Finite Element Model Simulations Associates With Hydraulic Fracturing
A Semantic Web Platform for Improving the Automation and Reproducibility of Finite Element...
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A Semantic Web Platform for Automating the Interpretation of Finite Element Bio-simulations
Dr. Ratnesh SahaySemantics in eHealth & Life Sciences (SeLS)
Insight Centre for Data AnalyticsNUI Galway, Ireland10-12-2014
SWAT4LS-2014, BerlinGermany
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Background – Hearing Loss
278 Million People
• Outer ear gets excited both by the sound waves propagate through the ear canal and strike the eardrum
• In the middle ear the ear drum vibrates generating pressure waves in the inner ear fluid chambers
• The inner ear turns pressure waves into electrical signals that our brain can understandSlide 2
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Background – Hearing Loss
• The ear drum vibrates generating pressure waves in the inner ear fluid chambers• The inner ear turns pressure waves into electrical signals that our brain can
understand
Infrastructure to integrate clinical knowledge, experimental data and inner ear models
Slide 3
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Inner Ear - Bio Simulation Model & System
PAK - FM
Slide 4
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SIFEM Project
Electrical Coupling Model
Micromechanics ModelFinite Element Model
Fluid Coupling Model
Slide 5
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Insight Centre for Data Analytics
GoalsAutomate the interpretation of finite element
(FE) biosimulations ...
Slide 6
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Insight Centre for Data Analytics
Motivational Scenario: Cochlear mechanics
Slide 7
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Insight Centre for Data Analytics
Characteristics of the FE Domain•Difficult to represent
• Physics, geometrical models, topological relations, algoithmic, mathematics
Slide 8
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Insight Centre for Data Analytics
Dimensions of a FE Bio-simulation
Slide 9
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Insight Centre for Data Analytics
Geometrical Model
Slide 10
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Insight Centre for Data Analytics
Physics Model
•FE equilibrium for solid
•FE equilibrium for fluid
Slide 11
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Insight Centre for Data Analytics
Numerical Models/Solvers
•Incremental-iterative implicit solution scheme
Slide 12
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Insight Centre for Data Analytics
Experimental Data
•A
Slide 13
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Insight Centre for Data Analytics
Lid-driven cavity flow
Slide 14
Physical Model
Solver
FEM Model
If there a vortex close to the lid?
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Insight Centre for Data Analytics
Lid-driven cavity flow
Slide 15
Physical Model
Solver
FEM Model
If there a vortex close to the lid?
definition of a simulation
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Insight Centre for Data Analytics
Numerical Data Interpretation
02 May 2014 Slide 16
description of the simulation
Rules using references to
anatomical, physical and data feature
elements
Is translated into
Multiple simulations
Feature extraction
Interpretation = rules applied over
data at the symbolic level
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Insight Centre for Data Analytics
Data View
Slide 17
Data Selection
y
0.05
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Insight Centre for Data Analytics
Feature Extraction
Slide 18
Minima=(0.055,-0.20)
fast increase
slow decrease
followed by (avg first derivative >
35)
velocity starts at 0 at the bottom
maximum velocity is
0.93at the lid
Based on the TEDDY ontology
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Insight Centre for Data Analytics
Data Interpretation Statements
Slide 19
:DataView1 :hasDimensionY :VelocityX .:DataView1 :hasDimensionX :DistanceFromTheCavityBase .:DataView1 :x0 “0.0"^^xsd:double .:DataView1 :y0 “0.0"^^xsd:double .:DataView1 :hasMinimumX “-0.055"^^xsd:double .:DataView1 :hasMinimumY “-0.20"^^xsd:double .:DataView1 :hasFeature :PositiveSecondDerivative .:DataView1 :hasBehaviour :BehaviourRegion1 .:DataView1 :hasBehaviour :BehaviourRegion2 .:BehaviourRegion1 :avgFirstDerivative “-3.63"^^xsd:double . :BehaviourRegion1 :hasFeature EndRegion . :BehaviourRegion1 :hasFeature :Decreases .:BehaviourRegion1 :hasFeature :DecreasesSlowly .:BehaviourRegion2 :avgFirstDerivative “33.35"^^xsd:double . :BehaviourRegion2 :hasFeature EndRegion . :BehaviourRegion2 :hasFeature :Increases .:BehaviourRegion2 :hasFeature :IncreasesFast .:BehaviourRegion1 :isFollowedBy :BehaviourRegion1 .: LidSimulation :hasInterpretation :ValidVelocityBehaviour .
Data Analysis Rule
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Insight Centre for Data Analytics
Data Analysis Rules
Slide 20
CONSTRUCT { :LidSimulation sif:
hasInterpretation :ValidVelocityBehaviour } WHERE {
?dataview rdf:type dao:DataView . ?dataview dao:hasFeature ?x . ... }
IF( minima(velocity) is negative AND decreases very slowly(velocity) AND
increases very fast (velocity) ) VALID VELOCITY BEHAVIOUR
SPARQL Rule
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Insight Centre for Data Analytics
Output Data Views
Slide 26
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Insight Centre for Data Analytics
Feature Extraction
Slide 27
:DataView1 :hasDimensionY :BasilarMembraneMagnitude .:DataView1 :hasDimensionX :DistanceFromTheCochleaBasis .:DataView1 :hasFeature :isSingleWave .:DataView1 :hasMaximumAmplitude “0.0031 "^^xsd:double.:DataView1 :hasMaximumY “0.0020 e^-6 "^^xsd:double .:DataView1 :hasMaximumX “14"^^xsd:double .:DataView1 :hasMinimumY “-0.0011 e^-6 "^^xsd:double .:DataView1 :hasMinimumX “17"^^xsd:double .
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Insight Centre for Data Analytics
Conceptual Model Excerpt
Slide 29
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Insight Centre for Data Analytics
Conceptual Model Excerpt
Slide 30
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Insight Centre for Data Analytics
Take-away message
•Contemporary science demands new infrastructures to scale scientific discovery in a complex knowledge environment.
•Numerical data is everywhere, not only in FE simulations.
•In this work we started exploring how to represent and extract numerical data features to a conceptual level.
Slide 31
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Insight Centre for Data Analytics
Future Directions•Better integration of the proposed representation and data analysis framework to the (TErminology for the Description of DYnamics) TEDDY conceptual model [EMBL-EBI].
•Use of the feature set and rules as a heuristic method to improve the simulation configuration space.
Slide 32
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Insight Centre for Data Analytics
SIFEM TEAM
Slide 33
•Andre Freitas
•Kartik Asooja
•Joao B. Jares
•Stefan Decker
•Ratnesh Sahay
Thank You !