CELLCOMP: Data-driven Mechanistic Modelling of Scalable Cellular Composites for Crash Energy Absorption

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The stringent and ambitious emissions targets within UK and the world are prompting the transportation industries to move towards zero-emission vehicles (e.g. electric cars, hydrogen cars). The biggest obstacles holding back the shift are safety concerns, low mileage range and high purchase prices. The high energy stored in the batteries or fuel cells of future vehicles poses a significant threat to passengers due to the potential fire or explosion in a crash. To accelerate the transition to net-zero, the future vehicles must reach similar level of crashworthiness achieved with conventional petrol/diesel cars, but significantly reducing the weight and at a relatively low cost. This exceptionally challenging target is motivating the development of lightweight, scalable and crashworthy structures (e.g. crash box, bumper) to protect the energy-storage devices. Synthetic cellular composites (CCs), inspired by natural materials (e.g. wood, composed of cellulose fibres and lignin matrix), are emerging lightweight materials for crash energy absorption. CCs are porous cellular materials with interconnected composite cell walls. With unique combinations of multi-phase material constituents and architecture, CCs are proven to absorb multiple times higher crash energy than many singe-phase cellular materials (e.g. polymer foams, honeycombs).To date, most existing truss-like CCs for energy absorption suffer from low throughput volume and poor recoverability, increasing the cost for manufacturing and maintenance. The vast design space considering architecture features, constituent materials and deformation mechanisms leads to a huge escalation in the complexity to predict energy-absorbing performance. Indeed, the lack of scalable manufacturing techniques and reliable models for crash assessments are the main barriers to increase the adoption of CCs in volume production transportation vehicles. This project aims to substantially address this challenge by integrating computational mechanistic models and data-driven approaches for predicting and optimising the crash performance of CCs.The proposed project will address two fundamental questions: (i) what is the role of microstructural topologies, constituent materials and strain rates on the energy-absorbing properties of CCs? (ii) how to efficiently program CCs to achieve desired crash responses while considering scalability and recoverability? To address these questions, this project will develop a new methodology to understand, predict and optimise the crush responses of CCs. This project will bring a unique team with multidisciplinary research expertise of scalable manufacturing, high strain-rate experimental testing, computational modelling and data-driven approaches. The project will employ a scalable manufacturing method to create new CCs for exploiting elastic buckling instabilities and minimising the localised junction failure, thereby enhancing their recoverability. The mechanical behaviours of material constituents and their architected CCs will be measured using high velocity testing facilities. Novel high-fidelity computational models will be developed to predict the buckling, plasticity and fracture responses of CCs under crushing loads. The structure-property relationships of CCs will be revealed by advanced machine learning algorithms, enabling the rapid and intelligent identification of optimised designs for desired applications. High velocity crash tests of optimised CCs prototypes will be conducted to evaluate their energy absorption and recoverability. The data-driven computational framework and scalable CCs prototypes developed in this project will shift the future computing paradigm and make future zero-emission vehicles safer and greener. The generic data-driven design tool will also open new avenues for efficient designs of other porous cellular materials, ranging from thermal insulation foams, acoustic metamaterials to artificial tissue scaffolds.