Models and Simulations of Functional Materials in Sustainable Energy Applications

Achieving a sustainable energy future depends on the development of advanced functional materials and scalable manufacturing processes for efficient energy conversion and storage. This thesis presents computational and theoretical modeling approaches to overcome limitations in the processing and optimization of such materials in energy applications. Scalable fabrication of organic photovoltaic (OPV) modules remains a challenge due to the lack of coating techniques capable of producing uniform, large-area films, an essential requirement for transitioning from lab-scale devices to full-sized modules. To address this, blade coating is investigated as a meniscus-guided, solution-processable method with high scalability. Several models are developed to predict wet film thickness, including a two-step Computational Fluid Dynamics (CFD) approach, alongside semi-empirical and theoretical models. While the CFD model provides the highest predictive accuracy, the alternative models enable rapid estimation across a wide range of process conditions. Based on these results, an accelerated coating strategy is proposed in which the coating speed is dynamically adjusted to minimize thickness variations. This approach offers practicable guidelines for large-scale OPV manufacturing. Further, this thesis addresses the understanding of nanocomposite materials for energy conversion systems such as proton exchange membrane fuel cells (PEMFCs). These materials, composed of randomly oriented, irregular nanotubes, require advanced modeling to assess their functional behavior. A multiscale framework is introduced, using a random-walk algorithm to generate realistic three-dimensional nanotube networks within representative volume elements (RVEs). To reduce computational complexity, a unit cell approach is employed. Finite Element Method (FEM) simulations are used to analyze percolation and conductivity, linking microscopic structural parameters to macroscopic material behavior. Together, these methods provide predictive tools and scalable design strategies for next-generation materials in sustainable energy systems and are applicable beyond the specific technologies examined.