Andrea Ferrero

Cryogenic chilldown and boil-off directly impact the efficiency, safety and operational margins of any system that handles cryogenic fluids, including propulsion, storage and transfer applications. These processes arise from the interaction between wall heat transfer, boiling regimes, and vapor-space dynamics, and are strongly influenced by geometry, initial thermal conditions, and operating procedures. As a result, well-resolved experimental data remain essential for the development and validation of transient two-phase models. This work presents an experimental investigation of liquid nitrogen chilldown and boil-off in a small-scale test facility composed of two cryogenic vessels, a feed tank and a return tank, connected by a horizontal transfer line. The setup is instrumented to measure temperature evolution, pressure and liquid levels, under controlled valve operations. Wall heat fluxes and mass flow rates are derived from these measurements through energy and mass balances. The influence of initial thermal conditions is examined by comparing chilldown into a pre-cooled transfer line with that of an ambient-temperature configuration under unpressurised flow. To assess the role of the driving mechanism, both unpressurised and pressurised flow transfer conditions are then considered for an ambient temperature transfer line and return tank. Boil-off behaviour in the feed tank is characterized at different liquid fill levels and further investigated through controlled heating using an immersed electrical heater. The resulting dataset offers experimental observations useful for studying chilldown transients, two-phase flow, and tank boil-off.

This work describes the experimental and numerical validation of a Multi-Fidelity (MF) model for the efficient simulation of Heat Exchangers (HX) based on Triply Periodic Minimal Surface (TPMS) lattices. The MF model is based on the porous media approximation developed in our previous work, where the porous lattice is modelled as a homogeneous medium characterised by variable permeability, Forchheimer, and heat transfer coefficients. Lattice coefficients are assumed to be dependent on the local lattice geometry and flow conditions, and are computed by a Machine Learning (ML) model. The ML model is trained on a database of LF simulations performed on individual lattice cells and a handful of High-Fidelity (HF) runs data. The resulting framework allows to run simulations at a fraction of the computational cost of a full-resolution CFD simulation. The validation of the proposed model is performed against both HF Conjugate Heat Transfer (CHT) simulations and experimental data. Experiments are performed on a series of Additively Manufactured (AM) HXs, tested under controlled thermal Boundary Conditions (BCs), at different inlet mass flow rates. Comparison with experimental data shows an excellent agreement for temperature distribution. A qualitative comparison with experimental measurements indicates that also the pressure drop is predicted correctly by the MF model. In particular, the MF model is able to capture the order of magnitude of the pressure drop and the overall trend with respect to the inlet mass flow rate. These results confirm the accuracy of the MF surrogate model and demonstrate that the proposed approach provides a reliable and computationally efficient tool for the simulation and optimisation of TPMS-based lattice HXs.

This study presents a reduced-order modeling framework for the shape optimization of a centrifugal pump. A database of CFD solutions is generated using Latin Hypercube Sampling over five design parameters to construct a reduced-order model based on proper orthogonal decomposition with radial basis function interpolation. The model predicts the flow field at the impeller–diffuser interface and pump outlet, enabling the estimation of impeller torque and total pressure rise. The active subspaces method is applied to reduce the dimensionality of the input space from five to four modified parameters. The sensitivity of the ROM is assessed with respect to further dimensionality reductions in the parameter space, POD mode truncation, and adaptive sampling. The model is then used to perform pump shape optimization via a quasi-Newton method, identifying the combination of the parameters that minimizes the impeller torque while satisfying a constraint on the head. The optimal result is validated through CFD analysis and compared against the Pareto front generated by a genetic algorithm. The work highlights the potential of model-order reduction techniques in centrifugal pump optimization.

This work explores solid rocket motor grain design, leveraging additive manufacturing techniques based on slurry deposition and UV-curing. The objective is to develop an automated design procedure to identify optimal solutions that meet mission requirements, exploiting new geometries and ballistic configurations previously unattainable with the classical mix–cast–cure manufacturing process. The design procedure is based on a stochastic optimization approach coupled with surrogate modeling of the pressure–time response, considering variable geometrical and ballistic parameters. Several surrogate models were tested after the creation of suitable databases through the computation of pressure evolution for different configurations. The most appropriate surrogate model was selected and applied within optimization routines to evaluate individual designs. The optimizer identifies the most suitable configuration to obtain the desired pressure–time response and to meet motor requirements. Different design approaches have been tested to evaluate the ballistic distribution’s influence on performance and how it can be leveraged to meet requirements, even with significant modifications in grain geometry. The results show that the proposed procedure is able to effectively achieve the expressed requirements, successfully handling the novel design environment. Furthermore, they highlight the strong influence of the ballistic distribution on performance and show how it can be successfully exploited to guide grain design.