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PNC, “Collaboration: Tools and Infrastructure” December 7, 2012

PNC, “Collaboration: Tools and Infrastructure” December 7, 2012. PrIMe : Integrated Infrastructures for Data and Analysis. Michael Frenklach. Supported by AFOSR, Fung. Combustion is Central to Energy. IMPACT ON SOCIETY Energy (power plants, car and jet engines, rockets, …)

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PNC, “Collaboration: Tools and Infrastructure” December 7, 2012

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  1. PNC, “Collaboration: Tools and Infrastructure” December 7, 2012 PrIMe: Integrated Infrastructures for Data and Analysis Michael Frenklach Supported byAFOSR, Fung

  2. Combustion is Central to Energy • IMPACT ON SOCIETY • Energy (power plants, car and jet engines, rockets, …) • Defense (engines, rockets, …) • Environment (pollutants, global modeling, …) • Space exploration • Astrophysics • Material synthesis • established practice of collaboration • Across different disciplines • Across different countries • There is an accumulating experimental portfolio • THEORY/MODELING LINKS FUNDAMENTAL TO APPLIED LEVEL

  3. experiments theory individual reactions model sensitivity reaction path … model reduction analysis • mechanism of: • ignition • laminar flames • NOx • soot • ... numerical simulations

  4. Methane Combustion: CH4 + 2 O2 CO2 + 2 H2O 1970’s: 15 reactions, 12 species 1980’s: 75 reactions, 25 species 1990’s: 300+ reactions, 50+ species Larger molecular-size fuels: 2000’s: 1,000+ reactions, 100+ species 2010’s: 10,000+ reactions, 1000+ species

  5. Methane Combustion: CH4 + 2 O2 CO2 + 2 H2O and yet The networks are complex, but the governing equations (rate laws) are known Uncertainty exists, but much is known where the uncertainty lies (rate parameters) Numerical simulations with parameters fixed to certain values may be performed “reliably” There is an accumulating experimental portfolio on the system

  6. Methane Combustion: CH4 + 2 O2 CO2 + 2 H2O but still Lack of predictability Lack of consensus

  7. PROBLEMS • current inability of truly predictive modeling • conflicting data in/among sources • poor documentation of data/models • no uncertainty reporting or analysis • not much focus on integration of data • resistance to data sharing • no personal incentives • no easy-to-use technology • no recognition of the problem

  8. models are not additive • data are not additive • need a system for synthesis of data

  9. PrIMe http://primekinetics.org Infrastructure for UQ-predictive modeling Process Informatics Model • Data sharing • App sharing • Automation

  10. Current status • registered members ~400 • countries ~15 • data records ~100,000 • apps~20 • active “players” • UCB (lead), NSCU, Stanford, MIT, Cambridge, KAUST, Tsinghua

  11. data organization: • conceptual abstraction • practical realization

  12. Conceptual Abstraction: Data Model Chemical Kinetics Model composed of have Chemical Reactions rate law data -parameter values -uncertainties -reference composed of Chemical Species have composed of thermo data transport data Chemical Elements have atomic masses

  13. molecular structure - - absorption coefficient spectra reactions thermo Practical Outcome:Trans-Disciplinary Collaboration combustion modeling quantum chemistry thermosciences diagnostics

  14. PrIMe Data Model: experiments • Experimental Record • reference • apparatus • conditions • observations • inner: XML • remote: HDF5, … • uncertainties • additional items • links, docs, … • video files, … • Data Attribute (QOI, ‘target’) • a specific feature extracted for modeling: • peak value • peak location • induction time • ratio of peaks • (from multiple experiments) … VVUQ data instrumental model archival record

  15. PrIMe Data Model • Initial Model: • “Upload your data to PrIMe Warehouse” (“give me your data”) • New, Distributed Model: • “You may, if choose, connectyour data to the communal system” • with a switch in the OFF position: “you can use the communal data and tools but your own data is private to you only” • “but please flip the switch to the ON position when you are ready to share your own data”

  16. same for apps • “Connect your codeto the communal system” • - you control your own code: • release version • user access, licenses • collect fees, if desired

  17. Technology: How • Remote server app—PrIMe Web Services (PWS) • no restrictions on platform • no restrictions on data formats • no restrictions on local programming language(s) • PrIMe Workflow Interface (PWI) is the only “standard” • developed, maintained, and controlled by the community

  18. PrIMe Dispatcher PrIMe Data Flow Network client machine PrIMe I n t e r f a c e PrIMe web services client data

  19. Big Data • excessively large data sets • do not move the data • but use “smart agents” (eg, HTML5 walkers) web services with user-reloaded tasks: fetch data features for user-requested analysis

  20. workflow component performs: • retrieves the pertinent kinetics model (via link in the dataset) • performs simulations on the fly for the conditions specified and builds a new surrogate model • performs UQ analysis combining the new surrogate model with the archived ones and the rest of the pertinent data • reports results workflow project user specifies conditions of interest • workflow component retrieves archived data: a set of relevant targets • target values and their uncertainty ranges • surrogate models developed for relevant targets • active variables and their uncertainty ranges data warehouse

  21. workflow component performs: • retrieves the pertinent kinetics model (via link in the dataset) • performs simulations on the fly for the new data and builds a new surrogate model • performs UQ analysis combining the new surrogate model with the archived ones and the rest of the pertinent data • reports results • adds the new data to the dataset and archives in Warehouse workflow project user specifiesa new setof data • workflow component retrieves archived data: a set of relevant targets • target values and their uncertainty ranges • surrogate models developed for relevant targets • active variables and their uncertainty ranges enrichment data warehouse

  22. focus on answering questions: prediction of (un)known observations

  23. focus on answering questions: prediction of an (un)known parameter

  24. focus on answering questions: prediction of multi-D correlations

  25. ANSWER QUESTIONS • What causes/skews model predictiveness? • Are there new experiments to be performed, old repeated, theoretical studies to be carried out? • What impact could a planned experiment have? • What is the information content of the data? • What would it take to bring a given model to a desired level of accuracy?

  26. A PARADIGM SHIFT from algorithm-centric view to data-centric view input output code data data

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