncertainty quantification, we propose a data-driven surrogate modeling approach that combines convolutional autoencoders (cAEs) with neural ordinary differential equations (neural ODEs). The present target application of an ML-based surrogate model to leading-edge multi-physics turbulence simulation is part of a paradigm shift in the deployment of surrogate models towards increasing real-world complexity. Sequentially, the cAE spatially compresses high-dimensional flow fields into a low-dimensional latent space, wherein the system's temporal dynamics are learned via neural ODEs. Once trained, the model generates fast spatiotemporal predictions from initial conditions and specified operating inputs. By learning a surrogate to replace the entirety of the time-evolving simulation, the cost of predicting an ignition trial is reduced by several orders of magnitude, allowing efficient exploration of the input parameter space. Further, as the current framework yields a spatiotemporal field prediction, appraisal of the model output's physical grounding is more tractable. This approach marks a significant step toward real-time digital twins for laser-ignited rocket combustors and represents surrogate modeling in a complex system context.

Figures (13)

• Figure 1: Workflow describing the construction and deployment of the neural ODE and decoder as a generative model.
• Figure 2: Ignition probability density functions (PDFs) marginalized over the input parameters $\xi_0$ through $\xi_{14}$, comparing ignition success (red) and ignition failure (blue) through kernel density estimates; green check marks denote the five parameters with the largest KL divergence between igniting and non-igniting cases. Full description of parameters in Table \ref{['tab:uncertainties']} in Appendix \ref{['app:uncertainty']}.
• Figure 3: Quantity of interest: spatiotemporal fields, (a) rendered simulation output of the successfully ignited combustor, (b) illustration of the integrated quantity acquisition, (1) light beam generation, (2) ray evolution according to the Eikonal equation, (3) Snell's law and occlusion, and (4) irradiance sampling, (c) successful and unsuccessful time-series for the computational infrared, and (d) computational Schlieren.
• Figure 4: Architecture schematic. (a) Encoder: a sequence of convolutional, with residual connections and pooling blocks maps the single–channel input to progressively smaller but deeper feature maps, which are then flattened and passed through fully-connected layers to obtain an 8-dimensional latent state $\mathbf{V}$. (b) NODE: the latent state is augmented with the control inputs ($\xi,t$), with two fully connected layers and the latent vector $\mathbf{V}$ is forecast for the duration of the ignition trial. (c) Decoder: the latent state $\mathbf{V}$ is lifted back to high dimension by fully-connected layers and a sequence of convolutional and upsampling blocks reconstructing the output field.
• Figure 5: (a) Evolution of the validation loss against epoch during the training of the AE, for choices of latent-space dimension hyperparameter $N_\ell = 1, 4, 8$ and $16$. (b) Representative loss during training of the NODE for the 200,000 iterations, for choice of hidden layer width hyperparameter $w = 400$. The horizontal black line indicates the loss criterion for a curriculum learning increment, and markers at spike locations denote curriculum updates.