Title:	Computational fluid dynamics and multiphysics modeling - Storage
DNr:	LiU-storage-2024-3
Project Type:	LiU Storage
Principal Investigator:	Magnus Andersson <magnus.andersson@liu.se>
Affiliation:	Linköpings universitet
Duration:	2024-06-10 – 2025-07-01
Classification:	20306
Keywords:

Abstract

Data is being generated through the LiU-compute-2024-20 project, involving several sub-areas within the field of Computational Mechanics and Engineering, dealing with numerical simulations of fluid flows; often coupled with other types of physics such as heat transfer, structural dynamics, fluid-structure interaction, chemical reactions, multiphase, etc. at different spatio-temporal scales. The different research areas (projects) is related to: - Cavitation-induced erosion in oil-hydraulic systems. This calls for very highly resolved multiphase CFD simulations, including cavitation and erosion modelling, coupled with co-simulation frameworks. - Additive manufacturing using lattice structures. Here, we seek to develop design tools based on topology and shape optimization for generating ultra-lightweight multifunctional solid-lattice designs for problem involving fluid flows and heat transfer, where high-fidelity (large/multi-scale) multi-physics simulations play a key role. - Biogas production efficiency. Here we focus on investigating different mixing processing using low/high-fidelity large-scale, multiphase CFD simulations coupled with compartmental modeling, which will be used to link CFD with kinetic models for investigating parameters that are affected by mixing. - Product development of multiphysics applications with large parameter space. Here, large-scale experiments and CFD methods impractical and the need for faster and more robust approaches for design performance analysis. This project seeks to investigate the possibility of using a CFD-driven machine learning framework, to reduce computational times, improve design efficiency, and accelerate the innovation cycle for real-world flow-related multiphysics problems. This will involve generation of large sets of CFD data used for the training phase, as well as for validating the predictability of the AI model. - Patient-specific models of cardiovascular flows. Apart from an increased understanding of the normal and abnormal blood flow in the human body we target intervention planning as well as follow-up and diagnostic aid for different reconstruction procedures. To establish such capability, a thorough understanding of normal flow conditions is required. We utilize the basic principles of fluid dynamics as well as the modelling and simulation capabilities from computational engineering and high-performance computing in combination with modern imaging modalities and image processing. With the introduction of very high-resolution CT (photon counting CT, PCCT) the need for HPC is increased significantly.