Adaptive CFD schemes for aerospace propulsion

The flow fields which can be observed inside several components of aerospace propulsion systems are characterised by the presence of very localised phenomena (boundary layers, shock waves,...) which can deeply influence the performances of the system. In order to accurately evaluate these effects by means of Computational Fluid Dynamics (CFD) simulations, it is necessary to locally refine the computational mesh. In this way the degrees of freedom related to the discretisation are focused in the most interesting regions and the computational cost of the simulation remains acceptable. In the present work, a discontinuous Galerkin (DG) discretisation is used to numerically solve the equations which describe the flow field. The local nature of the DG reconstruction makes it possible to efficiently exploit several adaptive schemes in which the size of the elements (h-adaptivity) and the order of reconstruction (p-adaptivity) are locally changed. After a review of the main adaptation criteria, some examples related to compressible flows in turbomachinery are presented. An hybrid hp-adaptive algorithm is also proposed and compared with a standard h-adaptive scheme in terms of computational efficiency

RANS closure approximation by artificial neural networks

Turbulence modelling remains a challenge for the simulation of turbomachinery flows. Reynolds Averaged Navier-Stokes (RANS) equations will still be used for high-Reynolds number flows for several years and so there is interest in improving their prediction capability. Machine learning techniques offer several strategies which could be exploited for this purpose. In this work, an approach to improve the Spalart-Allmaras model is investigated. In particular, the model is used to predict the flow around the T106c low pressure gas turbine cascade. As a first step, an Artificial Neural Network (ANN) is trained on the data generated by the original model. Then, an optimisation procedure is applied in order to find the weights of the network which minimise the error between the predicted results and the available experimental data. The new model is tested at different Reynolds numbers on the T106c cascade and on a wind turbine airfoil in post-stall conditions. Significant improvements are observed in the condition chosen for the optimisation. Future work will be devoted to the generalisation of the approach by including multiple working conditions optimisations and adding new physical variables as inputs of the ANN

Diagnostic and therapeutic challenges in the management of intermediate and frail elderly multiple myeloma patients

Multiple myeloma (MM) mostly affects elderly patients, which represent a highly heterogeneous population. Indeed, comorbidities, frailty status and functional reserve may vary considerably among patients with similar chronological age. For this reason, the choice of treatment goals and intensity is particularly challenging in elderly patients, and it requires a multidimensional evaluation of the patients and the disease. In recent years, different tools to detect patient frailty have been developed, and the International Myeloma Working Group frailty score currently represents the gold standard. It identifies intermediate-fit and frail patients requiring gentler treatment approaches compared to fit patients, aiming to preserve quality of life and prevent toxicities. This subset of patients is underrepresented in clinical trials, and studies exploring frailty-adapted approaches are scarce, making the choice of therapy extremely challenging. Treatment options for intermediate-fit and frail patients might include dose-adapted combinations, doublets, and less toxic combinations based on novel agents. This review analyzes the available tools for the assessment of frailty and possible strategies to improve the discriminative power of the scores and expand their use in real-life and clinical trial settings. Moreover, it addresses the main therapeutic challenges in the management of intermediate-fit and frail MM patients at diagnosis and at relapse