COMPIT 2010 in Gubbio - TUHH

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Index Ahmad F. Ayob, Tapabrata Ray, Warren F. Smith 7 A Framework for Scenario-Based Hydrodynamic Design Optimization of Hard Chine Planing Craft Volker Bertram, Patrick Couser 20 Aspects of Selecting the Appropriate CAD and CFD Software Markus Druckenbrod, Jochen Hundemer, Moustafa Abdel-Maksoud, Max Steden 30 Optimisation of Single and Multi-Component Propulsors Valery Mrykin, Vyacheslav Lomov, Sergey Kurnosov, Manucher Dorri 39 Training Complex for Training Submarine Motion Control Skills on the Basis of Virtual Dynamic Systems Jose Marcio Vasconcellos 48 Ship Structural Optimization under Uncertainty Jean-David Caprace, Frédéric Bair, Philippe Rigo 56 Multi-Criterion Scantling Optimisation of Passenger Ships Isabelle Toulgoat, Pierre Siegel, Yves Lacroix, Julien Botto 65 Operator Decision Modeling in a Submarine Matteo Diez, Daniele Peri 76 Two-stage Stochastic Programming Formulation for Ship Design Optimization under Uncertainty Kunihiro Hamada, Yoshifumi Takanobu, Kesavadev Varikkattu 90 Development of Ship Design Support System in Consideration of Uncertainty in Product Information Anthony S. Daniels, Morgan C. Parker, David J. Singer 99 Effects of Uncertainty in Fuzzy Utility Values on General Arrangements Optimization Verónica Alonso, Carlos González 113 The Challenge in Hull Structure Basic Design: From Traditional 2D Drawings to 3D Early Product Model Robert Hekkenberg 124 “The Virtual Fleet” – Use of Extended Conceptual Design Data for Trend Analysis of Inland Ship Characteristics Ralf Tschullik, Hannes Prommer, Pentscho Pentschew, Patrick Kaeding 132 A Concept of Topological Optimization for Bow Structures Monique Chyba, Michael Andonian, Dario Cazzaro, Luca Invernizzi 139 Trajectory Design for Autonomous Underwater Vehicles for Basin Exploration Marco Bibuli, Massimo Caccia, Renato Robino, William Bateman, Thomas Vögele, Alberto Ortiz, Leonidas Drikos, Albena Todorova, Ioanins Gaviotis, Francesco Spadoni, Vassiliki Apostolopoulou Robotic Tools to Assist Marine Inspection: The MINOAS Approach 152 4
Frank Borasch 166 A Digital Planning Tool for Outfitting: “DigiMAus” George Bruce, Mike Evans 176 Reducing Management Software Costs Thomas Koch 182 Validation and Quality Control of Design and Production Information - Applying Rule Based Data Mining and Business Intelligence Concepts to Engineering Roope Kotiranta 194 Extended usage of a Modern Product Model in Finite Element Analysis Hermann Lödding, Axel Friedewald, Lars Wagner 203 Rule-Based Resource Allocation – An Approach to Integrate Different Levels of Planning Detail in Production Simulation Tomasz Abramowski 213 Combining Artificial Neural Networks and Simulated Annealing Algorithm for Increasing Ship Transport Efficiency Tomasz Abramowski, Tadeusz Szelangiewicz, Katarzyna śelazny 221 Developing of a Computer System Aiding the Determination of Mean Long-Term Service Speed Reyhane Rostami, MohammadReza Matash Boroujerdi 235 Data Mining to Enhance the Throughput of Container Ports Rolf Oetter 243 Revitalizing Brazil’s Shipbuilding Industry - A Case Study Darren Larkins 249 Practical Applications of Design for Production Karel Wagner, Alain Wassink, Bart van Oers, Hans Hopman 259 Modeling Complex Vessels for Use in a 3D Packing Approach: An Application to Deepwater Drilling Vessel Design Thomas DeNucci, Hans Hopman 273 Optimization-Based Approach to Rationale Capturing in Ship Design Benedict Boesche 285 Improvement of Interoperability between Yards and Equipment Suppliers David J. Andrews, Tim P. McDonald, Richard G. Pawling 290 Combining the Design Building Block and Library Based Approaches to improve Exploration during Initial Design Dirk Steinhauer 304 GeneSim - Development of a Generic Data Model for Production Simulation in Shipbuilding Tommi Kurki 311 Utilization of Integrated Design and Mesh Generation in Ship Design Process Marcus Bole Interactive Hull Form Transformations using Curve Network Deformation 319 5
Page 1 and 2: 9 th International Conference on Co
Page 3: 9 th International Conference on Co
Page 7 and 8: A Framework for Scenario-Based Hydr
Page 9 and 10: Fig. 2: Detail flowchart on the opt
Page 11 and 12: to be able to solve a wide range of
Page 13 and 14: acceleration. Similar results were
Page 15 and 16: 3.4. Scenario results Two different
Page 17 and 18: At sea-state 2 (H 1/3 = 0.8m) and s
Page 19 and 20: NEU, W. L.; HUGHES, O.; MASON, W. H
Page 21 and 22: 3. The software selection process T
Page 23 and 24: o Development capacity of CAD vendo
Page 25 and 26: • Departments / groups suited to
Page 27 and 28: 1. Pre-processing: Pre-processing r
Page 29 and 30: www.ssi.tu-harburg.de/doc/webseiten
Page 31 and 32: propulsor should be higher than tha
Page 33 and 34: The FRIENDSHIP Framework software d
Page 35 and 36: ehaviour for both parameters. Table
Page 37 and 38: Fig.13: Thrust oscillation for sing
Page 39 and 40: Training Complex for Training Subma
Page 41 and 42: feasible option to improve the trai
Page 43 and 44: In addition, the submarine’s anim
Page 45 and 46: Fig.8: Visualization of Operation o
Page 47 and 48: References DORRI, M.K.; KORCHANOV,
Page 49 and 50: The selection operator allows the t
Page 51 and 52: This paper uses as case study the m
Page 53 and 54: indicates the Pareto frontier with
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Centre Girder.Max 29 28 Centre Gird
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Though a holistic approach to the s
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2.5 Global Criterion Optima Enginee
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sections in order to obtain compati
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Max solution has been obtained for
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Abstract Operator Decision Modeling
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Rule 6: With the MOAS sonar (Mine a
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4.2. Definition: Extension The use
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We obtain two extensions, which are
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- We define the defaults D: ¬ dete
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7. Conclusion. The default logic al
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partitioned into two sets as follow
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3. Two-stage stochastic programming
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drops and the two-stage stochastic
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Fig. 6: Unit transportation cost as
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Fig. 10: Unit transportation cost a
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Acknowledgements This work has been
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Loan model: loan = 0.8 ship cost ra
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The effect of analyses with uncerta
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3.3. Requirements to the system Whe
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There are three design plans (1), (
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(a) (b) (c) Table III: Solutions fr
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Abstract Effects of Uncertainty in
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ε is a number with G\U(0,σ) and [
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Increasing Sigma Time Histories 1 0
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5.1. Space # 040: Galley and Sculle
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5.4. Long Term Study of Optimizatio
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RUN Fitness Min. ZD Avg. ZD Min. SP
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RUN Fitness Min ZD Avg ZD Min SP Av
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The Challenge in Hull Structure Bas
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early design stages. The main advan
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Profile definition is mainly based
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Fig. 7: Example of an automatically
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6.3 Other output One of the advanta
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welding and painting are additional
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To overcome these issues, a model w
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lightweight, hydrostatic data and t
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This graph yields the expected resu
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OERS, B.J. Van; STAPERSMA, D.; HOPM
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Size optimization changes only expl
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3.2. Topology Optimization for Conc
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meshed with shell elements and rema
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Trajectory Design for Autonomous Un
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drastically since it determines a l
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assumptions, the inertia matrix (in
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For each of these missions, we will
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more powerful than for the first tw
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For mission 3, using the first thru
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References BREIER, J.A.; RAUCH, C.G
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wastage from the presence of corros
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The MINOAS system integrates a set
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significant improvements over appro
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over the 3D CAD model of the area u
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through porous materials (see Stauf
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obustness and reliability in challe
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ØSTERRGAARD, E. H.; MATARIC, M. J.
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2 Tool integration DigiMAus integra
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Fig. 4 : Navisworks visualization i
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2.4 Office and other Visualization
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2.7 Control in 3D In this perspecti
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2.10 Method documentation The metho
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Clifetime Cdevelopment Csupport n :
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6. Why Lego Bricks? The three archi
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Specific own platform plug-ins are
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process moves from global design ac
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Data correctness and consistency ch
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Fig.4: System Architecture Once the
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7. Rule Based Processing Rule based
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The editor component is required on
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Geneva. ISO 215 (2004), Industrial
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2. Modern product model - Solid bas
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The built-in output formats include
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3.3. Global loads For the purposes
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Fig. 6: First eigen modes of a crud
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Abstract Rule-Based Resource Alloca
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Fig.2: User groups in the productio
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4 Rule-based approach 4.1 Rule defi
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necessity in the winter. These outd
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Fig.6: Decision tree for the space
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Abstract Combining Artificial Neura
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x log sig( x) = , (12) 1− − x e
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the simulated annealing algorithm,
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ecause the relation between speed a
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Developing of a Computer System Aid
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f γ A = f µ , (2) Fig. 2: Oceans
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calculation methods of the phenomen
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In a similar way can be expressed t
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− the relative resistance increas
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a) 1,0 P VE [-] 0,8 0,6 0,4 0,90 0,
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For the so defined service margin w
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Data Mining to Enhance the Throughp
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maneuvering time and according to w
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The author emphasize that reality i
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time intervals was not appropriate
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The Role of IT in Revitalizing Braz
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The EAS strategy is to use the Petr
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7. Speed to Proficiency In order to
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Practical Applications of Design fo
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Minimize Fabrication / Assembly Com
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Fig. 3: Example of Minor Bulkhead u
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5.2 Improved Practice Fig. 7: Accur
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many constraints on the resulting s
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Modeling Complex Vessels for Use in
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The initial position values for the
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means to get an insight into the sy
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variables. The shape of the hull is
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the free space available above the
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noise or vibration, separation of h
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Furthermore the approach to the imp
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Optimization-Based Approach to Rati
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• Refine or ‘fine-tune’ exist
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elative positions between objects.
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New objective scores for the design
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5.3 Baseline Design Selection Fig.4
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distance in the max-min problem. If
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Improvement of Interoperability bet
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HATLAPA models (Fig.3) are more com
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10. Support by VSM and VDMA (associ
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(DBB) approach, are restricted by t
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3.1.1. A process for employing the
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(a) (b) (c) (d) Fig.3: Option Explo
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Fig.7: Max. length vs. total displa
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Fig.9: Number of Combined Float-Mov
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ather than the normal approach in w
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ANDREWS, D.J., PAPANIKOLAOU, A; ERI
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2. Aspects of Production Simulation
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4.1 Data structure As the goal of t
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and used engineering tools showed d
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Utilization of Integrated Design an
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The right side in Fig. 2 illustrate
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Fig. 6: Initial design of hopper an
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In most cases, it is desired to app
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Interactive Hull Form Transformatio
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3. Modern Hull Form Representation
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introducing a knuckle, Fig. 3. Rule
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P 0 P i P’ 0 ) P’ i P′= P + i
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1. The initial selected vertices ar
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In other cases, it may be necessary
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face must be extended to introduce
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Fig 13: A prototype of the intended
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Abstract Aerodynamic Optimization o
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commonly used in wind tunnel tests,
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Schmode (2002) applied a RNG k-ε m
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Table II presents the properties of
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Fig.10: Comparison of exhaust conce
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5.2. Selected results In order to o
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of a zoomed in region, namely a meg
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performance. The derivation of the
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each agent can always apply rule #1
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With four vehicles the area that ca
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References AKYILDIZ, I.F.; POMPILI,
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However, there is no global optimum
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are adjusted. The calculations are
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Fig. 3: Deep water trim curve for m
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consumption is analysed over relati
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3.2 Benefits for crew and ship mana
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A 3D Packing Approach for the Early
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of these changes. Subsequently, Sec
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• Soft object. Soft objects also
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such that the overlapping part is r
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• Connection object. Connection o
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main difference is that the topside
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Two objectives were maximised: pack
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ASMARA, A.; NIENHUIS,U. (2006), Aut
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Fig.1: General arrangement of the t
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Group Steady-state modeling Group A
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Fig.7: Architecture of Cascade-forw
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2.2.1. Calculation of steady state
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Fig.15: Scatter plot of model outpu
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Because of the usage of steady stat
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Nomenclature c coefficient & fit-fa
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Fig.1: Petroglyph of, possibly, a c
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The lack of a graphical user interf
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3.3. CAD supported logical modellin
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often used in the ship industry, bu
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7.2. Logical components Logical com
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So, input facilities are an integra
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A Decision Support Framework for th
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Some measures will change the fuel
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The re-active technical abatements
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adding water into the chamber. The
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selection of air emission controls
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I = I ∩ I ∩ I and I ⊂ I VOA V
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The objective function in an optimi
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MALLACH, E.G. (1994), Understanding
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etween Kristiansand in Norway and H
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validation dataset. In a recent ser
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(a) (b) Fig. 6: IR corridor test (a
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Fig.9: Number of passengers in each
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4. Conclusions Two passenger ship a
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positive effects on the energy effi
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higher fuel saving potentials and a
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For vessels with azimuthing pods in
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4. Use Case Examples The methodolog
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4.5. Losses and Load Distribution T
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Fig. 7: Mission tab The software pr
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The methodology thus places itself
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Development of a Methodology for Ca
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2.2 Process Driver Fig. 1: Processe
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Fig. 4 illustrates the product info
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Reconstruct calculation An addition
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References BENTIN, M.; SMIDT, F.; P
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While the path for the integration
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ultimately, zero in on the best des
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Below, the subject of collaboration
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1 P opt (C*) C opt (P*) 0 C* 0 1 Fi
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Most importantly: design constraint
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include, for example 5 : z 1 1 = 1/
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Z 1 , which is in this figure is sh
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It can be concluded that the goal o
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Behavior Models can be built from t
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GOUGOULIDIS, G. (2008), The Utiliza
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This procedure can be regarded as a
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corrosion, coating, deformation, cr
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detect an indication of a crack? Wh
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Product Data Management is a concep
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Project Part Instances is a relatio
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The starting point of the developme
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Abstract Requirements of a Common D
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Requirements management or system e
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However the industry has still not
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When design offices begin creating
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Fig. 5: Ship documentation as manag
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Although engineering objects are th
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Fig. 8: Example of UUID as implemen
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Abdel-Maksoud 28 Abramowski 213,221
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