Modern society is highly dependent on many different types of networks. However, once any of these critical infrastructures fail, it would have profound implications for all of us. Using simulation models, we can predict the impact of adverse events and decide on how solve these disruptions and its aftermath quickly – or even prevent them from happening in the first place.

The SIM-CI suite and Digital Twin City platform both incorporate a wide range of physics and statistical models regarding infrastructures, environmental influence, chemical processes and human behavior. These models allow us to create, validate and assess mitigation, contingency and other scenarios for critical infrastructures and urban processes with scientific accuracy.

In close cooperation with our international research & development partners, we are constantly working on extending and improving the scientific models and algorithms powering our Digital Twin City platform and near-real time simulations.

Network models

SIM-CI creates and/or tailors models for various infrastructures, such as electricity, gas, water, traffic, heating grids, sewage and telecommunication. The network models compute the network state, e.g. flow and pressure for water or gas and voltage and current for electricity.

SIM-CI develops models and algorithms in house, but also uses existing ones if they prove sufficient for our purposes. The models can be based on physics laws, empirical research equations or industrial standards. SIM-CI has created, for example, its own model for the simulation of a low voltage network. The basis of this model are still the laws like Ohm’s law and Kirchhoff’s circuit laws. This in-house created electricity network model is used in interdependency models such as the one that describes the relation between flooding and electrical networks.

Interdependency models

Infrastructures might also affect each other. Numerous examples can be thought of. For example, how does an electricity outage affect traffic flows? Or can stray currents cause corrosion in steel pipes? Another example is a broken water main that caused a large-scale gas outage (for more information on this interdependency, see use case).

The risks of interdependencies are often not well understood. SIM-CI models these interdependencies to offer insight and thus help in minimizing cascading effects in our infrastructures.

Deterioration and aging

SIM-CI also creates many types of ageing and deterioration models. One example is network failure probability estimation using Weibull distributions. Another one is the calculation of loss of lifetime for specific network components such as a transformer. These aging and deterioration models can be used in risk calculations or in condition-based maintenance planning.

Flooding models

In times of changing climate conditions, sea levels are expected to rise and more intense rain fall is anticipated. Hence, flooding might occur due to dike breach or heavy rain fall. SIM-CI employs existing software to calculate water heights and velocities in the case of a flood. The flooding model is used to calculate the impact of a flood on other networks like the electricity grid. Not only the inflow of the water can be modeled, but also the outflow during the recovery phase.

Weather influence models

In this final model type the effect of the weather on energy usage is investigated. The current model predicts the load on the electricity network based on weather forecasts. This model has been tested with sample data and an accuracy of 97% was obtained.

The models highlighted above are those that have been created or that are currently being worked on. SIM-CI is continuously developing new models and improving existing ones. This is necessary to form a better understanding of infrastructures and their interdependencies as a whole and will contribute to creating cities that are ‘Resilient by Design’.

Interested in seeing how scientific modelling and simulations can help you make the right choices regarding city planning, infrastructure protection and smart city resilience?

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