About

HiDALGO2 explores synergies among 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 above simulations is the use of numerical analysis of fluid flows by the Computational Fluid Dynamics (CFD) method, which is typically very compute-intensive.

Focus

HiDALGO2 focuses on the following conceptual aspects:

  • Technological
  • Multidisciplinary
  • Socio-scientific
  • Teamworking

HiDALGO2 implements 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 is 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 HiDALGO2 tools, a qualitative leap in terms of scale, resolution and accuracy of the results obtained is achieved. 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 optimisation 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.

Pilots

HiDALGO2 explores synergies among 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.

Urban air project


Our pilot explores how air flows through urban environments, evaluating its impact on pollution, wind conditions, human comfort, and city planning. To achieve this, we developed the Urban Air Flow computational model, utilising state-of-the-art HPC, mathematical analysis, and AI technologies

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Urban Building Model


Our pilot explores advanced building models to improve their integration with urban architecture. To keep the computational scale highly efficient, we utilize a simplified monozone model that calculates and provides critical source-term data for heat and air pollutants (such as CO2 and NOx) directly to the urban air pollution model.

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Renewable energy sources


This pilot advances energy production estimation for renewable energy sources, such as wind farms and solar panels, while also predicting potential damage to the infrastructure. To achieve this, we apply an uncertainty quantification study to our simulation models and run high-resolution ensembles on a much larger HPC scale.”

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Wildfires

Our pilot implements the advanced computational environment necessary to simulate wildfire-atmosphere interactions and smoke dispersion at multiple scales. We focus on evaluating risks and potential impacts driven by mesoscale and microscale fire behavior, specifically targeting the highly vulnerable Wildland-Urban Interface (WUI) zones.

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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.

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