Material Data Identification in an Induction Hardening Test Rig with Physics-Informed Neural Networks.

Physics-Informed neural networks (PINNs) have demonstrated remarkable performance in solving partial differential equations (PDEs) by incorporating the governing PDEs into the network's loss function during optimization. PINNs have been successfully applied to diverse inverse and forward problems. This study investigates the feasibility of using PINNs for material data identification in an induction hardening test rig.

Helmholtz's decomposition for compressible flows and its application to computational aeroacoustics.

The Helmholtz decomposition, a fundamental theorem in vector analysis, separates a given vector field into an irrotational (longitudinal, compressible) and a solenoidal (transverse, vortical) part. The main challenge of this decomposition is the restricted and finite flow domain without vanishing flow velocity at the boundaries. To achieve a unique and -orthogonal decomposition, we enforce the correct boundary conditions and provide its physical interpretation.

Aeroacoustic source term computation based on radial basis functions.

In low Mach number aeroacoustics, the known disparity of length scales makes it possible to apply well-suited simulation models using different meshes for flow and acoustics. The workflow of these hybrid methodologies include performing an unsteady flow simulation, computing the acoustic sources, and simulating the acoustic field. Therefore, hybrid methods seek for robust and flexible procedures, providing a conservative mesh to mesh interpolation of the sources while ensuring high computational efficiency.