This document discusses ArcelorMittal's use of numerical models for research and development. It introduces the Diabolo paradigm, which is a solution developed using ESI software to address some key issues with managing and sharing models. Specifically, the Diabolo paradigm creates a centralized model repository and toolbox to make models more reusable and easier to share across different users and geographic locations. It aims to support the full lifecycle of models and ease multi-domain simulations. Advanced use cases discussed include building physics simulation, optimization tools, and a statistical model toolbox.

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Welcome
Stéphane Jimenez, ArcelorMittal global R&D
November 20th, 2018

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The Diabolo Paradigm

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The Diabolo Paradigm
ArcelorMittal R&D develop numerical models since more than 30 years
Models are (very) heterogeneous in technology and structure …
Developers are geographically distributed …
Models are used and maintained by many different users over time …
Models are not easily shared nor re-used  can be redeveloped instead
Models Lifecycle is hard to manage  lost of [digital] knowledge
 huge ‘Time to Industrial Use’
Ease models sharing and reuse
Ease chaining and Multi-Domain simulation
Support Models LifeCycle
… enter a virtual circle to enhance our Digital Knowledge

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The Diabolo Paradigm
A gateway between R&D and Industry worlds
… from Innovation to industrialization
… from exploratory to standards
… to support Models Lifecycle
Tools, methods (standards) and organization

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The Diabolo Solution

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Module
Generator
Scientific documentation
Executable component
Technical specification and user help
C, FORTRAN, Java, Scilab, Python*, Excel**
A centralized Model Repository and a Depot
Model
Publication
Workflow

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 Authenticated access to the ArcelorMittal ATOMS depot
 Advanced user friendly tools : Data, Statistical, Analysis, Optimization, Plots
 Model access is filtered according to the user role
A centralized Model Repository and a Depot
Diabolo Model block
Diabolo Control (source) blocks
Diabolo Visualization (sink) blocks

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A powerful and user friendly Toolbox set
 Complete data port management thru a graphical interface
Model blocks embed Input and Output data multiplexors
 USER_BLOCK permit to easily program specific simulation blocks
 Advanced blocks comes with a user friendly interface
Edit
source code

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A powerful user friendly Toolbox set
In a simple (graphical) way
 Model Chaining
 Data Access
 Tools for Parametric studies
 Optimization and reverse analysis
Evolution of the cooling profile associated to each
individual
Evolution of the cost associated to the
population of one hundred individuals
Optimizing cooling profile with genetic algorithm (NSGA II) bock
Chaining ‘product model’ (flat carbon) with ‘process model’ (Pipe Forming)
Analysis of Sensibility

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 About 300 developers & users of model
Diabolo today … 120 users, 80 models

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Advanced Use Cases
The Building Physics simulator concept

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The Building Physics simulator concept
Optimization for Advanced Design System Toolbox (optimADS)
 Previously developed in an exploratory project in automotive by ArcelorMittal global R&D and INRIA
 Multi-objective optimization based on surrogate model construction
 A Non-dominated Sorting Genetic Algorithm is used to optimize the surrogate model
 Non dominated solutions are extracted using Scilab pareto_filter
 Easy configuration thanks to a user friendly interface
DACE for generation of Surrogate model
SVM-KM to reduce the number of variable of surrogate model
NSGA II for fitting of Surrogate model
A Java GUI o define
 Name of the file to save the calculated DOE
 List of optimization variables and their bounds
 List of optimization objectives and their bounds
 Scilab code for DOE discrete projection
 Scilab code for constraints on optimization variables

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The Building Physics simulator concept
Use of existing ArcelorMittal global R&D models, software and solvers for Buildings
 Price and weigh estimation of Steel framed buildings
 Mechanical constraints and thermal properties for “sandwich” panel facade
 Heating and cooling Energy
 LCA and Cost Modules
Building dimensions
Type of Slabs and Reinforce
Weight and thickness of Slabs
Total steel weight
Total steel frame price
Project definition
Building use, time life,
localization, type of facade,
% opening, king of heating
Grid span
Façade Thickness
Mechanic and Thermal
properties of Façade

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The Building Physics simulator concept
Use of existing ArcelorMittal global R&D models, software and solvers for Buildings
 Price and weigh estimation of Steel framed buildings
 Mechanical constraints and thermal properties for “sandwich” panel facade
 Heating and cooling Energy
 LCA and Cost Modules
CO2
Total cost
Heating + Cooling energy
Energy consumption
for heating and cooling
CO2 quantity
Primary Energy Demand
Non dominated solutions

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The Building Physics simulator concept
The global PIDO schema has been calculated in Diabolo
 The whole optimization is an automated process
 Calculation time is huge due to thermal calculation ( HPC !)
 The use of surrogate is essential to explore wide range of the space of solution
 Very promising results have demonstrated the Proof of Concept
Original combination of grid
span and façade thickness

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Advanced Use Cases
(Quick release of) Rich GUI applications for Process Study use

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Rich GUI applications for Process Study use
Generic architecture for a rich GUI application framework
 Use Scilab Engine API for model running
 Distribute Model as Diabolo Modules [Scilab Gateways]
 Develop generic Qt widget to capitalize graphic components
 Setup a Model Engine able to dynamically setup the GUI for a model
 Significant speed up the model delivery process
 20 single or chained models deployed in factories for Process Study Use
 More than 200 users

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Advanced Use Cases
The Statistical Model Toolbox

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The Statistical Model Toolbox
ArcelorMittal Statistical Toolbox
Automatic generation of linear and non linear models from industrial data
 Previously developed in Splus (commercial implementation or R language)
 Fully ported to Scilab V5 and currently porting to V6
 Will be release under two form : A Diabolo Toolbox and a Windows rich GUI application
NaN-toolbox for clustering and correlation
XLS_link for data import / export and reporting
Diabolo toolbox and Windows Rich GUI application
 Vapnik model to estimate and order influence of data
 Automated generation and optimization of models
 Analysis and filtering tools
Stixbox to calculate linear regression
Stability SPC
Vapnick curve
Foolproof
Performance

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The Statistical Model Toolbox
Lifecycle mgmt. for Statistical Metallurgical Models
Research Engineer
Process Engineer
Process
data
Study
Refinement
Validation
Quality Control
Product Qualification
Configuration Manager for the Quality Platform
Comit to Config Mgmt

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Thank you !