blog section, code


Dans la continuité des stages de Marc ALECIAN (2021), puis Mina CHAPON (2021), un outil Python a été développé pour calculer les facteurs de formes de rayonnement entre « facettes » planes.

Incontournables dans la détermination de la température moyenne radiante, les facteurs de formes sont un paramètre de première importance dans la détermination du confort thermique, notamment pour le rayonnement CLO & GLO (plus d’explications sur la page dédiée : Calcul de la MRT).

Après avoir été présenté à la conférence IBPSA France 2022, le code est désormais disponible sur Gitlab : et sur PyPi avec un simple pip install pyviewfactor !

blog section, Publication

IBPSA France 2022

AREP L'hypercube attended the IBPSA France 2022 conference (at Châlons-en-Champagne, may 19th-20th), to present a paper entitled " Computation of View Factors between polygons - Application to urban thermal and comfort studies« .

Work is underway to make these codes open-source, as a Python library - Stay tuned! Stay tuned!

Abacus of VF between a wall and an individual (cylinder)

Comparison of surface temperatures with and without taking into account the exact view factors

MRT computation with view factors

blog section, Publication

The PET comfort index included to the pyThermalComfort Python library

Our version of the Physiological Equivalent Temperature (PET) comfort index, published in 2018 in Building&Environment has made it to pythermalcomforta project from the Center for the Built Environment (CBE) at UC Berkeley (amongst others S. Tartarini & S. Schiavon)

pyThermalComfort includes many other comfort metrics (PMV/PPD, SET, DR... a bit lost? A recap here) and the steady state PET is naturally added for a wider diffusion of this reference model.

More information in the documentation !

blog section, Projects

National Grand Prize for Engineering - Syntec

L'hypercube is proud to have contributed to the Grand Prix National de l'Ingénierie 2021, won by AREP teams, for the transformation project of the Saint-Michel Notre-Dame station (RER C).

For the hypercube, it is the recognition of the Air Quality expertise, born during this project in 2017.
After preliminary study phases that were marked by the development of predictive models of air quality in train stations and a fruitful collaboration with the CSTB allowed us to develop a simulation framework to estimate the ventilation induced by train passages ("piston" effect),the main driving force of the air quality improvement on this project.

Subsequently, we had to deal with other site constraints, such as acoustics and flood control. It is precisely this collective and iterative work of the different engineering departments that was rewarded by the GPNI!

Below is a video presentation of the main challenges of the project:

Non classé

World UTCI and MRT

A "new" database has been made available by the EU, from the Copernicus project (free access, login required).

UTCI and T_{MRT} , August 31st, 2018


This dataset provides a complete historical reconstruction of the UTCI (Universal Thermal Confort Index, a thermal comfort index as well as the MRT (Mean Radiant Temperature, essential for the thermal comfortestimation), from 1979 up to today. Those parameters are computed by the ERA5-HEAT (Humen thErmAl comforT) model. It is based upon a reanalysis of observations from across the world, to provide a globally complete and consistent description of the Earth’s climate and its evolution in recent decades.


Technical data:

Data type Gridded
Horizontal coverage Global except for Antarctica (90N-60S, 180W-180E)
Horizontal resolution 0.25° x 0.25°
Vertical resolution Surface level
Temporal coverage 1979-01-01 to near real time for the most recent version.
Temporal resolution Hourly data
File format NetCDF
Conventions Climate and Forecast (CF) Metadata Convention v1.6
Versions UTCI v1.0
Update frequency Intermediate dataset updated daily in near real time, Consolidated dataset monthly updates with 2-3 month delay behind real time.


Non classé

160 Cores dedicated to building physics

Since 2017, The Hypercube has been performing detailed airflow simulations and implementing numerical optimization procedures. These methods require significant computing power, which until now has been provided by "virtual machines" in Microsoft's Azure cloud. The team thus had 24 bodybuilt "machines" (8 ultra-clocked logic cores and 56 to 112 GB of RAM), switched on on demand and allowing a high reactivity.

This solution, which has been in use for 2 years, limited us in terms of reactivity (IT maintenance) and connectivity (data transfer speed) with a non-negligible associated cost.

The question then arose, with the AREP IT team, of an opportunity to invest in an "on-premises" calculation server... It is now done! It has just been delivered, in kit form.

It is therefore a computing rack with 4 processors, 20 cores each capable of hyperthreading (virtualization of a second core), in other words, 160 cores dedicated to simulating building physics phenomena! This represents the equivalent of more than 25 high-performance computers (for example for 3D visualization).

160 cores in action !

Today, this "beast", with 256 GB of RAM memory, extensible up to 3TB, runs on the open source Linux operating system (Debian 9).

Many thanks to AREP IT Services for their advice, support and energy!

Non classé

Autonomous measurement station

An autonomous measuring station for diagnosis

Resulting from a partnership of more than two years between AREP and the electrical engineering speciality of the INSA of Strasbourg, here is an autonomous measuring station, started during previous projects (see L'Hypercube references) which was finalized in January 2019 thanks to the Technological Research Project of François-Alexandre Fournier, a GE5 engineering student at INSA: we thank him here for his unequalled investment!

Including a Raspberry PI base and low power wireless sensors Whisper node from WISEN, this station was custom developed for AREP's needs and includes a dozen temperature and humidity probes as well as a CO2 sensor. Particular attention was paid to energy consumption and robustness of operation during the project. Field tests are planned for 2019.

[ Autonomous station: Raspberry PI + 868 MHz communication with wireless sensors]