The Potential of Switchable Glazing in Cooling Dominated Climates

The design trend of most commercial and office buildings over the past three decades focused on attaining a façade design with the highest possible window to wall ratio. Whereas this approach appears to satisfy the aesthetic scope of developing buildings that look ‘modern and transparent’ to maximise on real estate value, the demand for...

Predicting turbulent dynamics with the convolutional autoencoder echo state network

The dynamics of turbulent flows is chaotic and difficult to predict. This makes the design of accurate reduced-order models challenging. The overarching objective of this paper is to propose a nonlinear decomposition of the turbulent state to predict the flow based on a reduced-order representation of the dynamics. We divide the turbulent...

Convolutional Autoencoder for the Spatiotemporal Latent Representation of Turbulence

Turbulence is characterised by chaotic dynamics and a high-dimensional state space, which make this phenomenon challenging to predict. However, turbulent flows are often characterised by coherent spatiotemporal structures, such as vortices or large-scale modes, which can help obtain a latent description of turbulent flows. However, current...

Short- And long-term predictions of chaotic flows and extreme events: A physics-constrained reservoir computing approach

We propose a physics-constrained machine learning method - based on reservoir computing - to time-accurately predict extreme events and long-term velocity statistics in a model of chaotic flow. The method leverages the strengths of two different approaches: empirical modelling based on reservoir computing, which learns the chaotic dynamics...

Auto-Encoded Reservoir Computing for Turbulence Learning

We present an Auto-Encoded Reservoir-Computing (AE-RC) approach to learn the dynamics of a 2D turbulent flow. The AE-RC consists of an Autoencoder, which discovers an efficient manifold representation of the flow state, and an Echo State Network, which learns the time evolution of the flow in the manifold. The AE-RC is able to both learn the...

A coupled model of transport-reaction-mechanics with trapping. Part I – Small strain analysis

A fully coupled model for mass and heat transport, mechanics, and chemical reactions with trapping is proposed. It is rooted in non-equilibrium rational thermodynamics and assumes that displacements and strains are small. Balance laws for mass, linear and angular momentum, energy, and entropy are stated. Thermodynamic restrictions are...

Computational modeling of Li-ion batteries

This review focuses on energy storage materials modeling, with particular emphasis on Li-ion batteries. Theoretical and computational analyses not only provide a better understanding of the intimate behavior of actual batteries under operational and extreme conditions, but they may tailor new materials and shape new architectures in a...