Chapter 1
Introduction
1.1 IN TRO D U CTIO N
For decades, ships have been designed using the well known ship design spiral which essentially is a sequential and iterative approach to the different stages of the design process. Concept design and preliminary design form the first two major stages o f the ship design spiral. The ship design spiral is presented in Figure 1.1. The four m ajor stages of design include the concept design, preliminary design, contract design and detail design [Taggart, 1980].
P R O PO R TIO N S AND P R E L POW ERING
L IN E S A N D BOOY P L A N
H Y D R O S T A T IC S A N D BO NJEANS
FLO O O A B LE L E N G T H AND F R E E B O A R D
A R R A N G E M E N T S HULL AND M A CHINERY
MISSION R E Q U IR E M E N TS
C O S T E S T IM A T E S
C A P A C IT IE S TRIM A N D IN T A C T S T A B IL IT Y
L IG H T S H P WEIGHT E S T IM A T E
S TR U C T U R E
C O N C E P T D E S IG N P R E L IM IN A R Y D E S IG N C O N T R A C T D E S IG N D E T A IL D E S IG N
V / / / / A
Figure 1.1 Ship design spiral
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The very first effort, concept design, translates the mission requirements into naval architectural and engineering characteristics. Essentially, it embodies technical feasibility studies to determine such fundamental elements of the proposed ship as its length, breadth, depth, draft and other characteristics that meet the speed, range, cargo capacity, deadweight and other requirements. The process of concept design starts w ith scanty information from the owner which includes the type of ship, cargo capacity, its speed and route and finally leads to the determination of the major ship characteristics affecting cost and performance. A proper selection of the main dimensions and the form coefficients of the ship ensures the attainment of the mission requirements such as desired speed and endurance, cargo capacity and deadweight, maneuverability and seakeeping. The completion of the preliminary design phase gives precise information about the vessel to be built and the initial cost estimate. It also provides a basis for the subsequent development of the contract plans, specifications and detailed design. The contract design stage yields a set of plans and specifications which form an integral part of the shipbuilding contract document. It encompasses one or more loops around the design spiral, thereby further refining the preliminary ship design. The final stage of ship design is the process of detailed design which refers to the development of detailed working plans.
The spiral approach may be inefficient and ineffective as such a methodology provides very little scope for life cycle considerations and can result in a feasible ship, rather than an optimal ship. Moreover, in a competitive market it is expected that only economically optimum designs are going to be built.
Over the last quarter of a century, optimization methods have increasingly become an integral part of most computer aided design systems, largely because effective design synthesis can be performed on the basis of a simultaneous consideration of all the design requirements and the competing solutions can be ranked. There have been a number of applications of optimization methods to the preliminary design phase. The methods range from basic design graphs and tabular data, simple linear relations and linear programming methods, nonlinear optimization methods such as those of Hooke and Jeeves or Nedler and Mead, and sequential unconstrained minimization techniques (SUMT) to the more recent ones based on goal programming and decision based design. These methods usually deal with the design problem isolated from reality ignoring life cycle considerations and solve the problem with simplified assumptions. Life cycle modelling is an important consideration which increases the importance of the early design stages as major design decisions are generally made at these stages based on scanty and imprecise information which have far reaching effects on the product being designed.
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1.2 M OTIVATION, O B JE C T AND SCOPE O F TH E W ORK
Design is a process of converting information that characterizes the needs and requirements for a product into knowledge about the product. Preliminary ship design is one o f the major design phases concerned with the selection of the principal particulars, hull form, power, preliminary arrangement of machinery and major structural layout. The early phases of design, i.e. the concept and the preliminary design stages, largely rest on decision making involving multiple measures of merit in an imprecise and incomplete information environment.
The construction of a mathematical framework involves the development of mathematical abstractions corresponding to selected aspects of a problem situation.
The appropriateness of a mathematical formulation depends on a number of factors which include the accuracy of problem representation, validity of the assumptions, availability of data for modelling, model transparency, cost of computation and finally the validity and the acceptability of the results for practical use. An effective model construction requires creativity as well as knowledge about the modelling paradigm and the problem domain.
A critical review of the preliminary ship design models reveals a wide variety of mathematical modelling using many different variables and their ratios, constraints, objective functions and solution methodologies. The major limitations of the preliminary ship design models are discussed here in brief under different heads. This eventually leads to the identification of a need for the development of an effective and efficient preliminary ship design framework.
V ariable identification : The variables used in different preliminary ship design optimization models are quite diverse. As an example, Sen[1992] has used a set of eleven design variables (length between perpendiculars Lpp, breadth B, draft T, depth to upper deck D, block coefficient CB, waterplane area coefficient Cw, prismatic coefficient CP, speed V, propeller diameter Pdia, propeller pitch and weight of water ballast). Pal[1981] has used a set of five variable ratios (x(l) a function of length- beam ratio, x(2) a function of beam-draft ratio, x(3) a function of prismatic coefficient, x(4) a function of length-cube root of the fish-hold volume and x(5) a function of depth-draft ratio) in the design of a fishing trawler. Zanic et al.[1992]
have used a set of seven design variables (Lpp, B, T, D, CM, CP, and Cw) in their optimization model for stem trawler design. Moreover, at times variable ratios have also been used as system variables for the ease of modelling and reducing the number of design variables in the problem as the method of variable identification has not been formalized.
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P a rtially discrete nature of the variables : Most ship design optimization models assume continuous values for all the design variables. A designer may want to represent the variables Lpp, B and D as discrete values along with the variables - T, Cb, Cm, and Cw assuming continuous variable values. Such situations can arise from the practical shipbuilding point of view where the variables Lpp, B and D represent discrete values instead of continuous values or in the design of a containership where Lpp, B and D are preferred to increase in steps corresponding to the container dimensions. These prevailing ship design models normally truncate or round off the values o f the system variables at the end of the optimization process thus ignoring the effects of the truncation or the rounding off process.
C on strain t definition and classification : The problem of constraint classification and the method of constraint modelling also varies quite substantially with different preliminary ship design optimization models. The term constraint has been used interchangeably with objectives, with prefixes like hard and soft, and at times constraints have been considered along with the objective function. These differences have primarily occurred because of the variety of optimization methods that have been used in these models.
Definition of objective function : The definition of the objective function has also exhibited large variations. Sen[1992] has considered the minimization of required freight rate (RFR), achieving the target number of twenty feet equivalent unit (TEU) and the minimization of water ballast as the objective, whereas Mistree et al.[1991]
have considered the objective to be the maximization of return on investment (ROI), the attainment of the required number of TEU, attainment of freeboard, and the attainment of the required transverse metacentric height etc.
Solution methodology : The solutions of the ship design problem using different models have been arrived at using simplified assumptions of local functional linearity while the existence of the multimodal nature of the optimization problem has been overlooked. The models have also failed to suggest a suitable methodology for finding the weights corresponding to various objectives for a typically multiple objective formulation.
Selection methodology : In a situation where multiple competing feasible designs evolve, the need for a selection procedure can often arise requiring the selection of the best alternative from among a set of pareto optimal or near optimal designs. Such methods are not provided for decision support in any of the existing preliminary ship design models.
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U ncertainty modelling : An uncertainty analysis of the designs has been carried out in isolation rather than in an integrated framework. Moreover, a suitable methodology to evaluate the relative influence of the nondeterministic parameters like freight rate, fuel oil price etc. on the objective function is not available.
O th er design support tools : A ship designer is rich in terms of design data but may seriously lack information or empirical relations for a particular performance estimate. No suitable tool or methodology is available to support the designer in such imprecise and incomplete information situations.
Owing to the presence of a large number of drawbacks and the possible limitations of the preliminary ship design models, further research is required towards developing an integrated framework to carry out the process of preliminary ship design efficiently and effectively in a real life perspective. Multiple criteria decision making allows a designer to develop, analyze and finally select designs through explicitly defined mathematical models (optimization) and preference criteria (selection) and provides a scope to incorporate the life cycle considerations into design.
The foregoing discussion has identified the drawbacks and limitations of the prevailing ship design models. The main objective of the present work is to develop a multiple criteria decision making framework for preliminary ship design eliminating the serious limitations and drawbacks of the past design methods with an aim to provide the designer with efficient and effective tools for design and decision support.
A holistic approach integrating the process of knowledge representation, information management and structured information processing aimed at a more exact representation of the design problem has been the basis for the development of the multiple criteria decision making framework for preliminary ship design in this work.
The integrated framework comprises of two major subsystems : a multiple objective decision making subsystem and a multiple attribute decision making subsystem. The multiple objective decision making subsystem deals with the development of design alternatives from a set of implicit relations which relates to a process of optimization and design synthesis, whereas the multiple attribute decision making subsystem deals with the selection of the best design from a list of already identified designs based on a set of incommensurable and conflicting criteria.
Mathematical model building begins with the process of variable identification. The process of variable identification has been formalized here and the concept of unit
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approach has been proposed. The unit approach as a tool for variable identification is particularly attractive for large systems involving a number of estimation methods and a number of intermediate variables where conventional methods based on a degree o f freedom analysis and subsequent variable identification may not be practically feasible. The application of the unit approach yields the result that the preliminary design variables are the principal dimensions of the ship (Lpp, B, T and D) and the form coefficients (CB, CM and Cw). From a practical point of view, the variables Lpp, B and D should represent discrete variable values. To incorporate the partially discrete nature of the preliminary ship design system variables a partial discretization module has been developed. This unique methodology thus efficiently avoids post optimal roundoff or truncation analysis. The process of partial discretization is based on a single stage minimization of the difference between the weight and the buoyancy of the ship, as explained in Section 3.4.
Constraint definition and classification have also been formalized in the present model. Constraints are classed into system constraints type A which includes the limits on the system variables and their ratios, and system constraints type B comprising of constraints arising from physical laws, owners' specifications, or statutory regulations.
It is widely accepted that the criterion of prime importance to preliminary ship design must be of an economic nature, giving full weight to technical factors. In the present work, the net present value index has been chosen as the objective to be maximized for commercial vessel designs. However, for the design of special purpose vessels other objectives like seakeeping or maneuvering performance may be considered as the primary objective at the preliminary design stage. The proposed framework also allows a weighted summation of multiple objectives which may arise for special purpose vessel designs.
The preliminary ship design problem without simplifying assumptions thus turns out to be a multivariable nonlinear constrained optimization problem. The optimization of a nonlinear objective function subject to nonlinear constraints may result in multimodal function behaviour which essentially is a problem of global optimization.
Global optimization problems cannot be solved in general without simplifying assumptions. In the present study, heuristic based methods are applied to the solution of the preliminary ship design problem which require no simplifying assumptions of linearity and can arrive at practically acceptable solutions of the problem in an affordable time span. Enhanced simulated annealing and the enhanced genetic algorithm have been used in this work with problem specific modifications in addition to pure random and random multistart methods to solve the preliminary ship
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design optim a it ion problem. The application of heuristic based optimization methods for the solution of the ship design problem results in a number of pareto optimal or near optimal designs. These designs have marginally varying objective function values but differ quite substantially in their principal dimensions and form coefficients. Hence, a secondary set of criteria are necessary to identify the best design from a set of competing designs. A secondary set of criteria have been identified and the subsequent process of design selection is based on methods of multiple attribute decision making. The analytic hierarchy process and the weighted least square technique have been used for the selection process.
The design relations for various estimates of steel weight, machinery weight etc. may often be outdated due to changing statutory requirements and technological innovations. Artificial neural network based support tools have been developed and incorporated in the present framework to assist the designer in such situations. The network can learn correlated patterns between sets of input data and corresponding target values and can handle problems involving data which are imprecise and noisy.
The network for this purpose is of the feedforward type undergoing supervised training through the process of back propagation minimization of error. The training of the network is based on the modified Marquardt Levenberg algorithm (Appendix B). The application of this algorithm provides a faster training of the network and is also particularly attractive as the step sizes of the variables are implicitly taken into account. Two independent nets have been incorporated in the framework: the first to predict the container capacity based on the principal dimensions and the speed of the vessel and the second to predict tanker capacity based on the principal dimensions of the vessel. A concept exploration model for a containership design has also been developed based on a hierarchical neural network structure. The concept exploration model can both provide good starting points for models relying on local optimization methods and also be used independently for a quick design appraisal.
The proposed ship design model utilizes empirical formulae and statistical correlations for the estimates of hull weight, building cost, operating cost etc. The fuel oil cost, freight rate and port charges are some of the non-deterministic input parameters used in the model based on information or prediction that invariably contain uncertainty. The framework allows an uncertainty analysis based on Monte Carlo simulation, where the designs can be studied under both probabilistically varying non-deterministic input parameters and the inherent error of the estimation methods. Such an analysis provides important information about the confidence interval of the objective function (economic assurance of NPVI for commercial vessels). It also provides a scope to assess the relative influence of the nondeterministic input parameters on the objective function.
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The developed framework is also capable of supporting a global ship design trend study. Such a study is used for predicting future ship design trends under different market scenarios. Moreover, it also provides a scope to compare and crosscheck the model behaviour with the variations expected logically.
The design framework has been tested with a number of design examples covering the m ajor classes of ship types. The examples include merchant ship types, viz. a containership (capacity carrier), a bulk carrier (deadweight carrier), and tanker (a liquid cargo deadweight carrier), as well as a service oriented vessel, i.e. an offshore supply vessel in which deck area and the payload volume are the primary service requirements.
The framework has been developed in a modular manner which allows the estimation methods to be added or updated easily. The FORTRAN 77 source code of the preliminary ship design framework runs into approximately 400,000 lines and runs on the HP9000/340 system of the Department of Ocean Engineering and Naval Architecture, Indian Institute of Technology, Kharagpur.
