Alliez, Pierre

Brunetti, François

Vadez, Vincent

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HAL Id: hal-02367540https://hal.inria.fr/hal-02367540Submitted on 18 Nov 2019HAL is a multi-disciplinary open accessarchive for the deposit and dissemination of sci-entific research documents, whether they are pub-lished or not. The documents may come fromteaching and research institutions in France orabroad, or from public or private research centers.L’archive ouverte pluridisciplinaire HAL, estdestinée au dépôt et à la diffusion de documentsscientifiques de niveau recherche, publiés ou non,émanant des établissements d’enseignement et derecherche français ou étrangers, des laboratoirespublics ou privés.First steps of numerical simulation using ArtificialIntelligenceVincent Vadez, François Brunetti, Pierre AlliezTo cite this version:Vincent Vadez, François Brunetti, Pierre Alliez. First steps of numerical simulation using Artificial In-telligence. European Space Thermal Engineering Workshop 2019, Oct 2019, Amsterdam, Netherlands.￿hal-02367540￿ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003 Page 1First Steps of Numerical Simulation using Artificial IntelligenceVincent Vadez DoreaFrançois Brunetti DoreaPierre Alliez Inria Sophia Antipolis - MéditerranéePage 2ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  Project-Team Titane‣ Geometric modeling of 3D scenes from measurement data‣ Analysis, reconstruction, approximation‣ Computational geometry, geometry processing, machine learningCentresAntennasSaclayIle-de-FranceRennesBretagne AtlantiqueBordeauxSud-OuestLilleNord EuropeParisNancyGrand estGrenobleRhône-AlpesSophia AntipolisMéditerranéeNantesPauMontpellierLyonStrasbourgPage 3ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  CONTEXT AND MOTIVATIONS‣ Additive manufacturing yields increasingly complex objects‣ Reduced weight via topology optimization‣ Many more facets elements are required to describe these free-form shapes, which are later added to the full satellite model.Image from www.eos.infoPage 4ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  CONTEXT AND MOTIVATIONS‣ Context of real-time simulation & sensibility‣ Radiative thermal simulation is time-consuming: O(n²) complexity for the view factors, with n the number of faces of the mesh.‣ Full simulation intractable on the complete satellite model, in a reasonable time.‣ A thermal-aware geometric approximation process is required, allowing real-time simulation and beyond (multi-physics simulation and predictions).Image from www.ata-e.comPage 5ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  PROBLEM STATEMENT‣ Input: Complex surface mesh (many facets and occlusions, created by a CAD software)‣ Output: Approximated model respecting the view factors of the thermal nodes‣ Guarantees: Error bounds under wide range of configurations and conditions‣ Goal: Optimize trade-off between accuracy and timePage 6ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  DIRECTIONS‣ Topic of Ph.D. thesis: Design a geometric approximation method preserving a simulation-aware error metric rather than a geometric error metric. ‣ Application to space thermal analysis: view factor computation and model reduction‣ Goals: ‣ Compute reference view factors‣ Compare with approximated view factors‣ Evaluate simulation with approximation‣ Utilize supervised machine learning to automate the reduction process, leveraging a large training dataset.Image from www.warontherocks.comPage 7ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  VIEW FACTORView factors depend on 3 components: • area of the faces• distance between them• orientationPage 8ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  RELATED WORK‣ Accelerating computation of view factors‣ Jacques, Masset, Kerschen: Ray tracing enhancement for space thermal analysis: isocell method , 27th Space Thermal Analysis Workshop, ESTEC.Page 9ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  RELATED WORK‣ Schröder, Hanrahan: On the Form Factor between Two Polygons.Proceedings of ACM SIGGRAPH 1993.→ Closed form solution for the view factor between two convex       polygons‣ Walton: Calculation of Obstructed View Factors by Adaptive Integration. NIST Report, 2002.Closed form (without obstruction)Page 10ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  ACHIEVEMENTS‣ Hierarchical geometric data structure: AABB-tree(fast intersection queries)‣ Closed form solution when full visibility (Schröder)‣ Quadrature in the presence of occlusions: ‣ Point-based (via bounded centroidal Voronoi diagrams)‣ Triangle-based (recursive longest bisection) Reference view factorsPage 11ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  Bounded Centroidal Voronoi Diagrams (CVD)Tournois, Alliez, Devillers: 2D Centroidal Voronoi Tessellations with Constraints.Lloyd iterationLloyd iterationsPage 12ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  VIEW FACTORS (VF)EmitterVF value01Page 13ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  COMPARISONSMaximum difference of view factors#quadrature pointsMax error with e-Therm [Dorea]Max error with View3D [Walton]Min error with bothPage 14ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  COMPARISONSComparison with e-Therm [Dorea]Comparison with View3D [Walton]#quadrature pointsStandard deviation of view factorPage 15ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  COMPARISONSComparison with e-Therm [Dorea]Comparison with View3D [Walton]#quadrature pointsAverage error of view factorPage 16ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  RANDOM SAMPLING VS CVDRandom uniform sampling Bounded Centroidal Voronoi View factor exact valueResults with two random triangles configuration and an obstacle between themView factor value#quadrature pointsPage 17ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  RANDOM SAMPLING VS CVDRandom uniform sampling Bounded Centroidal Voronoi View factor exact valueResults with two random triangles configuration and an obstacle between themView factor value#quadrature pointsPage 18ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  VARYING #LLOYD ITERATIONSResults with two random triangles configuration and an obstacle between themView factor value#Lloyd iterationsPage 19ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  ON-GOING WORK‣ Approximation method guided by preservation of the viewfactors. We keep the constraint of the thermal nodes in order tocompare the matrices of the radiative surfaces per nodes.Page 20ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  ON-GOING WORK‣ Simplification via face clustering in order to best approximate the thermal nodes.‣ Comparison with the reference calculation thanksto thermal nodes:‣ Main idea: compare the radiative surfaces by nodematrices from the reference calculation case and the approximation oneIllustration of the thermal nodes with e-ThermPage 21ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  WHAT’S NEXT: SUPERVISED MACHINE LEARNING‣ Goal: learn geometric error metric able to govern an automaticapproximation algorithm so that the resulting thermal simulation is asaccurate as possible to a reference calculation.‣ Constraints = thermal nodes, so we can compare the radiative surfaces ofeach node before and after approximation.[Jacobson] Thingie10K (training dataset)Page 22ESTEW – DOREA – 2019 – Réf: DOR/REP/2019/003  THANK YOU‣ Contacts:‣ vincent.vadez@dorea.eu‣ pierre.alliez@inria.fr‣ francois.brunetti@dorea.eu

First steps of numerical simulation using Artificial Intelligence

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First steps of numerical simulation using Artificial Intelligence

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