HPC and Big Data Technologies for Global Systems
(c) HiDALGO2
HiDALGO2 continues the mission of its predecessor project HiDALGO and aims to explore synergies between modelling, data acquisition, simulation, data analysis and visualisation along with achieving better scalability on current and future HPC and AI infrastructures to deliver highly-scalable solutions that can effectively utilise pre-exascale systems.
The project focuses on five pilots from the environmental area: improving air quality in urban agglomerations, energy efficiency of buildings, renewable energy sources, wildfires and material transport in water. The common feature of the modelling of the above simulations is the use of numerical analysis of fluid flows by Computational Fluid Dynamics (CFD) method, which is typically very compute-intensive.
Pilots
Urban air project
In this pilot we work around the evolution of air in urban areas considering pollution, wind, comfort and planning. The core of our work here is the Urban Air Flow computational model that is massively based on modern HPC, mathematical, and AI technologies.
Urban Building Model
Here we focus on advanced building models for better integration with urban architecture. We aim to provide a source term for heat and air pollutants (CO2 and NOx) to the urban air pollution model. We will use a simplified monozone model to keep the problem size reasonable.
Renewable energy sources
We aim to advance energy production estimation from renewable energy sources, such as wind farms and solar panels, and also predict damages to the RES infrastructure. We will achieve this by applying uncertainty quantification study to the simulation models and by running the ensembles on a larger scale.
Wildfires
To simulate wildfire-atmosphere interactions and smoke dispersion at various scales, we will implement the computational environment necessary in order to assess the risk and potential impacts induced by mesoscale and microscale fire behaviour in the vicinity of and within WUI zones.
Material Transport in Water
Αdvanced numerical simulations for a better understanding of the complex process of pollution transport in rivers, offering a means to enhance control and prevention strategies. Coupling the High-Performance Computing multiphysics framework waLBerla with the C++ framework for large-scale, high-performance finite element simulations, HyTeG.
Focus
HiDALGO2 focuses on the following conceptual aspects:
- Technological
- Multidisciplinary
- Socio-scientific
- Teamworking
HiDALGO2 is going to implement a rich set of functionalities, services, model adaptations, co-design benchmarks as well as actively work on establishing collaboration with other projects, initiatives and communities. In order to achieve the relevant impact for the project and measure its success, four above-mentioned categories have been detailed by a set of specific objectives.
Goals
The goals and ambitions of the HiDALGO2 project go far beyond the current state of knowledge and application possibilities, exploring areas that have not been explored so far. Thanks to HiDALGO2, it will be possible to make a qualitative change allowing us to look at the current state of knowledge of the analysed problems from a new perspective.
Through the progress achieved in the project, a qualitative leap in terms of scale, resolution and accuracy of the results obtained is expected. This can be compared to using a microscope with much higher magnification to study extremely small organisms. Something that was previously invisible to the researcher suddenly becomes available and allows you to understand the essence of things.
Scalability
HiDALGO2 puts high emphasis on issues related to the scalability of solutions, the best adaptation of the software to the infrastructure (co-design) by using the appropriate benchmarking methodology and algorithmic optimization methods. This enables the efficient use of top-notch HPC systems to simulate complex structures with much greater accuracy not achievable for calculations using Cloud solutions.
The quality of our solutions is assessed by uncertainty analysis carried out in ensemble runs mode. HiDALGO2 actively contributes to user communities from the EU by addressing the skills gap and sharing knowledge under organised specialised workflows and training.
Runtime: 01/2023 - 12/2026
