Aquaculture is currently the fastest growing food sector in the world and the open oceans are seen as one of the most likely areas for large-scale expansion [1], [2], [3]. The global demand for seafood is continuing to rise sharply, driven by both population growth and increased per capita consumption, whilst wild-capture fisheries are constrained in their potential to produce more seafood. A recently funded EC project, the Blue Growth Farm – BGF (GA n. 774426, 1st June 2018 – 30th September 2021) aims at contributing to this world need with an original solution. The Blue Growth Farm proposes an efficient, cost-competitive and environmentally friendly multi-purpose offshore farm concept. It is based on a modular floating structure, moored to the seabed, meeting requirements of efficiency, cost-competitiveness and environmental friendless, where automated aquaculture and renewable energy production systems are integrated and engineered for profitable applications in the open sea. In the present paper, the overall engineering approach developed to carry out the research work is presented, described and justified. Different technical and scientific challenges are addressed through an integrated industrial engineering design approach, where all disciplines are tuned to achieve the Blue Growth Farm main targets. These are represented by: i) guaranteeing expected nominal fish production thanks to advanced automation and remote control capabilities; ii) minimizing the pollution introduced at marine ecosystem level when exploiting the marine natural resources, whilst increasing the social acceptance and users community agreement; iii) maximizing the electricity production in the Blue Growth Farm potential installation area ecosystem to provide energy supply to the on-board electrical equipment and to dispatch the extra produced electric energy to the land network. Preliminary engineering design results are promising to demonstrate effective increase of safety and efficiency by reducing on-board human effort and consequently risks at offshore, thus to make commercial-scale open ocean farming a reality. The present paper introduces overall concepts and design methodology whilst other companion works submitted at OMAE2019 [4], [5], [6] provide insight of specific aspects of the Blue Growth Farm project elaborated during the first six months activity.
The Blue Growth Farm" is an ongoing H2020 European project, aimed to the development, engineering and demonstration of a new floating multi-purpose platform concept, devoted to aquaculture, and wind-wave energy production. Due to the significant complexity of the coupled dynamic behavior of the proposed concept, model tests are essential to investigate the most relevant physical phenomena and validate/calibrate the pertinent numerical models. However, the realization of a scaled model of such structure is by itself quite challenging, since each sub-system follows its own scaling laws and requires different strategies to minimize scale effects. The aim of the present paper is to describe the arrangement of the experimental activities, discussing the mechanisms of the scale factor at different scales and related results accuracy, scaling strategies and test environments. The two-phase framework and the scaling strategies proposed may be also useful for future activities on similar concepts.
Abstract. This paper presents the RBF4AERO benchmark technology platform, developed in the framework of the EU-funded RBF4AERO project. The platform enables the so-called Benchmark Management System (BMS) used for benchmark submission and results reporting. The BMS is deployed using three modules, namely the Graphical User Interface (GUI), the Workflow Manager (WM) and the Benchmarking Database System (BDS) which cooperate during the whole optimization benchmark life-cycle. The GUI is the only component which interacts with the end-user. It enables the optimization benchmark submission, along with the progress, results and computational platform resources monitoring. The configuration of the Optimization (OT) and the Morpher Tool (MT) is a pre-requisite for the optimization benchmark submission. In an optimization scenario the WM, which is practically the controller of the system, queries the OT in order to get a table of samples and gives back the results of the simulator (for instance a CFD tool). The evaluated individuals serve as training patterns of a Response Surface Model (RSM) which is, then, used for an Evolutionary Algorithms based optimization. The resulting 'optimal' solution(s) are delivered back to the WM for re-evaluation on the CFD tool. For each evaluation on the CFD tool, when a new geometrical shape is required, the computational grid is morphed using the MT based on radial basis functions. 4156Massimo Bernaschi et al.
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