Horizon

WP1:
Synthesis of benchmark coolants and characterisation of their critical rheological and transport properties (e.g., shear/elongational viscosity and equilibrium/dynamic surface tension).
Test rigs for spray visualisation and impact on heated targets set-up and instrumented; this includes appropriate nozzles, sensors, imaging and laser systems configuration and testing.
Assessment of temperature measurement techniques and sensors.
Design, manufacturing and set-up of linear and optical e-motor have been.
Design of spray injection cooling system.

WP2:
Molecural dynamics simulations, complementing the experiments of WP1 for operating points for which measurements will be either time consuming or with low accuracy.
Machine Leaning platform for enabling constitutive equation providing the mathematical relationship between the strain-rate history and the corresponding tensorial stress.
Spray modelling: theoretical constitutive models, accounting for the contribution of polymeric chains in the stress tensor of the momentum conservation equation and embedding the ML term have been implemented in OpenFOAM . This CFD modelling framework simulates from ‘first-principles’ the size distribution of the structures (ligaments and satellite droplets) forming during near-nozzle atomisation/fragmentation processes for the examined cooling fluids.
On-the-fly adaptive grid refinement provides the required resolution near the interface of the atomising liquid. Sufficient number of numerical simulations will allow the formulation of a sub-grid-scale viscoelastic-atomisation model for predicting jet/spray primary break-up, as function of the liquid properties and injector geometry.

WP3:
Numerical simulations utilising complex numerical grids required for considering the geometry of the motor windings.
Analytical functions for surface parametrisation (e.g., surface approximation polynomials such as Bezier or Splines) required by the ML algorithm to facilitate continuous input values have been implemented.
AVL-FIRE is currently extended to incorporate the atomisation SGS model developed in WP2.
Datasets will be used for the training of the ML-tool.

WP4:
Establish the proof-of-concept.
Experimental verification in a motor offering optical access; instrumentation incorporated as part of WP1 will produce the relevant operating data, temperature recordings, efficiency and power output. In this prototype device
Spray visualisation experiments will validate the ML model of WP3.
The ML tool will be further utilised for parametric/optimisation studies aiming to identify the optimum combination of geometries and coolant properties for different types of e-motors.
Verification and synthesis of the optimal polymer mixtures with measurements.
Model predictions and guidelines for the cooling of e-motor designs for future applications, such as heavy-duty machines, marine and aviation applications.