Towards Streaming Prediction of Oscillatory Flows: A Data Assimilation and Machine Learning Approach

TL;DR

This paper introduces a combined Data Assimilation and Machine Learning framework to predict oscillatory fluid flows from limited data, enabling phase-specific modeling and real-time updates for complex, non-stationary flow configurations.

Contribution

It presents a novel methodology integrating DA and ML for phase-resolved prediction of oscillatory flows, addressing challenges of transient features and real-time adaptation.

Findings

Effective phase-specific flow prediction demonstrated

Method captures transient flow features accurately

Potential for real-time digital twin applications

Abstract

Data-driven methods have demonstrated strong predictive capabilities in fluid mechanics, yet most current applications still focus on simplified configurations, often characterised by statistical stationarity or limited temporal variability. This work proposes a methodology that combines Data Assimilation (DA) and Machine Learning (ML) to predict flow configurations that exhibit cyclic behaviour over time. Starting from limited, sparse high-fidelity measurements and a low-fidelity numerical model, the DA approach performs data fusion to obtain complete and accurate flow state estimations in time. This complete dataset is used to train multiple ML tools, which are applied across different phases of the flow cycle to augment the model's predictions when high-fidelity data might not be available for the DA application. The methodology is applied to the analysis of an oscillating cylinder in a laminar regime using a sliding-window approach, in which separate models are trained for specific flow conditions to ensure each model specialises in flow dynamics representative of a phase of the oscillation period. This phase-resolved learning enables the efficient capture of transient features that would be challenging for a single global model. The results highlight the potential of this method to study complex flow configurations with oscillatory features in which neither the flow nor the cycle is known a priori, in particular by exploiting real-time training and updates, as is commonly done in digital twins, which require continuous model correction and adaptation.