Hasil untuk "astro-ph.HE"

Khan Tahsin Abrar

Srinivas Raman, Ronald Chow, Peter Hoskin et al.

Derek Long

Astro Industries, USA

David Lindley

Winfried Bauer

Das Unternehmen Schneider Electric baut den weltweiten Vertrieb seiner RSM- und RDM-Motoren um: Seit dem 1. Januar 2026 übernimmt die Astro Motorengesellschaft mbH & Co. KG, Geestland, die Bestellabwicklung und Kundenbetreuung für kleinste, kleine und mittlere Auftragsgrößen dieser Antriebsreihen sowie für zugehörige Gehäuse und Stellnocken.

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.

Jonathan MacArt

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.

Harish Parthasarathy

Amirreza Rouhi

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?

Astro Calisi

S. F. He, J.-Y. Wei, C. Eyzaguirre

Burkhard Fuchs

General Electric Co., Philadelphia, PA (USA). Astro Space Div.