HiDALGO urban air pollution pilot based on CAMS data

Zoltán Horváth, Bence Liszkai, Ákos Kovács, Tamás Budai and Csaba Tóth

Széchenyi István University, Győr, Hungary

HiDALGO – EU founded project #824115

CAMS 4th General Assembly and User DayBudapest, 16-20 September 2019

Vision of HiDALGO’s Urban Air Pollution

Provide citizens and policy makers

with forecast and reanalyses

at very high resolution

for urban air pollution

using

science and

supercomputing resources

through an easy web-interface.

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Agenda

1. The global challenge: improve urban air quality

2. The HiDALGO digital twin for urban air pollution

3. Demonstration to Győr and Stuttgart

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Global challenge: improve urban air quality

• 3 million deaths attributable to ambient air pollution, by WHO| https://www.who.int/phe/health_topics/outdoorair/databases/en/

• Traffic is emitting >40% of several contaminants (e.g. NO2)

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https://www.who.int/phe/health_topics/outdoorair/databases/en/

Global challenge: improve urban air quality

• EC regulates air quality management and allows the use of computational models for reporting (see Directive 2008/50/EC).

• EC provides forecasts for air quality from CAMS: | European AQ – Ensemble hourly forecasts and analyses

| AQI for every 3 hours, for several cities, one value for the whole town

• However, …

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Global challenge: improve urban air quality

• Example: Győr, Hungary. NO2 simulation with 3D geometry

| Hot spots occur even when overall city values are OK

| High resolution validated simulation is needed→ need of CFD & HPC

| recent activities are starting within FAIRMODE as well

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The HiDALGO digital twin for urban air pollution

• Background: | H2020: MSO4SC, CoeGSS

| Hungarian-ESF-projects: SZE FIEK (GINOP)

• HiDALGO – Center of Excellence for HPC and Big Data for Global Challenges| H2020 CoE project, from December 2018 until November 2021

| provides HPC, HPDA infrastructure by experts, and

pilot services based on the infrastructure for global challenges

| HiDALGO is to do ”the heavy weight lifting for modelling - HPC, HPDA and algorithms - of global challenges and some finale mile runs”

| Technical coordinator: HLRS, coordinator: ATOS

• Goal of HiDALGO urban air polution (UAP) pilot: develop a service for UAP with very high resolution

• Demonstration area: Győr, Hungary (of 130.000 habitants)

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What is a model based digital twin?

• HiDALGO UAP as a digital twin

• Digital twin = digital replica of a real physical asset for which| digital image is based on computational simulations of physical models,

| connected with sensor measurements to the real asset,

| models updated continuously upon measurements,

| gives real time answers to questions on the real asset (based on modelorder reduction)

• More details:

see the booklet by EU-MATHS-IN on

technologies for digital twinning (much)

beyond the state-of-the-art

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HiDALGO digital twin for urban air pollution - goals

• Highly accurate simulation of urban air pollution

| Real 3D geometry of the city

| High resolution mesh: 1 m at street level

| Online and real time sensor data from sensor networks(cameras with plate number recognition and low cost AQ)

| Traffic emission: from SUMO simulation or statistical data

| Weather forecasts from ECMWF

| CAMS data for background concentration, local emissions, and long distance emission, all on coarse grid

| Highly accurate simulation (CFD) for wind and dispersion

• Model order reduction and ensemble modelling for the pollution

• Service to be developed, aim: CAMS use case

• Traffic management (model predictive control) based on the digital twin

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The HiDALGO digital twin for urban air pollution

• Sensors1: Intelligent camera based sensor network of the traffic| Implementation is ongoing by Adaptive Recognition Hungary (ARH) and

Hungarian Public Road Ltd (MK)

| Plate number recognition and loop detector data

| Generate full trip information, origin-destination matrix

| Data will be anonymised and transported real time to SZE directly

| SUMO model will be updated real time based on data assimilation

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The HiDALGO digital twin for urban air pollution

• Sensors2: Weather and background pollution data| Weather and pollution sensor data are assimilated into simulation for

predictions and reanalyses

| Weather (forecast and reanalyses) data are provided by ECMWF through

| data exchange and postprocessing via Python scripts (now)

| REST API service of ECMWF (to be developed in HiDALGO)

| Weather data are used for

| boundary conditions for the city wind field computations

| advanced physical models (with radiation, humidity, etc; to be developed)

| Pollution data are used from the CAMS simulations and observations

| background concentration,

| local emissions,

| long distance emission, all on coarse grid

to be developed

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The HiDALGO digital twin for urban air pollution

Workflow of the initial version (MSO4SC 3DAirQualityPrediction)

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Overview of workflow

Module configuration

#!/bin/bash# Parameters for the dispersion module

#### Fluent configurations ####FLUENT_BINARY="fluent"NUMBER_OF_CORES=2FLUENT_CUSTOM_COMMAND_LINE_OPTIONS=""

#### Simulation and model parameters ####SIMULATION_START_TIME="2017-05-10 00:00:00"ITERATION_STEADY_FOR_INITIALIZATION=30ITERATION_TRANSIENT_PER_TIMESTEP=5TIMESTEP_SIZE_SECONDS=60NUMBER_OF_TIMESTEPS=30

# No2 concentration calculation ppb = no2.mass.fraction*1e9*46/28# 20 [ppb]=0.00000001217[no2-mass-fraction]NOX_BACKGROUND_MASS_FRACTION=0.00000001217

#### Geometry definitions ##### STL Surface definitions (surface_name stl-path)STL_SURFACES[0]="surface_2m slicer_surface.stl"

# Point definitions (name x y z)POINTS[0]="central_point 66.40663 69.79992 16.45698"POINTS[1]="side_point 58.4459 67.79778 16.45698"

#### Output controls ##### Full domain outputsCASE_AND_DATA_SAVING_ENABLED="True"CASE_AND_DATA_OUTPUT_PREFIX="model_result"CASE_AND_DATA_SAVING_PERIOD=10

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ENCAS_SAVING_ENABLED="True"ENCAS_OUTPUT_PREFIX="model_result"ENCAS_SAVING_PERIOD=10

STATE_MATRIX_SAVING_ENABLED="True"STATE_MATRIX_OUTPUT_PREFIX="state-matrix"STATE_MATRIX_FIELDS="nox-ppb x-velocity y-velocity z-velocity"STATE_MATRIX_SAVING_PERIOD=10

# Monitors (geometry-name field)MONITORS[0]="central_point nox-ppb"MONITORS[1]="central_point x-velocity"MONITORS[2]="central_point y-velocity"MONITORS[3]="central_point z-velocity"MONITORS[4]="side_point nox-ppb"MONITORS[5]="surface_2m nitrogen-dioxide"

#Plots (surface1,surface2,surfaceN field min-val max-val) wher min-val and max-val are optionalPLOTS[0]="wall_ground,wall_building velocity-magnitude"PLOTS[1]="surface_2m nitrogen-dioxide 0 0.0000001085"PLOTS[2]="surface_2m nox-ppb 0 50"PLOTS[3]="surface_2m velocity-magnitude"PLOTS_SAVE_PERIOD_TIMESTAMPS=1PLOTS_RESOLUTION="1920x1080"

• Input parameters| set in a text file (e.g. see that below for the dispersion module),

| some of them edited in the portal GUI (in TOSCA blueprint)

| input-output files are standardized (→provides opportunity for changingsolvers)

The HiDALGO digital twin for urban air pollution

• Demonstration and validation to Győr

| Area: 4 km x 4 km x 0.8 km

| Mesh: 800.000 octree cells; meshsize: from 2 m (street) to 50 m

| Computation time: 1/3 of simulation time on 16 cores

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• ANSYS Fluent module for wind and dispersionsimulation (transient Navier-Stokes with turbulencemodelling, diffusion-advection-reaction of NOx, O3; transient boundary conditions)

• Experiments with an open source, free software aswell (e.g. OpenFOAM)

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Dispersion computation

The HiDALGO digital twin for urban air pollution

Application to Stuttgart

Application to Stuttgart (test of the HiDALGO urban air pollution pilot)

| All preprocessing steps took 5 person days

| 3D geometry is generated from Open Street Map - Video

| Meshing is done via in-house octree-mesher

| Traffic is simulated with SUMO based on synthetic data

| Weather data are from ECMWF forecast to 2019-05-25

| Northern wind of 2 m/s, in most of the day

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geo-stuttgart0-video.mp4

Further applications – illustrations to Stuttgart

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Towards the digital twin

Model order reduction (MOR) of the air flow computation and dispersion modelling (ongoing)

| Snapshot matrix compilation is solved from workflow

| SVD for one use case (with transient wind boundary conditions forthe Navier-Stokes equations (Re=10^9) and transient emission): s. values drops 2 magnitude with < 20 s. bases vectors) → goodstarting point for the MOR

| Note: HPC is used at compositionof the reduced (i.e. computationallycheap) models only; at productiononly cheap models will run.

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The HiDALGO digital twin for urban air pollution

• Usability: run the simulation on HPC from simple, web based portal→ HPC is reachable for policy makers easily!

• Now the MathSO portal operates→ online demonstration!

https://youtu.be/RV1Tg7-Rl1c demonstration video

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https://youtu.be/RV1Tg7-Rl1c

Conclusions and further steps

Done: operational simulation infrastructure of urban air pollution with| CFD for dispersion

| HPC (use of supercomputers)

| Easy-to-use web based user interface

| Fast preprocessing, enabled by developed tools

Next steps| Physical model to be developed

| Implementation of model order reduction for faster simulation

| Coupling with CAMS data

| New indicators to be worked out according to high resolution

| More requirements gathering

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THANK YOU !

QUESTIONS ?

Prof. Zoltán HorváthSzéchenyi István UniversityEgyetem tér 1.9026 Győr, HungaryPhone: +36-96-613657Email: horvathz@math.sze.hu

HiDALGO – EU founded project #824115