About the project

Objective
In this project, Princeton University and KTH Royal Institute of Technology plan to develop a family of C3.ai-enabled methods for learning, optimization, and system stability analysis of grid-tied inverters and power electronics-based power systems. The goal is to develop a unified machine-learning platform for power electronics, power systems, and data science research. A bottom-up approach, from modelling a single inverter to modelling a cluster of inverters connected as a microgrid, will be used as a motivating case study to show a holistic hierarchical modelling approach supported by C3.ai.

Background
Distributed and renewable energy resources are massively deployed in electric power grids, driven by the sharp cost reduction and the demand for carbon neutrality. The grid of the future will be supported by clouds of distributed and renewable energy resources. Power electronics Inverters are pervasively needed to connect renewable energy resources to the grid, thanks to their full controllability over electric power. On the other hand, to meet a wide range of grid requirements, these grid-tied inverters are commonly equipped with sophisticated control systems, which pose challenges to the stability and control of power grids, threatening the energy security of modern society.

Crossdisciplinary collaboration
This project is a collaboration between KTH and Princeton University.

Watch the recorded presentation at Digitalize in Stockholm 2022 event:

About the project

Objective
The Learning in Routing Games for Sustainable Electromobility (RoSE) project will employ large-scale simulation, learning, and game theory to develop sustainability-aware traffic routing tools. The tools will leverage and fuse heterogeneous, noisy, and often incomplete data from various sources, such as infrastructure condition data, traffic flow data, and power distribution grid data. The key contribution is to account for operational costs, infrastructure condition deterioration, and environmental externalities in the design of socially desirable, sustainable traffic routing mechanisms. We will address questions such as: How should heavy-duty vehicles be routed at scale to find a good trade-off between operational costs, sustainability, and electric power grid constraints?

Background
The transportation sector is the largest contributor to greenhouse gas emissions worldwide (about 24% of CO2 emissions in the EU and about 28% in the USA). The electrification of road transportation presents an opportunity to defer emissions from roads to electric power generation. However, the ambition to achieve zero-emission mobility requires a new, sustainability-oriented approach to transportation planning at a societal scale, respecting infrastructural constraints and individual incentives while being resilient to infrastructure component failures and data uncertainty. A promising approach is using digital traffic routing tools that maximise green energy utilisation while respecting other vital environmental constraints.

Crossdisciplinary collaboration
RoSE is a collaboration between KTH (EECS and ABE school) and MIT (Department of Civil and Environmental Engineering) in Cambridge, USA.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

About the project

Objective
This project plans to develop AI-based CFD simulation to realise accurate and fast predictions of urban climate and the built environment. This study will focus on two aspects of developing the AI-based model: the AI-based turbulence model by learning the “behaviour” of turbulence and the AI-based surrogate model. To train and test the artificial neural network (ANN) model, this project will collect experimental data on indoor and outdoor airflow from on-site and lab measurements. In terms of accuracy, the predictions by AI-based models are expected to be within a 10% difference from that of conventional CFD simulations. Regarding efficiency, AI-based models are expected to be at least ten times faster than conventional CFD simulations.

Background
The urban climate determines the environmental quality in urban areas by removing or dispersing the airborne pollutants generated by human activities or providing cleaner external (rural) air. Studying urban climate and its impact on built environments would help provide guidelines and tools for urban planners and building engineers to evaluate the environmental quality in our living space. Given the practical difficulties of performing city-scale or multi-scale experiments, accurate simulation and fast decision-supporting tools are urgently needed to provide pollutant mitigation strategies for researchers, urban planners, environmental engineers and decision-makers. The existing development of such tools has been plagued mainly by three scientific challenges: computational speed, accuracy, and robustness.

Crossdisciplinary collaboration
The researchers in the team represent the KTH ABE School, the Department of Computer Science and Engineering, and the Blekinge Institute of Technology.

Watch the recorded presentation at the Digitalize in Stockholm 2023 event:

Activities & Results

Find out what’s going on!

Coupled framework
We develop a coupled CFD – deep learning framework where we substitute only the turbulence model of CFD with an MLP

Flow fields from literature
A. Room simulation mixed convection indoor airflow [1]. Data used to train the MLP.

B. Office simulation with displacement ventilation [2].

C. Building array simulation outdoor airflow [3].

Compared to standard CFD simulation using RNG k-ε model the new coupled framework is:
A. 5 % faster*

B. 7 % faster*

C. 2 % faster*

Download presentation from Open Research Day 21 April as pdf.

Publications

We like to inspire and share interesting knowledge!

Calzolari, G. and Liu, W. Deep learning to replace, improve, or aid CFD analysis in built environment applications: a review. Building and Environment, 2021, 206, 108315. https://doi.org/10.1016/j.buildenv.2021.108315.

Calzolari, G. and Liu, W. A deep learning-based zero-equation turbulence model for indoor airflow simulation. Proceedings of the 5th International Conference on Building Energy and Environment (COBEE 2022), Paper No. 1196, Montreal, Canada.