Transient simulation of triboelectric nanogenerators considering surface roughness

主要目标是扩展现有的数值框架并处理与时间相关的问题的复杂性。这一功能将允许模拟实际的TENG并以前所未有的精度预测输出电流。为了使数值模型更接近实际设备的物理，我们将包括粘弹性材料行为和接触层的粘性相互作用。此外，为了计算短路电流，我们将控制接触力学和静电学的耦合PDE的有限元模型与电路常量方程的时间求解器相结合。这些发展将得到MoFE[5]提供的尖端科学计算工具的支持，从而实现展示模型预测能力所需的大规模并行模拟。我们将利用格拉斯哥大学的本地HPC设施、Archie-West超级计算机中心和云资源进行这些研究。此外，该项目还将与正在对邓丽君进行实验研究的材料与制造研究组的同事合作。因此，第二个目标是为Teng的数值模拟提供一个高效、可访问和可靠的工具，帮助我们的合作者加快新设备的设计、优化和原型制作。我们将使用JupyterHub将整个模拟管道(模型设置、后处理、可视化)移动到Web浏览器中，而计算将在MoFE中使用本地或云HPC功能执行。

the primary objective is to extend the existing numerical framework and handle complexities related to the time-dependent problem. This functionality will permit simulation of an actual TENG and predict output current with unprecedented accuracy.To bring the numerical model closer to the physics of an actual device, we will include viscoelastic material behaviour and adhesive interaction of contacting layers. Furthermore, to compute the shortcircuit current, we will combine the finite element model for coupled PDE governing contact mechanics and electrostatics with the time-solver for electric circuit ODE. These developments will be underpinned by cutting-edge scientific computing tools available in MoFEM [5], enabling massively parallel simulations required to demonstrate model's predictive capabilities. We will perform these studies using local HPC facilities at the University of Glasgow, ARCHIE-WeSt supercomputer centre and cloud resources.Moreover, the project will include collaboration with colleagues at the Materials & Manufacturing Research Group who are doing experimental research on TENG. Therefore, the second objective is to provide an efficient, accessible and reliable tool for numerical simulation of TENG, assisting our collaborators in accelerating the design, optimisation and prototyping of new devices. We will use JupyterHub to move the whole simulation pipeline (setting-up of the model, post-processing, visualisation) into a web browser, while computations will be performed in MoFEM using local or cloud HPC capabilities.