1. MASI ProgrammeTekes Programme Report 3/2010 20052009Final Report Niina Holviala (ed.)

2. Niina Holviala (ed.) VTT Technical Research Centre of FinlandMASI Programme 20052009Final ReportTekes Programme Report 3/2010 Helsinki 20103 3. Tekes, the Finnish Funding Agency for Technology and InnovationTekes is the main public funding organisation for research and development (R&D) in Finland.Tekes funds industrial projects as well as projects in research organisations, and especially promotesinnovative, risk-intensive projects. Tekes offers partners from abroad a gateway to the key technologyplayers in Finland.Tekes programmes Tekes choices for the greatest impact of R&D fundingTekes uses programmes to allocate its financing, networking and expert services to areas that areimportant for business and society. Programmes are launched in areas of application and technologythat are in line with the focus areas in Tekes strategy. Tekes programmes have been contributing tochanges in the Finnish innovation environment for twenty years.Copyright Tekes 2010. All rights reserved.This publication includes materials protected under copyright law, the copyright for which is held byTekes or a third party. The materials appearing in publications may not be used for commercial purposes.The contents of publications are the opinion of the writers and do not represent the official position of Tekes.Tekes bears no responsibility for any possible damages arising from their use. The original source must bementioned when quoting from the materials.ISSN 1797-7347ISBN 978-952-457-498-3Page layout DTPage OyPrinted by Libris Oy, Helsinki 20104 4. ForewordThe modelling and simulation of objects and phenomena have a long history, espe-cially with regard to experimental modelling. In developing and learning new things,humanity has faced a need to try to identify the right direction and choices by cal-culating and trialling in advance, as accurately as possible, the object or phenome-non under study. Modelling and simulation offer an opportunity to explore the un-known, and to clarify matters that could not otherwise be measured or evaluated.They might also be compared to a compass helping to orient us in the fog of igno-rance towards the right target and destination.The development of numeric methods, and particularly the rapid developmentin the information technology sector in recent decades, have created ever better,more versatile and quicker possibilities for exploring the unknown. These possibili-ties are utilised ever more extensively in various industrial sectors and research insti-tutes, in Finland and in other countries. For some time now, Finland has been at thevery cutting edge in many areas of modelling and simulation.In 2005, Tekes MASI programme was launched to support the developmentof the Finnish modelling and simulation sector and to promote the utilisation in in-dustry of the sectors tools and methods. The central aims of the programme wereidentified as the aggregation of phenomena models into wider and more integrat-ed models, more extensive implementation and utilisation of the sectors tools andmethods in the business of small and medium-sized companies especially, and thedevelopment and strengthening of the range of services offered by the sector. Theaim of this was to effect renewal across Finnish industry and business, and to lift thecompetitiveness of both.Tekes, the Finnish Funding Agency for Technology and Innovation has al-ready participated in the development and funding of the Finnish simulation sec-tor through numerous technology programmes and separate funding schemes. Ex-amples of Tekes programmes that preceded the MASI programme include the LIEK-KI 2 Combustion and Gasification Research Programme 19931998, the Compu-tational Fluid Dynamics Technology Programme 19951999, and CODE Modellingof Combustion Processes 19992002. The MASI programme can be seen as the con-tinuation of, and most recent link in, this chain of programmes. MASI has been moreexpansive in nature than its predecessors, however, and with it Tekes has sought awider impact on the modelling and simulation field in Finland.The programmes expansive nature and its targeting at several different areas ofindustry and business also meant that the programmes target group and the con-tent of the projects covered a rather wide and comprehensive area of Finnish mod-5 5. elling and simulation expertise. In addition to annual seminars, several mini-seminarsand other events were held during the programme, for which the target group waschosen by further narrowing down the factors that linked projects. This achieved thedesired networking across projects of a similar type and representing the same sector.The MASI programme was the focus of great interest and demand from bothcompanies and research institutes from the moment it was launched. In financialterms the programme was also realised on a wider scale than originally planned. Afurther indication of the importance and development activity of the Finnish mod-elling and simulation sector is the direction of some of the sectors funding demandduring the final years of the MASI programme into other Tekes programmes, suchas the Digital Product Process programme, the Academy of Finlands Research Pro-gramme in Computational Science, and the Strategic Centres for Science, Technol-ogy and Innovation launched during the programme.As the MASI programme concludes, it is time to turn our gazes to the futureand ponder what comes next. The expertise developed during the course of theprogramme should be carried forward for the benefit of Finnish business and soci-ety. Development work in the sector and cultivation of expertise will certainly con-tinue in Tekes other programmes, in the Academy of Finlands programmes, and inthe Strategic Centres for Science, Technology and Innovation.For its part, the programme has furthered the development of a multidiscipli-nary outlook and operational model in Finland, and promoted a culture of co-op-eration both within Finland and internationally. One notes improvements in Finn-ish expertise in many different areas of modelling and simulation. Utilisation of theexpertise developed in the projects and the results of the projects has had, and willcontinue to have, a significant impact on Finnish society and business more broadly.Tekes sincerely thanks all of the people, companies, research bodies and otherpartners involved in planning and realising the MASI programme. Tekes would liketo say thank you very much to the members of the Steering group, and to its chair-man Harri Turpeinen, for directing the programme. We would like to offer our par-ticularly warm thanks to Pekka Taskinen, who co-ordinated the programme with im-agination and efficiency, and to his supporting staff at VTT Technical Research Cen-tre of Finland.Helsinki, April 2010Tekes6 6. ContentsForeword ............................................................................................................................................................................51At the conclusion of the MASI programme .................................................................................9 General description ............................................................................................................................................9 Areas of emphasis and funding for the programme..................................................................10 The programmes trajectory and results.............................................................................................11 Observations made during the course of the programme ...................................................142Research projects .......................................................................................................................................... 17 Multiobjective optimization and multidisciplinary decision support MASIT01 ....17 Symbiosis between plant and computational models (SIMBIOT) MASIT02............20 Inverse problems and reliability of modelling MASIT03 ......................................................22 Multiphase chemistry in process simulation (VISTA) MASIT04 ........................................25 Statistical phenomena in virtual design of machines (MARTSI) MASIT05.................29 Modelling and simulation of coupled problems in mechanics and electrical engineering MASIT06...........................................................................................................33 Modeling and simulation of dissolved and colloidal substance flows in TMP- and DIP-processes MASIT07 ................................................................................................36 Scientific computing and optimization in multidisciplinary applications (SCOMA) MASIT08 ........................................................................................................................................38 Automated generation of 3D topographic visualisations MASIT09 .............................42 Improvement of evacuation safety in large buildings by the combined simulation of fire and human behaviour MASIT10..................................................................45 Modelling and simulation of manufacturing systems for value networks (MS2Value) MASIT12 ...................................................................................................................................48 In silico models of disease pathogenesis and therapy (TRANSCENDO) MASIT13 ...52 From discrete to continuous models for multiphase flows MASIT14 ..........................55 Virtual engineering in design, training and completion of demanding maintenance work tasks (VIRVO) MASIT15...................................................................................59 Modelling changing needs of consumers (KULTA) MASIT16 & MASIT36 ..................62 Combining multiblock and CFD modelling (LOVI) MASIT17 ............................................66 Utilisation of simulation in industrial design and resulting business opportunities (SISU) MASIT18...............................................................................................................70 7 7. Multi-scale flow modelling (MUSCA) MASIT19 ..........................................................................73 Nonlinear temporal and spatial forecasting: modeling and uncertainty analysis (NoTeS) MASIT20 ........................................................................................................................78 Genuinely three-dimensional user interfaces in product design and animation (HandsOn) MASIT21 ...........................................................................................................83 Developing chemometrics with the tools of information sciences (CHESS) MASIT23 ......................................................................................................................86 Modelling and simulation in software engineering (MoSSE) MASIT24 ......................90 Innovative simulation method of multi-phase chemistry (InnoSim) MASIT25 .....93 Development of the 3D power plant simulator MASIT27 ..................................................95 Flow physics and modelling (FLOPHY) MASIT28......................................................................97 Modelling interfacial partitioning in multi-phase systems (INTER) MASIT29 ....... 102 Ice-structure interaction modelling and simulation (STRUTSI) MASIT30 ............... 105 Automatic testing of control systems in the integration phase of intelligent mobile machines (TINAT) MASIT31 ...................................................................... 110 Design and modeling of printable electronics applications (DEMOprint) MASIT32............................................................................................................................. 113 Industrial application of PhaseField modelling (PhaseField) MASIT33..................... 116 Qualitative methods in virtual design of machines (KVALIVE) MASIT34 ................. 120 Combining simulation and optimisation with building draft and HVAC planning MASIT35 ....................................................................................................................... 123Annexes 1The projects of the MASI programme................................................................................... 126 2MASI steering group ........................................................................................................................ 137Tekes Programme Reports in English ................................................................................................... 1388 8. 1 At the conclusion of the MASI programmeGeneral description Other factors in the changing op- In connection with the prepa- erational environment are illustrated inrations, a series of seminars were ar-Background and preparation for figure 1. ranged in which representatives of thethe programmeIn connection with the prelim- industrial and research communitiesTekes began to investigate the state inary investigation, interviews werewere able to express their opinions onand needs of the modelling and sim-conducted with a wide range of rep- the programmes key areas, as well asulation sector in Finland in 2004 with resentatives from Finnish industry andits orientation and emphases. On thea view to launching a national devel-research, and their opinions on the basis of its preparations in 2004, Tekesopment programme. The driving forc-programmes necessity and relevance launched the Modelling and Simula-es behind the investigation included were surveyed. The investigation illus- tion Technology Programme (MASI) atthe considerable development of infor- trated the pressing need for a devel- the beginning of 2005. It was decidedmation and communication technolo- opment programme for the model- that the MASI programme would takegy and the resulting possibilities, tight- ling and simulation sector in Finland, so place over five years (20052009) wither global competition, and pressures toTekes decided to begin preparations toan estimated total budget of 92 millionsave in consumption of energy and rawthat end. euros, whereof Tekes contribution wasmaterials. estimated at half. Aims of the programmeFigure 1. Acting forces in the operational environment at the beginning of the MASIThe key aims of the MASI programmeprogramme. were: The widespread implementation in industry of modelling and sim- ulation Innovation of modelling and sim- ulation processes The creation of new commercial opportunities With its MASI programme, Tekes sought more diverse and more widespread use of modelling and simulation in Finnish industry. The strategic aim of the pro- gramme was to promote interdiscipli- narity and international co-operation, as well as networking between compa- nies and research bodies.9 9. The goal was more rapid techno-3. Development of services and busi- Timeline, scope and funding logical development in the range andness processes: such as develop-As far as Tekes was involved, the pro- application of tools and methods. Inment of service and business ex-gramme was situated within a funding terms of the tools and methods of mod-pertise, use of modelling in deci-framework in order to define and lim- elling and simulation, a move in the di-sion making and in support of busi- it specific goals on the one hand, and rection of more extensive and com-ness management, addition of busi-the programmes size on the other. For prehensive models was sought. The ness expertise, scenario calculationthe programmes whole five-year peri- aim was to combine various phenom-at various phases of the value chain, od a total budget around the 92 mil- ena-based models into a larger whole. and new methods from business lion euro mark was aimed for, where- This way they could be used to pro- studies for tangibly realising theof Tekes proportion would be an esti- duce more extensive and comprehen-benefits of modelling initiatives.mated 46 million euros. MASI success- sive solutions to the various problems 4. The use and productisation of fully achieved its overall aim, and even and processes in which the phenomenamodelling in the simulation sec-surprised with the high demand for en- are expressed. Another aim was to ag- tor, utilisation of visualisation, crea-terprise projects. Final total funding for gregate phenomena-level, system-level tion of modelling services, market- the programme amounted to 98.3 mil- and decision making models, so that the ing, support for use, questions of re-lion euros. During the course of the knowledge of different sectors could be sponsibility and IPR issues.programme funding was allocated to more broadly utilised in problem solv-104 company initiatives with a total ing and decision making. It was hopedA general criterion of the programme volume of 72.3 million euros, whereof that the co-operation offered by the was the attempt to create holistic re- Tekes funding contribution amounted MASI programme would result in syner-search endeavours which would pave to 31.8 million euros (43.9%). In terms of getic benefits and the results adoption the way for a stronger international po- public research, the programme fund- in Finnish business life.sition and improve the conditions fored a total 35 projects, each of which, asthe emergence of new expertise and joint ventures, were divided into sever- Areas of emphasis andco-operation.al subprojects. The total volume of re- funding for the programme Areas of emphasis The following areas of emphasisFigure 2. Distribution of funding to various projects according to size of enterprise, were chosen as premises for the pro- and Tekes funding shares for enterprise and public research projects. grammes preparation: 1. Models of phenomena and theircombination: such as new modelsof phenomena, connecting of newand old models (reaction kinet-ics, biotechnology, materials, heattransfer, currents, stochastic meth-ods etc.) and co-ordination of phe-nomena levels. 2. Methods and tools of modelling:such as optimisation, data analysis,SOM, validation, programming, math-ematics, measurements, 4-D (time de-pendence), the models life span, visu-alisation and animation.10 10. Figure 3. Funding shares of different industries. Tekes funding covered 44% of the The programmes trajectorytotal 72.3-million-euro volume of enterprise projects.and resultsThe programmes direction andproject choicesFunding share of enterprise projects in MASI programmeYears 20052009, 72 million euros The steering group made up of businessrepresentatives provided direction onthe programmes emphases of content,11,7 Minternational aspects, business needs,Energy and environment industriesand perspectives in terms of variousMechanical engineering industrieslines of business. It also proposed meas- 31,8 M Forest and chemical industries13,1 Mures for realising the project. The steer-Telecommunication and electronicsing group took part in determining se-Other industry sectorslection criteria for research projects andTekes share of fundingdeciding project selection policies in7,6 Mconnection with funding applications.7,3 MA programme team made up of0,8 M Tekes experts was charged with sup-porting the programme manager infunding decisions, selection of practicalmeasures, and monitoring of projects. Asearch projects amounted to 26 million public research co-operation in- project manager and a project co-ordi-euros, with Tekes contributing 21.2 mil- itiatives were launched, whose nator nominated by Tekes implement-lion euros (81.5%). Figure 2 shows the steering groups were required to ed the practical measures determineddistribution of funding to large-scale include representatives of enter-by the steering group within the frame-enterprises (60 projects), small and me- prises,work of the co-ordination project.dium sized enterprises (44 projects), utilisation of the results of researchand the proportions made up by pub-projects was promoted, as was theEnterprise fundinglic research (36 projects). The shares ofemergence of companies offeringAt the beginning of the programmeTekes funding allocated to enterprises services in the new sector,in January 2005, an application roundand public projects are presented sep-small and medium sized enterpris-directed at enterprises was opened.arately in the graph. Funding shares ones in particular were encouraged Thereafter enterprises had the oppor-an industry-specific basis are presented to broaden their expertise and totunity to apply for funding for the en-in figure 3. adopt modelling and simulation tire duration of the programme. In en- methods and tools in their devel-terprise projects, the aim was to devel-Steps in the programmes opment work, op modelling and simulation applica-realisation seminars, workshops and net- tions, and to encourage implementa-In order to realise the aims of the pro- working events were arranged.tion of research results and new meth-grammeods to meet the needs of business life. product development initiatives A steering group comprising representa- were launched with companies tives from business life was establishedResearch funding offering or utilising modelling andto direct the programme, and its task was Two research application rounds were simulation services, to issue guidance on the programmesarranged for public research projects wide-ranging and interdisciplinarystrategic emphases and policies.in 2005 and 2007. Demand for fund- 11 11. ing was extremely high, which meant and further the spread of modelling ex- workshops were arranged to consider that only around 15% of project appli-pertise between different experts and particular issues in modelling and sim- cations were approved funding in theenterprises. During the course of the ulation. Training in project leadership first round. Research projects from the programme the emphasis was on pro-was arranged for the individuals re- first round were launched at the begin- moting commodification and exploita-sponsible for projects, alongside an in- ning of 2006. tion of research results and co-opera-troduction to Tekes practices, consid-Before the second applicationtion between enterprises. eration of the best approaches for uti- round, an interim evaluation of the pro-lising results, and specialist communi- gramme was carried out in the form of Seminars, workshops and eventscations training with a view to making a web survey in order to adjust the em- Activities during the programme com-distribution of information more effi- phases of ongoing funding. The devel- prised activities internal to projects, cient. opment of instruments needed in busi- and joint programme activities. Re- Various functions and events were ness decision making was added to search projects operated and assem- arranged within the framework of the the programmes list of priorities. The bled according to their own timelines.co-ordination project as follows: research round was carried out in two Work proceeded under the guidance phases. Applicants for ongoing fundingof management groups and projectFive annual seminars were given the opportunity to present leaders, in accordance with project Opening seminar: Helsinki 2005 their aims for follow-up projects and re- plans and co-operation agreements. Jyvskyl 2006 port results from the first phase to theProjects independently arranged in- Tampere 2007 programmes steering group and Tekes ternal workshops and reporting of re- Vantaa 2008 programme team. In order to perfect sults in the form of an extended man- Concluding seminar: Helsinki 2010 the programme, a further applicationagement group meeting, for example. round was established in which inten- They served as a productive forum for Eight subject seminars tions for new projects could be de- exchange of information and discus- Validation 2006 clared. As a result of this intentionssion among business and researchers. Multiobjective Optimization 2006 round, the second round for researchThe events were generally directed at Global Optimization 2007 applications proper saw funding grant-parties who were involved in the initi- Data, information and knowledge ed to around half of applicants. Theative. In many projects, active personalin chemical technology 2008 projects were launched at the begin-contacts between business representa- Simulations for industry Simind ning of 2008. tives and researchers played a key role 2008In addition to Tekes ordinary fund- and clearly furthered exploitation of re- Measurements and modelling 2008 ing conditions for research projects, sults later on. Modelling and Simulation in projects were required to meet the pro-International co-operation tookFinland and in USA 2009 gramme-specific conditions of at leastplace both within research projects Virtual Reality and Remote three parties to research as well as re-and in the form of events organised byOperation Day 2009 alisation of an international dimension the programme and trips. during the period of the project. TheThe programme promoted inter-Four workshops projects were required to form a steer- action between bodies operating in The state of computational fluid ing group comprising experts from in- the sector, and offered projects jointdynamics in Finland 2007 terested enterprises together with rep- events for exchanging up-to-date in- Utilisation of modelling and resentatives from research bodies. Theformation. The annual seminars were simulation 2007 purpose of these additional require-among the programmes major events. Interim evaluation 2007 ments was to increase the breadth ofAround 160220 individuals partici- Future need for forums in the co-operation, promote innovative re-pated in the events. In addition to the modelling and simulation sults by combining areas of expertise,annual seminars, various seminars and sector 201012 12. Six project manager days were announced in the form of news-gramme. As one form, participation inTekes international activities 2005paper advertisements and pro-different interest areas events was tri-Project manager days 2005, 2006 gramme leaflets as well as Tekes news- alled in the form of briefings. Subjects & 2008letters. Information related to the pro- were selected with the event organ-Communications training 2007gramme was distributed with the help isers. This opportunity for precision of website, by sending newsletters presentations of projects was market-Two area seminarsand by publishing news magazines.ed to different parties, such as associ-Tekes international activities and The web pages featured seminar pres- ations and enterprise representatives MASI, Pori 2005 entations, yearbooks and descriptionsin meetings of the research projectsModelling in process industry,of research projects. The programmesmanagement teams. Lappeenranta 2006 mailing list stretched to over a thou- sand contacts. Subjects were market- Results and achievements of theThree fair pavilions and presentations ed to the press in the form of press programmeTechnology Fair, Jyvskyl 2006 releases. In terms of the research By its very nature, the MASI programmeAIChE Annual Conference, Phila- projects, annual reports constituted was an extremely variegated entity delphia, USA 2008 a more detailed source of informa- producing expertise and expertise-in-Subcontracting Fair, Tampere 2009 tion on the projects aims and achieve-tensive tools. As a result of the nature ments. The annual reports were pub-of the programme, tangible resultsActivities reflected the cross-sector na-lished in both printed form as part of and new achievements are distributedture of the programme. Each party wasTekes report series, and in pdf form on widely, and observation of benefits willinterested in quite detailed questions the MASI homepage online.take place over a longer interval. Mod-and expertise related to their own In connection with the annualelling and simulation are often ongo-needs. In the more general subject ar- seminars, projects were offered the op-ing processes for enterprises, result-eas interest focused on the key ques-portunity of a single oral presentationing in constant development. The on-tions of modelling and simulation, and poster presentation each year. Atgoing transfer of results into enterpris-such as validation, verification, validity the opening and concluding seminars, es expertise and elements of their op-of data and its representative render- the programmes projects and sectorserational systems makes measurementing, optimisation and in some sec- enterprises were offered the opportu-of quantitative results almost impossi-tors measurement and comprehen-nity to participate with their own stand ble. Qualitative results may be consid-sion of phenomena. Visualisation, vir- in small-scale fairs. Within the frame-ered instead.tual environments and presentation work of the projects co-ordination,of results were all further subjects ofparticipation in fairs took place threeProgramme activitiescommon interest. Around 4060 ex-times in the form of an independentDuring the course of the MASI pro-perts typically participated in thepavilion presenting MASI. Enterprisesgramme, several new patterns of co-seminar events, and around 20 per- and commercially interesting researchoperation emerged among various re-sons took part in the workshops andprojects were offered the opportunitysearch groups, and between researchtraining events. Detailed needs were to present their products and expertisegroups and enterprises at the nationalhandled in the projects own semi- at fair seminars. The research projectslevel. International co-operation alsonars, and they were also taken care of were responsible for the publication ofincreased. Behind the networking be-in connection with the projects eve-projects scientific results and for disser- tween various actors lay in part Tekesryday activities.tation works.requirement for research projects in Various strategies for commu-terms of their size, international co-op-Communications nicating the possibilities and results eration and enterprise participation.Funding application rounds and con-of modelling and simulation were The seminars, trips and other eventstent descriptions for the programmes brainstormed and trialled in the pro-organised as part of the programme13 13. made meetings possible, and thereby The projects carried out by enter- Observations made the emergence of new contacts and prises in the MASI programme can beduring the course of patterns of co-operation. With the help divided into development projects by the programme of the programme activities, it was pos-enterprises offering modelling services, sible to distribute information about and on the other hand projects by en-Investigations concerning general questions related to the sector,terprises utilising the services and ex- the sector and to inspect the trends in the devel- pertise in their own business. In both During the course of the programme, opment of operations. groups significant progress took place the views of researchers were heard in both the development of tools and and business representatives were in- Research projects methods and in relation to their utilisa-terviewed. The investigations carried The results of the research projects car- tion. According to the larger enterpris- out and the views of persons repre- ried out in the programme will be uti-es, more efficient, tailored applicationssenting the sector emphasised the lised by Finnish business life and enter- were adopted. In some projects re- MASI programmes current relevance prises in phases, by way of the enter-search was carried out on adjusting theand its role in developing the Finn- prises own projects. The timespan fornew methods to business, and infor-ish innovation environment. There are the utilisation of the results of researchmation was applied to development of a number of particularities, but a few projects is 510 years on average. Never- holistic systems. Interest focused on, for matters have been singled out below. theless, a significant number of concrete example, service concepts, remote op- The MASI programme had an in- examples of enterprises utilisation of the erations and solutions for subcontract-vestigation carried out under the lead- results of research projects could alreadying arrangements, as well as command ership of VTT Technical Research Cen- be seen during the programme. Prom- of the international market and optimi-tre of Finlands research professor Ol- ising results and new finds reported by sation of ones own processes. li Vent, which evaluated the state of several business representatives indi-One aim of the MASI programmemodelling and simulation in the key cate utilisation of modelling and simula- was the increased utilisation of model-fields of Finnish business. The investi- tion, as does the independent continu-ling and simulation, and their extend- gation mapped future challenges and ation of research projects by enterprises ed use as an everyday tool in Finnish in-perceived opportunities. following the programme. The research dustry and business irrespective of the During the MASI programme, the projects have found in their projects size of the enterprise. A large number USAs National Science Foundation car- new focuses of development, which are of Finnish SMEs were indeed involved,ried out an investigation into the state funded through Strategic Centres forboth in funding and following pub- of modelling and simulation in the Science, Technology and Innovationlic research projects, and in participat-United States and worldwide. The in- initiatives or as part of other nationaling in activities with their own researchvestigation group, led by professor Sha- programmes. This will guarantee thatand development projects. Although ron Glotzer, visited several countries in research expertise in modelling and from a funding and numerical perspec-Asia and Europe. The results of the in- simulation continues to develop after tive, participation by SMEs was over-vestigation led to the January 2009 the programme, too. shadowed by the contribution of large- MASI seminar Modelling and Simula- scale enterprises, the programme did tion in Finland and in USA. The investi- Enterprise projects much to pave the way for the manifes-gation states, for example, that in future One of the programmes key aims was totation of its results in SMEs on a muchmodelling and simulation may come to create new business and services for thewider scale in coming years. Thus, the cover almost all areas of human activi- sector. One example of the realisationresults in relation to SMEs were posi- ty, it will be cheaply available to all, and of this aim is the emergence during the tive, and several new enterprises inter- it will become an easy and efficient tool programme of new enterprises offering ested in utilising the innovations wereset for all places. In other words, eco- modelling and simulation services.involved in the project. nomic competition will tighten as a re-14 14. Figure 4. Some of the MASI participants at AIChE conference.sult of the development of modelling ly on modelling and simulation exper-prepare for increased investment inand simulation.tise. Consequently, engineering scienc-the sector in future. New areas of rap-Modelling and simulation exper-es will also largely be built in future on id development include open sourcetise is especially sorely needed to re-modelling and simulation. Mathemat-service, economical parallel proces-solve the major challenges and com-ical modelling and computational sci-sors, visualisation devices, 3D devel-plex systems facing humanity, such asences lay behind the development and opment, information exchange plat-climate change, production of alterna- expertise. forms for software, massive, fast-con-tive energies, environmental issues andAccording to VTTs investigation,nection networks, etc. The possibilitiesdisease control. This expertise will offer in our key areas of industry we lie near offered by technological developmentabundant opportunities to those en-the very top, and in some areas we ac- for improving algorithm developmentterprises who are involved in the devel- tually lead. However, the operationaland programming engineering willopment, and who are able to adopt theenvironment is constantly changing certainly offer a national competitivenew implements. Key areas of research, and with increasing speed. Althoughadvantage to those countries that in-such as biology and medicine, ma-in individual sectors Finnish exper- vest in it. On the other hand, success-terial technologies and social scienc- tise lies at the very top in internation-ful modelling and simulation expertisees, as well as natural sciences, also re-al comparison, we still have reason to and its application are always based on 15 15. world-class expertise in each area of Training and creation of expertisePerhaps it would be worth con- application. On the basis of the investi- Many key issues emerged from the in- sidering a suitable requirement level gation, each area of application has no vestigation led by professor Glotzer,without expecting everything desira- shortage of challenging targets, whichone of the most important of which ble in a single package. In future it will is why development needs on the one was the quality of modelling and simu- be worth paying greater attention to hand and opportunities on the oth-lation training in the United States and modelling and simulation expertise, its er are substantial. How we succeed in worldwide. According to the investiga- role and place in the Finnish education future depends solely on joint invest-tion, not enough experts are trained tosystem. From whence will enterprises ments and the activeness and exper- meet the requirements of the model-recruit their experts, how will they carry tise of enterprises.ling and simulation sector. The same out their own training in these subject situation can be seen in Finland. In areas, and how will development take Concern in discussions aboutmany areas Finland leads the way inplace? An operational culture increas- ongoing funding expertise, but the group of experts is ingly focused on subcontracting may On the basis of contacts, concern was nevertheless too narrow to spread theweaken understanding within enter- noted about the sustainability of re- benefits of modelling and simulation prises of the possibilities of modelling search funding in the sector followingextensively enough across the indus- and simulation, and decrease commu- conclusion of the programme. Fund-trial and research sectors. The in-depth nication between actors which is based ing has been clearly directed towards knowledge demanded in the sector on needs. How to ensure the develop- the subject area in some Strategic Cen- was seen as a major problem. Exper-ment of expertise in enterprises already tres for Science, Technology and Inno-tise in modelling and simulation alone offering services, and preserve enter- vation programmes (Forest Cluster and is not sufficient, rather, comprehensive prises operational ability in the small FIMEC), but there is still reason to draw knowledge is needed of each special- domestic market? In enterprises strat- attention to funding of generic topicsist area. In thermodynamics or chem- egy and operational design, it is worth in the modelling and simulation sector. istry, for example, one needs strong considering modelling and simulation They may be excluded from funding expertise in the sectors processes, asexpertise and the future competitive when making choices based on short- well as preferably strong expertise in edge achieved with its help. term results or sector-specific criteria. the area of programming. When social Care should also be taken to maintain skills, language skills and other more communication between the academ- general business-related knowledge ic community and enterprises with re- is added to the list of employee crite- gard to issues in modelling and simula- ria, it is only natural that top-level ex- tion. This should be taken into account perts are rare. when developing a new higher educa- tion system.16 16. 2 Research projectsMultiobjective optimization and multidisciplinary decision support MASIT01Objectivesin nonlinear multiobjective optimiza- support (JYU, HSE, TUT). Even thoughStrategic, operative as well as decisiontion and multiple criteria decision mak-each of these research units did havemaking in general necessitates taking ing. Because the interests of these re- its own goals they all did share much inseveral conflicting criteria simultane- search units are highly overlapping itcommon. The objective of this projectously into account. When dealing with has been convenient to share experi-was to find areas where knowledge cancomplex problems this requires appro- ences and do research in tight collabo- be shared in such a way that researchpriate decision support tools. By multi-ration. Each research unit has lots of ex-in each unit was supported by the oth-objective optimization, we mean find- perience and knowledge related to op- er units. For example, application cen-ing the best solution in the presence oftimization, modelling and applicationstred research units (UKU, TKK, TUT) of-several conflicting criteria. Tools of mul- and, thus, all these strengths have beenfered valuable real world problems andtiobjective optimization enable con-combined in the project.inspiration to units that were workingsidering complex problems and inter-The core of the research has been with more general methods and toolsdependencies in them as entirenessesbuilt around computationally demand-(HSE, JYU). On the other hand, thesewithout artificial simplifications. ing multiobjective optimization and general methods and tools were usedInteractive multiobjective optimi-multiple criteria decision making. Theto solve application problems.zation methods provide a decision mak-central goal of this project was to de-In addition to cooperative re-er a possibility to learn about the inter-velop new decision support approach-search, the aim of the project was inrelationships between the criteria andes and multiobjective optimizationsharing of the obtained knowledge re-direct the solution according to her/hismethods designed especially for com-lated to multiobjective optimization inpreferences. Unfortunately, commercialputationally challenging nonlinear andgeneral. In this way, the companies in-optimization software packages con- mixed integer problems. In all develop- volved were given a great opportuni-tain no such methods. Practical expe- ment, the focus has been on methods ty to learn how multiopjective optimi-rience has shown a need for computa-and tools that are as application inde- zation methods can be utilized to im-tionally efficient methods in both non- pendent as possible. This makes it pos- prove their individual goals and appli-linear and mixed-integer problems withsible to extend the results of this project cation specific processes.multiple objectives.to a variety of other application branch-This project did consist of five re-es of multiobjective optimization.Resultssearch units at the University of Jyvsky-Research in this project was relatedThe main results of the project are newl (JYU), Helsinki School of Economicsto real world applications in the fieldsmethods and approaches that are doc-(HSE), Helsinki University of Technolo- of energy (TKK, UKU), mechanics (TUT),umented in over 40 publications rang-gy (TKK), Tampere University of Tech- and radiotherapy (UKU), and, on the ing from scientific articles and pro-nology (TUT) and University of Kuo- other hand, to software tools (JYU) and ceedings papers to technical reportspio (UKU). These units share an interestgeneral purpose methods for decisionand other scientific publications. Dur- 17 17. ing the project, research units also pro- doctoral dissertation titled Reference optimization problems that are signifi- duced three doctoral and two other ac-point based decision support tools forcant to the companies. This co-opera- ademic degrees. The results of the re-interactive multiobjective optimizationtion, where actual design problems are search were presented in over 40 pres-describes the results obtained. solved, shall continue after this project. entations given in different internation- Two doctoral dissertations with ti- At UKU, for the first time, an in- al and national conferences or semi-tles A systematic procedure for analy- teractive multiobjective optimization nars. In what follows, we shortly high- sis and design of energy systems and method was employed in radiotherapy light some main results and related col-Modelling biomass-fuelled small-scaletreatment planning model based on laboration. CHP plants for process synthesis opti-Boltzmann Transport Equations (BTE). In At JYU, a new intelligent decisionmisation describe the results obtained addition, interactive multiobjective op- support system (based on the inter- at TKK. In addition to these, also a newtimization was used also to internal ra- active NIMBUS method and its imple- method for heat exchanger network diotherapy, brachytherapy, and our ap- mentations IND-NIMBUS and WWW-synthesis was developed. Together withproach was tested with clinical exam- NIMBUS produced in earlier projects)JY and HSE a tool that integrates IND-ples (at Kuopio University Hospital). In were developed and applied to appli-NIMBUS-software and GAMS-modellingboth cases results are highly promising. cation areas of UKU and HUT. This new system was developed. With this tool, This research was made in collaboration system design was extended with de- GAMS can be used to solve multiobjec- with JY and HSE. Furthermore, UKU de- cision support tools developed in col-tive optimization problems interactive- rived depth-averaged flow and ener- laboration with HSE. The decision sup-ly. The method was used in synthesis of gy equations for plate heat exchanger port system developed at JYU did play heat exchanger network problems. Ad-modelling. These are used to optimize a crucial role in the project, because it ditionally, the method was used withdifferent plate geometries using single offered an interface which did make multiple objectives to optimize oxyfu-and multiobjective optimization. After possible to utilize the developed meth- el power plant concepts.optimization, the most interesting ge- ods in solving the application problems.At TUT, a general algorithm for ometries can be investigated with more In addition, JYU did also lots of meth- finding the Pareto optimal solutions of accurate 3D modelling and experimen- od development and international re-multiobjective mixed integer problems tal measurements. search collaboration which had a greatwas developed. The algorithm was ver- impact on research made in the otherified by solving a number of mathe- Publications research units. matical test problems having distinct Summary of essential publications At HSE, the emphasis was on gen-properties. Furthermore, structural de- Eskelinen, P. 2008. Reference point based eral method development in tight col- sign applications associated with trussdecision support tools for interactive laboration with JYU and internation-optimization were formulated and multiobjective optimization, Doctoral al researchers. As an example, meth-solved by the algorithm. The results dissertation, A-334, Helsinki School of ods based on trade-off informationfrom the preliminary testing providedEconomics, Helsinki. were studied with JYU and related toideas for enhancing the algorithm fur-Eskelinen, P., Miettinen, K., Klamroth, K. this, experiences were exchanged with ther to make it computationally more & Hakanen, J. Pareto Navitagor for UKU. Also a new innovative interactiveeffective. During the algorithm devel- Interactive Nonlinear Multiobjective method was developed where the de-opment, experiences were exchanged Optimization, OR Spectrum, to appear. cision maker is allowed to navigate inwith JYU and TKK. As the knowledge on Klamroth, K. & Miettinen, K. 2008. the Pareto optimal solution set of a mul- computational algorithms for mixed in- Integrating Approximation and tiobjective optimization problem andteger problems has gradually increased,Interactive Decision Making in explore potential solution alternatives.co-operation with some of the indus- Multicriteria Optimization, Operations A version of this method was integrated trial partners of the project has been Research, 56(1), 222-234. into the IND-NIMBUS environment. Thestarted by identifying and formulating18 18. Lyytikinen, M., Hmlinen, T. & HDR Brachytherapy, Reports of SWECO Marine OyHmlinen, J. A fast modelling toolthe Department of Mathematical Patria Aerostructures Oyfor plate heat exchangers based Inaformation Technology, Series B, Wrtsil Finland Oyon depth-averaged equations,Scientific Computing, No. B 14/2008, Fortum OyjInternational Journal of Heat and MassUniversity of Jyvskyl, Jyvskyl. Kuopio University HospitalTransfer, to appear.Savola, T. 2007. Modelling Biomass-Fuelled Metso Power OyMela, K. 2006. AlgoritmiSmall-Scale CHP Plants for Process M-real Oyj, Andritz Oymonitavoitteisen eplineaarisen Synthesiss Optimisation, Doctoral Foster Wheeler Energia OysekalukuoptimointitehtvnDissertation, TKK Dissertations 75, PA Consulting Group Oy,ratkaisemiseksi. Diplomity,Espoo. Varian Medical Systems Finland OyTampereen teknillinen yliopisto,Tveit, T.-M. 2006. A systematic procedureTampere.for analysis and design of energy Contact informationMela K., Koski J. & Silvennoinen, R.systems. TKK Dissertations 27, Helsinki Kaisa Miettinen2007. Algorithm for generating theUniversity of Technology, Espoo.University of JyvskylPareto optimal set of multiobjectiveTel. +35850373 2247nonlinear mixed-integer optimizationProject volumekaisa.miettinen@jyu.fiproblems. In Proceedings of 3rd AIAAFor time perioid 1.8.200531.12.2008http://venda.uku.fi/research/paperphysics/Multidisciplinary Design Optimization the total funding has been 1149 325 hyvatietaa/Specialist Conference. Honolulu, USA. where Tekes share has been 1038 000 Ojalehto, V. 2008. Nkkulmiamonitavoiteoptimoinnin NIMBUS-Project participantsmenetelmn eri toteutuksiin, pro University of Jyvskylgradu -tutkielma, Jyvskyln yliopisto, Helsinki University of TechnologyJyvskyl. Helsinki School of EconomicsRuotsalainen, H., Miettinen, K., Palmgren, Tampere University of TechnologyJ.-E. & Lahtinen, T. 2008. Interactive University of KuopioMultiobjective Optimization for Danfoss Oy 19 19. Symbiosis between plant and computational models (Simbiot) MASIT02 Abstract International cooperation Halmevaara, K. & Hytyniemi, H. The aim of the Simbiot project is to August 2005: A two-day plant Dynaamisten simulointimallien find a plant model and software com- modelling seminar on the ISO parametrien virittminen data- ponent based approach to compu-15926 standard.pohjaisilla tilastollisilla menetelmill. tational models in process industry. October 2005: The projectAutomaatiopivt 2007. Suomen The approach should work for mod-assisted the Automation Societyautomaatioseura, 2007. (in Finnish) els in different levels of details. Integra- in organising an OPC Theme Day. Halmevaara, K. & Hytyniemi, H. Managing tion with plant modelling means that October 2006: The 2006 OPC UAComplexity in Large Scale Control the computational models will be in- theme day. Systems. Proceedings of the 1st IFAC tegrated part of the other information November 2006: A one day Workshop on Applications of Large model of the plant i.e. they can be ini- seminar on the current status of Scale Industrial Systems (ALSIS). tialized from the information model of plant model standardisation. Helsinki Stocholm, 3031 August the plant and they can produce results2006. to the same information model. Soft- In November 2005 and in DecemberHalmevaara, K. & Hytyniemi, H. ware components are used in order to 2006 the project organised an industri-Simulointiavusteinen prosessien achieve modular and extensible frame-al workshop on multi-scale modelling.suorituskyvyn optimointi Iterative work. Different levels of details are ana- The project has participated in theRegression Tuning. Automaatiopivt lysed and methods for multiscale/mul-standardisation work of the data trans-2005, Suomen automaatioseura, 2005. tiphysics modelling are defined. The fer models of the Standardisation Cen- (in Finnish) main result of the project is an open on-tre for the Process Industry. Halmevaara, K. & Hytyniemi, H. Tuning tology based modelling and simulation of multi-parameter systems using environment where information con- Publications multivariate regression and numerical tent can be modeled using a relational Halmevaara, K. & Hytyniemi, H. 2005.optimization methods. Proceedings graph based data model (RDF) and sim- Performance Optimization of Large of the 6th International Conference ulation engines and components canControl Systems Case study on a on Intelligent Processing and be integrated to the same environment.Continuous Pulp Digester. Proceedings Manufacturing of Materials (IPMM). The project was divided into sixof the 15th IFAC World Congress,Salerno, Italy, 2529 June 2007. work packages:Prague.Huhtanen, R., Hnninen, M. & Pttikangas, Environment for developmentHalmevaara, K. & Hytyniemi, H.T. 2007. Coupling of Computational and use of virtual models (WP 1)Application of Elastic Intuitions toFluid Dynamics Codes with System Linking of the companion modelProcess Engineering. Proceedings of Codes: A Case Study, VTT Research solver to the environment (WP 2)the 9th Scandinavian AI ConferenceReport VTT-R-00742-07. Espoo, Iterative Regression Tuning (SCAI). Espoo, 2527 October 2006.Finland, 13 p. method (IRT) (WP 3)Halmevaara, K. & Hytyniemi, H. Data- Karhela, T. & Kuikka, S. Prosessilaitoksen CFD and structural analysis (WP 4)based Parameter Optimizationelinkaarenaikaisen tiedonhallinnan Application of phase fieldof Dynamic Simulation Models. palvelukehys (Framework for process approach to industrial flow Proceedings of the 47th Scandinavianindustry plants life-cycle information calculation (WP5) Conference on Simulation andhandling). Automaatiopivt 2005, WP6 for project administrationModelling (SIMS). Helsinki, 2829 Suomen automaatioseura, 2005. and coordinationSeptember 2006. (in Finnish)20 20. Laurila, T., Tong, C., Huopaniemi, I.,Villberg, A. 2007. Design Challenges of Project participants Majaniemi, S. & Ala-Nissila, T. 2005. an Ontology based Modelling and VTT Technical Research Centre of Dynamics and Kinetic Roughening ofSimulation Environment. Masters Finland (semantic models, large scale Interfaces in Two-Dimensional ForcedThesis, HUT. process simulation, CFD-modelling and Wetting, Eur. Phys. J. B 46, p. 553561. Villberg, A., Lehtonen, T., Kondelin, K.simulation)Lehtomki, M. 2007. Dynaamisen & Karhela, T. Applying Semantic Helsinki University of Technology simulaattorin parametrien Modelling Techniques in Large Scale(optimization, multi variable methods, datapohjainen virittminen kytten Process Simulation. IFAC Workshop on statistical mechanics, phase-field rekursiivista lineaarista mallinnusta.Applications of Large Scale Industrial models) Diplomity, HUT. (in Finnish) Systems (ALSIS). Helsinki Stocholm,Pttikangas, T., Manninen, M., Ilvonen, M.,3031 August 2006. Contact information Huhtanen, R. & Luukkainen, M. 2006.Tommi Karhela Symbiosis Between ComputationalProject time scaleVTT Technical Research Centre of Finland Fluid Dynamics and Plant Models. 1.1.200531.12.2006 Tel. +358 40 582 2274 VTT Research Report VTT-R-08582-06,tommi.karhela@vtt.fi Espoo, Finland, 32 p.Project volumeSaukkonen, M. 2006. HydrodynamicTekes, VTT, Fortum Nuclear Services, Modeling of Two-Phase Flows underJaakko Pyry, Intergraph Finland. Non-Equilibrium Conditions. Masters Total funding 686 000 , Thesis, TKK. Tekes share 500000 Villberg, A. & Karhela, T. Solving Matrix Equations in Large Scale Dynamic Simulation of Flow Networks. Mathmod 2006. The 5th International Conference on Mathematical Modelling. February 2006, Vienna, Austria. 21 21. Inverse problems and reliability of modelling MASIT03 Background Results of the projection is unknown. From the The accuracy of modelling is alwaysMCMC applications: estimating pa- theoretical point of view it has been limited. This is due to the idealiza-rameters and noise. Lappeenran- studied under which conditions the tions in the model itself, and due tota University of Technology. The re-projection orientations are uniquely re- errors in measurement that are need- search in the Lappeenranta University coverable from ideal noiseless projec- ed to calibrate the model against re-of Technology group focuses on nov- tions. The practical question is how to al data. A proper estimation of the im-el methods in computational statistics, recover them given a set of noisy pro- pact of noisy data is most often ham-and promotes the use of them in prac- jection images from a real experiment. pered by the fact that the phenomena tical industrial applications. For a safe in- A new method of recovering the pro- studied are nonlinear, while the stand-terpretation of modelling results, the er-jection orientations has been proposed. ard statistical theory employed only isror bounds of the predictions should be A similar method has been considered valid for linear models. Medical imag- determined. For nonlinear models, thisin a simpler case, with dimensionality ing, industrial flows or remote sensingonly recently has become possible withreduced by one, i.e. with two dimen- provide some typical application areas the advent of new statistical samplingsional objects and one dimensional of inverse problems. Recognizing and methods, especially the MCMC (Markovprojection. In this simpler case a nec- solving an inverse problem is a radicallychain Monte Carlo) methods. We stud-essary and sufficient condition under broader topic than standard parameteried adaptive methods by which, underwhich the projection orientations can fitting, particularly since the problems given a priori knowledge, the error var-be recovered from the geometric mo- are typically ill-posed. iance may be estimated by sampling it ments of the images has been proven.together with the unknown parameter This result considerably strengthens the Objectives vector of a model. The methods have previously published results. Recently, new computational toolsapplied to industrial applications on Process tomography and mod- have emerged in this field, but they various fields, as suggested by the col-elling of approximation errors. Uni- have not yet been transferred to com-laborating and funding companies: log versity of Eastern Finland, Kuopio mon use. This project aimed to improve tomography, numerical modelling, de-campus. The focus of the research has this situation on a number of industrial-sign and optimization of heat exchang-been on modelling and compensation ly important applications. A special fo- ers, forest inventory by laser scanning.of approximation errors and on com- cus is on quantitative estimation of un- Several ongoing PhD studies continueputational methods related to tomo- certainties of prediction, due to both this work after the MASI project, manygraphic imaging. The approximation modelling errors and noisy data. funded by the interested companies. error methods were applied to severalReconstruction of virus struc-linear and nonlinear test cases, for ex- Project implementation ture from cryo electron images. Uni-ample, dealing with errors due to nu- The project was carried out as co-op-versity of Helsinki. The purpose of the merical discretization and/or uncer- eration of five different universities and project was to develop methods to re- tainties caused by unknown parame- the Finnish Meteorological Institute.construct the scattering density func-ters in the model. We have also stud- Each of the research groups has suc- tion of a virus particle given data from aied the modelling of approximation er- cessfully developed novel approaches cryo electron microscope experiment.rors due to the truncation of computa- in their planned tasks.Because the orientation of the virus par- tional domain in an application relat-ticle on the stage of the microscope is ed to electrical geophysical tomogra-unknown, it follows that the orientationphy. As a result, the dimension of the22 22. inverse problem can be reduced if the These coordinates can be determinedware for simulating the radar responsesapproximation error resulting from theby available imaging methods. Anoth- corresponding to arbitrary modulationdomain truncation is modelled and tak-er line of research has been the study ofpatterns. The planned radar echo sim-en into account in data processing. Re- Gaussian beams, which are waves that ulator has been completed. The simu-cently we have studied the use of sta-are always concentrated around one lator can be used for studying nov-tistical and approximation error meth-point in space. Such waves could, forel radar modulation and analysis prin-ods in a source inversion problem inexample, be acoustic, electromagnetic, ciples. The goal has been to study thewhich the objective is to characterizeor seismic. An attractive feature of Gaus- performance of the SMPRF modulationthe source of some (possible hazard-sian beams is that the equations gov-principle. This is currently being workedous) substance based on remote sens-erning their propagation are much sim- on together with Eigenor Oy. The newing measurements at different time in-pler than the full wave equation: Gaus-methods allow simultaneous measure-stants. In the field of process tomogra-sian beams propagate along charac- ment of all ionospheric regions, spacephy the focus of research has been on teristic curves. The second subproject debris, and meteor head echoes.electrical impedance tomography (EIT).was modelling of complex kinetics sys- MCMC methods for model se-We have studied non-stationary three- tems. The first target has been simula-lection. Finnish Meteorological In-dimensional imaging, optimization oftion of metabolic processes in the hu- stitute. Finnish Meteorological Institutecurrent injection patterns, estimationman body. The main accomplishmentstudied sampling methods (Reversibleof velocity fields and the use of a nov-of this subproject is the release of the Jump MCMC) that help to select be-el electrode configuration. The objec-simulation tool called Metabolica. tween different modelling options. Thetive has been to develop on-line data Linear inverse problem solver, methodology was successfully appliedprocessing methods for industrial tom-weather radar simulation software. to remote sensing satellite data pro-ographic applications.Sodankyl Geophysical Observato- duced by the GOMOS instrument on-Wave equation, complex kinet- ry. FLIPS (Fortran Linear Inverse Prob-board ENVISAT satellite. GOMOS pro-ics systems. Helsinki University of lem Solver) is a Fortran95 module de-duces concentration profiles for vari-Technology. The research has con- veloped to solve large (especially overous gases in the atmosphere by inver-centrated on two topics: direct and in- determined) statistical linear inverse sion of the measured occultation data.verse problems for the wave equationproblems. FLIPS was from the start de- The problem of model selection is toand modelling of complex kinetics sys-veloped to be able to handle large scale determine to which extent various gas-tems. A new algorithm was developed problems, so extra attention was paides may be inverted and how the aero-that focuses the energy of a wave using to the memory management and low sol should be modelled. All the modelsiteration of measurements. Such tech- memory footprint. It is possible to feed are approximations that depend on thenique can be used, e.g., to heat up anthe data into FLIPS piece-by-piece toassumed particle size distributions, vol-object by generating a heat source lo-keep the memory consumption as low cano activity etc., which are unknowncated at a point inside the object. The as possible. Another feature of FLIPS in-beforehand. The final result includes thealgorithm has many potential appli- clude the possibility to marginalize un- model uncertainty and several accept-cations. For example, one could use itknowns or add new unknowns at anyed models can be averaged to produceto heat a cancer, while leaving the sur-point of the operation, and the abilitymore realistic model predictions. Arounding tissue unharmed. The mainto delete data that was already fed into computer code that implements RJM-advantage of this algorithm is that itthe system. FLIPS is currently used in the CMC was created during the project.does not require much information new EISCAT data analysis system for find-It essentially depends on the adaptiveabout the properties of materials insideing the ionospheric plasma parametersMCMC methodology that has been de-the object: To focus the wave to a pointfrom the incoherent scatter radar meas-veloped together with the LUT groupone only needs to specify the geodes- urements. The goal of weather radarbefore, and extends that methodologyic coordinates for that particular point. simulation software is to develop soft-to the model selection problem. 23 23. Figure 1. Automatic forest segmentation by laser derived features. Laine, M. & Tamminen, J. 2008. Aerosolmodel selection and uncertaintymodelling by adaptive MCMCtechnique, Atmospheric Chemistryand Physics, 8(24), pages 76977707.Seppnen, A., Voutilainen, A. & Kaipio,J.P. 2009. State estimation in processtomography reconstruction ofvelocity fields using EIT, InverseProblems 25:085009.Virtanen, I. I., Vierinen, J. & LehtinenM.S. 2009. Phase coded aperiodictransmission sequences. AnnalesGeophysicae.Project time scale1.1.200831.12.2009Project volumeTotal 681 774 , Tekes share 562 000 Project participants Lappeenranta University of Technology Commercial impact, Dissemination most precise method available. The LUT Finnish Meteorological Institute Several of the methods developed ingroup featured on the main TV news of University of Helsinki the project have been taken to indus-YLE just before the Copenhagen Cli- Helsinki University of Technology trial or operational use, for example in mate Conference with this theme. University of Eastern Finland, applications in remote sensing, chem-Kuopio Campus ical engineering, engineering mechan-International cooperation University of Oulu ics or virtual design of machines As anThe project participants belong to the Sodankyl Geophysical Observatory example, MASIT03 has had a significant Centre of Excellence in Inverse Problems, impact on the development of for-Academy of Finland, with extensive na-Contact information est inventory services at Arbonaut Ltd.tional and international collaboration. Heikki Haario Through algorithms originally invented Lappeenranta University of Technology in this project, Arbonaut has become Most essential publications Tel. +358 400 814092 the market leader in operational for-Dahl, M. F., Kirpichnikova, A. & Lassas, M. heikki.haario@lut.fi est inventory in Finland, and is about to 2009. Focusing waves in unknownwww.lut.fi/mafy assume that same position in Sweden media by modified time reversal in the course of 2010. Apart from for-iteration, SIAM Journal on Control and est inventory for timber sourcing, an-Optimization, 48:839858. other application of the same meth-Ketola, J. & Lamberg, L. An algorithm ods has emerged in the fight againstfor recovering unknown projection climate change. The Sparse Bayesian orientations and shifts in 3-D method for measuring forest biomass tomography. (to be published in developed in MASIT03 is currently the Inverse Problems and Imaging)24 24. Multiphase chemistry in process simulation MASIT04ObjectivesMethods ResultsThe goal of the VISTA project was to im-Generic methods for coupling the ther-Generalprove performance in both metals andmodynamic multi-phase approach with In the VISTA project bo Akademi Uni-materials processing, in power produc-computational fluid dynamics (CFD), re- versity, TKK, University of Oulu and VTTtion and paper manufacturing by im- actor and process simulations were de-have jointly developed new advancedplementing generic free energy meth-veloped. Force balance and Gibbs free methods to utilize rigorous multi-phaseods into process simulators. The specif-energy minimisation techniques were chemistry in industrial process models.ic applications included solid-gas dep- used together with flow simulation to The models provide improved perform-osition models in heat recovery boilers,predict multi-variant operation win-ance in both metals and materials man-casting and processing of steel and al- dows for high-temperature process-ufacturing, in power production and pa-loys, reactions in gasification and flash es. Multi-phase reactors in flow condi- per manufacturing technology. The par-conditions and simulation of aqueoustions were calculated by using the con- ticular applications are listed as follows:process chemistry and fibre suspen- strained free energy method. Interfac- predictor model for flash smeltersions. The project was targeted to im-es and databases for the use of multi-heat recovery boiler depositionsprove present simulation programs component streams in process flow- nitrogen/argon switch control forwith new algorithms and data as wellsheet simulation were developed.steel converteras to create entirely new commercialsoftware products.Figure 1. Use of multi-phase Gibbs energy models in advanced process simulation.25 25. inclusion trajectory predictor for Figure 3. Particle trajectories with chemical composition in flash smelter heat recoverysteel-making boiler. multi-phase chemical balance &pH simulator for forest industry release and dissolution of gasesin boiler water and desalination black liquor flash gun simulationand boiling point elevationpredictor new software products forcommercial and engineering use Scientific and technical progress In addition to the practical industrial ap- plications, a number of methodical ad- vances have been developed, which combine various physical and chem- ical phenomena into industrial multi- physics process models. These include use of coupled thermochemical parti-tion of inclusion trajectories in bloom data can be incorporated to a visual cle growth and CFD models combinedcasting of steel with and without elec- virtual reality animation, which can be with a physical fouling model, calcula- tromagnetic stirring, a combined ther-used to support decision-making both modynamic and CFD model for nitro-in process design, technology market- gen/argon blow in steel converters, ing and plant management. Figure 2. Calculated particle paths coupling bubble growth and nozzle In VISTA project, two software with electromagnetic stirring (right) flow modelling for splashplate spray of products for such commercial uses, viz. in the bloom caster and without black liquor and combination of mul-AFROK for rotary kilns and CheMac for electromagnetic stirring (left). tiphase chemistry to flowsheeting boil- pulp washing and bleaching has been er/evaporators. created. In addition, the ICA model, In several applications the minimi- which combines multi-phase thermo- sation of Gibbs free energy was used fordynamics with simulation of steel solid- the multi-phase equilibrium chemistry ification is in a semi-commercial stage and thermodynamics. An entirely new of development. New unit operations approach using immaterial constraints with advanced features have been in- in the free energy minimisation was in- cluded in the flowsheet routines Apros troduced. The advantage of thermody-and Balas. namic in calculation of process chemis- try is their abundant state property da-Impact of VISTA project ta, which is practical in validation of in- The commercial utilization of the dustrial models. Use of state propertiesproject results has commenced in two in process simulation further improvesdirections. First, simulation models can model reliability and reduces or elimi- be used to improve performance and nates need of expensive trial runs andproductivity of mill-scale processes. In pilot-experiments. The comprehensivesteelmaking, the consumption of ar-26 26. Figure 4. Non-condensable components in flashing vapour of the desalination gon is the second highest operation-process. Calculation with (CA) and without free energy data.al cost of the mill, and it is estimatedthat savings exceeding 0.6 M/year canbe achieved by cutting its unneces-sary use. The other example closely re-lated to this project is an average pa-per mill, where raising the operation-al factor by one per cent correspondsroughly +1 M increase in annual turn-over. Model supported scale-up andchoice of equipment materials usual-ly give even larger benefits. Secondly,combining multi-phase chemistry withCFD and flowsheet routines createsnew opportunities for knowledge in-tensive software products and services.As the expertise developed in Fin-land in multi-phase process simulationis world-wide competitive, internation-al sales of such software products maybe expected. The simulation models al-so support marketing of technologyexport in process, energy and environ-Figure 5. Development of new software products (VTT, Process Flow Oy).mental industries.The Constrained Free Energymethod (CFE), developed by VTTs re-searchers in VISTA project and pub-lished in 2006, was given the Best PaperAward of the Calphad Journal in Penn-sylvania State University in May 2007.Under the auspices of the VIS-TA project, M.Sc. Peter Blomberg hasworked as a a research exchange stu-dent in Massachusetts Institute of Tech-nology to combine the CFE methodwith an MIT-based transformed Gibbsenergy calculation method particular-ly focused on the thermodynamics ofbiochemical processes (MASIT25 In-nosim). 27 27. Figure 6. CALPHAD Journal Best Paper 2006 award for VTTs VISTA research. Project time scale 1.4.200530.6.2008 Project volume 1 512 000 , Tekes share 1 084 800 Project participants VTT bo Akademi University TKK University of Oulu Process Flow Oy Ltd SimTech Oy Project manager Pertti Koukkari VTT Technical Research Centre of Finland, Process Chemistry Tel. +358 40583 4092 pertti.koukkari@vtt.fi DisseminationKoukkari, P. & Pajarre, R. 2006. Calculation The project results have been present-of constrained equilibria by GibbsAdditional information ed in 23 scienteific papers and interna-energy minimization, Calphad. Vol. 30 Pekka Taskinen tional conference presentations. Two in-(2006), 1826.VTT Technical Research Centre of Finland ternational seminars were held in Fin- Li, B., Brink, A. & Hupa, M. 2009. CFD Tel. +358 40558 4954 land on VISTA topics. investigation of deposition in a heat pekka.taskinen@vtt.fi recovery boiler: Part II - deposit growth Selected publications modelling. Progress in Computational Brink, A., Li, B. & Hupa, M. 2009. CFDFluid Dynamics, 9(8), 453459. investigation of deposition in a heatRiipi, J. & Fabritius, T. 2007. Surface recovery boiler: Part I - a dual layertension of liquid Fe-N-O-S alloy, ISIJ particle conversion model. Progress inInternational, 47, No 11, 15751584. Computational Fluid Dynamics, 9(8), 447452. Jrvinen, M., Krn, A. & Fabritius, T. Detailed numerical modelling of gas- liquid and liquid-solid reactions in steel making processes, Scanmet III, 3rd International Conference on Process Development in Iron and Steelmaking, 8-11 June 2008, Lule, Sweden. Volume I, p. 347355.28 28. Statistical phenomena in virtual design of machines (MARTSI) MASIT05Background ic equations. Real-time simulation allows ObjectivesExisting off-line simulation and visuali-the work environment to be altered. The The goal of the current project is to cre-zation tools for R&D of machines ignoreimpact of operator response and per-ate operational models that would per-statistical effects caused by users andformance on the stochastic perform- mit stochastic effects to be consideredwork processes. Statistical tools can be ance of a mechatronics machine can be in the simulation of a mechatronics ma-used to evaluate the performance datameasured. This would be impossible or chine. A foundation for achieving theseobserved during the simulations. Statis- at least very time consuming, in tradi- goals is the development of real-timetical representation of the data will bet- tional off-line simulation. The real-time simulation tools and methods that canter portray the machine usage over a models must, of course, be sufficiently be used to simulate a machines per-longer period of operation. To make this realistic such that feedback from opera-formance over a longer period of oper-possible a general framework for the im- tion can be used in the product designation. Real-time simulation will be usedplementation of real-time simulation asprocess. All significant aspects of the to take into account certain stochasticpart of the product design cycle is re-machines operation must be modelledlike variations between operators. It isquired. During real-time simulation thein order to be useful in assessing theclear that the driver forms a systemicmachine operator is directly connect-overall reliability of the machine. The process with the machine according toed to the simulation model. The simula-interface between operation and real- his sensory motoric skills and thus thetion can take into account the user feed-time model must be realistic to insureoverall dynamics of the human-ma-back before the first physical prototypesthat the operator responses and com-chine system is determined by the sub-are manufactured. This is a challengingmands are representative. This requires systems and their interconnections astask because the dynamic machine op- both visual sensing, but also sound shown in Figure 1.eration is then connected to subjectiveand motion which can be provided byoperator rather than a set of mathemat-means of a motion base.Figure 1. Interconnections between subsystems in a human operated machine.29 29. Figure 2. Real-time simulator of a log crane. Real-time simulationimplementation in productdevelopmentResultsStochastic Input Variables forMultibody Dynamic SimulationsThe crucial and of key importance as-pect of MBD simulations is the externalforcing representation in time-domain.This representation is necessary for pre-diction of fatigue reliability of virtualcomplex mechanical system. Currentmethods used to formally assess uncer-tainties include Monte Carlo (MC) sim-ulations, and linear and nonlinear ap-proximations of the system response.MC approach is costly and the accura-cy of the estimated statistical proper-ties improves with square root of thenumber of runs. Statistical linearizationand nonlinear approximation methodscompute several parameters but do notcapture essential features of the nonlin-The proposed final goal of devel-same time being able to characterize ear dynamics (as revealed only by spec- oping a framework for the implemen- the stochastic operation of a machinetral analysis). tation of real-time simulation as partduring its life cycle. There are two main methods pro- of the product design cycle will be re-posed for quantification of stochastic fined with the use of statistical meth- Project implementation input parameters (uncertainties). The ods for defining machine work cycles. The goals of the project were achieved first methodology was developed for Expected work cycles can later be fur-by means of a series of work packagestreating complex loading spectra, and ther evaluated by means of off-line as shown below.named as Rainflow Analysis and Hidden simulation. A demonstration simulator Real-time operating environmentMarkov Chains. The method for find- for a log crane (see Figure 2) was de- development ing Markov model or a hidden Mark- veloped in LUT. Rigid body modellingov model that fits a measured rainflowThe goal of the project can be con- Flexible body modelling matrix has been derived. This estimat- densed into an optimization problem. Modelling of mechatronics ed model can be used for generation of The goal is to minimize the numbercomponents load sequences for fatigue evaluation. of required off-line simulations which Motion sensing developmentThe Markov method originally used for maximizing the reliability of the simula- Distributing computingsimulation and approximation of real tions that are performed. This presents Development in real-timeload sequences, but it also can be ap- the challenge that the simulationsintegrationplied to any type of numerical load da- should be both simple, while at the Probabilistic tools developmentta. In our case the data for load spectra30 30. characterization and analysis were ob-simulation example. The log crane sim- for flexible bodies were developed. Thetained from the numerical arrays of out-ulator was used to produce simulationtelescopic joint model based on con-puts produced by MBD real-time simu-data of log crane work cycles for statis-straint primitives and contact positionlation with human-in-the-loop interac-tical analysis.interpolation was implemented in thetion (HIL). The log crane model consists of 15 simulation code.Random order of loading was sim-rigid bodies interconnected by 17 joints.ulated by ten different hypothetical op- The kinematics includes two closed Collision modellingerators and the most interesting load- loops, which are opened. The hydrau- Collisions play an important role ining histories of log lifting operation cy-lics of lift and jib booms are modelledmaking simulators more realistic. Es-cle lift and jib cylinder forces were se- using the semi-empirical approach. The pecially in the simulator training, unre-lected as proto-type loading spectra formodel also includes the collision mod- alistic collisions make the whole simu-simplified mechanical system namedels between the log and the ground aslation unbelievable to the operator. Iffurther as toy case. The algorithm of well as the log and the log grapple. The the operator does not think he is driv-external force vector implementationsimulation time step using 3.4 GHz P4ing a real machine he will not learn tointo MBD simulations is still under de- processor is 0.0018 s with visualization operate the machine. As a test case avelopment. The main hypotheticalrate of 40 frames per second.log forwarder was modelled in a waytechnique is the second method men-that a log can be lifted into the for-tioned above named Polynomial Chaos Description of the structuralwarder using a contact based grip-per.flexibility in real time simulationTheory (PCT), which has been already In order to enable the gripping of thesuccessfully applied in structural me-The simulation of dynamics of flexible log, a collision model using the colli-chanics and in fluid mechanics stud-bodies was studied using floating framesion intersection surface circumfer-ies. The application of PCT in MBD sim- of reference method. The method is ence was developed. The developedulations not studied yet enough and tobased on the use of reference motion method enables a stable grip on theones knowledge it has not been pre-with vibration modes of bodies. This log as well as the contacts betweenviously applied to MBD simulations forenables the use of method in dynam-the different bodies.external force representation for real- ics simulation since reference motiontime inputs. This approach conceivablycan handle large rotations that usual- Motion platform controlenables the simulation of MBD systems ly cause difficulties in flexibility analysis. Feeling of motion is important in simu-to produce results with error bars; sim-The floating frame of reference method lators especially in vehicle and mobileilar to the way the experimental resultsis suitable to be used in real-time sim- machine simulators. Control of a plat-are often presented. More-over, the pro-ulation since its computational require- form affects a lot to the created feelingposed methodology allows the quanti-ments are reasonable. The method was of motion. Control consists of signal fil-fication and modelling of stochastic in-studied in Lagrangian dynamics solu- tering, inverse kinematics and a controlput variables in both time and frequen- tion. The study resulted to a simulation loop. Platform gets its reference valuescy domains. code suitable for simulation of flexible from a dynamic model.multibody dynamics of academic lev- Acceleration data from the dynam-Real-time simulationel examples. ic model cant be directly used in theThe study of semi-recursive methods Since telescopic boom structures motion platform control because thewith rigid bodies resulted to simulationare widely used in mobile working ma-platform has limited workspace. The ac-code that enables the simulation of ap- chines the modelling of telescopic joint celeration signal must first be filtered. Aplication size mechanisms in real-time. with flexible bodies was studied. Thewashout-filter is commonly used in theBoth open-loop and closed loop kine-literature search of simulation of joint platform control. It notices only thematics can be handled in the simulation constraints with flexible bodies gavehigh frequency accelerations and re-code. A log crane simulator was used as poor results so the constraint primitivesturns the platform smoothly to initial 31 31. state after movements. The classical Publications Project time scale washout-filter is very simple to use but Summary of essential publications1.6.200531.12.2007 the parameters of the filters are case-Korkealaakso, P., Rouvinen, A., Moisio, S. & specific and the classical washout pro- Peusaari, J. Development of a real-time Project volume duces errors to control signals. Adaptive simulation environment MultibodyTotal 620 000 , Tekes share 558 000 washout is more intelligent and its filterSystem Dynamics. Volume 17, parameters changes during the simula- Numbers 23, April 2007. p. 177194.Project participants tion so it can adapt to different cases. Sallinen, J., Eskola, T. & Handroos, H. 2008. LUT Institute of Mechatronics and With the adaptive washout it is possi-Design of a Motion Platform for a Virtual Engineering ble to achieve a better signal than withMobile Machine Simulator by Utilizing LUT Laboratory of Fatigue and Strength the classical.6-D Measurements and Inverse LUT Department of InformationThe problem with the washout-fil-Dynamics Analysis. p. 127130. The 7thTechnology: Laboratory of tering is that it doesnt notice the lowInternational Conference on Machine Communication Engineering frequency accelerations. To improve Automation (ICMA2008), September University of Joensuu, Laboratory of feeling of motion, low frequency ac-2426, Awaji, Japan.Applied Mathematics celerations must also be described and Sallinen, J. 2008. Liikealustan suunnittelu John Deere Forestry Oy it can be done by tilting the platform. liikkuvan tykoneen simulaattoriin. Kalmar Industries Oy Hence the force affecting to user inMasters Thesis, LUT. Sandvik Mining and Construction Oy low frequency accelerations can be de- man, R., Handroos, H. & Eskola, T. Wrtsil Finland Oy scribed by using the gravitational force. Computationally Efficient Two-Regime Flow Orifice Model For Real-TimeContact information Impacts Simulation. EUROSIM2007, SeptemberHeikki Handroos The project led into commercialization9-13, Ljublijana, Slovenia. Lappeenranta University of Technology of the real-time simulation technology man, R., Handroos, H. & Eskola, T. 2008.Tel. +358 40510 7599 and establishment of spin-off company Computationally Efficient Two-regimeheikki.handroos@lut.fi MeVEA Ltd providing commercial real-Flow Orifice Model for Real-time time R&D and user training simulators Simulation, Simulation Modelling for industry (www.mevea.com)Practice and Theory 16, p. 945961.man, R. 2007. Hydraulisen kuristinmallin ja liikealustan ohjauksen kehittminen reaaliai-kasimulointiin. Masters Thesis, LUT.32 32. Modelling and simulation of coupled problems in mechanics and electrical engineering(KOMASI) MASIT06Backgroundtion, identification and validation of the equation methods is that the discre-Today, modern computers together with magnetoelastic model required con- tized matrix equation becomes ill-con-advanced numerical algorithms havestruction of measurement devices. Forditioned if the frequency is low or themade it possible to simulate and analyzesimulation of working vehicles coupled mesh density is high compared to thelarge complex structures. Therefore nu- hydraulic and flexible multibody me- wavelength. This yields inefficient iter-merical simulation has become an inte-chanics model is required. ative solutions and if the frequency isgral part of research and development in very low even the direct solution be-several fields of technology. Prime exam- Project implementation and results comes impossible. In this project newples include structural mechanics, elec-One of the main problems in the tradi- surface integral equations were devel-tromechanics, acoustics, fluid dynamics tional electromagnetic surface integraloped and implemented to overcomeand heat transfer. Despite their differenc-es the above areas share common math-Figure 1. Electromagnetic field simulation of a hollow plastic box on top of a metallicematical tools: the use of partial differ-plate calculated using the new