# TemporalFlowViz: Parameter-Aware Visual Analytics for Interpreting Scramjet Combustion Evolution

arXiv: https://arxiv.org/abs/2509.04834 · HTML: https://mlsanity.com/paper/2509.04834/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 16 of its 45 references that are in the map.
- First posted on arXiv 5 Sep 2025.

## Builds on (most cited first, 16 of 16)

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- [Optimized Cartesian K-Means](https://mlsanity.com/paper/1405.4054/) (2014)
- [ParamsDrag: Interactive Parameter Space Exploration via Image-Space Dragging](https://mlsanity.com/paper/2407.14100/) (2024)
- [AuraGenome: An LLM-Powered Framework for On-the-Fly Reusable and Scalable Circular Genome Visualizations](https://mlsanity.com/paper/2507.02877/) (2025)
- [Fast Approximate K-Means via Cluster Closures](https://mlsanity.com/paper/1312.3061/) (2013)
- [SalienTime: User-driven Selection of Salient Time Steps for Large-Scale Geospatial Data Visualization](https://mlsanity.com/paper/2403.03449/) (2024)

## Abstract (arXiv)

Understanding the complex combustion dynamics within scramjet engines is critical for advancing high-speed propulsion technologies. However, the large scale and high dimensionality of simulation-generated temporal flow field data present significant challenges for visual interpretation, feature differentiation, and cross-case comparison. In this paper, we present TemporalFlowViz, a parameter-aware visual analytics workflow and system designed to support expert-driven clustering, visualization, and interpretation of temporal flow fields from scramjet combustion simulations. Our approach leverages hundreds of simulated combustion cases with varying initial conditions, each producing time-sequenced flow field images. We use pretrained Vision Transformers to extract high-dimensional embeddings from these frames, apply dimensionality reduction and density-based clustering to uncover latent combustion modes, and construct temporal trajectories in the embedding space to track the evolution of each simulation over time. To bridge the gap between latent representations and expert reasoning, domain specialists annotate representative cluster centroids with descriptive labels. These annotations are used as contextual prompts for a vision-language model, which generates natural-language summaries for individual frames and full simulation cases. The system also supports parameter-based filtering, similarity-based case retrieval, and coordinated multi-view exploration to facilitate in-depth analysis. We demonstrate the effectiveness of TemporalFlowViz through two expert-informed case studies and expert feedback, showing TemporalFlowViz enhances hypothesis generation, supports interpretable pattern discovery, and enhances knowledge discovery in large-scale scramjet combustion analysis.