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MACCCR 5 th Fuels Research Review September 17, 2012 Michael Frenklach Supported by AFOSR PrIMe Next Frontier: Large, Multi-dimensional Data Sets.

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Presentation on theme: "MACCCR 5 th Fuels Research Review September 17, 2012 Michael Frenklach Supported by AFOSR PrIMe Next Frontier: Large, Multi-dimensional Data Sets."— Presentation transcript:

1 MACCCR 5 th Fuels Research Review September 17, 2012 Michael Frenklach Supported by AFOSR PrIMe Next Frontier: Large, Multi-dimensional Data Sets

2 PrIMe Cloud Infrastructure: ‒ Data Flow Network ‒ Remote Server: PrIMe-RMG ‒ Interfaces ‒ Big Data Other new developments: ‒ Species identification app ‒ UQ: Statistical sampling of the feasible set... PrIMe with Humanities

3 PrIMe Process Informatics Model  Data sharing  App sharing  Automation

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5 domain 2 web page Present-day Science Sharing: via web-page access database apps Internet domain 1 web page

6 science domain 2 database apps Internet science domain 1 PrIMe Science Sharing: via web-service data/app access

7 science domain 2 database apps Internet science domain 1 PrIMe Science Sharing: via web-service data/app access client web service data flow network client workflow app

8 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”

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

10 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

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

12 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

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14 Created ~2 years ago ‒installed by professional programmers ‒implemented on Reaction Design site Modified June 2012 ‒can be installed by users ‒implemented with RMG at MIT site ‒installed by first-year grad students!

15 installation manual

16 User creates a PrIMe Workflow (PWA) project User submits a request: “create a reaction model for …” The request activates RMG code at MIT server User receives when the model is generated User retrieves the model or it “moves” along the PWA project to the next component

17 client machine client data PrIMe web services binary XML – HDF5 e.g., reaction model: GRI-Mech 3.0

18 input data for UQ bypassing Warehouse species identification via crowd-sourcing UQ: sampling within the feasible region comparison between interval-to-interval UQ and rigorous Bayesian parallelization of Chemkin II

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24 experimental uncertainty M(x1,x2)M(x1,x2)  feasible set prior knowledge

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27 An ongoing collaborative study with Jerome Sacks, National Institute of Statistical Sciences Rui Paulo, ISEG Technical University of Lisbon Gonzalo Garcia-Donato, Universidad de Castilla-La Mancha Bayesian simulations: no simplifying assumptions, but utilize the Solution Mapping strategy for numerical efficiency

28 Execution time of flame simulations with a large acetylene model

29 Execution time of flame simulations with a hydrogen model

30 A collaborative project of PrIMe with Humanities: Berkeley Electronic Cultural Atlas Initiative

31 “Study of Buddhist Texts” PrIMe is used to predict the past The abstracted dots represent “panes”

32 A collaborative project of PrIMe with Humanities: Berkeley Electronic Cultural Atlas Initiative Berkeley Institute of Information: “Editors Notes”

33 Remote-server app and new apps −RMG: interface (with MIT, Bill Green) −Communal/User tools: Cantera (with NCSU, Phil Westmoreland) −Big Data: feature collection for UQ (with Utah, Phil Smith) Enabling new science infrastucture −ALS-data analysis (with NCSU; Phil Westmoreland) −Species IDs (with Kaust; Mani Sarathy) −H2-O2: automation/addition of flame targets (with Tsinghua, Xiaoqing You) −Submission of Chemkin mechanisms (with Kaust and Tsinghua)


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