Descrizione del progetto
Approccio avanzato ai test sulle strutture composite complesse
Il progetto D-STANDART, finanziato dall’UE, cercherà di sviluppare metodi rapidi ed efficienti per creare modelli di durabilità di strutture composite su larga scala, con stampaggi arbitrari in condizioni realistiche. Utilizzando campioni generici, i ricercatori svilupperanno nuove metodologie per determinare i parametri dei materiali associati alla loro durata sotto carico ciclico. Questi modelli altamente fedeli dovrebbero aiutare a simulare la crescita dei difetti in stampaggi di varie dimensioni in funzione del carico ciclico e della velocità. L’uso di modelli surrogati di intelligenza artificiale dovrebbe contribuire ad accelerare lo sviluppo, l’adozione e la commercializzazione di componenti avanzati. Saranno identificati due casi d’uso rilevanti per il settore aerospaziale e per le energie rinnovabili, al fine di convalidare le prestazioni di durabilità modellizzate delle strutture in composito.
Obiettivo
Advanced composite manufacturing is becoming crucial in the global sustainable drive for a climate-neutral future as enabler of light-weight structures in, for example, the aerospace and wind energy sector. Their increased relevance has raised the importance of damage tolerance and durability of composite structures, currently dealt with time-consuming and inaccurate techniques. Hence the objective of D-STANDART is to develop fast and efficient methods to model the durability of large-scale composite structures with arbitrary lay-ups under realistic conditions (loads, environment).
New test methodologies will be developed using generic specimens to correctly quantify material parameters that determine the durability of composites under cyclical loading. Material characterisation will be used in high-fidelity models to simulate defect growth in various lay-ups and at various scales as a function of cyclical loading and rate. To apply these models in an industrial design environment, D-STANDART will make use of Artificial Intelligence (AI) surrogate models, trained using test data from the project and historical test data to easily adapt to different design parameters and complex lay-ups, thereby accelerating the development, uptake, and commercialisation of advanced components. Two use cases have been selected to validate the modelled durability performance both in the aerospace and renewable energy sectors. Furthermore, circularity and sustainability will be assessed via dedicated life-cycle assessment, life cycle costing and cost-benefit analysis.
This 36-month €5.6M valued action involves, 9 partners (3 universities, 2 RTOs, 2 industry, and 2 SMEs) from four countries (United Kingdom, The Netherlands, Germany, and France). The consortium will be supported by an Advisory Board formed of 6 End users embracing the value chain to validate requirements, guide on relevant approach to certification, and finally support results uptake, in tight alignment with EMCC.
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HORIZON-RIA - HORIZON Research and Innovation ActionsCoordinatore
1059 CM Amsterdam
Paesi Bassi