Energetic Systems of Tomorrow,from traditional wood stove to « IOT » multi-functional solution

ENERGETIC SYSTEMS OF
TOMORROW
FROM TRADITIONAL WOOD STOVE TO « IOT »
MULTI-FUNCTIONAL SOLUTION
Projects Optifoyer & PCC80, supported by Wallonia
Project SMARTHEAT, supported by EU
TEKK Tour Nov. 6th, 2017 Copyright Cenaero

Who are we ?
– Private Research Center in Simulation,
Optimisation, Data analytics
• Aerospace & Transport
• Manufacturing & Process industries
• Built environment & Smart Cities
– Stakeholders
• Technological & Numerical industries
• Public & Private decision makers
• Business & Scientific clusters
– Walloon HPC center operator (« zenobe »)
Safran partnership 2007
Creation 2002
HQ in Gosselies
Commercial subsidiary in
Paris
Composite
Workshop
1 Start-up
50+ FTE
50+ % PhD
5+ Mio € of Turnover
Incl. 40% industrial revenues
(2015)
30+ projects
R&D (2015)
20+ Regional SME
collaborations (2015)
Tier-1 HPC center
(14,000+ cœurs)

Cross-fertilisation … From plane to building
1 Pilot city
(Charleroi)
Energy & Buildings
team
10+ PPP R&D
projects
30+ companies
collaboration (75+ %
PME) 5 (+3) jobs
created
Factories 4.0
Precast performance assessment tools
BIM-to-manufacturing lines (HMI)
Quantity take-off managment (ERP)
Advanced Products & Processes
Energy (Boiler, Ventilation, Fuel cell, …)
Structural/thermal composites & parts
Smart Buildings
Support to Certification
Control & data analytics
Software & App
New built environments
Microclimate in districts
Energy in building stock & grid
Big/geo-data analytics

The challenge of Energy in Buildings
• Directive for moving to NZEB ask to
• ReduceReduceReduceReduce heatingheatingheatingheating needsneedsneedsneeds (NWE)
… hence moving the problems on
ventilation (IAQ) and overheatingoverheatingoverheatingoverheating riskriskriskrisk
• Increase renewable shares
… hence coping with intermittence and
new activeactiveactiveactive systemssystemssystemssystems emergence
• EU expecting impact for Energy in Buildings
EeB H2020 program : “(…) reducing its (EU) energy consumption by as much as
50%, the construction sector is today on a critical path to help decarbonise the
European economy by 2050”

The Stûv strategy in a nutshell
– Diversify Energy source, from wood to pellet & gas
– Ensure Design & Performance links as highly differenciating
elements
• Highly efficient combustion
• Cost/Use trade-off consideration
– Adapt to the market & trends
• Multifunctional use in NZEB
• Think beyond the building block
– Be pro-active on environmental impact
• Ensure low emission (trough high performance & particule filtering)
• Contribute to the scientific diagnostic on air quality
Today

Stûv P10 development: iterative diffuser design
with HPC (CFD simulations)
HPC - 5 designs
Time~ 2designs/day
Resource ~ 128 cores/design
Tests - 5 designs
Time ~3 days/design
Resource ~1 FTE
(+) Time-to-market reduced
(+) Development Cost mastered
(+) Simulation serves for know-how
increase on dominant heat transfers in
a quick mode (design de-materialised)

Market competitive element outcomes
• Aesthetism : Optimal visual quality of
the flame
• Efficiency : High combustion level
reached
20 x CO less than norm (CO
normalised testing)
• New Patent
• RED DOT Award in July 2016 (Product
Design – Allemagne)
• Out-performing capacity demonstrated
by Test Achats (September 2017)

Conventional design & operation tools not suited
Towards a multi-fonctional and active solution

Making of the HPC-till-IoT link our strategy
• De-materialisation mandatory due to the augmented scale (virtual
prototyping)
• Simulation AND data must work together towards Stove 4.0
Produce anywhere and anytime!
• Our data & simulation « boxes » to play with

White box approach serving design & prototyping
Integrated 1D /3D White Box approach
= Scale managment (space & time)
3D CFD local model
(comfort, stove
efficiency optimisation)
1D (TRNSYS) local model
(Season-based assessment,
Building dynamics & regulation)
Optimal design in comfort &
efficiency (local/global)
Regulation function estimate
serving IoT/control strategy
Produce anywhere and anytime!

• Neuronal Network trained with white box experiments (TRNSYS)
• 2-para. stove heat model equation (Thermal mass & Ratio convective/radiative)
• 4-para. building (Type 56) model (2 in/out EPB-compliant envel. & 2 ventil. distribution modes)
• Identify the most influential parameters based on the NN
• KPI : Overheating & Stove consumption
Stove's thermal mass and convective
ratio, impact on overheating Sensitivity in Stove consumption
Explore concepts with Black Box models
6 para
TRNSYS
2 KPI
+ Mass
+ Overheating
lasts (hence
impacts)
+ Convection
+ Overheating
(questionable wrt.
Local/stratification model)
Limited impact of ventilation
distribution MODES & envelop
(since EPB compliant)
Design parameters impact >80%!
possible optimisation

• White Box Stove model (Thermal mass & Ratio convective/radiative)
Grey Box replacement based on measurements on prototype
RC OpenModelica Grey Box model
• IN : Combustion power
• OUT : Delivered power (moremoremoremore
realistic value for Type56realistic value for Type56realistic value for Type56realistic value for Type56)
… and use it in the full
TRNSYS model
Add realism to simulation with Grey Box
Grey Box model parameter
calibration (9 variables)
OUT(expe)
OUT(model)
IN

Energetic Systems of Tomorrow,from traditional wood stove to « IOT » multi-functional solution

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Energetic Systems of Tomorrow,from traditional wood stove to « IOT » multi-functional solution