Thanks to the ever-growing resources available at supercomputing centers, High 
Performance Computing (HPC) analyses of flow configurations including several 
complex concurring aspects are becoming an established reality. Thus, the 
development of reliable numerical strategies capable of providing an accurate 
representation of multi-physics problems is a timely central challenge in 
Computational Fluid Dynamics (CFD). The accurate prediction of numerous flow 
features of unstationary flows, such as aerodynamic forces, is driven by the 
precise representation of localized near-wall dynamics. In the last decades, 
several numerical strategies have been proposed to handle these two problematic 
aspects. Among these, the Immersed Boundary Method (IBM) has emerged as one of 
the most popular approaches. Among the state-of-the-art proposals reported in 
the literature, the IBM developed by the team shows favorable features of 
accuracy and efficiency. The main open challenge with IBM is the representation 
of wall turbulence, which is a governing aspect in most engineering cases. The 
wall resolution required increases with the Reynolds number as finer coherent 
structures are observed, leading to a rapid rise in computational resources. 
Therefore, a strategy based on online Data Assimilation combined with data 
stream learning is proposed to train a new generation of IBM methods, with the 
aim to obtain high precision with limited computational resources. More 
precisely, the data-driven strategies will aim to identify and optimize new IBM 
formulations capable to represent complex features of the flow, such as 
separation and re-attachment of the boundary layers.
The research group has recently developed a software able to perform online 
sequential data assimilation exploiting a reduced-order technique referred to as 
multigrid Ensemble Kalman Filter (MGEnKF). Following some works recently 
proposed in the literature, data stream learning will be integrated within the 
code to derive new paradigms for data-augmented IBM.
Objectives: the work of the candidate will aim for the development of the 
following tasks:
1. Implementation of data streaming machine learning techniques within the C++ 
software developed by the research group, aiming to reconstruct an augmented IBM 
formalism. The AI learning procedure will be fed by online data generated by the 
MGEnKF.
2. The performance of the code will be assessed via the analysis of 
progressively more complex scale-resolved turbulent flows. The test cases 
investigated include the turbulent channel flow and, in case of success, complex 
test cases such as a radial pump will be investigated. For every test case 
considered, high fidelity DNS / experimental data are already available from 
previous analyses of the research group.
Host Institution: Arts et Métiers - LMFL. The candidate will be based in Lille 
(France).
Scientific Leader: M. Meldi (marcello.meldi@ensam.eu)
Candidate’s profile: the candidate must have strong competences in machine 
learning techniques, ideally for applications with streaming data. A PhD degree 
in this area of expertise is required. In addition, skills in the numerical 
simulation of turbulent flows and / or IBM tools and / or OpenFOAM are welcome, 
but they are not mandatory.