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Rafael de Góngora 354 A New Collaborative Engineering Management Tool for Shipbuilding Martijn Neef, Anthonie van Lieburg 363 Applications of Self-Organizing Systems in Maritime Environments Christian Massow, Ivan Siksne-Pedersen 378 Computer Integrated Planning and Resource Management in Shipbuilding Patrick Couser, Andrew Mason, Garth Mason, Cameron R. Smith, Brian R. von Konsky 391 Artificial Neural Networks for Hull Resistance Prediction Reidar Tronstad, Antonio Rodriguez 403 Automation Tools in the Design Process Andreas Bortfeldt 419 A Heuristic for the Container Pre-Marshalling Problem David E. Hess, William E. Faller, Edward S. Ammeen, Thomas C. Fu 430 Neural Networks for Naval Applications Mario Dogliani, Dracos Vassalos, Tom Strang 447 Evacuation Notation – a New Concept to Boost Passenger Evacuation Performance in the Cruise Industry M.S. Seif, E. Jahanbakhsh 459 Neural Networks Model for Ship Maneuver Wilfried Abels, Stefan Krüger 470 Correlation Strategy for Propeller Excited Pressure Fluctuations Index of authors 479 Call for Papers COMPIT’05 at last page Email the authors to obtain an electronic copy often containing figures in colour. 4
Towards Intelligent Simulation-Based Design Volker Bertram, ENSIETA, Brest/France, volker.bertram@ensieta.fr Abstract Proposed models for the ship design process fail to convince. The classical design spiral model is frequently rejected by modern ship designers, but design is iterative and several basic elements are repeated used, albeit not always in same order. The paper gives some historical perspective before reviewing some modern developments in computer-aided ship design, drawing frequently, but not exclusively, on the experience of the author. Techniques covered include knowledge and decision, generation of solutions, analysis and visualization. 1. Introduction The design process is often described as a design spiral following the initial model of Evans (1959). This model has been ''refined'' by proposing spiraling cones, Andrews (1981), or interconnected spirals, Mistree et al. (1990), but in essence it was the spiral in disguise, Fig.1. This model for ship design has been rejected frequently, as the sequence of analyses and decisions is not as sequential as the spiral models suggest. Ship design seems to be instead a rather chaotic process of combining various analyses with ‘intelligent’ or ‘creative’ generation of solutions, sometimes in parallel, which are then analyzed, evaluated, possibly rejected or modified. The sequence of these elements in the design process changes even for absolutely the same design task from designer to designer. There is no absolute criterion what is the best design process sequence. While different designers may well apply the same tools (or at least tools of comparable performance power), some designers manage to get better results. Evans (1959) Andrews (1981) Mistree et al. (1990) Fig.1: Ship design has been frequently described as a design spiral process I see this in rough analogy to nature. The DNA is composed of the 4 same basic elements, whether it is the DNA of a bacterium or the DNA of a Nobel prize laureate. Apparently the proper combination of the same 4 simple elements can make a big difference. Similarly, one could use music as an analogue with a (relatively) small number of string, keys or notes used by masters and beginners alike, but with big differences. Rather than speculating whether computers in the (distant) future may outperform humans in the design process, I focus on the “basic elements” of ship design and highlight some recent developments. The ship design process remains computer-aided, but the computer accelerates the process and improves the “classical toolkit” of the designer. The following is neither mutually exclusive, nor collectively exhaustive. It is merely intended to bring some structure into the review. Basic elements in ship design are then for me: 1. Generation (creative, intelligent or systematic creation of solutions) 2. Analysis (evaluation, simulation, computation) 3. Visualization (representation, communication) 4. Decision (knowledge, identification, learning) 5
Page 1 and 2: 3 rd International Conference on Co
Page 3: Patrick Hupe, Uwe Langbecker, Mubas
Page 7 and 8: ectly damage in terms of greenhouse
Page 9 and 10: Fig.7: FEA of a catamaran made of c
Page 11 and 12: Set the small parts fixing the part
Page 13 and 14: main benefit of 3-d design lies in
Page 15 and 16: Applic. Mar. Industries, COMPIT, Ha
Page 17 and 18: Use of Genetic Algorithms in Propel
Page 19 and 20: 3. Fitness Function Formulation The
Page 21 and 22: where µ α and µ θ are the chose
Page 23 and 24: From another point of view and unde
Page 25 and 26: Y X Benchmark First Optimum Fig.8:
Page 27 and 28: A Systematic Study on Posing and So
Page 29 and 30: expressed as functions of the wave
Page 31 and 32: 4.4. Optimization with modeFRONTIER
Page 33 and 34: Fig.5: SIMPLEX optimization and Par
Page 35 and 36: References HOFFSCHILDT, M.; BIDLOT,
Page 37 and 38: 2.2. Tribon Product Information Mod
Page 39 and 40: 3. New Process The new process is b
Page 41 and 42: 4. Conclusions and next step The ne
Page 43 and 44: 2. E-Commerce of goods versus servi
Page 45 and 46: Main steps on ePING’s road-map ar
Page 47 and 48: anon. client new client registered
Page 49 and 50: Preparing an offer can be hierarchi
Page 51 and 52: Acknowledgment This work was partia
Page 53 and 54: The focus within this paper is the
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conditions have to be prevented. Fo
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Ship 2 LPP 182.390 m B 26.00 m T D
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Fig.9: Ship 1; Simulated Crash stop
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Using Simulation in Evaluating Bert
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ship. To shorten the time spent by
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travelled by each SC are totalled t
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around time is the highest against
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to alternative berths instead of pl
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Appendix 1: Data Collected from Con
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Abstract Introduction to BALTPORTS-
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- at the level of operational plann
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- External interoperability - In ad
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5. Setting up of the Baltic Sub-Reg
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Robot Applications in the Field of
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Fig.3: The main components of the D
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The precision of the process depend
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The frame is further divided into 2
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1. Periodicity on the time line, pe
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Quality measures. Let J be the inst
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6.2 Mutation A random number out of
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7.1 Adaptation of Theorem 2 in our
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8. Conclusions and scope for future
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meet the needs of the user. Typical
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2-digit level (e.g., 220 - Engineer
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eviewed in dialogue boxes and on Ex
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Fig.3: Example tanker (HMI BRENTON
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Fig.7: Assigned material tabular en
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Fig.11: Design ship 2-digit SWBS Re
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Communication in Ship Design Networ
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number and volume of communicated i
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The investigated collaborations are
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The four principle communication pa
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6. Information content In design cl
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References ERIKSTAD, S.O., HAGEN, A
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intensive. The terminal's own drive
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A distribution of decision competen
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the order for planning the task to
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3.400.000 Total costs 3.200.000 3.0
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Visualisation of Ship Motions in Wa
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3.3. Panelisation and information e
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5.2. 3D visualisation Fig.4: Refere
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6.2. Examples of the visualisations
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7. Conclusions The presented toolki
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Fig.2: Possibilities for robotic in
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Experiments with the underwater cam
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Fig.13. and 14: Cleaning of the cha
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Acknowledgements The authors want t
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Fig.2: Result of Industrial Enginee
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2. Industrial engineering system fo
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4. Next steps In the next phase, th
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organizations and organizational un
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artifact’s context and extend the
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We take the ship survey process, wh
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device multimedia capabilities: Sur
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4. Future Work The mobile services
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FEM Supported Alignment of Power Tr
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2.2. Causes First researches result
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Fig.5: Sufficient tooth grip patter
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4. Alignment of a RoRo vessel’s p
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4.3. Calibration/Validation of the
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Optimisation of the Survivability o
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Table I: Current UK & US Damage Sta
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Fig.2: Damage Density Distribution,
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An Air-to-Surface Missile (ASM) is
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length according to the existing re
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the use of a stochastic optimizatio
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Each MOGA loop requires approximate
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JIASIONOWSKI, A. (2002), An integra
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Design problems are typically chara
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o planned: systematic; o ad-hoc: op
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o Provide justification for decisio
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o o o o o o Guide the user through
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- for some systems, the initial com
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feasible. The practical implementat
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PARK, J.H.; STORCH, R.L. (2002), "O
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where: a, b - coefficients (weights
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C Fig.2: The fuzzy domain of the bu
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5.2. Ship fuzzy domain in an open a
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PIETRZYKOWSKI, Z. (2003a), Modellin
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means that the number of intersecti
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4. Manipulation of Sets of Data Poi
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Fig.2: The selection function defin
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Let us define the root node of a se
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Fig.4 took up to several minutes on
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Applying Meta-Models to the Probabi
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The assessment of the damage stabil
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Fig.1 shows clearly an important ch
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one is willing to do, the smaller t
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Depending on the number of simulati
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Since none of the points of the lat
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CHEN, S.; WANG, L.; WU, X.; WANG, X
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alance between ship resistance and
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frequent movements of the pitch tha
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"commanding" by itself since a vari
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The numbers shown in the 3 rd row o
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angle of attack of the water flow o
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stationary, i.e. all variables rema
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the engine map runs through the ove
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References GRIMMELIUS, H.T.; STAPER
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their output to be random too. Runn
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simulation runs. It typically takes
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When the system initiates, the star
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The user is guided through various
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• Port Planning & Development •
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Knowledge-based Concurrent Engineer
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or puts components aside to make mo
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Technical specs Make preliminary ca
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ios, and knowledge about the design
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are applied afterwards, they can be
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calculations etc.) The product rela
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For finding the dependency of reque
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GIASSI, A. (2003), Multidisciplinar
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already mentioned. It is based on t
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2.2.2 Geometry Geometry covers the
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2.3 Simulation The simulation engin
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As far as the simulation engine inc
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time results. Table I: Mustering ti
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simulation means. The required time
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design consultants. Starting from a
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different severity, e.g. warning or
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• Execute calculation (including
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and ghostscript, Gnuplot, Ghostscri
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References CASTOR; An open source d
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an accurate description of a densit
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(a) UDS and CDS Schemes (b) Gamma S
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Figure 3: Typical unstructured grid
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(a) 0 degree (b) 5 degrees - Port s
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(a) Computations (b) Experiments Fi
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(a) X/L = +0.10 (b) X/L = +0.20 (c)
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4 Conclusion This paper has describ
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State of the art in climbing and wa
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2.1 REST 1 and REST 2 climbing robo
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Figure 5: ROWER 1 system. The full
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Acknowledgements The REST climbing
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ÖJu‚ÓuwØ›t²o"t²Ós²^hÔ
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X X X X X X X X Z Z Z Z Z Z Z Z Y Y
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30 25 20 Conjugate gradient 30 25 2
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CARENA is a naval architectural sof
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Fig.6: Cargo vessel carrying “spe
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used especially for shape optimizat
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easily given as simple auto-update
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and mutation concepts for evolving
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govern agents’ interaction with t
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Create generation behaviour Sequent
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Side Casing Hoister Deck Car Deck C
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The selection of the best individua
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Appendix A Table A-I: Optimisation
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2. Project Management tools in ship
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Fig.1: Example of document properti
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Fig.4: Example of query view on doc
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The Product Data Management is comp
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Abstract Applications of Self-Organ
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EMERGENT GLOBAL STRUCTURE LOCAL INT
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dynamically structured, the system
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After a leakage has been detected,
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EXIT door door corridor exit device
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adaptive. Furthermore, from a imple
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The main objective for our approach
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LUCAS, C. (2003), Self-organizing s
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• As a result of the above-mentio
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Table I: Exemplary matching of Refe
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Yard Program time Mile-stone Plan d
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In the following we start with a di
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Material Information Machines Proje
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module (SpaceMan) supports the allo
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³× ÖØÖÓÒ Ñ×ÙÖ Ó Ø Ö
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ÑÓÖ ÙÖØÐÝ¸ Ö Ó ÓÚÖ
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10 10 Exp ANN Exp ANN 7.5 7.5 Cr [x
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7 Cr [x 1000] 6 5 4 3 2 0.2 0.4 0.6
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Automation Tools in the Design Proc
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Fig.1: Visual LISP integrated devel
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• Bentley’s adoption of VBA •
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To these outstanding features, it h
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4.2.1. Automation The objective of
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• Direction of the start of the c
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• The FDE program processes the p
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Database access The FORAN System us
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A Heuristic for the Container Pre-M
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When they arrive in the terminal ya
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(3) The number of all badly placed
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still greater than zero, the minimu
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• For each dirty stack s exactly
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in the remaining 7 instances no mor
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DOF motion. Then, alter the input f
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learn of any delay that can occur b
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40 20 30 10 x-x0 (m) 20 roll (deg)
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Since, among the velocity component
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Y (m) -1700 -1500 -1300 -1100 -900
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system framework, the details of it
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Real-time Nonlinear Simulation (RNS
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design modifications are made, and
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Evacuation Notation - a New Concept
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• r(t): response time, which acco
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• The time tk: the time correspon
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5.1. Quantitative assessment Quanti
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Table 2: Summary of total and model
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notation: besides an objective asse
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Neural Networks Model for Ship Mane
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&r V G • • r = a = ( uî & & +
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V a Estimating Forces F Calculating
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Fig. 5: Error variation for the net
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Fig 8: Ship's path in Zigzag test 3
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[4] ALMAN, P.R., BERTSCHE, W.R., BO
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2 Numerical Methods RANSE methods l
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As a conclusion of these parameter
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For correlation of propeller data t
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quadratic functions result in littl
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Index by authors Abels 470 Akinfiev
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