Enterprise AI Analysis

Particle trajectories often contain inaccuracies due to tracking errors. To address this, a novel outlier detection method based on a Long Short-Term Memory (LSTM) neural network was developed. LSTMs are uniquely suited for sequential data analysis, capable of modeling long-range temporal dependencies within particle traces, making them highly effective at identifying and filtering defective matches that traditional methods might miss.

The LSTM network features a ragged input layer for variable sequence lengths, an LSTM layer with 64 units and L2 regularization, followed by batch normalization and a dense output layer with sigmoid activation. This architecture allows the network to classify traces as correct or faulty based on subtle patterns in particle displacement and directional changes.

Training utilized 170,000 synthetic traces, equally divided between correct and faulty examples derived from laminar and vortex flow fields, with random fluctuations. The traces were transformed into sequences of relative distances and angles between successive particles, enabling the LSTM to learn robust motion patterns. The model achieved a 93.5% overall accuracy, with a recall of 97.6% and a precision of 90.4%.

Particle Tracking Velocimetry is fundamental to understanding fluid dynamics. Traditional PTV methods, such as multi-frame, cross-correlation, and relaxation techniques, often face limitations in scenarios with high particle displacement, density, or complex rotational and turbulent flows. These methods require significant manual tuning and can become unreliable as flow conditions become more challenging.

The combined SOM and LSTM framework offers a robust, versatile, and efficient solution. The SOM's advanced matching capabilities, refined through automated hyperparameter calibration, ensure accurate particle tracking even in high-ξ flows. The LSTM's ability to identify and filter incorrect traces ensures the integrity of the data, providing a unified and powerful tool for reliable PTV analysis in dynamic and complex environments. This framework reduces the need for manual intervention and adapts to previously unseen flow fields.

This research broadens the applicability of PTV beyond complex plasmas, extending its benefits to fields such as atmospheric physics and industrial fluid dynamics. Future work will explore integrating additional machine learning models for advanced particle trace analysis, including turbulence classification and flow regime identification, unlocking new capabilities for understanding complex fluid behaviors at an enterprise level.