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What issimulation data management?

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What is simulation data management?


Simulation is key to digital product development, manufacturing, and lifecyclemanagement. “Simulation” also known as “Computer Aided Engineering (CAE)” or“Analysis” is the use of computer tools, which are based on fundamental physical laws,to evaluate aspects of product performance.

Companies everywhere want to take advantage of simulations in order to develop betterproducts faster and cheaper. They want to extend simulations early, during product conceptand architecture development, and they want to replace physical testing for product validationas much as possible. However, they struggle to manage their simulation data, which isincreasing in size and complexity by orders of magnitude as more numerous and moredetailed simulations are performed.

The effective management of simulation data and process information is increasinglyimportant as simulation becomes a core business practice and organizations rely on simulationresults as the basis for business decisions. “Simulation Data Management (SDM)” is atechnology which uses database solutions to enable users to manage structures of simulationand process data across the complete product lifecycle. SDM artifacts can be data, models,processes, documents and metadata relevant to modeling, simulation, and analysis.

An often-asked question is, “We alreadyhave a product data management systemfor our (CAD) data, why not just use it forour simulation (CAE) data?” It isconvenient to understand the requirementsto manage simulation data through acomparison with other data and processesmanaged in the PLM environment.Simulation poses some unique challengesfor both data and process.

Simulation generates huge amounts ofdata that has a short lifecycle of days orweeks. The majority of simulation data iscreated in iterative learning cycles or istemporary reference data that might bedeleted once the product functionalperformance has been assessed and designdecisions have been made.

Characteristics of simulation data



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Complexity and size of dataSimulation has many data types, andrequires significant extensions to astandard PLM data model to representsimulation objects, data sets and processrecords. Simulation comprises manyphysics domains, and each domain mayrequire a different solver and a differentrepresentation of the product geometry. A single part, for example, an automobilewindshield, may have dozens of differentrepresentations in the simulation modelsthat include it. Indeed, somerepresentations are abstractions thatcontain no explicit information on thegeometry.

Simulation data forms can be quitecomplex, and often involve the time historyof multiple field values (scalars, vectors andtensors) on discrete locations of the model.

Large file sizes bring another dimension ofcomplexity to Simulation DataManagement. CAE results files may beorders of magnitude larger than, forexample, CAD component geometry files.

Size, complexity, and the work-in-process(WIP) nature of simulation data are specialaspects that need to be addressed.

Distributed dataStrategies for SDM must face the issue thatthe data is often distributed. Very largefiles may be generated on remote HPCsystems and large files may be generatedon local clusters or high-end desktopcomputers. It may not be feasible, ordesirable, to move all simulation data intoa single physical repository. Indeed, manylarge analysis output files may be deletedafter a relatively short period of time. SDMenables the key results of manyexperiments to be consolidated into asingle enterprise database.

Visualization and collaborationThe size, complexity and data structureissues noted above bring particularchallenges for simulation data visualizationand collaboration.

Highly compressed representations ofsimulation results, which comprehendcomplex field data across a wide range ofphysics, can be effective for collaborationin a simulation environment.

Process and contextEngineering simulation typically involves acomplex sequence of steps, the result ofwhich is an assessment of the design andor product performance. The context andthe process are key to the value ofsimulation data. SDM involves tracking the“how” and the “why” of data as it iscreated. A simulation model should betraceable to the design information used togenerate it, and a simulation result shouldbe traceable to the input data and to theapplication software version used togenerate it. This process and contextinformation is the core feature of an SDM,and it enables reuse of data and processes.

Business processes and rulesSimulation process requirements may bevery different than those of otherfunctions. Examples are change control,and data authority for creation anddeletion.

Enterprise accessThe number of groups across theenterprise that need to access simulationdata is significantly fewer than thoseinterested in other product data, such asCAD. However, managing access control,security, and access to software licensesacross a range of computing resources canbe an extreme challenge.During the simulation lifecycle there arethree distinct phases with different goalsand objectives.




Data gathering and model preparation This first phase involves creating productconfigurations and bills of materials, andthen building and detailing the simulationmodels. Access to a wide range of productdesign information is required, includingproduct requirements, geometry,connections (welds and bolts), materialproperties, loads, etc. For example,specialists at Automotive OEMs often dothis phase as a service, and they heavilyutilize the enterprise PDM system. There isalso a need for simulation-specific librariescontaining occupant models, multi-bodydynamics subsystems, and the like. SDM isnot just for the automotive and aerospaceindustries; SDM can be utilized over arange of simulation activities which areutilized in the chemical, pharmaceutical,and consumer goods industries.

Active work-in-process. During this phasethe analysis work is ongoing and may beexploratory in nature and the large analysisoutput files created may be temporary.Indeed most of the output data created inthis phase will be deleted shortly aftercreation but which simulation models andresults data will need to be retained is notknown a-priori. Simulation Data

Management in this phase should be astransparent as possible to the user and beintegrated with the simulation processes.The capture of key results, process andcontext information in this phase addssignificant value.

This phase includes recommendations anddecisions made during productdevelopment, and continues through finaldesign release and start of production.

Sustain to end of lifecycle“Final” simulation information is retainedto support manufacturing, productchanges, and customer field issues duringthe remainder of the product’s life cycle.This information is also for reuse – it mayform the basis to investigate new productsor product improvements.

This stage includes the archiving ofsimulation data to retain a record of theinformation used in the decision makingprocess. Effectively, this re-quires contextand process information as well as thedata. Requirements for compliance,regulations, and legal discovery must bemet.

Simulation lifecycle

The simulation process may vary from acomplex chain of events and interactions toa simple job submission. The process maybe manual, semi-automated, or fullyautomated. The process (user for manualprocesses) knows the information that isrequired such as the identity of every pieceof data used together with their respectiverelationships along with what softwaretools were used. It is highly suggested thatwhenever possible that the simulationinformation should be extracted from theprocess automatically as it is run, to avoid

errors and lack of validity associated withmanual entry of this information.Automated capture of the simulationinformation from the process is key tounlocking the value of managed simulationdata.

It is also important that the process andcontext information (metadata) be robust.It is desirable, for example, to preserve thelink between the input file and alightweight report when deleting a largeresults file.

Process, context, and metadata capture



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An SDM system must have basic func-tionality, and should manage the processand context information necessary forsimulation data. At a minimum, it shouldprovide:

� User management, system access,and security

� User roles, groups, and permissions

� Base data management functions:versions, groups, status, andchange control

� Automatic and manually generatedmetadata that allows data objectsto be unambiguously identified andclassified.

� Relationships and links that allowthe association of data objects forexample, parent child relationshipsthat establish the provenance andpedigree of data objects.

� Search capabilities based on filecontext, content, metadata, andrelationships.

Additional functionality may include:

� Work flows that allow handoff ofwork: For example, documentapproval.

� Process templates that describetasks and then automate metadatacreation.

� Lightweight visualization andcollaboration tools that interface tomultiple data formats.

� The ability to embed or integratewith simulation processes

� The ability to integrate in the PLMenvironment: PDM, CAT, require-ments, materials, etc.

� Capability to create and manageconfigurations and bills of material.

� Integration in the CAE environment,particularly for pre- and post-processing, and job submission tocompute clusters and HPCs, so thatdata management and metadatacollection are as unobtrusive andautomatic as possible.

SDM system functionality

The base functionality that an SDM systemmust provide is the management ofsimulation data, and information describingits context.

Process automation options run the entiregamut: at one extreme, the SDM containsno process definitions, but is used as anexternal store by automation tools, perhapsthrough an API. This simplest conceptualapproach is akin to a shared electronicfiling cabinet, an extension of the commonUnix shared drive. The primary means ofaccessing information is by a (perhapsconfigurable) hierarchy of attributes. Thissystem does not control the processes thatcreate the data, be they manual orautomated. It should provide a means forprocesses to populate and update data,and to capture metadata.

At the other extreme, process definitionsand automations are completelyencapsulated in the SDM, which cannotmanage data created or modified outsidethe system. “Vertical applications”, whichautomate specific tasks, are at thisextreme.

Most SDM implementations are betweenthese extremes – they enable some degreeof process definition and automation, andthey are able to bank external data andmetadata.

Process automation may yield largebenefits, but may be expensive toimplement. The cost to implement andmaintain customizations must be carefullyweighed against the ongoing andprojected benefits.

Process management and automation




At the other extreme, process definitionsand automations are completelyencapsulated in the SDM, which cannotmanage data created or modified outsidethe system. “Vertical applications”, whichautomate specific tasks, are at thisextreme.

Most SDM implementations are betweenthese extremes – they enable some degreeof process definition and automation, andthey are able to bank external data andmetadata.

Process automation may yield largebenefits, but may be expensive toimplement. The cost to implement andmaintain customizations must be carefullyweighed against the ongoing andprojected benefits.

Often, in an organization there are a lotmore diverse tools used in CAE/simulationcompared to CAD/PDM. So rapiddeployment approaches in smaller groupsto begin with that does not require theentire CAE/simulation organization to comeup with a unified strategy may have to beconsidered as part of a deploymentstrategy (bottom-up approach).

PLM integrationThe choice of an SDM strategy should becarefully weighed against the requirementsfor customization, configuration, andintegration with other systems. Thisincludes not only initial costs, but provisionof the skills needed to maintain and furtherdevelop the system.

The SDM strategy should not be developedand deployed without considering its partin the total PLM strategy. An SDM systemwill need integration not only withsimulation applications, but also withelements of the PLM ecosystem, forexample:

� CAD and other design data

� Material properties

� Test data

� Product performance requirements

� Product configurations and Bills ofMaterials

� Workflows for change management

An SDM system should enhance end-userproductivity while also providing broaderbenefits like improved information fordecision-making. Dictates to use systemsthat reduce end user productivity will likelyfail.

SDM capability delivered as part of thenative data authoring tools (CAE pre, post-processors or simulation tools) can reducethe barriers to adoption and provides abottom-up approach to implementingSDM.

Business case. The business case for SDMcan be made by efficiency gains within thesimulation function, particularly wheresimulation processes are repetitive andapplied to similar product definitions suchas in the automotive industry. Somecompanies have realized large enterprise-level gains in product development time,quality, and cost.

Please refer to the NAFEMS Business Valuefrom Simulation Process and DataManagement white paper which addressesboth the benefits derived from the coredata management system and theadditional gains achievable through processautomation.



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Further resourcesThe following are recommended:

� NAFEMS publication “Simulation Data Management Survey Report”(order reference R0102 from NAFEMS)

� NAFEMS publication “Business Value from Simulation Process and Data Management”(order reference R0104 from NAFEMS)

� NAFEMS working group on Simulation Data Management

� NAFEMS SDM Conferences

� CIMdata / CPDA website

� Vendor websites

Glossary

CAD Computer-aided design

CAE Computer-aided engineering (simulation)

CAT Computer-aided test

DMU Digital mock-up

PDM Product Data Management

OEM Original Equipment Manufacturer

PLM Product Lifecycle Management

WIP Work In Process

HPC High Performance Computing



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What is - NAFEMS Home engineering analysis and simulation · PDF fileWhat is simulation data management? S ... processes, documents and metadata relevant to modeling, ... filing cabinet,