This paper presents Parallel World Framework as a solution for simulations of complex systems within a time-varying knowledge graph and its application to the electric grid of Jurong Island in Singapore. The underlying modeling system is based on the Semantic Web Stack. Its linked data layer is described by means of ontologies, which span multiple domains. The framework is designed to allow what-if scenarios to be simulated generically, even for complex, inter-linked, cross-domain applications, as well as conducting multi-scale optimizations of complex superstructures within the system. Parallel world containers, introduced by the framework, ensure data separation and versioning of structures crossing various domain boundaries. Separation of operations, belonging to a particular version of the world, is taken care of by a scenario agent. It encapsulates functionality of operations on data and acts as a parallel world proxy to all of the other agents operating on the knowledge graph. Electric network optimization for carbon tax is demonstrated as a use case. The framework allows to model and evaluate electrical networks corresponding to set carbon tax values by retrofitting different types of power generators and optimizing the grid accordingly. The use case shows the possibility of using this solution as a tool for CO2 reduction modeling and planning at scale due to its distributed architecture.

A spouted bed has been simulated through a Computational Fluid Dynamic model using the Two Fluid Method and validated against experimental data. A sensitivity analysis has assessed the influence of the characteristic parameters on the solution. Among them, the accurate selection of the drag law seems to have the strongest influence on the results. In order to extend the capabilities of Ansys Fluent, Di Felice's drag law was also considered through a User Defined Function. The assessment of the granular phase and its kinetic, collisional and frictional forces, is highly relevant to achieve a correct prediction of the particle velocity profile. The specularity coefficient appears to be more influencing than the restitution coefficient, but both parameters are useful to optimise the model. Overall, the prediction of the particle vertical velocity is accurate whereas the height of the fountain is slightly over-predicted.

This Article illustrates how a dynamic knowledge graph approach in the context of The World Avatar (TWA) project can support the decarbonization of energy systems by leveraging the existing energy storage system (ESS) selection framework to assist in the selection and optimal placement of the ESS. TWA is a dynamic knowledge graph based on the Semantic Web and its associated technologies, with intelligent agents operating on it. The agents act autonomously to update and extend TWA, and thus it evolves in time. TWA also provides the ability to consider different scenarios, referred to as parallel worlds, allowing for scenario analysis without mutual interference. A use casethe addition of a battery energy storage system to the Singapore Jurong Island electrical networkis introduced to demonstrate the application of this approach. The domain ontology, OntoPowSys, was extended to describe and instantiate the relevant ESSs considered in the use case. This extension is described in the Article using the description logic syntax. The Article also outlines the details of how the various agents involved in the use case are being integrated into TWA. The use case also highlights how the parallel world framework can facilitate scenario analysis by considering different scenarios without affecting the real-world representation.