We know the digital twin as a combination of one or more data sets and a visual representation of the physical object it mirrors. Digital twins are morphing to meet the practical needs of users. In asset-heavy industries, optimizing production, improving product quality, and predictive maintenance have all amplified the need for a digital representation of both the past and present condition of a process or asset.
An
industrial digital twin is the aggregation of all possible data types
and data sets, both historical and real-time, directly or indirectly
related to a given physical asset or set of assets in an easily
accessible, unified location.
The collected data must be trusted and contextualized, linked in a
way that mirrors the real world, and made consumable for a variety of
use cases.
Digital twins must serve data in a way that aligns to how operational decisions are made. As a result, you may need multiple twins, as the type and nature of decisions are different. A digital twin for supply chain, one for different operating conditions, one that reflects maintenance, one that's for visualization, one for simulation—and so on.
What this shows is that a digital twin isn't a monolith, but an ecosystem. To support that ecosystem, you company/group need an efficient way of populating all the different digital twins with data in a scalable way.
- Introduce the objectives and challenges of building thermal regulations.
- Understand how to calculate the thermal balance of a room using manual methods.
- Acquire or update knowledge to solve thermal problems.
Dynamic
Energy Simulation with Indoor Air Quality Models
The HiDALGO2 Urban Building Pilot integrates Dynamic Energy Simulation with indoor air quality models for urban buildings, leveraging advanced algorithms and high-performance computing. Focused on both city-scale and building-scale analyses, it aims to enhance energy efficiency, comfort, and sustainability. This project aligns with global initiatives like the European Green Deal, emphasizing a holistic approach to urban environmental challenges.
This training course will focus on the following points:
Objectives:
- Enhance energy performance, indoor air quality, and human comfort in urban buildings.
- Focus on computational methods for modeling and optimizing urban environments.
Technical Workflow and Challenges:
- Comprehensive workflow integrating mesh generation, parallel mesh adaptation, and multi-fidelity models.
- Challenges in large-scale mesh generation for accurate computational modeling.
- Development of scalable solutions for complex urban building simulations.