Towards a formal description of models and workflows

Transcription

1 Towards a formal description of models and workflows Heinz A Preisig Process Systems Chemical Engineering NTNU, Trondheim, Norway MoDeNa - FP7 ++

2 Computational engineering The vision that we can predict the behaviour of a material a product a process a procedure Materials project US : properties of over 80,000 materials and screened 25,000 of these for Li-ion batteries... We can compute: molecule structures based on quantum mechanic concepts coarse-grained elements of the material in the geometrical space compute behaviours of fluids in complex geometries but the machines are not big / fast enough

3 Multi-scale modelling: Oignon approach electronic atomistic microscopic mesoscopic macroscopic device -_ -_ length scale time scale 2-3 order of magnitude per step

4 Multi-scale modelling: scale integration surrogates macroscopic device mesoscopic & surrogate for lower scales length scale time scale 2-3 order of magnitude per step

5 Formulation of the mathematical problem object - plant, process, material.. problem domain: - design - operations - phys props. - structure - mech props. mimiced behaviour: mathematical model mathematical problem

6 Formulation of the numerical problem mathematical problem instantiation specific problem numerical problem

7 Finding the solution numerical problem solution Solvers: - equation solvers - integrators - root solvers - optimisers -. instantiation

8 Current : vertical integration For each specific problem: model (may be default) model manipulation (often not) instantiation (main effort) numerical problem (implied) solver instantiation (usually default) solution (often as number or graph)

9 Target structure The core model is in the centre All other models are derived Activities form a whirlpool enable interactions between paths enable direct comparisons enable linking sequences of activities

10 Models are central

11 Models from models from models...

12 Model-centered view Model is the centre of all activities Specific models are all derived from a mother model The mother model is derived from a generic model Generic model is captured in an ontology

13 What is an ontology...and information science, an ontology is a formal representation of knowledge as a set of concepts within a domain, and the relationships between those concepts. It is used to reason about the entities within that domain, and may be used to describe the domain. In theory, an ontology is a ''formal, explicit specification of a shared conceptualisation''. An ontology provides a shared vocabulary, which can be used to model a domain -- that is, the type of objects and/or concepts that exist, and their properties and relations. (source: WikiPedia)

14 What are ontologies a formal representation of knowledge as a set of concepts within a domain relationships between those concepts formal, explicit specification of a shared conceptualisation provides a common vocabulary

15 Ontologies - new? no more like generating a more modern, better looking chair

16 Wish-list: model-based engineering Something to: assemble models build, modify & maintain Define my problem Solve my problem Evaluate solution Carry information to the next problem

17 Wish : Workbench IDE for computer-based engineering Ontology editor - context specific Graphical modelling editor network models Assumptions implementation Split of topologies Model reduction to eliminate states Mathematical problem definition Instantiation tool Solver wizard Solver instantiation tool Analysis tool Graphics tool for visualisation Statistical analysis Factory tool

18 Constructing first section of the IDE attribute editor model assembler appearance editor graph editor attributes automaton editor item info equation editor rules editor structure info rules context editor equations model lib task rules library

19 Wish-item: Domain specific model ontology Construct: network models basic principles (conservation dynamics) augment with transport, kinetic, geometry, definitions, transformations, energy surfaces, potentials, EOS,... information must be build in lower-triagonal form: start with graph & keep on adding one piece of information at the time that may be a function of the others consistent model ontology

20 Ontology structure chem eng macroscopic I [base model] Graph = (nodes, arcs) attributes characterising nodes and arcs equations using attributes as index sets lower triagonal, bipartite superstructure (choose from alternative where appropriate)

21 Ontology list of node attributes type nature physical information morphology distributed lumped dot dynamics constant dynamic event-dynamic token mass energy momentum information phase gas liquid solid user spec species A B C D

22 Ontology arc attributes type nature physical information directionality uni-directional bi-directional token mass energy momentum information mechanism heat vol work mech work el work phase gas liquid solid user spec species A B C D

23 Ontology structure chem eng macroscopic II [simulation] conditions (intensive state of the reservoirs) initial conditions for all dynamic parts time interval solver solver parameters solution

24 Ontology equipment design [design] criterion model as constraint set of conditions solver solver parameters solution

25 Equation ontology

26 Adding control

27 Wish-item: Graphical model editor Model assembly tool: support for hierarchical graph construction hierarchical refinement saving/loading of sub-models (complete/incomplete) top-down & bottom up design plug-in domain-specific ontology adds user-interface behaviour adds graphic component appearance integrates with model library model reduction tool

28 Wish-item: Model assumptions time-scale / length-scale assumptions small/large capacity small/large transfer small/large transposition method: mostly singular model reduction methods mathematical procedures method: builds on mainly superposition on ext automatic - only method control

29 Math Order-of-magnitude assumptions singular perturbation leads to equilibrium condition small / large capacities B A null space on fast network eliminates A fast flow fast reaction separation of networks into fast and slow networks may be done for different conserved quantities separately topology splitting A B topology overlap A+B

30 Workflows There is an ANSI/ISA-88 standard for unit process that can be used as template for a task Tasks defines numerical problems instantiated model == model plus all data solver and its instantiation output structure and target storage workflow control == automaton current state + input/condition next action, next state

31 Factory: stand-alone target app Design domain specific model ontology network model mathematical model problem instantiation - including all data solver solver GUI compile and slice instantiated GUI assemble process/problem specific application Result : stand-alone application with GUI

32 Factory: multi-physics task Design all model ontologies all network models define GUI overall multi-physics model as network workflows allocate resources for each task generate script execute script provide results for analysis

33 Issue of distributed computing generating the script (above) large data sets (transfer data or program) availability of programs (licences) exception handling computation error (diverging solution, zero devide ) wrong solution solution not feasible too large / too slow very large dimensionality are they necessary? control? adaptation?

34 Thermo factory realisation chosen chemical system e v a l u a t o r c o d e data base - experimental - computational approximate properties & derivatives differentiation EOS_1 EOS_1 g e n U H G Legendre A

35 Conclusion Main issue: split model generation from assembling coding evaluation derive all models from a mother model through adding assumptions reducing model using algebra model libraries of finished and partial models

36 Conclusion Workbench for modelling assemble individual network model link network models factory for stand-alone apps and multi-physics scripts Software farms? Central computing centres? Distributed computing?

37 Current work: generic multi-scale ontology Objective or question: Do we have a hand-full of concepts that cover the whole model space? Mass balance Hamiltonian Behaviour theory - port-hamiltonian systems Time-scale and length scale assumptions Stochastic / particle vs macro / field