Computational Fluid Dynamics

The computational fluid dynamics (CFD) has a relevant impact on the design of civil and military aviation aircrafts with the first notable achievement dating back to the late 1980s with the integration of the engine nacelle with the wing of the Boeing 737. There are several issues to be addressed in CFD. The ones related to physical modelling such as turbulence and transition especially in adverse-pressure-gradient flows with separations. In particular the focus is on hybrid RANS-LES methods and machine-learning techniques that have shown great potentiality to modify and enhance the functional form of a model. Flows at low-Reynolds number present several modelling issues because cannot sustain adverse pressure gradients and often separate in laminar regime. These flows have a renewing interest because, in the compressible regime, characterize the flight in the low-Martian atmosphere. An other crucial field of interest is the drag reduction that impacts the fuel consumption and the environmental footprint of an aircraft.

Hybrid RANS-LES and wall-modelled LES are modelling strategies that try to combine the efficiency of a RANS turbulence model near the wall with a LES treatment in the region away from the wall. Issues related to the RANS-LES interface directly impacting the scale-resolving capability of the LES mode need to be addressed. The interest for the compressible aerodynamics of low-Reynolds number flows has recently grown for the possible use of flying machines for exploring the Martian surface. Separation occurring in the laminar regime and the possible formation of a bubble are likely the most important and critical phenomena of these flows. Techniques of drag reduction need to be explored. Several methods have been studied to delay laminar-turbulent transition or to modify the turbulent structures of the boundary layer. The natural laminar flow (NLF) technology aims at extending the laminar region as much as possible. The riblets consist of streamwise grooved surface and are able to reduce friction drag in the turbulent part of the flow. These devices should be made more effective and reliable.

Full-length manuscripts are welcome in the following areas:

• Hybrid RANS-LES and wall-modelled LES: RANS-LES interface and switching. Grey-area mitigation i.e. the delay in the formation of LES-resolved initial instabilities when switching from RANS to LES. Interaction between modelled and resolved turbulence. Injection of resolved turbulence upstream the LES region. Interaction between resolved wall-bounded turbulence and shock-wave.

• Machine-learning methods for turbulence modelling: Physics-informed neural networks. Statistical inference and uncertainty quantification.

• Low-Reynolds number flows: Modelling of laminar separation bubbles. Transition modelling for flows separating in laminar regime. Simulation and modelling of compressible flows operating in environmental conditions typical of the low Martian atmosphere.

• Drag reduction: Modelling and design of airfoils/wings with a large extension of laminar conditions. Modelling and simulation the effect of riblets on flow characteristics. Effect of different shapes and arrangements of the riblets.

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Article types

This Research Topic accepts the following article types, unless otherwise specified in the Research Topic description:

• Brief Research Report
• Data Report
• Editorial
• FAIR² Data
• FAIR² DATA Direct Submission
• Hypothesis and Theory
• Methods
• Mini Review
• Opinion
• Original Research
• Perspective
• Review
• Technology and Code

Keywords: Turbulence modelling, Transition modelling, Low-Reynolds number, Low Mars atmosphere, Machine learning, Drag Reduction, Riblets

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