Characterising, understanding and predicting the performance of structural power composites

Dramatic improvements in power generation, energy storage, system integration and light-weighting are needed to meet increasingly stringent carbon emissions targets for future aircraft and road vehicles. The electrification of transport could significantly reduce direct CO2 emissions; however, battery energy and power density limitations pose a major technological barrier. The introduction of multifunctional structural power composites (SPCs), which simultaneously provide mechanical load-bearing and electrochemical energy storage, offers new possibilities. By replacing conventional materials with SPCs, electrical performance requirements could be relaxed, and vehicle mass could be reduced; however, for SPCs to outperform monofunctional systems, significant performance and reliability improvements are still required. The use of computational models to support experimental efforts has so far been overlooked, despite wide recognition of the benefits of such a combined approach. The aim of this work was to develop predictive finite element models for structural supercapacitor composites (SSCs), and use them to investigate their mechanical, electrical, and electrochemical behaviour. A unit cell modelling technique was used to generate realistic mesoscale models of the complex microstructure of SSCs. The effects of composite manufacturing processes on the final performance of SSCs were investigated through characterisation and modelling of compaction and manufacturing defects. Numerical predictions of the elastic properties of SSCs were evaluated against data from the literature; and the presence of defects was shown to significantly degrade performance. Motivated by the large series resistance of SSCs, direct conduction models were developed to better understand electrical charge transport. Based on investigations of various current collector geometries, design strategies for the mitigation of resistive losses were proposed. To enable analysis of the combined mechanical-electrochemical behaviour of SSCs, an ion transport user element subroutine was developed but could not be validated. Overall, this work demonstrates that substantial improvements in the mechanical and electrical properties of SSCs are possible through control of the composite microstructure. The models developed in this work provide guidance for the optimisation of manufacturing processes and the design of new SSC architectures, and underpin the future certification and deployment of these emerging materials.Open Acces

Evaluation of process-structure-property relationships of carbon nanotube forests using simulation and deep learning

This work is aimed to explore process-structure-property relationships of carbon nanotube (CNT) forests. CNTs have superior mechanical, electrical and thermal properties that make them suitable for many applications. Yet, due to lack of manufacturing control, there is a huge performance gap between promising properties of individual CNTs and CNT forest properties that hinders their adoption into potential industrial applications. In this research, computational modelling, in-situ electron microscopy for CNT synthesis, and data-driven and high-throughput deep convolutional neural networks are employed to not only accelerate implementing CNTs in various applications but also to establish a framework to make validated predictive models that can be easily extended to achieve application-tailored synthesis of any materials. A time-resolved and physics-based finite-element simulation tool is modelled in MATLAB to investigate synthesis of CNT forests, specially to study the CNT-CNT interactions and generated mechanical forces and their role in ensemble structure and properties. A companion numerical model with similar construct is then employed to examine forest mechanical properties in compression. In addition, in-situ experiments are carried out inside Environmental Scanning Electron Microscope (ESEM) to nucleate and synthesize CNTs. Findings may primarily be used to expand the forest growth and self-assembly knowledge and to validate the assumptions of simulation package. Also, SEM images can be used as feed database to construct a deep learning model to grow CNTs by design. The chemical vapor deposition parameter space of CNT synthesis is so vast that it is not possible to investigate all conceivable combinations in terms of time and costs. Hence, simulated CNT forest morphology images are used to train machine learning and learning algorithms that are able to predict CNT synthesis conditions based on desired properties. Exceptionally high prediction accuracies of R2 > 0.94 is achieved for buckling load and stiffness, as well as accuracies of > 0.91 for the classification task. This high classification accuracy promotes discovering the CNT forest synthesis-structure relationships so that their promising performance can be adopted in real world applications. We foresee this work as a meaningful step towards creating an unsupervised simulation using machine learning techniques that can seek out the desired CNT forest synthesis parameters to achieve desired property sets for diverse applications.Includes bibliographical reference