How collaborative multidisciplinary optimization can improve efficiency and maximize profits in ship design

Written by Alberto Clarich
Jan 17 2020 · 7 min read

Ship design has been changing very fast over the past years, just like any other engineering field, to keep pace with technological innovations and competitors in the market.

The spread of numerical models and simulation software, along with the increasing power of computational resources, provided the designer with a wider range of alternatives to analyze. But the overwhelming availability of data coming from different sources, including numerical models, measurements, regulations, along with the financial aspects, may introduce the risk of losing control.

The process of designing a ship has to take into account numerous interdependent components that interact within the system, with the necessity of sharing the contribution of different teams that don't necessarily talk the same technical language.

The challenge: a flexible and collaborative ship design platform

The challenge is therefore how to conjugate the differing requirements and expertise into a single design process, starting as early as the conceptual phase.

It is in fact particularly important for the designer - and even more so for the shipowner - to rapidly choose a solution that minimizes the costs and maximizes the profit so that the project can attract funds from banks and investors, regardless the typology of the ship (passengers, cargo or LNG/CNG carrier).

Any decision or modification to the baseline project should be done as soon as possible in order to minimize the waste of resources and delivery time.

An efficient ship design platform today needs to be flexible, multidisciplinary and collaborative.
Alberto Clarich

An efficient ship design platform today needs to be:

  • flexible, to allow an easy modification in the process without losing the previous work of the designers
  • multidisciplinary, to include any discipline concerned in the system within the same design process and from the beginning
  • collaborative, to allow each expert to share their contribution, in such a way that any stakeholder or decision-maker can have immediate feedback on the status of the entire project and on the profit margin that can be obtained

This platform should allow the designers to easily integrate any parametric model, CAE or numerical, into an automated workflow, so that any change of the design parameters can be evaluated immediately and automatically.

Nowadays it is in fact no longer advisable for the designers to manually rebuild complex CAE models from scratch each time any variant to the design project needs to be evaluated. The time to build any model can vary from days to weeks, and any change may produce important delays in delivery.

The traditional “cut and try” approach must, therefore, be replaced by an efficient process automation platform that seamlessly integrates with most common CAE software, based on API language or simple template files. This allows the designer to build a CAE model once, defining the parameters of interest, and leaving the evaluation of the design variants to the automated process integration platform.

The traditional "cut and try" approach to building CAE models must be replaced by an efficient process automation platform that seamlessly integrates with the most common CAE software.
Alberto Clarich

From intuition-based engineering to AI-empowered decision making

The process described above no longer relies on the evaluation of design alternatives based on the designer’s experience and intuition. Rather, it relies more and more on the guidance of Artificial Intelligence and machine learning.

modeFRONTIER optimization platform from ESTECO is fully integrated with a wide range of AI algorithms, like Genetic and Evolutionary Algorithms, gradient-based and deterministic methodologies, as well as hybrid or multi-strategy methodologies that combine the robustness of the main algorithms with the convergence rapidity of the Response Surfaces, such as Neural Networks or Machine Learning.

These algorithms are particularly efficient to find the optimal combination of the design parameters to satisfy the objectives and constraints defined in the design project, and are able to achieve this through the evaluation of a limited number of design simulations,

Ship design typically involves objectives coming from several disciplines or departments (e.g., minimize the drag force on the hull, respect structural constraints on the various structures of the ship, minimize fuel consumption or maximize payload). All of these can be included in a multidisciplinary optimization process set up.

The power of cross-discipline collaboration

In a typical MDO process, the expertise of professionals from different disciplines is required. Therefore, it is of paramount importance to use a common platform to share the contributions of all departments in a way that each section can stand independently from the others (in other words, that the process set up provides useful insights without requiring specific knowledge in a field different from one's own).

A successful example of cross-disciplinary collaboration is the innovative CNG ship carrier project GASVESSEL, funded by the European Union under Horizon 2020 Program. ESTECO took part in the project providing its technology to industrial partners coming from various fields (energy, Oil&Gas, Naval engineering).

The aim of the Gasvessel project was to prove the techno-economic feasibility of a new CNG (Compressed Natural Gas) transport concept, enabled by a novel patented Pressure Vessel manufacturing technology (based on lightweight composite overwrapped vessels) and a new conceptual ship design including safe on and off-loading solution.

Learn more about how ESTECO Technologies were used in the Gasvessel project

Alberto Clarich

Alberto Clarich is Head of Engineering Team at ESTECO and member of NAFEMS Italy Steering Commitee. He received a PhD in “Innovative Parameterisation and Optimisation Methodologies in Aeronautic Field”, University of Trieste (2003), in collaboration with Dassault Aviation, having a background as Mechanical Engineer. He begun working at ESTECO in 2004 as Optimization expert.

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