In Italy, INFN coordinates the research in the field of astro-particle physics. The supported experimental activities include the study of the cosmic radiation, the search of gravitational waves, the study of dark universe, general and quantum physics, and the study of the neutrino properties. A rich program of experiments installed on the earth, in the space, and underground or underwater is being supported to provide a possible answer to some of the most relevant open questions of particle physics, astrophysics, and cosmology. A short overview of the ongoing effort is presented.
John A. Seed, Helen R. Sharpe, Harry J. Futcher
et al.
AbstractTreatment of [Ph3EMe][I] with [Na{N(SiMe3)2}] affords the ylides [Ph3E=CH2] (E=As, 1As; P, 1P). For 1As this overcomes prior difficulties in the synthesis of this classical arsonium‐ylide that have historically impeded its wider study. The structure of 1As has now been determined, 45 years after it was first convincingly isolated, and compared to 1P, confirming the long‐proposed hypothesis of increasing pyramidalisation of the ylide‐carbon, highlighting the increasing dominance of E+−C− dipolar resonance form (sp3‐C) over the E=C ene π‐bonded form (sp2‐C), as group 15 is descended. The uranium(IV)–cyclometallate complex [U{N(CH2CH2NSiPri3)2(CH2CH2SiPri2CH(Me)CH2)}] reacts with 1As and 1P by α‐proton abstraction to give [U(TrenTIPS)(CHEPh3)] (TrenTIPS=N(CH2CH2NSiPri3)3; E=As, 2As; P, 2P), where 2As is an unprecedented structurally characterised arsonium‐carbene complex. The short U−C distances and obtuse U‐C‐E angles suggest significant U=C double bond character. A shorter U−C distance is found for 2As than 2P, consistent with increased uranium‐ and reduced pnictonium‐stabilisation of the carbene as group 15 is descended, which is supported by quantum chemical calculations.
Jeppe Madsen, George Madden, Efrosyni Themistou
et al.
Facile labelling of both blocks of a pH-responsive diblock copolymer with different fluorophores allows monitoring of polymer aggregation and deprotonation.
Lightweight materials have become integral in diverse sectors such as transportation, energy, and healthcare. Their varied microstructures and properties present significant potential for applications from load-bearing components to multifunctional structures. However, a major challenge lies in the heterogeneous material properties and vast design space of materials, impeding effective design and optimisation.My talk will address this challenge in two parts. Firstly, I will explore mechanics-based approaches to model the failure of materials. This will encompass a wide range of scenarios, from fracture, crushing behaviour, ballistic impact to liquid-solid impact of materials. Secondly, I will showcase the application of machine learning approaches for the design of porous architected materials, focusing on optimisation strategies. By bridging mechanics and machine learning, our work aims to unlock new possibilities in material design and optimisation.
Prediction and control of complex flows remain a significant challenge for engineering systems. Turbulent flow predictions generally require Reynolds-Averaged Navier–Stokes (RANS) simulations and Large-Eddy Simulation (LES), though their predictive accuracy can be insufficient for flow control optimization, and non-Boussinesq turbulence and/or unresolved multiphysical phenomena can preclude qualitative fidelity in certain regimes. For example, in turbulent combustion, flame–turbulence interactions can lead to inverse-cascade energy transfer, which violates the assumptions of many RANS and LES closures. We develop adjoint-based, solver-embedded data assimilation methods to augment the RANS and LES equations using trusted data and embedded higher-fidelity simulations. This is accomplished using Python-native flow solvers that leverage differentiable programming techniques to construct the adjoint equations needed for optimization. We present applications to canonical turbulence, shock-dominated flows, aerothermodynamics, and flow control and discuss the potential of adjoint-based approaches for future machine learning applications.
Turbulent friction drag is an inevitable source of power consumption for aircrafts, trains, pipelines, and many other industrial applications. Significant efforts are ongoing to design and study drag-reducing mechanisms, e.g. riblets, superhydrophobic surfaces and blowing/suction. In this seminar, I focus on an active controlling mechanism based on spanwise surface oscillation, leading to the generation of a streamwise travelling wave. The mechanism has been extensively studied via direct numerical simulation (DNS); it has shown the potential to reduce drag by 40%. Owing to the expensive computational cost of DNS, the prediction models for drag reduction are derived based on datasets with friction Reynolds numbers less than 2000. Motivated by Intellectual Ventures, a team of researchers have built their experimental and large-eddy simulation capabilities to study the travelling wave actuation at friction Reynolds numbers beyond 10000. Thus, providing the opportunity to study the efficacy of this mechanism at a flow regime closer to that of ground and air vehicles. As a member of this team, I present our findings. My presentation evolves around two major themes: 1) Do the high Reynolds number data agree with the past prediction models? If not, what emerging flow physics are related to the disagreement? 2) Stokes layer is an important mechanism in this problem. How does this mechanism interact with the near-wall turbulence? and how this interaction manifests in the drag reduction?