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Providing structure to experimental data: A large scale - - PowerPoint PPT Presentation

Oct 19, 2022 363 likes •686 views

Providing structure to experimental data: A large scale heterogeneous database for collaborative model validation Jim Oreluk Arun Hegde Wenyu Li Andrew Packard Michael Frenklach SIAM NUMERICAL COMBUSTION 17 APRIL 4, 2017 Overview

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APRIL 4, 2017 SIAM NUMERICAL COMBUSTION 17

Providing structure to experimental data: A large scale heterogeneous database for collaborative model validation

Jim Oreluk Arun Hegde Wenyu Li Andrew Packard Michael Frenklach

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APRIL 4, 2017 SIAM NUMERICAL COMBUSTION 17

Overview

  • Introduction
  • Giving structure to experimental data
  • PrIMe Data Warehouse
  • New PrIMe application
  • front-end application to the CCMSC coal database (filter, visualization, and export data)
  • Bound-to-Bound Data Collaboration workflow for model validation
  • Summary
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  • Predictive modeling starts with validation
  • Experimental data stored in various file formats

– CSV, Excel, tab delimited, ASCII, etc. – No standard

  • Each record requires specialized knowledge of how the data was stored

– Can be an incomplete record of experiment with missing information

  • We would like automated access to data

– Without structure, query requests are quickly intractable across a diverse collection of data

  • Efficiently discover validation data to incorporate in the model validation process

Introduction

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Providing Structure to Experimental Data

  • What is PrIMe?

– Data Warehouse – repository of experimental records – Applications – aid in development of predictive models

  • Transformation of information into a usable form
  • PrIMe’s data models use XML schemas to provide structure

– Contains complete information of an experiment – Experimental data is stored in XML or HDF5 files

  • Storage of raw experimental data and derived properties

– Ability for instrumentation modeling

primekinetics.org

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CCMSC Coal Database for V/UQ CCMSC efforts

Dataset unit U = ( U, L, M ) Dataset unit

U2 = (U2, L2, M2)

Dataset unit

U3 = (U3, L3, M3)

Dataset unit

U4 = (U4, L4, M4)

Dataset unit

U5 = (U5, L5, M5)

Dataset unit

Ue = ( Ue, Le, Me )

crowdsourcing

  • International Flame Research Foundation, Livorno, Italy
  • Sandia National Laboratory, Livermore, CA

269 Solid Fuels & Blends Fossil, Biomass, Sludge, Waste, Char 2710 Data Groups collected from 1016 Records Varying Conditions (Temperatures, %O2, %H2O, Gas Mixture) Experiment Types: Devolatilization, Char oxidation In collaboration with Salvatore Iavaron and Alessandro Parente, Université Libre de Bruxelles

leveraging existing cloud infrastructure and data models

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CCMSC Coal Database

primekinetics.org github.com/oreluk/coalDB

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CCMSC Coal Database

Select Experiments Plot & Export Data

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CCMSC Coal Database

Fraction of Weight Loss Char Temperature

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Char Oxidation Example

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Char Oxidation Example

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Char Oxidation Example

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Char Oxidation Example Char Oxidation Example

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Char Oxidation Example

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Char Oxidation Example

Experimental Data of Utah Skyline coal from Sandia’s Laminar Entrained Flow Reactor Features: CO2 or N2 diluent Initial Particle Diameter: O2: H2O: Validation data at 399 different gas conditions & heights above burner

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Bound-to-Bound Data Collaboration (B2BDC)

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Bound-to-Bound Data Collaboration (B2BDC)

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Bound-to-Bound Data Collaboration (B2BDC)

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Bound-to-Bound Data Collaboration (B2BDC)

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Bound-to-Bound Data Collaboration (B2BDC)

Parameter space QOI space

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B2BDC Model Validation Workflow

Char Oxidation Model (Instrument + Physics)

Response [Image of Particle Temp distribution & highlight QOI]

Particle Temperature

Scenario Parameters,

CCMSC Coal Database

Uncertain Parameters

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B2BDC Model Validation Workflow

Response [Image of Particle Temp distribution & highlight QOI]

Particle Temperature

Scenario Parameters,

CCMSC Coal Database

Uncertain Parameters Char Oxidation Model (Instrument + Physics)

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B2BDC Model Validation Workflow

Response [Image of Particle Temp distribution & highlight QOI]

Particle Temperature

Scenario Parameters,

CCMSC Coal Database

Uncertain Parameters Char Oxidation Model (Instrument + Physics)

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B2BDC Model Validation Workflow

Response [Image of Particle Temp distribution & highlight QOI]

Particle Temperature

Scenario Parameters,

CCMSC Coal Database

Uncertain Parameters Dataset Unit Char Oxidation Model (Instrument + Physics)

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B2BDC Model Validation Workflow

Response [Image of Particle Temp distribution & highlight QOI]

Particle Temperature

Scenario Parameters,

CCMSC Coal Database

Uncertain Parameters Dataset Unit Consistency Analysis

Dataset Unit Dataset Unit Dataset Unit Dataset Unit

Dataset Unit Dataset Char Oxidation Model (Instrument + Physics)

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Validation through consistency

Model Form Transport

  • Diffusion of oxidizer to particle surface
  • Diffusion of products from particle surface

Scalar consistency measure:

If all constraints are expanded by at least 26% the inconsistency can be resolved. If all constraints are expanded by no more than 19% the inconsistency cannot be resolved.

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Validation through consistency

Model Form Transport

  • Diffusion of oxidizer to particle surface
  • Diffusion of products from particle surface

Scalar consistency measure:

If all constraints are expanded by at least 26% the inconsistency can be resolved. If all constraints are expanded by no more than 19% the inconsistency cannot be resolved.

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Validation through consistency

Scalar consistency measure: Model Form Transport

  • Diffusion of oxidizer to particle surface
  • Diffusion of products from particle surface
  • Diffusion of oxidizer through coal particle

– coal particle is a porous medium with internal surface area

Uncertain Kinetic Parameters

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Validation through consistency

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Summary

  • Developed new data models for coal data
  • Easy filtering through a diverse collection of experimental data
  • B2BDC based test-bed for exploring parameter and model form uncertainty

– With a consistent dataset we can do prediction of posterior QOI or parameter bounds, and sample the feasible set for correlations between parameters and QOIs

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Acknowledgements

This work is supported as a part of the CCMSC at the University of Utah, funded through PSAAP II by the National Nuclear Security Administration, under Award Number DE-NA0002375.