Project information
Seed project name
Machine learning stabilized steady-state advective-diffusive heat transport
Seed project manager
Winston Mmari (Principle investigator)
Other seed project members
Björn Johannesson, Welf Löwe, Björn Lindenberg and Jonas Nordqvist, Linnaeus University
Participating organizations
Linnaeus University
Financier
Linnaeus University Center (Lnuc) for Data Intensive Sciences and Applications (DISA)
Timetable
20 Nov 2023 - 19 March 2024
Subject
Building technology, computer technology and mathematics (Department of Building Technology, Department of Computer Science and Media Technology, Department of Mathematics, Faculty of Technology)
More about the project
Solutions of advective–diffusive transport problems continue to be of interest in many areas of science and engineering, such as heat and mass transfer and pollutant dispersion in air, soils, and water.
They are useful for a variety of applications, such as providing initial or approximate analyses of alternative pollution scenarios, conducting sensitivity analyses to investigate the effects of various parameters or processes on contaminant transport, extrapolation over large times and distances where numerical solutions may be impractical, serving as screening models or benchmark solutions for more complex transport processes that cannot be solved exactly, and for validating more comprehensive numerical solutions of the governing transport equations.
The project is part of the research in the research groups Forestry, Wood, and Building Technology, Data Intensive Software Technology and Applications (DISTA) and in Linnaeus University Center (Lnuc) for Data Intensive Sciences and Applications (DISA)
