strategic multiscale

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IntegratedStrategic Multiscale

A NewVirtual Multiscale

Science - Engineering Environment

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Alessandro Formica

December 2013


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Alessandro Formica, December 2013 All rights reserved

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TABLE OF CONTENTS1. Multiscale and The Future of Technology Innovation, Engineering andManufacturing... pag. 3

2. Integrated Strategic Multiscale Framework Architecture.. pag. 7

3. Integrated Multiscale Science - Engineering Framework.. pag. 113.1 Architecture. pag. 113.2 Multiscale Data, Information and Knowledge Analysis and Management Systempag. 123.3 Multiscale Science Engineering Information Space. pag. 203.4 The Information Driven Concept and Analysis Scheme... pag. 263.5 Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers. pag. 293.6 Multiscale Multiresolution Multiphysics Testing, Experimentation and Sensing. pag. 33

3.7 Integrated Multiscale Science Engineering Analysis Strategies.. pag. 363.7.1 Methodologically Integrated Multiscale Science - Engineering Strategies pag. 363.7.2 Multiscale Science - Engineering Analysis Schemes. pag. 43

3.8 Designing the R&D and Engineering/Manufacturing Processes. pag. 463.8.1 R&D and Engineering/Manufacturing Process Architecture pag. 463.8.2 R&D and Engineering/Manufacturing Strategy Management System.. pag. 513.8.3 Integrated R&D and Engineering Analysis Strategies. pag. 54

4. Integrated Multiscale Science Engineering Technology, Product and ProcessDevelopment (IMSE-TPPD) Framework.. pag. 574.1 IMSE-TPPD Architecture and Overview pag. 574.2 Multiscale Multidisciplinary Science Engineering Cyber Extended EnterpriseFramework. pag. 584.3 Computer Aided R&D, Engineering and Manufacturing /Processing (CARDE-MP)Framework.... pag. 59

4.3.1 Architecture.. pag. 594.3.2 Multiscale Manufacturing and Processing.. pag. 604.3.3 Multiscale Environmental Monitoring and Impact Analysis..... pag. 64

4.4 Multiscale Science Engineering Virtual Testing pag. 694.5 Virtual Multiscale Innovative Technology and Systems Development Framework pag. 72

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4.6 Virtual Multiscale Life Cycle Engineering Framework.. pag. 754.7 Multiscale Science Engineering Knowledge Integrator and Multiplier (KIM) .Computing Information Communication Infrastructural Framework....... pag. 77

5. Integrated Multiscale Science Engineering Technology and SystemsDevelopment (IMSE-TSD) Framework.. pag. 80

5.1 Framework Architecture and Objectives.. pag. 805.2 Multiscale Multidisciplinary Science Engineering Cyber Extended SystemsFramework.. pag. 845.3 Computer Aided R&D and Engineering (CARDE-MP) Framework pag. 865.4 Computer Aided Design of Systems (CADS) Framework.. pag. 875.4.1 Introduction pag. 875.4.2 Architecture and Functionalities.. pag. 885.4.3 Multiscale Environmental Monitoring and Impact Analysis... pag. 91

5.5 Multiscale Science Engineering Virtual Testing pag. 955.6 Virtual Multiscale Innovative Technology and Systems Development Framework . pag. 985.7 Virtual Multiscale Life Cycle Engineering Framework.. pag. 101

5.8 Multiscale Knowledge Integrator and Multiplier CIC Framework.. pag. 103

About the Author.. pag. 105


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1. Multiscale and The Future of TechnologyInnovation, Engineering and Manufacturing

Computational Multiscale has become a key asset in the R&D and Engineering World and an important

element for Technology, Products and Processes Innovation. Multiscale methods helped to establish abridge between Science and Engineering and the related domains of knowledge. Continuous advances inComputational Methods (Virtual Environments) and High Performance Computing provided the basis todefine a new vision of Multiscale we refer to as "Strategic Multiscale.

The term Strategic means that Multiscale Methodologies are applied not only to improve Modeling andSimulation Methods, but, also, to improve in a significant way R&D, Engineering and ManufacturingOrganization, Structure and Strategies.

Complexity of Products and Manufacturing Technologies and the related R&D and Engineering Processes iscontinuously increasing: researchers and engineers have to manage, integrate and coordinate an everwidening spectrum of analytical, computational, experimental, testing and sensing models, methods and

techniques.

A Fundamental Goal of the Integrated Strategic Multiscale Framework is to address thisChallenge outlining a set of new concepts, methods and environment to Design the R&D andEngineering Process

A Distinguishing Element of the Strategic Multiscale Framework is the new concept of MultiscaleModeling and Simulation as Knowledge Integrators and Multipliers and Unifying Paradigm forScientific and Engineering Domains.

This new methodological Science - Engineering Framework allows us to give a New Dimension andMeaning to the term Virtual as far as Engineering and Manufacturing are concerned. and introducea New Field: Virtual Technology Innovation, which is the connection element between Science andEngineering/Manufacturing Domains.

The Integrated Strategic Multiscale Framework defines a Comprehensive Theoretical andMethodological Environment to design and implement a New Generation of Virtual Science Based Technology Innovation, Engineering and Manufacturing Strategies where MultiscaleModeling and Simulation become Pivotal Elements of the R&D and Engineering ManufacturingWorld overcopming classical divisions between the Computational and the Experimental, Testingand Sensing Areas.

A Fundamental Characteristics of the Integrated Strategic Multiscale Framework is to allow for a

smooth, continuous, efficient, structured and timely transfer of scientific knowledge inside theTechnology Development, Engineering and Manufacturing Processes and related ComputationalFrameworks.

The concept of Multiscale as Unifying Paradigm is not new. In the mid of ninenties, several researchers inthe Chemical Engineering Field (Sapre and Katzer, Leou and Ng, and Villermaux) and the author of thisdocument (Alessandro Formica) highlighted the need of a comprehensive Multiscale approach as a keyStrategy to establish a new Unifying Paradigm in order to enable a better correlation between scientidficand engineering advances and related knowledge domains. Later on, Prof. Charpentier, past EuropeanFederation of Chemical Engineering President highlighted again the strategic relevance of this conceptualscheme.


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The definition of new Frameworks like the Integrated Computational Materials Engineering (ICME) andthe Integrated Compuitational Materials Science Engineering (ICMSE) ones and the launch of the USPresidential Materials Genome Initiative (MGI) put the bases for a wide Industrial Application of MultiscaleScience Engineering Integration Strategies and Frameworks

The European Union FET FLAGSHIP Human Brain Preoject (HBP) is a demonstration of the StrategicValue of Multiscale Science Engineering Integration. To fully understand processes and related

relationships characterizing Brain Functions and Functionalities over the whole range of scalea and Brainorganization levels and the fundamental relationships with diseases, new (Multiscale) Computational,Experimental and Data Analysis Methodologies, Techniques and Strategies will be developed and applied.New (Multiscale) Methodologies will also be functional to develop a New Generation of (Multilevel) NonVon Neumann Computing Systems.Engineering and Manufacturing are quickly changing. Science hasalready become a key issue and value for both the fields and this trend will become increasingly important inthe coming years . Many Projects have clearly demonstrated that, today, is possibile to use Multiscale(Science Engineering) Computational Methodologies to Design and Manufacture new inherentlyHierarchical Multiscale Materials, Devicews, Components and Systems (Nano To Macro Integration).

Fig. 1 Multiscale (Nano To Macro) System Design (MIT)

Multiscale as Unifying Paradigm for Chemical EngineeringProf. Charpentier, past European Federation of Chemical Engineering (EFCE) President, at the 6thWorld Congress of Chemical Engineering - Melbourne 2001, described his Vision of Multiscale asStrategic Paradigm for Chemical Engineering. We report his words : One key to survival in

globalization of trade and competition, including needs and challenges, is the ability of chemicalengineering to cope with the society and economic problems encountered in the chemical and related process industries. It appears that the necessary progress will be achieved via a multidisciplinary andtime and length multiscale integrated approach to satisfy both the market requirements for specific enduse properties and the environmental and society constraints of the industrial processes and theassociated services.

This concerns four main objectives for engineers and researchers:

(a) total multiscale control of the process (or procedure) to increase selectivity and productivity,

(b) design of novel equipment based on scientific principles and new methods of production: processintensification,

(c) manufacturing end-use properties for product design: the triplet processus-product-processengineering,(d) implementation of multiscale application of computational modeling and simulation to real-lifesituations: from the molecular scale to the overall complex production scale.


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Multiscale Science Engineering Integration implies that we can not only define a Science DrivenEngineering, but, also a Engineering Driven Science. Multiscale Computational Methodologies(Virtual Engineering and Manufacturing) should consider the impact of these global trends over theirdevelopment, structure and related implementation strategies in order to define their Future.

It is to be highlighted that the awarding of the Nobel Prize in Chemistry to three scientists for thedevelopment of Multiscale Models for Complex Chemical Systems has helped to create the optimalintellectual and scientific context to introduce high level Projects and Initiatives in the MultiscaleScience Engineering Integration field

Integrated Strategic Multiscale Framework Goals:

Defining a New Organization and Structure of the R&D and Engineering World: Designing the R&Dand Engineering Process Architecture

Defining a New Frontier for Virtual Worlds and Application Strategies: Virtual Multiscale Science Based Technology Innovation, Engineering and Manufacturing

easing knowledge transfer between the different stages of the R&D and Engineering/Manufacturingprocess

integrating and Strcuturine Data, Information and Knowledge from the Scientific and EngineeringWorlds

defining new cooperation and partnering schemes among academy, research, and industry . In the newscience-engineering context, engineering can become an important driver for science, overturninghistoric relationships and dependencies and putting the bases for a new way of doing science andengineering. Not only advances in science can be stimulated and driven by technology progress and theneed to solve specific technological and engineering problems, but research strategies will be more andmore influenced by technology roadmaps and vice versa.

putting the bases to define a new structure and organisation for the research and industrial world based,from an Infrastructural point of view, on a new generation of Multiscale Multidisciplinary Science Engineering Cyberinfrastructures and, from a methodological point of view, on the here describedFramework to bridge the gap between disciplines and the different scientific and engineering approaches.

enabling new Technological Engineering Solutions (Multiscale Engineering: From Multiscale Analysisto Multiscale Design). New Frameworks enable the design of inherently Hierarchical MultiscaleSystems (materials, structures components, products and processes) which is a fundamental condition tofully exploit in the industrial environment the potentialities of Nano and Micro Technologies.

In such a context it is possible to realize a real fusion between

science-driven engineering and engineering-driven science

which represents a key goal of the Strategic view of multiscale.

The Royal Swedish Academy of Sciences has awarded the Nobel Prize in Chemistry for 2013to Martin Karplus of Universit de Strasbourg, France and Harvard University, Cambridge,MA, USA; Michael Levitt of Stanford University School of Medicine, Stanford, CA, USA; andArieh Warshel of the University of Southern California, Los Angeles, CA, USA "for the development of multiscale models for complex chemical systems


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General ReferencesDavid L. McDowell, Jitesh H. Panchal, Hae-Jin Choi. Carolyn Conner Seepersad, Janet K. Allen, FarrokhMistree, 2010. Integrated Design of Multiscale, Multifunctional Materials and Products - Published byElsevier .

Oden , J.T. , Belytschko , T. , Fish , J. , Hughes , T.J.R. , Johnson , C. , Keyes , D. , Laub , A. , Petzold , L. ,Srolovitz , D. , Yip , S. , 2006 . Simulation-based engineering science: Revolutionizing engineering sciencethrough simulation . In : A Report of the National Science Foundation Blue Ribbon Panel on Simulation-Based Engineering Science . National Science Foundation : Arlington, VA .

Olson , G.B. 1997 . Computational design of hierarchically structured materials . Science, 277 ( 5330 ) ,1237 1242 .

Alessandro Formica. Fundamental R&D Trends in Academia and Research Centres and their Integrationinto Industrial Engineering Report drafted on behalf of European Space Agency, July 2000

Alessandro Formica, Multiscale Science Engineering Integration A New Frontier for Aeronautics, Spaceand Defense, Italian Association of Aeronautics and Astronautics (AIDAA), March 2003


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2. Integrated Strategic Multiscale FrameworkArchitectureThe theoretical and methodological basis of the Strategic Multiscale Framework is constituted by thefollowing key elements:

The extension of the Model concept to the Experimentation, Testing and Sensing Fields giving a newmeaning to the Virtual Engineering and Manufacturing concept and approach. In this context, a newVision of Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers andUnifying Paradigm for Scientific and Engineering Methodologies and Knowledge Domains has beendefined. Multiscale Modeling and Simulation integrate the full spectrum of science and engineeringmethodological approaches and knowledge environments. This new Vision puts ComputationalFrameworks and High Performance Computing at the center of the R&D andEngineering/Manufacturing World even more than classical Virtual concepts and approaches.

The Multiscale Science-Engineering Information Space concept to integrate data, information andknowledge from computational models and methods and experimental, testing and sensing models and

techniques to develop, validate and apply Computational Models. Uncertainty Quantification (UQ) andQuantification of Margin of Uncertainties (QMU) have become critical issues as the relevance ofModeling and Simulation is continuously increasing.

The Information Driven Analysis concept and scheme which, together with the Science Engineering Information Space concept is a key element to shape Integrated R&D andEngineering/Manufacturing Analysis and Design Strategies, following the Multiscale Modeling andSimulation as Knowledge Integrators and Multipliers concept and application environment.

New Multiscale Science Engineering Data, Information and Knowledge Management Systems basedupon the Multiscale Maps concept

New Multiscale Methods to Model, Simulate and Design the Technology Development andEngineering/Manufacturing Processes and Products Life Cycle: Virtual Multiscale Innovative

Technology and Systems Development Framework and Virtual Multiscale Life - Cycle EngineeringFramework and Environmental Impact Analysis

The Integrated Strategic Multiscale Framework is constituted by:

A common theoretical, conceptual and methodological core we refer to as Integrated MultiscaleScience Engineering Framework [described in this document]

Four Application Frameworks

Integrated Multiscale Science Engineering Technology, Product and Process Development(IMSE-TPPD) Application Framework [described in this document]

Integrated Multiscale Science Engineering Technology and Systems Development (IMSE-TSD)Application Framework [described in this document]

Multiscale Science Based From Space To Earth Application Framework [described in the Multiscale Science - Engineering From Space To Earth document]

Multiscale Science Based Education, Information and Communication Application Framework[described in the Multiscale Science Based Education, Information and Communication document]

A key distinguishing feature of the Integrated Strategic Multiscale Framework is that all the fourApplication Frameworks characterize themselves for having a common theoretical, conceptualand methodological basis described in the Integrated Multiscale Science EngineeringFramework


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Integrated Strategic Multiscale Framework ArchitectureA New Virtual Multiscale Science Engineering World

Basic Conceptual, Theoretical and Methodological Framework

Integrated Multiscale Science Engineering Framework

Integrated Multiscale Science Engineering Technology, Product

and Process Development

(IMSE-TPPD) FrameworkAnalysis And Design of a NewGeneration of Materials, DevicesSystems, and related ManufacturingProcesses

Integrated Multiscale ScienceEngineering Technology and

Systems Development

(IMSE-TSD) Framework

Analysis And Design of a NewGeneration of Civil And InfrastructuralSystems

Multiscale Science BasedFrom Space To Earth Framework

A New Vision and Cultural Policyfor Space: Virtual Space Station

New Multiscale Science BasedEarth Monitoring Framework

A new Smart City vision:Multiscale Science BasedFrom Space To Earth VirtualCyber City

New Education, Information andCommunication Strategies And

Frameworks Based Upon TheSpace Environment

Multiscale Science Based

Education, Information and Communication Framework

A New Integrated Framework which, for the first time, applies, in a sistematic way,the Strategic Multiscale concept and methods to the Education, Information andCommunication Fields


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The Integrated Strategic Multiscale Framework embodies the following Elements:

Integrated Multiscale Science Engineering Framework Described in the Chapter 3 - whichrepresents the theoretical, conceptual and methodological core of the Integrated Strategic MultiscaleFramework and the basis the several Strategic Multiscale Application Frameworks. Key Elements: Multiscale Data, Information and Knowledge Analysis and Management System Multiscale Science Engineering Information Space Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers Multiscale Multiresolution Experimentation, Testing and Sensing Methodologically Integrated Multiscale Science Engineering Strategies The Information Driven Concept Multiscale Science - Engineering Analysis Schemes R&D and Engineering/Manufacturing Process Architecture R&D and Engineering Analysis and Design Strategy Management System Integrated R&D and Engineering Analysis Strategies

Integrated Multiscale Science Engineering Technology, Product and Process Development (IMSE-TPPD) Application Framework - Described in the Chapter 4 - Multiscale Multidisciplinary Science Engineering Enterprise Framework Computer Aided R&D, Engineering and Manufacturing/Processing (CARDE-MP) Framework

which implements the Integrated Multiscale Science Engineering Framework Multiscale Manufacturing and Processing Multiscale Environmental Monitoring and Impact Analysis

Multiscale Science Engineering Virtual Testing Virtual Multiscale Innovative Technology and Systems Development Framework Virtual Multiscale Life Cycle Engineering Framework The Multiscale Knowledge Integrator And Multiplier Computing, Information and Communication

(CIC) Infrastructural Framework

Integrated Multiscale Science Engineering Technology and Systems Development (IMSE-TSD)Application Framework - Described in the Chapter 5 - Multiscale Multidisciplinary Science Engineering Cyber Extended Systems Framework Computer Aided R&D and Engineering/Manufacturing (CARDE-MP) Framework which

implements the Integrated Multiscale Science Engineering Framework Computer Aided Design of Systems (CADS) Framework Multiscale Science Engineering Virtual Testing Virtual Multiscale Innovative Technology and Systems Development Framework Virtual Multiscale Life Cycle Engineering Framework Multiscale Knowledge Integrator and Multiplier CIC Framework

Multiscale Science Based From Space To Earth Application Framework Described in thehomonymous document -

A New Vision and Cultural Policy for Space: Virtual Space Station New Multiscale Science Based Earth Monitoring Strategies and Environments

A new Smart City vision: Virtual Multiscale Cyber Extended City

A new Multiscale Science Based From Space To Earth Education, Information andCommunication Framework


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Multiscale Science Based Education, Information and Communication Application Framework- Described in the homonymous document -

Multiscale Science Engineering Language Multiscale Language Implementation Framework

Multiscale Analysis and Presentation Settings and Schemes Application Areas

Education and Training Programs (Courses, Lessons, Lectures, Seminars

Information and Communication Programs Information Programs Documentaries Meetings Conferences University Industry -Public Bodies Society Communication Environments

Communication Programs Multiscale Science Based Talk Shows Multiscale Webs

Multiscale Science Based Entertainment Programs


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3. Integrated Multiscale Science - Engineering Framework

3.1 Architecture

Main elements of the Conceptual and Methodological Framework are:

Multiscale Science - Engineering Data, Information and Knowledge Analysis and Management System

Multiscale Science Engineering Information Space

Information Driven Multiscale Science Engineering Analysis Concept and Schemes

Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers and Unifying Paradigm for Scientific and Engineering Methodologies and Knowledge Domains The role of Multiscale as Unifying Paradigm and Language for Science and Engineering was discussedby Alessandro Formica, some years ago in the book - Computational Stochastic Mechanics In a Meta-

Computing Perspective December 1997 - Edited by J. Marczyk pag. 29 Article: A Science BasedMultiscale Approach to Engineering Stochastic Simulations.

Multiscale Multiresolution Multiphysics Testing, Experimentation and Sensing

Methodologically Integrated Multiscale Science Engineering Methodologies

New Methods, Tools and Strategies to Design the R&D and Engineering Analysis Process

Integrated Multiscale R&D and Engineering Analysis Strategies


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3.2 Multiscale Science Engineering Data, Information and KnowledgeManagement SystemA critical issue for a wide diffusion of the science based engineering analysis and design approach in theindustrial field is the availability of Software Environments (CAD/CAE/CAM/CAP) specifically conceived

for multiscale science engineering strategies and applications. Today, notwithstanding the growingdiffusion of multiscale inside university, research, and even industry, software environments(CAD/CAE/CAM/CAP) specifically conceived to implement multiscale science-engineering integrationvisions and strategies are still in their starting phase. The lack of software environments specificallyconceived to implement a multiscale science-engineering integration strategy represents a fundamentalhurdle to a large scale implementation of multiscale inside innovative technology development andengineering/manufacturing/processing fields.

The new Data, Information and Knowledge Management System proposed in this Document rests on theconcepts of:

Multiscale Multiphysics Multiresolution Maps

Multiscale Multi Abstraction Level Knowledge Domains

The Multiscale Multiresolution Maps here described is an extension of the Map concept discussed byAlessandro Formica in the Multiscale Science Engineering Integration: A new Frontier for Aeronautics,Space and Defense White Book published on March 2003 by Italian Association of Aeronautics andAstronautics.

Multiscale Multiresolution Maps are Multiscale Multiresolution Information and Knowledge Structuresdescribing complex networks of relationships and interdependencies between a large spectrum ofInformation Variables characterizing Systems Structure and Dynamics. Relationships andinterdependencies between Information Variables are worked out applying several mathematicaltechniques such as multivariate analyses and neural networks to raw data coming from a wide range ofData Sources (analytical and computational models, data bases, experimentation, characterization, testingand sensing). covering the full spectrum of scales (from atomistic to macro) and the full spectrum ofdisciplines: Multiscale Maps structure Data and Information and, accordingly, they represent a step toturn Information into Knowledge. Maps are organized in a hierarchical way: A Map can incorporate a setof lower level Maps. For instance: a Multiscale Physical Map linked to a specific Process (HypervelocityImpact, Combustion or Explosion, for instance) can be constructed by assembling a range of MultiscalePhysical Maps describing more elementary physical (chemical and biochemical) phenomena (fracture,fragmentation, phase change,..) related to a specific material or component of a System.Representations can be static and dynamic. Multiscale Maps incorporate error analyses and uncertaintyquantification methods. Multiscale Maps make an extensive use of Static and Dynamic GraphicRepresentations.

Multiscale Multi Abstraction Knowledge Domains are a further organization level for Information andKnowledge. A Knowledge Domain Structures can aggregate several Maps related to one or more scales ofthe same typology or of different typologies related to the same or different operational conditions, analysisand design hypotheses and solutions. Maps can be set up and integrated inside a Knowledge Domainapplying several aggregation and clustering schemes. Knowledge Domains can be organized in aHierarchical Way. Knowledge Domains can be related to specific R&D and Engineering/ManufacturingTasks and Phases. They can track Knowledge structure and organization as we transition from a R&D andEngineering Phase and Task to another one.

Multiscale Maps and Multiscale Knowledge Domains allow for an effective insertion and managementof the more fundamental knowledge (basic and applied research) inside the sequence of TechnologyDevelopment and Engineering phases. At each step/phase of the R&D and Engineering Process , MultiscaleMaps and Knowledge Domains are built taking full advantage of the knowledge get in the previousstep/phase. Several typologies of Maps are foreseen which describe relationships among variables, structuresand processes:


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A) R&D And Engineering (for systems of any kind of complexity) Multiscale Analysis and Design Variable Maps tracking relationships between Analysis and Design

Variables . Multiscale Analysis and Design Variable Maps are built applying statistical analysis schemes(multivariate, PCA) or other techniques like neural networks to data coming from several sources: databases, computation, analytical theories, experimentation, testing, sensing. Data integration and fusiontechniques are applied to reconcile and integrate data coming from different sources characterized by arange of accuracy and reliability degrees. Multiscale Analysis and Design Variable Maps describerelationships between variables and parameters used to characterize Systems Behaviour over a fullrange of space and time scales and disciplines.

Multiscale Physics Maps identifying the Physical, Chemical and Biochemical Phenomena andProcesses considered fundamental to carry out a specific task and describe relationships andinterdependencies among them. The following table illustrates a textual version of a simplified PhysicsMap:

Multiscale Architectural/Structural Maps describing relationships between the hierarchy of Sub-Systems, Components, Devices, Materials and Elementary Structures constituting an EngineeringSystem (or System of Systems) of arbitrary level of complexity. This kind of Maps incorporates aspecial set of Elements referred to as Interfaces which describe interconnections amongArchitectural/Structural constituents inside a scale and among different scales.

Engineering System Multiscale Monitoring and Control Maps describing (Hierarchical) Networks ofSensors and Control Devices and Systems and their relationships with Elements to be monitored andcontrolled (described in the Multiresolution Multiscale Architectural/Structural Maps). TransformationProcesses induced by control actions are described thanks to Multiresolution Multiscale Physics Maps

and Multiresolution Multiscale Architectural/Structural Maps. This kind of Maps describes thequantities monitored and controlled, time and space resolution, sensing and control devicescharacteristics and operational schemes

Multiscale Materials (Tantalum) Characterization (Livermore) Physics Map

Atomistic length-scale modeling input : interatomic potentials (calculated with quantum mechanics)output: dislocation generation, motion and interaction with other defectsscale physics: properties of individual defects (dislocations, vacancies, interstitials, dopants),defects mobility, diffusion, clusters, surface reactions

Microscale length scale modeling input: dislocation generation, motion and interaction with other defectsoutput: yield and hardening rules for single crystalsscale physics : defect interactions, precipitates, dislocation reactions, the early stages of voidgrowth, grain boundaries and the interactions between dislocations and grain boundaries

Mesoscale Modeling input: yield and hardening rules for single crystalsoutput: mesoscale models of polycrystal aggregates (100s of grains)scale physics : shear band, dislocation walls, collective dynamics of microstructure, interfacediffusion, grain coarsening, recrystallization, crack growth, fracture

Mesoscale Homogenization / Continuum Model input: mesoscale models of polycrystal aggregates (100s of grains)output: pressure and strain path dependent yield surface for continuum code hardening.scale physics : polycrystal plasticity, temperature fields, hydrodynamic motion, textures,microstructures homogenization, anisotropic hardening.


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Multiscale Functional Maps describing relationships between Engineering System and relatedArchitectural/Structural Elements and Functions performed

Multiscale Requirements - Performance Property Structure Maps describing relationships betweenRequirements, Performance, Structural Elements and related Properties over the whole scales andrepresentation levels.

Multiscale Performance Property Structure Manufacturing/Processing Maps describing theimpact of Processing techniques over the network of Performance - Structure - Property relationshipsover the whole scales and representation levels.

B) Manufacturing and Processing Multiscale Manufacturing and Processing Systems Architectural, Functional and Monitoring &

Control Maps describing: the (multiscale/multilevel) architecture (hierarchical networks of units [from Plants to Cell,

Robots and Machines/Tools] at different scales and complexity levels of any kind ofManufacturing/Processing Systems and related interconnections and interdependencies (materialflow). At the highest abstraction level, a Unit can represent a whole Manufacturing/ProcessingSystems incorporating several Plants and other Elements. The representation scheme is recursive: AUnit can be decomposed into a network of simpler Units, a simpler Units can be, in turn, bedecomposed into other networks of even more elementary Units over the whole hierarchy of scalesand representation levels, as needed.

the full spectrum of functions carried out by the units constituting Manufacturing/ProcessingSystems and their relationships and interdependencies. Multiscale Physics Maps and MultiscaleStructural Maps are applied to describe physical, chemical and bio-chemical transformations andprocesses occurring at and over the full spectrum of Units.

the (Hierarchical) networks of multiscale monitoring and control (M&C) devices and systems overthe full spectrum of scales and levels This kind of Maps describes the quantities monitored andcontrolled, time and space resolution, sensing and control devices characteristics and operational

schemes the (Hierarchical Network) of Inspection Systems, their Functions and Operational Modes

Multiscale Multilevel Manufacturing Processes Execution Flow: this kind of Maps describes, for eachspecific Manufacturing Process of any level of complexity the execution flow (manufacturing/processsequence of steps) throughout the full set of Plants. Process Units, Cells, Machines/Robots,., the workperformed at each step, the characteristics of the Unit, the structural/chemical/physical transformationsworked out (also using Architectural/Structural Maps and Physics Maps), the inspections performed, thematerials flow..

Multiscale Manufacturing Systems any level of the hierarchy - Operational Modes - Environmental Emission Maps Theses Maps represent a new Generation of Maps specificallyconceived to evaluate the impact on the Environment of Manufacturing/Processing Systems for a widerange of operational conditions and design solutions. Maps describe relationships among ManufacturingSystem (any level), its Operational Modes and related Emissions (any kind).

Multiscale Maps represents a key element of a new Multiscale Computer Aided Research,Development, Engineering (CARDE-MP) Software Systems. Main objectives:

Developing new schemes allowing for a more in-depth analysis and structuring of data, informationand knowledge and related correlations and interdependencies

Integrating the full spectrum of Data Sources (Data Bases, Analytical Theories, ComputationalModels, Experimentation , Testing and Sensing). The Information Space and the Modeling andSimulation as Knowledge Integrators and Multipliers concepts and methods can ease this kind ofIntegration

Developing new CAD/CAE/CAM/CAP Environments specifically conceived to Design and Producenew Hierarchical Multiscale Nano To Macro Multifunctional Systems in the context of an IntegratedScience Engineering Approach


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Multiscale Maps are indexed and related to specific R&D and Engineering Tasks and Phases, DesignHypotheses and Design Decisions and Operational Conditions.

The Multiscale Science Engineering Data, Information and Knowledge Management System records,organizes and manages all the previously defined Maps and Knowledge Domains. Each Map andKnowledge Domain is characterized by a set of Tags which link it to a specific task, phase and operationalconditions and analysis and design hypothesis inside the R&D and Engineering Analysis and DesignProcess.

Fig. 2 Physics Map Example (from Overview of the Fusion Materials Sciences Program Presented by S.J. Zinkle, Oak Ridge National Lab Fusion Energy Sciences Advisory Committee Meeting February 27, 2001Gaithersburg)

This figure depicts a Information Structure like the proposed Multiscale Physics Maps. In this case theMultiscale Physics Map describes relationships between physical phenomena and chemical/physicalstructural transformations linked to Radiation Damage Process for Metals

A cluster of Multiscale Physics Maps, linked to specific physics phenomena or processes, architecturalelement and operational conditions, can define what can be called a Physical (Chemical and Biochemical)Phenomena and Processes Knowledge Domain. Knowledge Domains are managed by the MultiscaleScience Engineering Data, Information and Knowledge Management System .


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Multiscale Multilevel Architectural and Structural Maps

Any System of arbitrary degree of complexity (an air transportation system, an energy production system,an aerospace vehicle, a chemical plant, a structure, a nanotechnology device, a nanostructured material), canbe recursively broken down in a set of simpler (macro, meso, micro, nano and atomistic) Architectural and

Structural Elements and Interface/Interconnection Elements. Interconnections and Integration developalong two lines: Horizontal (same scale) and Vertical (different scales). We distinguish two kinds ofSystems: Technological Systems and Natural Systems where the Technological System (or System ofSystems) operates.

Fig. 3 Two dimensional multilevel multiscale view of an aircraft. ( from the Validation Pyramid and the failure of the A-380 wing Presentation given by I. Babuska (ICES, The University of Texas at Austin), F. Nobile (MOX, Politecnico di Milano, Italy), R. Tempone (SCS and Dep. of Mathematics, Florida StateUniversity, Tallahassee) in the context of the Workshop Mathematical Methods for V&V SANDIA ,

Albuquerque, August 14-16, 2007

Three new features distinguish this kind of Maps and related Multiscale Multilevel Science EngineeringCAD Systems:

Multiscale Multilevel Architectural/Structural Element Networks Analysis and Description. New CADSystems should describe the full set of multiscale multilevel (inside a single scale) Architectural andStructural Elements of a System (or System of Systems) - including the Operational Environment -and related interconnections. Interconnection Elements describe two way interactions betweenElements. This feature is of particular importance for System Engineering analyses and if we like toassess the impact of the System upon the environment where it operates and the effects of theEnvironment on the System for the whole Life Cycle and the whole spectrum of operational conditionsincluding extreme ones and accidents.

Zooming and Selected Multilevel Multiscale view capabilities . Users should have the possibility to selecta full spectrum of views at different levels of resolution, scales and abstraction ways. Multiple viewsshould be visualized in order not to lose connections among different levels of abstraction, resolution andscales. The zooming function should allow users to transition from a levels of abstraction, levels andscales in an interactive way.

Multi Abstraction Levels: we can select groups (clusters) of architectural/structural elements of different

typologies over a spectrum of scales and resolution levels as needed to carry out specific analyses anddesign tasks.


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This kind of Maps gives a comprehensive picture of the:

Architectural and Structural and Interface/Interconnection Elements (from macro to atomic levels asneeded) which constitute a system and its related Horizontal and Vertical organization: from theSystem (or System of Systems) down to elementary structures (atoms/molecules, groups of atoms andmolecules)

Materials, Energy, Chemical and Biochemical Substances Flow (pollutants emitted toward the NaturalSystem for instance,) among the Elements constituting the System or System of Systems Analysis and Design Variables their relationships and interdependencies and links between Analysis

and Design Variables and Architectural and Structural Elements

Properties of the full set of Architectural and Structural Elements Performance and Requirements for the full set of Architectural and Structural Elements. Performance are

calculated and/or measured during the R&D and Engineering Process while, Requirements are definedand refined by designers.

Architectural and Structural Maps evolve along the Technology Development and Engineering Analysis andDesign Process thanks to Analysis and Design Modules and Strategy Modules. Maps are built using theavailable knowledge; as analysis and design activities proceed, they are interactively modified. DifferentMaps can be linked to different Architectural Hypotheses and Decisions for different purposes and tasksduring the R&D and Engineering Process. Maps are recorded, organized and managed in specificArchitectural and Structural Map Data Bases. Architectural and Structural Elements Maps are relatedto: Functional Maps Monitoring and Control Maps

Physics Process Maps

Multiscale Monitoring and Control Maps

This kind of Maps gives a comprehensive picture of the Multiscale Multilevel Networks of Monitoring andControl Devices and Systems their interconnection schemes and their functionalities and operational modes.Multiscale Monitoring and Control Maps are related to: Architectural and Structural Maps Physics Maps (Physical and Bio-Chemical Phenomena and Processes Monitored and effects of

Control actions)

Multiscale Functional Maps

We define two types of Functional Maps. The first one, which can be called Direct Functional Map, describes Functions carried out by the

System and the full hierarchy of its Elements. Direct Functional Maps link Architectural/StructuralElements to Functions and they describe what functions are performed by Architectural/StructuralElements.

The second one, which can be called Inverse Functional Map relates Functions toArchitectural/Structural Elements over the full spectrum of hierarchy levels

Functional Maps are linked to:

Architectural and Structural Maps

Physics and Processes Maps

Functional Maps defined during the Technology Development and Engineering Process are recorded,organized and managed by specific Functional Maps Data Bases. Maps are indexed in such a way as torelate them to specific R&D and Engineering Phases and Tasks.


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Multiscale Multiphysics Maps

We use the term Physics to indicate a more or less complex cluster of elementary physical and biochemicalphenomena/processes occurring inside a scale or developing over a spectrum of scales.Phenomena/Processes are, for instance, failure, stress corrosion cracking erosion, phasetransformation, A Process can be broken down in a full hierarchy of more elementary Processes andPhenomena. The distinction between processes and phenomena is, to some extent, arbitrary. It is amatter of opportunity. Phenomena and Processes can concern more Architectural/Structural Elements.

Physics Maps are linked to:

Architectural/ Structural and Functional Maps . Monitoring and Control Maps Requirements - Performance Property Structure Maps Performance Property Structure - Processing Maps

Multiscale Manufacturing Systems any level of the hierarchy - Operational Modes - Environmental Emission MapsPhysics Maps are software environments which describe :

the full set of physical (biological and chemical, as needed) phenomena and processes which rule thedynamics of Architectural/Structural Elements (Interconnection Elements included) of a Systemunder analysis/design for a specific Task and their interactions inside a scale and over differentscales.

The full hierarchy of (geometrical, physical and biochemical) Architectural/Structuraltransformations related to a specific set of Phenomena/Processes linked to a specific R&D andEngineering Task .

Relationships between the full hierarchy of processes, phenomena and Architectural/Structuraltransformations for a specific Task

Maps are indexed in such a way as to relate them to specific R&D and Engineering Phases and Tasks.

Physics Maps are linked to Multiscale Methodologically Integrated Strategy Maps described in theParagraph 3.8.1. Multiscale Methodologically Integrated Strategy Maps describe what ComputationalModels, Experimentation, Testing and Sensing Techniques/Procedures are applied to analyze specificphysical phenomena/processes and their interconnection networks, sequence of execution and data. PhysicsMaps are built using the available knowledge, as R&D and Engineering proceed, they are interactivelymodified.

Physics Maps defined during the R&D and Engineering Process are recorded, organized and managed byspecific Physics Maps Data Base.

Integration of the previously defined Multiscale Maps allow to correlate:

functions to physical phenomena and processes (linking Multiscale Functional Maps with MultiscalePhysics Maps

Properties (Multiscale Architectural/Structural Maps) to Physics (Multiscale Physics Maps)


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Multiscale Performance Properties Structure Processing Maps

The definition of the Performance Properties Structure Processing relationships has become acornerstone of the modern Materials Science and Engineering and R&D and Engineering at all.Prof. Gregory Olson, Northwestern University has been one of the pioneers of this strategy. Prof. Olson

described this approach in a Science Magazine article: Vol. 277 (29 August 1997) pp. 1237-1242.

Fig. 4 (from Questek) illustrates the application of a Performance Properties - Structure Processing Mapto the design of new alloys.

Performance Properties Structure - Processing Maps are indexed in such a way as to relate them tospecific R&D and Engineering, Phases and Tasks. Performance Properties - Processing Structure Mapsdefined during the R&D and Engineering Process for different purposes and tasks are organized andrecorded in the Performance Properties - Structure - Processing Map Data Bases The MultiAbstraction Level feature of the Maps can be seen in the figure: each box is a specific abstraction level. Each

Box refer to a cluster of processes occurring over u spectrum of scales and resolution levels.

This kind of software environments contribute to characterize and manage relationships between processingand manufacturing activities and the resulting architecture/structures

These Maps identify :

defects (typology, physical and chemical characteristics, density and distribution : statistical anddeterministic analysis) linked to specific processes and manufacturing activities and steps

bio chemical and structural features and transformations linked to specific processes and manufacturingconditions, procedures and technologies

This kind of Maps are related to Multiscale Physics Maps


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3.3 Multiscale Science - Engineering Information SpaceThis concept was presented by Alessandro Formica in the Report Fundamental R&D Trends in Academiaand Research Centres and Their Integration into Industrial Engineering (September 2000), drafted forEuropean Space Agency (ESA). The Multiscale Science-Engineering Information Space is associated toany analytical, computational model/method, and experimental, testing and sensing procedure and technique

applied to a specific task. The Multiscale Science-Engineering Information Space defines: what spectrum of information about physical/biological/chemical phenomena and processes at what level of accuracy and reliability

can be get by a computational model or experimental/testing/sensing technique/procedure applied in aspecific context for a specific task.

A set of model variables characterize analytical and computational models. A set of method variablescharacterize the specific method applied to perform simulations. A set of system variables characterizesthe system to be modeled and simulated or subjected to experimental, testing and sensing analyses. A set ofexperimental, testing and sensing variables characterizes experimental, testing and sensing techniques andprocedures.

The Science Engineering Information Space also applies to cluster of computational models andexperimental/testing/sensing techniques/procedures linked through multiscale multiphysics couplingschemes. In this case we can define coupling scheme parameters which describe the method used tocouple models and/or experimental/testing/sensing techniques/procedures.

With the term system we refer to the system (materials, device, component,.) under analysis.. A setof variables describe the geometrical, biological, chemical and physical structure of the system.

With the term Operational Environment, we refer to External Fields and Loading Conditions

With the term model we refer to the mathematical/computational representation of the system underinvestigation. A set of variables characterize and describe the models (boundary conditions, external

fields/loading conditions, space and time dimensions, discretization techniques, particles number andtypology,.). In the proposed framework we extend the concept of Model to theExperimental/Testing/Sensing world as explained in the Paragraph 2.4

With the term method we refer to the specific deterministic and statistical analytical and computationalmethod (Monte Carlo. Classical Molecular Dynamics, Quantum Molecular Dynamics, DensityFunctional Theory, Dislocations Dynamics, Cellular Automata,).

With the term experimental/testing/sensing technique and procedure variables we refer to thevariables which describe technical characteristics of the experimental and testing apparatus and thespecific operational modes and conditions (globally referred to as procedure)

Information Space ConstructionTo build the Information Space of a specific (single scale or multiscale) computational model withreference to a specific system and analysis task (fracture, delamination, oxidation,), we perform a set ofsimulations, varying in a systematic way parameters/variables which characterize the physical (chemical andbiochemical) phenomena/processes of interest in the context of a specific task including external forces.Then, we validate computational models using a set of experiments, tests and sensing measures to track theboundaries of the Information Space and evaluate accuracy and reliability (Uncertainty Quantification UQ). Information Spaces can be built also for experimental, testing and sensing techniques and procedures.In this case a Cross Validation strategy is applied which foresee the comparison of a spectrum ofexperimentation, testing and sensing techniques.

The next page Box synthetically describes the key role and significance played by new Verification &Validation Strategies (Uncertainty Quantification and Quantification of margin of Uncertainty) for theComputational field.


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The Predictivity and Validation IssuesThe National Nuclear Security Program (NNSA), in the context of the Advanced Simulation and

Computing (ASC) Initiative, established the Predictive Science Academic Alliance Program(PSAAP) focusing on the emerging field of predictive sciencethe application of verified andvalidated computational simulations to predict the behavior of complex systems where routineexperiments are not feasible. The goal of these emerging disciplines is to enable scientists to makeprecise statements about the degree of confidence they have in their simulation-based predictions.Five PSAAP Centers have been created:California Institute of Technology: Center for the Predictive Modeling and Simulation of High-Energy Density Dynamic Response of Materials; Purdue University: Center for Prediction ofReliability, Integrity and Survivability of Microsystems (PRISM); Stanford University: Center forPredictive Simulations of Multi-Physics Flow Phenomena with Application to Integrated HypersonicSystems; University of Michigan: Center for Radiative Shock Hydrodynamics (CRASH); Universityof Texas at Austin: Center for Predictive Engineering and Computational Sciences (PECOS)

The following text, drawn from the Presentation Can Complex Material Behavior be Predicted?Given by Prof. Michael Ortiz, Caltech PSAAP Center Director, at the DoE NNSA StockpileStewardship Graduate Fellowship Program Meeting Washington DC, July 14, 2009, illustratesobjectives and approach underlying the general PSAAP Strategy and Methodology concerningValidation and Predictivity challenges:

PSAAP Caltech High-Energy-Density Dynamic Response of Materials (Hypervelocity Impact Application Field) Center objective: rigorous certification of complex systems operating under extreme conditions. lOverarching Center objectives: Develop a multidisciplinary Predictive Science methodology focusing on high-energy-density

dynamic response of materials Demonstrate Predictive Science by means of a concerted and highly integrated experimental,

computational, and analytical effort that focuses on an overarching ASC-class problem: Hypervelocity normal and oblique impact at velocities up to 10km/s

Overarching approach: A rigorous and novel Quantification of Margin of Uncertainty (QMU) methodology will drive

and closely coordinate the experimental, computational, modeling, software development,verification and validation efforts within a Yearly Assessment format

Two issues deserve to be highlighted:

The central role of the Uncertainty Quantification and Quantification of Margin ofUncertainty issues in the context of the Computational Models Validation effort to shape R&Dand Engineering activities. This vision can be, to some extent, related to the previously illustratedconcepts: Multiscale Science Engineering Information Space, Range of Validity andInformation Driven R&D and Engineering Strategy

The key role of Computational, Analytical and Experimental Efforts Integration. New(multiscale) experimental techniques and analytical (theoretical) developments are fundamental todevelop and apply new and more powerful (predictive) computational models and strategies. TheVision is in line with our Methodologically Integrated R&D and Engineering approach


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Range of Validity is the range of the Multiscale Science-Engineering Information Space inside whichwe can get a set of information from specific models/methods and experimental, testing and sensingprocedures/techniques and possible coupling schemes at a certain level of accuracy and reliability(uncertainty quantification).It is of fundamental relevance to determine how the Range of Validity changes as model, method,experimental & testing and coupling scheme variables change. The range of validity is a key element todetermine (for a specific task) :

how good computational models and experimental, testing and sensing techniques and couplingschemes should be to get Information we think to be needed to carry out a task at a predefined error anduncertainty level.

how to define the right mix of computational models/methods and experimental & testingprocedures/techniques and coupling schemes to get what we think to be the right information at the rightlevel of accuracy and uncertainty to perform a specific R&D and Engineering analysis and design task..

Fig. 5 (Center for Computational Materials Design NSF) describes a framework to define in a formal waythe Range of Validity (or Applicability Domain) of a model

The Multiscale Science-Engineering Information Space formalizes what, today, is being performed in anempirical and semi-empirical way. Such a formal procedure allows us to rigorously evaluate the relativeweight of the several model/method/technique variables as function of the Information Space and the

best research/development paths for computational models/methods and experimental & testing techniquesto address specific challenges.

The Multiscale Science-Engineering Information Space concept and method enables researchersand designers to jointly define development roadmaps for computational models and experimental,testing and sensing techniques.


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The need of defining the Information Space associated to computational method and experimentaltechniques, in the context of the Verification & Validation process, has been analyzed, for instance, byTim Trucano in Uncertainty in Verification and Validation: Recent Perspective Optimization andUncertainty Estimation, Sandia National Laboratories Albuquerque, NM 87185-0370 SIAM Conferenceon Computational Science and Engineering, February 12-15, 2005, Orlando, Florida - SAND2005-0945C.

Fig. 6 The figure (from the previously quoted document) illustrates the Information Space concept

Thanks to the Multiscale Science Engineering Information Space concept and method, it is possible todefine Costs/Benefits Function for models/methods and related coupling schemes as referred to differentTechnology Development and Engineering tasks. Benefits are referred to the Information get and Costs

to the resources needed to develop, validate and apply models/methods/techniques/coupling schemes. Thiskind of Function could be useful to Technology Development and Engineering Project Managers to bettermanage and allocate human, organizational and financial resources.

The Multiscale Science Engineering Information Space and the Range of Validity concepts can berelated with new Verification and Validation (V&V) strategies and methods. Uncertainty Quantification(UQ) is a key challenge for Computational Science and Engineering. UQ and Quantification of Margin ofUncertainty (QMU) [performance (measured) vs. requirements (set)] , are becoming (have already become)one of the new driver and objective for the Computational World. The Predictive Science Academic AllianceProgram (PSAAP) managed by US National Nuclear Security Agency (NNSA) is a clear example ofapplication of these statements.

The Multiscale Science-Engineering Space is of fundamental importance to define and implementMethodologically Integrated Multiscale Science-Engineering Strategies which foresee the coherentuse of several different single and multiscale computational models and methods, and several differentsingle and multiscale experimental, testing and sensing techniques working over a full range of scales.

The Multiscale Science Engineering Information Space is becoming of increasingly importance forScience and Engineering because for a specific tasks is common using a spectrum of computational modelsand a spectrum of experimental techniques and methods. Integration calls for rigorous methodologies todetermine what kind of Information can be get from computations and what from experimentation, testingand sensing.


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According to the previous analysis, the Multiscale Science-Engineering Information Space concept andmethod is instrumental to identify:

shortcomings and limitations of computational models/methods and related multiscale multiphysicscoupling schemes for specific R&D and Engineering tasks

development lines (roadmaps) for computational models and methods and multiscale coupling schemesto achieve specific R&D and Engineering objectives

shortcomings and limitations and development lines (roadmaps) for experimental, testing and sensingtechniques and procedures and related multiscale multiphysics coupling schemes

integrated roadmaps for jointly developing multiscale multiphysics analytical, computational and (multiscale) experimental, testing and sensing techniques to deal with specific R&D and EngineeringTasks

integrated strategies for jointly applying multiphysics multiscale analytical, computational and(multiscale) experimental, testing and sensing techniques/procedures to deal with specific R&D andEngineering Tasks


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3.4 The Information Driven Concept and Analysis SchemeThe relevance of Information, as a key element to shape R&D and Engineering Strategies, is winning anincreasing attention. Several studies have been performed, for instance, by Jitesh H. Panchal, Janet K. Allen,David L. McDowell and colleagues at Georgia Institute of Technology. Alessandro Formica highlighted therole of Information to drive modeling and simulation strategies in the White Paper HPC and the Progress ofTechnology : Hopes, Hype and Reality published in US by RCI Ltd on February, 1995. In this documenthe discussed the concept of Engineering Information Analysis. The issue was also dealt with in the contextof the Accelerated Insertion of Materials (AIM) Program (1999) managed by US DARPA. The followingtext is drawn from DARPA Proposer Information Pamphlet BAA 00-22 clearly describes the theme andrelated challenges:

The need for an Information-Driven strategy . .There are many interrelated technical challengesand issues that will need to be addressed in order to successfully develop new approaches for acceleratedinsertion. These include, but are not limited to, the following:The construction of the designers knowledge base: What information does the designer need and to what

fidelity? How does one coordinate models, simulations, and experiments to maximize information content?

What strategies does one use for design and use of models, computations, and experiments to yield usefulinformation? How can redundancies in the data be used to assess fidelity ? The development/use of modelsand simulation: What models are required to be used and/or developed in the context of the designerknowledge base? How can models of different time and length scales be linked to each other and toexperiments? How can the errors associated with model assumptions and calculations be quantified? Howcan models be used synergistically with experimental data ?

The use of experiments: Are there new, more efficient experimental approaches that can be used toaccelerate the taking of data? How can experiments be used synergistically with models? How can legacydata and other existing data base sources be used ?

The mathematical representation of materials: How can one develop a standardized mathematical language

to: describe fundamental materials phenomena and properties; formulate reliable, robust models andcomputational strategies; bridge interfaces; and identify gaps between models, theory and experimentalmaterials science and engineering? How can this representation be used to develop hierarchical principles

for averaging the results of models or experiments while still capturing extremes ?

In the context of the Integrated Multiscale Science Engineering Framework, Information is a keyelement which, to a large extent, drives and shapes R&D and Engineering Strategies.

The term Information Driven means that R&D and Engineering/Manufacturing strategies for specificTasks have to address what can be called The Information Challenge for R&D and Engineering :

What Information at what level of accuracy and reliability (uncertainty quantification) is needed toaccomplish a task

What Relationships and Interdependencies between analysis and design variables should be tracked overa full range (as needed) of space and time scales to accomplish a task

What kind of information sources (analytical, computational, experimental, testing and sensingmodels/techniques) are needed and how they can be combined to get the previously identifiedinformation


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Accordingly, the following key issues define the The Information - Driven Analysis Scheme for R&Dand Engineering/Manufacturing: Select a set of scales and resolution levels (the choice is not unique and it is related to a specific Phase

and Task)

identify physical phenomena, geometry and variables at the different space and time scales whichinfluence the dynamics of a system at the reference scale at a certain level of accuracy and fidelity(different scenarios for accuracy and fidelity can be taken into account).

identify at a qualitative and quantitative level relationships and interdependencies among phenomena,geometry, equations and variables at the different scales

assess how and to what extent (qualitative and quantitative evaluation) the capability of gettinginformation thought to be needed to describe the dynamics of the system at the reference scale at acertain level of accuracy and fidelity is affected by the spectrum of phenomena at the other scales.

assess how requirements defined at a scale determine and affect requirements to the other scales Thedefinition of how information and requirements propagate in a qualitative and quantitative way (in adeterministic and/or probabilistic fashion and taking into account uncertainties) from a scale to anotherscale, from a resolution level to another resolution level, is a key step to : effectively deal with physics aswell as with system and process complexity

Assess what Information at what level of accuracy and reliability is thought to be needed to accomplish aR&D and Engineering task . Thought to be needed means that the process is iterative, we start withsome hypotheses and just Multiscale Science Engineering Strategies and related Data, information andKnowledge Analysis schemes and tools give us the possibility to improve evaluation about theInformation needed to execute the task. Example : What Information (what physical and chemicalphenomena and processes related to materials, structures and chemically reacting flows and theirinteractions) at what level of accuracy and uncertainty should we know to analyze the dynamics of aThermal Protection Systems of an Hypersonic Vehicle for a specific operational environment?

Evaluate what physical length scales and related physical and biochemical phenomena rule the dynamicsof the system under analysis for a specific Tasks, what is the relative weight, what are relationshipsand interdependencies between phenomena and processes inside a scale and between different scales (tobe described thanks to Multiscale Maps) .

Evaluate what Information at what level of accuracy and reliability can existing analytical, computationalmodels, experimental, testing and sensing techniques and related coupling scheme give us (to bedescribed using the Multiscale Science Engineering Information Space) .

Assess what characteristics (Information Spaces) should new models/techniques and related couplingschemes have

Assess what combination of old and new computational, analytical, and experimental/testingmethodologies at different levels of scale and resolution do we need to get the right information at theright level of accuracy and completeness for the different tasks in the different R&D and design stages.A critical step for the rational design of the R&D and engineering processes is a proper selection,integration, and sequencing of computational and analytical models and experimental/testingmethodologies with varying degrees of complexity and resolution. To do that we have to define the Science-Engineering Information Space associated to each methodology.

Assess how good analytical and computational models, experimental, testing and sensing techniques andrelated coupling schemes should be to get the previously identified information thought to be needed toaccomplish a task. How good means evaluating how much physical realism should be incorporatedinto the models and what scales hierarchy has to be taken into account. Not in all the cases, of course,we really need complex multiscale methodologies going down to the Schrdinger equations: simplesingle scale models can be accurate and reliable enough.


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Note: This kind of Information is critical to evaluate what new analytical and computationalmodels and what new experimental, testing and sensing procedures/techniques should bedeveloped and integrated to deal with a specific analysis task. It is important to identify notonly what we know, but, in particular, what we do not know, what we should know, howwe should know it (what combination of scientific and engineering methodologies andtechnologies should be needed). In this context, the lack of Knowledge becomes andimportant element to guide Strategies.

Furthermore, another very critical issue is that we need a rational approach to link advances in thedifferent methods at the different scales with the new information we need to meet challenges in thedifferent tasks in the different stages of the R&D and engineering process. How do we effectively andtimely evaluate the impact of scientific methodological and information advances at an atomic,molecular, and grain (for materials) level on new technological and engineering solutions if we do nothave conceptual and methodological (multiscale) frameworks to link methods and information at thedifferent scales: from atomic to continuum? The Multiscale Science-Engineering Information Spacecan represent a first step to deal with these critical issues. If we like to shape new cooperative schemesbetween industry, from one side, and academia and research, from the other side, we have to definespecific methodologies to evaluate the industrial and technological value of new scientificmethodological advances.

It is to be highlighted that this Analysis Scheme is adaptive and iterative. It should be carried out at thestarting time of any R&D and Engineering/Manufacturing Phase and Task using available data, informationand knowledge and formulating hypotheses: Results get during the execution of a Phase and related Taskswill provide data, information and knowledge that allow to update and improve the Analysis Scheme andinitial Hypotheses Phase after Phase, Task after Task.

The Information-driven approach is a fundamental element to assess if, where, when and to what extentwe have to go down along the hierarchy of scales. Not in all the cases, of course, we should go down until

Schrodinger equations from the continuum. Dont Model Bulldozers with quarks (Goldenfeld and Kadanoff, 1999)


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3.5 Multiscale Modeling and Simulation as Knowledge Integrators andMultipliers and Unifying Paradigm for Scientific and EngineeringMethodologies and Knowledge Domains

The Vision of Multiscale Modeling & Simulation as Knowledge Integrators and Multipliers

(KIM) and Unifying Paradigm for Scientific and Engineering Knowledge Domains and(Experimentation, Testing and Sensing) Methodologies characterizes the Integrated MultiscaleScience-Engineering Framework and it represents the conceptual context inside which theFramework is applied to R&D and Engineering Processes. The KIM notion was presented byAlessandro Formica in the: HPC and the Progress of Technology : Hopes, Hype, and Reality RCI. LtdManagement White Paper February 1995

Multiscale Multiphysics Modeling and Simulation can be regarded as Knowledge Integrators andMultipliers (KIM) and Unifying Paradigm for Scientific and Engineering Knowledge Domains andMethodologies because Multiscale Models are able to integrate and synthesize, in a coherent framework,Data, information, and Knowledge from:

a number of disciplines, a wide range of scientific and engineering time and space domains, multiple scientific and engineering models (science-engineering integration) linked by a spectrum of

coupling schemes.

a wide spectrum of Computational, Experimentation, Testing and Sensing Multiscale Science Engineering Data and Information Spaces built during the development, validation, application andimprovement phases of the same Multiscale Models

several Maps generated by a wide range of methodologies (analytical theories, computation,experimentation, testing and sensing) during the development, validation, application and improvementphases of the same Multiscale Models

In this context, we propose to extend the concept of Model to include not only its mathematicalformulation, but, also, Information Spaces and Maps linked to it for specific tasks. We also extendthe concept of Model from the Computational to the Experimental, Testing and Sensing WorldThis Vision give a New Dimension to the Virtual Engineering and Manufacturing Concept andStrategy and Science Engineering Integration Methodologies and Environments open the way todefine a New Field: Virtual Technology Innovation

Multiscale Information Spaces and Multiscale Maps embody and organize Data, Information andKnowledge get by the full spectrum of analytical theories, a set models at different scales and the relatedexperiments, tests and sensing measures used to develop, validate and improve them.It is to be highlighted that all the existing Modeling and Simulation concepts, application strategies andmethodologies, such as Virtual Prototyping , Simulation - Based Design, Simulation - Based

Acquisition, Simulation Based Engineering Science (SBES) and Virtual Engineering, can be consideredas particular cases of this more general concept and strategy.

We would like to emphasize that the KIM concept puts Multiscale Modeling and Simulation and, accordingly, HPC, at the centre of the R&D and Engineering/Manufacturing Processes much more than the classical Virtual Engineering and Manufacturing and Simulation Based Engineering Science concepts. Multiscale Modeling and Simulation become a key element to shape complex (multi and single scale) Experimental, Testing and Sensing Strategies.

The concept of Model as Knowledge Integrator is certainly not new. This view, in the mid of nineties,was clearly described in the chemical engineering field by James H. Krieger, in the article ProcessSimulation Seen As Pivotal In Corporate Information Flow - Chemical & Engineering News, March 27,1995. The text reported the following statement of Irving G. Snyder Jr., director of process technology

development, Dow Chemical : "The model integrates the organization. It is the vehicle that conveysknowledge from research all the way up to the business team, and it becomes a tool for the business toexplore different opportunities and to convey the resulting needs to manufacturing, engineering, andresearch." . In the same article other companies such as BNFL and Du Pont expressed similar points of view.


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Note: Continuous advances in computational modeling and computing power makes it possible to buildcomputational models which simulate the experimental or testing apparatus, the system to be probed andrelated interactions. This kind of modeling is an interesting asset to plan experimentation, testing andsensing and analyze results.

Key element of the KIM Vision is the extension of the concept of Model to the Experimental, Testing andSensing World as detailed in the following:

The Concept of Experimental, Testing and Sensing Model

In the proposed theoretical and methodological framework it is necessary to extend the concept ofModel from the Computational to the Experimental, Testing and Sensing World. In the context of theExperimental, Testing and Sensing World, for Model, as referred to a specific Experimental, Testing,Sensing activity carried out with specific techniques, working in a specific operational mode andprobing a specific system for a specific task, we mean an Information and Knowledge Structure thatdefine: Characteristics (structure, composition, initial dynamics state, boundary conditions, external loadings)

of the System to be probed Characteristics of the equipment in terms of resolution, scale, physical and biochemical phenomena

which can be probed

Characteristics of the specific Experimental, Testing and Sensing operational conditions and modesapplied for specific R&D and Engineering Tasks

The Multiscale Science Engineering Information Space related to it Multiscale Physics Maps .

As in the Computational World, it is easy to define the concept of Multiscale Experimental, Testing andSensing Model. In this case the Information/Knowledge Structure refers to a cluster of differentequipments and it embodies information about: Interaction schemes among the different equipments

Data and Information Flow among the different equipments

Multiscale Computational Modeling and Multiscale Experimentation Integration Materials Research Society Bulletin

An important recognition of the key strategic relevance of the development of multiscale experimentaltechniques and their integration with multiscale computational modeling comes from the article Three-Dimensional Materials Science: An Intersection of Three-Dimensional Reconstructions and Simulations(Katsuyo Thornton and Henning Friis Poulsen, Guest Editors), published in the Materials ResearchSociety (MRS) Bulletin June 2008.

..For example, by combining a nondestructive experimental technique such as 3D x-ray imaging on acoarse scale, FIB-based 3D reconstruction on a finer scale, and 3D atom probe microscopy at an even

finer scale, one has an opportunity to capture materials phenomena over six orders of magnitude inlength scale. This will bring materials researchers closer to the ultimate dream of a direct validation ofmultiscale models, both component by component and ultimately as an integrated simulation tool. Inconjunction with the advances on the modeling side, such comprehensive experimental information isseen as very promising for establishing a new generation of models in materials science based on first

principles..


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Even if attention to the integration issue is positively increasing, particularly for models development andverification and validation phases, there are still conceptual and methodological relationships not thoroughlyexamined between challenges and advances in modeling and simulation, and progress and challenges inexperimental, testing and sensing techniques. Experience is showing us that ever more complex and largescale computations call for increasingly sophisticated and expensive experimental/testing/sensing techniquesboth in the model development, validation and improvement phases. Advances in modeling and simulationare intimately linked to progress in experimental, testing and sensing methods and techniques and vice versa.A direct correlation and strong mutual dependencies, in the model development, validation and improvementphases, exist between the two fields sometimes regarded as antithetic. It is important to take into accountthat, if computational methods and computing technologies are continuously progressing, also experimental,testing and sensing techniques are making continuous significant progress.

It is sufficient to think at the impact on materials research that the Scanning Tunneling Microscopy (STM)and Atomic Force Microcopy (AFM) techniques have had.

It is advisable to consider a joined development of new Computational Methods and Strategies with new Experimentation, Testing and Sensing Development Techniques and Strategies and vice versa.

Furthermore more and more complex and powerful 3D and 4D experimental, testing and sensing techniquesincreasingly call for complex computational models to interpret, analyse and organize data and defineintegrated measurement and characterization strategies.A priority target is to develop a unified conceptual context to synergistically take advantage of advances inboth the fields and not only for the computational models development and validation phases, as it occurstoday, but, also, in the application phase. All of that in the context of Integrated Frameworks and StrategiesAn effective R&D and Engineering Strategy should find the way to synergistically take advantage ofadvances in both the fields. In several cases, today, advanced HPC/Modeling/Simulation andexperimental/testing/sensing programs are conceived and managed as separated realities. This situationcan lead to costs increase and hamper and limit the effectiveness of both the programs. The new Visionreconcile development streams and roadmaps in the two fields.

In the R&D and Engineering Process, today and, more and more, in the future, we have to integrate a fullspectrum of (interdependent and interlinked) scientific and engineering models and codes with a widespectrum of experimental, testing and sensing (scientific and engineering) data with a full spectrum ofscientific and engineering analytical formulations. Data get from experimentation, testing and sensing coversseveral physical and biochemical disciplines and domains and several different space and time scales. It isclear that, increasingly, we have to deal with very complex interaction patterns intra the experimentation,testing and sensing world, intra the computational modeling world and inter the experimentation,testing, sensing and computational modeling worlds. Multiscale Science Engineering Information Spaces,Multiscale Maps and the Kim vision can be a first step to realize this integration. The KIM concept is afundamental theoretical and methodological basis. Methodologically Integrated Multiscale Science -Engineering Strategies are built upon it. Classical Modeling & Simulation Application Strategies in theinnovative technology development field are significantly hampered and limited the following fundamentalcontradiction: when we develop innovative technologies and innovative engineering solutions, we oftenenter a territory where theories are not well developed and reliable, and the availability of experimentaland testing data is fragmented or lacking at all. Accordingly, we face a fundamental and intrinsic problem:

Modeling & Simulation is the reference strategy to limit risks, costs, and development times by heavilyreducing the resort to complex and expensive experimental and testing activities. However, contrary to whathappens in the mature or evolutionary technology environment, we cannot adopt this strategy because westill need very significant experimental and testing activities to develop and validate the neededcomputational models.That is what is called a classical Catch 22 situation: (i.e.) a situation which involves intrinsiccontradictions.


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This contradiction is certainly not ignored. In the presentation Modeling and Simulation in the F-22Program held on 3 June 98, Bgen Michael Mushala, F-22 System Program Director, highlighted this issue.We quote his exact words :

A Catch 22 :

>> Increased Reliance on Simulation Requires High Confidence in the Modeling

>> High Confidence in the Modeling Requires High Quality Flight Test Data

How to get out of this contradiction? We think that single scale and independent computational andexperimentation, testing and sensing science and engineering strategies are not up to the challenge, A partialway forward can be the application of the new Vision of Modeling and Simulation and, in particular, ofsome of its key constitutive elements:

Multiscale Maps the Multiscale Science Engineering Information Space concept. which enables the definition in a

formal way of what kind of information at what level of accuracy and reliability can be get by single andmultiscale computational, experimental, testing and sensing models and techniques.

A new concept of computational model which include not only mathematical and physical (chemical andbiochemical, as needed) formulations, but, also, Data, Information and Knowledge (Multiscale Maps)linked to it when applied to a specific task

The extension of the model concept to the experimental, testing and sensing world Definition of the Applicability Conditions and Predictability Criteria for (single and multiscale)

Computational models which guide the application of Modeling and Simulation and their integrationwith experimentation, testing and sensing (Methodologically Integrated Multiscale ApplicationStrategies)

It should be taken into account that, notwithstanding important advances in the Verification & Validation(V&V), Uncertainty Quantification (UQ) and Quantification of the Margin of Uncertainty (QMU) fields,methodologies to rigorously validate computational models outside the range of availability of experimental,testing and sensing data, are still, to a large extent, an unsolved issue.


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3.6 Multiscale Multiresolution Multiphysics Experimentation, Testingand Sensing

The term Multiscale means the ability to probe phenomena and processes occurring over a spectrum ofspace and time scales

The term Multiresolution refers to the analysis of phenomena and processes inside a single scale, butwith a range of different resolution degrees The term Multiphysics means the analysis of a spectrum of phenomena and processes referred to

several physical and biochemical domains, inside a specific scale or over a range of

strategic multiscale

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