Computational engineering is a discipline that deals with the development and application of computational models and simulations, often coupled with high-performance computing, to solve complex physical problems arising in engineering analysis and design as well as natural phenomena.
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Facts
Researchers
6
Projects
3
Postdocs/PhDs
2
Video: NORCE
Research in the field of Computational Engineering focuses on the development and application of mathematical and numerical models to analyze complex and uncertain systems for gaining knowledge and insights into the systems. Computational engineers use a physics and data driven approach to solve problems.
The ultimate goal of both data science and computational engineering is to support decision making, but the main emphasis in data science is data whilst the main emphasis in computational engineering is models (including data-driven models).
Project
Prospective individualization of contrast dose in CT angiography of coronary vessels
In a CT examination of the heart, it is desirable to achieve a good contrast charge in the blood vessels without giving more contrast than necessary.
HU measured over time with a region of interest with a width of approximately 2/3 of the aortic diameter, centrally placed in the aorta ascendens, gives a time-density curve.
We will use patient data and mathematical models to optimize the amount of contrast given. It is important to take images at the optimal time, i.e. when the contrast is in the coronary arteries. Normally, this is done with the help of a preliminary examination in which a small dose of contrast is given, a so-called test bolus. We suggest using the information from the test bolus together with our mathematical models to more accurately predict which dose we need to give to achieve an optimal contrast charge in the coronary arteries. In this way, for each individual, the exact amount of contrast that is necessary for the examination to be optimal can be adjusted. The project is divided into two parts. The first part deals with the validation of our mathematical models, while the second part deals with the individualization of the amount of contrast.
Palliative strategies for single-ventricle heart disease management
Almost 1% of babies worldwide, and 500-600 babies in Norway, are born with clinically significant structural defects in their heart (congenital heart disease, CHD).
A normal heart has two pumping chambers known as ventricles, one of which circulates oxygenated blood and the other which sends deoxygenated blood to the lungs. Single-ventricle heart malformations are the most serious type of CHDs because the baby has only one functional ventricle. The mixing of oxygenated and deoxygenated blood streams in the single ventricle results in extremely low arterial blood oxygen saturation which is fatal. These patients undergo a series of surgeries within the first year of their life to survive. The surgeries are designed to separate the vena caval veins (which return deoxygenated blood to the heart) from the heart and connect them directly to the pulmonary arteries, resulting in Fontan circulation. The peculiar Fontan physiology causes non-pulsatile pulmonary flow and elevated venous pressure due to the lack of a subpulmonary ventricle, which can lead to serious complications such as liver disease, protein-losing enteropathy, and eventually failure. The aim of this project is to provide innovative palliation for the growing population of patients with failing Fontan. One of our novel solutions to the Fontan paradox that is clinically feasible and self-powered is the Venous Ejector Pump (VEP). In silico evaluations using computational fluid dynamics as well as in vitro pulsatile tests revealed that the proposed solution has the potential to provide meaningful support for Fontan patients.
This project is supported by Equinor Akademia programme.
We are focused on utilization and development of state-of-the-art computational engineering techniques such as lattice Boltzmann method (LBM), to establish effective tools for gaining knowledge and understanding of hemodynamics in the cardiovascular system.
The picture shows flow of a non Newtonian fluid in a Fontan prosthesis.
These methods are aimed at providing more accurate and detailed simulations of blood flow and better understanding of the complex interactions between blood cells, plasma and intricated geometries. The goal is to use these tools to improve our understanding of the cardiovascular system and to support the development of diagnostic and therapeutic approaches for cardiovascular diseases. The LBM method is a recent advancement in computational fluid dynamics, and particularly in blood flow simulation. Because of its local operations, it is straight forward to integrate, benefit from automated and efficient mesh pre-processing, and is an excellent candidate for highly scalable parallel processing. LBM is a mesoscopic particle-based method, and one has easier access to the underlying physics and more possibilities to include realistic physical descriptions of important phenomena. The LBM is hence, an advantageous approach for simulating high-Reynolds-number flows and non-Newtonian fluids, such as blood. Its capability to model complex geometries and fluid flows make it a valuable tool in hemodynamic modeling and investigations. Furthermore, LBM's ability to accurately capture the key physical characteristics of complex fluids makes it a suitable candidate for such simulations.
The picture shows flow of a non Newtonian fluid in a Fontan prosthesis. The project is supported by Equinor Akademia programme.