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

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Presentation on theme: "PNC, “Collaboration: Tools and Infrastructure” December 7, 2012 Michael Frenklach Supported by AFOSR, Fung PrIMe: Integrated Infrastructures for Data and."— Presentation transcript:

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

2 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 mechanism of: ignition laminar flames NO x soot... individual reactions model model reductionanalysis numerical simulations experiments theory sensitivity reaction path …

4 Methane Combustion: CH O 2  CO H 2 O 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, species

5 Methane Combustion: CH O 2  CO H 2 O 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 and yet

6 Methane Combustion: CH O 2  CO H 2 O Lack of predictability Lack of consensus but still

7 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 Process Informatics Model  Data sharing  App sharing  Automation

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

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12 DATA ORGANIZATION : conceptual abstraction practical realization

13 Chemical Kinetics Model Chemical Reactions Chemical Species Chemical Elements composed of have atomic masses rate law data -parameter values -uncertainties -reference have thermo data transport data

14 reactions - combustion modeling quantum chemistry diagnostics thermosciences thermo molecular structure spectra absorption coefficient

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

16 Initial Model: “Upload your data to PrIMe Warehouse” (“give me your data”) New, Distributed Model: “You may, if choose, connect your 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”

17 “Connect your code to the communal system” - you control your own code: release version user access, licenses collect fees, if desired

18 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

19 client machine client data PrIMe web services PrIMe Data Flow Network PrIMe Dispatcher

20 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

21 workflow project user specifies conditions of interest workflow component retrieves archived data: a set of relevant targe ts target values and their uncertainty ranges surrogate models developed for relevant targets active variables and their uncertainty ranges data warehouse 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

22 workflow project 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 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 enrichment user specifies a new set of data

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26 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?

27 from algorithm-centric view to data-centric view outputinput code data


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