Machine Characterization

Introduction

The Machine Characterization tool is used to define the machine characteristics and features in term of dimensions, power absorption, cost, mass, etc. The module is a special type of Deployment Design Tool, and it should be used at the start of a DTOcean+ project together with the Site Characterization module.

Structure

This module’s documentation is divided into four main sections:

  • Tutorials to give step-by-step instructions on using the Machine Characterization toll for new users.

  • How-to Guides that show how to achieve specific outcomes using the Machine Characterization tool.

  • A section on background, theory and calculation methods that describes how Machine Characterization works and aims to give confidence in the tools.

  • The API reference section documents the code of modules, classes, API, and GUI.

Functionalities

The main purpose of the Machine Characterization tool is to collect information about the machine to be deployed in the array. The data is subdivided in three categories:

  • General – includes system mass, materials, rated capacity, installation depth, etc.

  • Dimensions - includes linear dimensions, areas and volumes

  • Model - define the main features of the machine related to its power production, such as Cp (Tidal) and CWR (Wave).

The Machine Characterization tool can be run in three differen mode, simplfified (complexity 1), intemedieate (complexity 2), and advanced (complexity 3) .

While the General and Dimensions inputs do not change with the mode (complexity), the Model section depends on it.

For the simplified analysis, only a single value efficiency is required for the machine. For the intermediate analysis, an efficiency function or multi-parameters model is requried for the machine. For the advance analysis, a more complex model is required.

Workflow for using the Machine Characterization module

The workflow for using the Machine Characterization module can be summarised as 1) provide General inputs 2) provide Dimensions inputs 3) provide Model inputs 4) lock the data to ensure results consistency

The case of advanced analysis of a wave energy converter, the workflow is modified as follow: 3.a) provide Model inputs 3.b) estimate linear potential theory coefficients using a BEM solver.