Updates on engineering and architecture jobs available around the world
PhD opportunity in an IFD-funded project at DTU, Department of Civil and Mechanical Engineering: Utilize vast datasets to build digital twins, optimize residential heating systems, and reduce energy costs in district heating networks. Leverage your knowledge in simulation and data analysis towards the cutting-edge of sustainable energy solutions.
Project Overview
District Heating companies are urgently undergoing a transition towards using large heat pumps and low-temperature heat sources. The success of this shift largely depends on the reduction of supply temperatures in district heating networks due to their significant effect on the Coefficient of Performance (COP) of heat pumps. However, the transition is hindered by apartment buildings that maintain unnecessarily high operating temperatures through their heating pipes, necessitating network operators to set their supply temperatures high enough to accommodate these apartments’ demands.
Addressing this issue and reducing these temperatures could result in annual savings of around 14 billion euros within the EU and between 300 to 350 million euros in Denmark, making this an extremely high-priority task.
In collaboration with leading heat-invoicing companies, our project intends to resolve this problem by tapping into extensive datasets from the digital heat cost allocators (HCAs) already in use in apartment buildings. These devices, which are mounted on radiators, measure heat output and wirelessly transmit the data. Over a hundred million HCAs have been installed worldwide, and the EU requires their near-universal implementation by 2027.
We can use the data from HCAs to create a detailed visualization of heating distribution at the radiator level. This approach allows us to understand where heating is primarily utilized. By merging this data with the main district heating meter data, we can create accurate digital twins for near real-time identification of performance inefficiencies and optimal control strategies. These digital twins will help guide building operators and heat providers and enable optimal control of heating substations located in basements without directly interfering with residents. This non-intrusive aspect significantly enhances the feasibility of our approach.
PhD Opportunity
This project offers a unique PhD opportunity as part of an IFD-funded Grand Solutions initiative. You will have the chance to work alongside leading heat-invoicing companies, district heating companies, and housing organizations, all aiming to decrease supply temperatures using this extensive data set.
The data will be securely piped into a platform for your research while you develop and test open-source tools to maximize this opportunity. This project represents the first initiative to employ such vast datasets on a large scale.
Your PhD project will act as the cohesive force bringing together various project elements, offering a highly collaborative and visible role. You will gain invaluable skills from driving open-source R&D using these large datasets in a rapidly growing industrial sector with enormous potential.
Responsibilities and Qualifications
At the project’s commencement, you will be expected to replicate existing methods for constructing digital twins to reduce supply temperatures in several well-instrumented buildings. Based on initial findings, your task will be to enhance, test, scale, and refine these methods in additional buildings while integrating intelligence, like streamlined digital twins, scalable insights, visualizations, and machine learning, when appropriate.
Application procedure
To apply, please read the full job advertisement, by clicking the ‘Apply’ button.
