Error Estimation of Euler Method for the Instationary Stokes–Biot Coupled Problem

In this paper, we study a finite element computational model for solving the interaction between a fluid and a poroelastic structure that couples the Stokes equations with the Biot system. Equilibrium and kinematic conditions are imposed on the interface. A mixed Darcy formulation is employed, resulting in continuity of flux condition of essential type. A Lagrange multiplier method is used to impose weakly this condition. With the obtained finite element solutions, the error estimators are performed for the fully discrete formulations.

Physics informed deep learning for coupled flow and poromechanics. Mandel’s problem

We evaluate the performance of the physics informed deep learning paradigm for solving the Biot system modeling coupled flow and poromechanics using Mandel’s problem analytical solution. The solution presents a unique set of challenges to the deep learning paradigm, such as the disparity in expected orders of magnitude of the output variables as well as the non-monotonicity and steep gradient in one of the output variables in a certain spatio-temporal domain. We tackle those challenges in this work and comment on the effect of activation function and minimization algorithm on the deep learning framework.

K-Means Clustering-Based Electrical Equipment Identification for Smart Building Application

With the development and popular application of Building Internet of Things (BIoT) systems, numerous types of equipment are connected, and a large volume of equipment data is collected. For convenient equipment management, the equipment should be identified and labeled. Traditionally, this process is performed manually, which not only is time consuming but also causes unavoidable omissions. In this paper, we propose a k-means clustering-based electrical equipment identification toward smart building application that can automatically identify the unknown equipment connected to BIoT systems. First, load characteristics are analyzed and electrical features for equipment identification are extracted from the collected data. Second, k-means clustering is used twice to construct the identification model. Preliminary clustering adopts traditional k-means algorithm to the total harmonic current distortion data and separates equipment data into two to three clusters on the basis of their electrical characteristics. Later clustering uses an improved k-means algorithm, which weighs Euclidean distance and uses the elbow method to determine the number of clusters and analyze the results of preliminary clustering. Then, the equipment identification model is constructed by selecting the cluster centroid vector and distance threshold. Finally, identification results are obtained online on the basis of the model outputs by using the newly collected data. Successful applications to BIoT system verify the validity of the proposed identification method.