Quantum-classical analogies in limit modes of heat transfer through turbulent flow

Operation co-funded by EU cohesion policy (European Social Fund (ESF)) under the programme Research Development and Education - CZ - ESF/ERDF (2014CZ05M2OP001), managing authority: MŠMT/odbor Řídicího orgánu OP VVV. The source names no beneficiary for this operation. Location: South Moravian Region (NUTS CZ064); Czechia. Budget: total eligible expenditure EUR 163,335.36; EU co-financing EUR 138,835.06 (co-financing rate 85%), as stated by the source. Runs 2018-10-01 to 2021-12-31. Source: Kohesio, EU cohesion policy projects managed by national and regional authorities (not CORDIS); selected because its title uses a quantum word. Summary from Kohesio (English machine translation by the source): The project combines theoretical and experimental research of the marginal modes of heat transfer by turbulent flow of classical and quantum fluids at low temperatures. The aim is to formulate a uniform description of the laws of heat transfer in a quantum liquid (in superfluid helium-4 in the so-called heat counterflow process) and in the classical liquid (normal helium-4 in the Rayleigh-Bénard convection process), and the development of a new and potentially breakthrough experimental method of visualisation of both types of flow.

Original source.

Source: Kohesio (EU cohesion policy projects), European Commission, Directorate-General for Regional and Urban Policy (DG REGIO). Retrieved 2026-10-21. Licence: CC0 1.0 (Cohesion Open Data Platform, dataset 2014-2020 Kohesio projects). Selected metadata, adapted presentation; the publisher's record is authoritative. No endorsement by the source is implied. Licence: public domain dedication (CC0 1.0).

AI-assisted summary: The project, co-funded by the EU, focuses on researching heat transfer in turbulent flow of classical and quantum fluids at low temperatures. It aims to describe heat transfer laws in superfluid and normal helium-4, running from 2018 to 2021. Suggested technology pillar: simulation. Generated by AI from the public source text; it may contain errors and the original source is authoritative.