New job offer job_96db2c3393bd61f985a151514a8d414c
Title: Improving solar-receiver efficiency by manipulating turbulence in heat-collecting, fluid-conveying channels: from physical understanding to Machine-Learning-based optimisation
Date: 2021-11-26 13:17
Authors: lionel agostini
Job_Location: CNRS-Université de Poitiers-ISAE ENSMA, 11 Boulevard Marie et Pierre Curie Téléport 2, BP 30179 86962 Futuroscope Cédex
Job_Duration: 6 months (possibility of a Phd after the internship)
Job_Employer: CNRS - Pprime institute
A research programme is proposed in which the efficacy of near-wall turbulence control is investigated in efforts to increase the heat transfer at minimum drag penalty in ducts that convey heated air within a solar receiver to a power-extracting device. The attached proposal sets out a research programme that aims to explore the effectiveness of near-wall control strategies for maximising the fluid temperature subject to an upper limit on the material temperature, at a minimum penalty to the frictional losses.
This is to be done by computational investigations - based on Direct Numerical Simulations (DNS) as well as Large eddy simulation (LES) - of combinations of flow-control methodologies, both active and passive, aided by the application of different Machine Learning algorithms (ML), all together as a means of optimising the balance between maximum heat transfer and minimum frictional loss. Some ML algorithms are dedicated to dimensionality reduction and low-dimensional dynamical identification while other ML algorithms are used for designing an optimal control law.
The research does not specifically aim to facilitate the construction of improved receiver design. Rather, it entails a series of fundamentally-oriented studies on generic receivers subjected to control and idealised heating scenarios, the aim being to derive answers to basic questions on the response of the flow to the proposed control methods in respect of heat transfer and drag