Effect of Prandtl number on heat transport enhancement in Rayleigh-B\'enard convection under geometrical confinement

We study, using direct numerical simulations, the effect of geometrical confinement on heat transport and flow structure in Rayleigh-B\'enard convection in fluids with different Prandtl numbers. Our simulations span over two decades of Prandtl number $Pr$, $0.1 \leq Pr \leq 40$, with the Rayleigh number $Ra$ fixed at $10^8$. The width-to-height aspect ratio $\Gamma$ spans between $0.025$ and $0.25$ while the length-to-height aspect ratio is fixed at one. We first find that for $Pr \geq 0.5$, geometrical confinement can lead to a significant enhancement in heat transport as characterized by the Nusselt number $Nu$. For those cases, $Nu$ is maximal at a certain $\Gamma = \Gamma_{opt}$. It is found that $\Gamma_{opt}$ exhibits a power-law relation with $Pr$ as $\Gamma_{opt}=0.11Pr^{-0.06}$, and the maximal relative enhancement generally increases with $Pr$ over the explored parameter range. As opposed to the situation of $Pr \geq 0.5$, confinement-induced enhancement in $Nu$ is not realized for smaller values of $Pr$, such as $0.1$ and $0.2$. The $Pr$ dependence of the heat transport enhancement can be understood in its relation to the coverage area of the thermal plumes over the thermal boundary layer (BL) where larger coverage is observed for larger $Pr$ due to a smaller thermal diffusivity. We further show that $\Gamma_{opt}$ is closely related to the crossing of thermal and momentum BLs, and find that $Nu$ declines sharply when the thickness ratio of the thermal and momentum BLs exceeds a certain value of about one. In addition, through examining the temporally averaged flow fields and 2D mode decomposition, it is found that for smaller $Pr$ the large-scale circulation is robust against the geometrical confinement of the convection cell.Comment: 25 pages, 11 figures, and 1 table in main tex

Modeling, Optimization, and Control of Down-Hole Drilling System

This dissertation investigates dynamics modeling, optimization, and control methodologies of the down-hole drilling system, which can enable a more accurate and reliable automated tracking of drilling trajectory, mitigating drilling vibration, improving the drilling rate, etc. Unlike many existing works, which only consider drilling control in the torsional dimension, the proposed research aims to address the drilling dynamics modeling and control considering both coupled axial and torsional drill string dynamics. The dissertation will first address optimization and control for vertical drilling, and then resolve critical modeling and control challenges for the directional drilling process. In Chapter 2, a customized Dynamic Programming (DP) method is proposed to enable a computationally efficient optimization for the vertical down-hole drilling process. The method is enabled by a new customized DP searching scheme based on a partial inversion of the dynamics model. Through extensive simulation, the method is proved to be effective in searching for an optimal drilling control solution. This method can generate an open-loop optimal control solution, which can be used as a guide for drilling control or in a driller-assist system. In Chapter 3, to enable a closed-loop control solution for the vertical drilling, a neutral- delay differential equations (NDDEs) model based control approach is proposed, specifically to address an axial-torsional coupled vertical drilling dynamics capturing more transient dynamics behaviors through the NDDE. An equivalent input disturbance (EID) approach is used to control the NDDEs model by constructing the Lyapunov-Krasovskii functional (LKF) and formulating them into a linear matrix inequality (LMI). The control gains can be obtained to effectively mitigate the undesired vibrations and maintain accurate trajectory tracking performance under different control references. The works on Chapter 2 and Chapter 3 are mostly for vertical drilling, and the remaining of the dissertation will focus on modeling and control for directional drilling. Chapter 4 proposes a dual heuristic programming (DHP) approach for automated directional drilling control. By approximating the derivative of the cost-to-go function using a neural network (NN), the DHP approach solves the “curse of dimensionality” associated with the traditional DP. The result shows that the proposed controller is robust, computationally efficient, and effective for the directional drilling system. To validate the DHP based control method using a high-fidelity directional drilling model, a hybrid drilling dynamics model is proposed in Chapter 5. The philosophy of the proposed modeling approach is to use the finite element method (FEM) to describe curved sections in the drill string and use the transfer matrix method (TMM) to model straight sections in the drill string. By integrating different methods, we can achieve both modeling accuracy and computational efficiency for a geometrically complex structure. Compared to existing directional drilling models used for off-line analysis, this model can be used for real-time testbeds such as software-in-the-loop (SIL) system and hardware-in-the-loop (HIL) system. Finally, a software-in-the-loop real-time simulation testbed is built to test the designed DHP based controller in Chapter 6. A higher-order hybrid model of directional drilling is implemented in the SIL. The SIL results demonstrate that the designed DHP based controller can effectively mitigate harmful vibrations and accurately track the desired references

Terahertz Characterisation of Living Plant Leaves for Quality of Life Assessment Applications

This paper presents preliminary results on employing Terahertz (THz) technology for measuring the water contents of leaves. The main purpose of this work is to highlight transmission constraints of terahertz radiation through the plants in the THz frequency region. Multiple leaves of plants are examined using the THz Swissto12 system, and the effect of thickness and water contents on transmission loss and attenuation are observed at different frequency regions, which can lead to meaningful information to study and analyse the existence of any pesticides in leaves with terahertz frequencies. The results of this paper pave the way for applicability of terahertz frequencies for sensing the quality of life in plants

DoubleH: Twitter User Stance Detection via Bipartite Graph Neural Networks

Given the development and abundance of social media, studying the stance of social media users is a challenging and pressing issue. Social media users express their stance by posting tweets and retweeting. Therefore, the homogeneous relationship between users and the heterogeneous relationship between users and tweets are relevant for the stance detection task. Recently, graph neural networks (GNNs) have developed rapidly and have been applied to social media research. In this paper, we crawl a large-scale dataset of the 2020 US presidential election and automatically label all users by manually tagged hashtags. Subsequently, we propose a bipartite graph neural network model, DoubleH, which aims to better utilize homogeneous and heterogeneous information in user stance detection tasks. Specifically, we first construct a bipartite graph based on posting and retweeting relations for two kinds of nodes, including users and tweets. We then iteratively update the node's representation by extracting and separately processing heterogeneous and homogeneous information in the node's neighbors. Finally, the representations of user nodes are used for user stance classification. Experimental results show that DoubleH outperforms the state-of-the-art methods on popular benchmarks. Further analysis illustrates the model's utilization of information and demonstrates stability and efficiency at different numbers of layers

HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. Under such observation, we tend to investigate the feasibility of a memory-friendly model with strong generalization capability that could boost the performance of HTC without prior statistics or label semantics. In this paper, we propose Hierarchy-aware Tree Isomorphism Network (HiTIN) to enhance the text representations with only syntactic information of the label hierarchy. Specifically, we convert the label hierarchy into an unweighted tree structure, termed coding tree, with the guidance of structural entropy. Then we design a structure encoder to incorporate hierarchy-aware information in the coding tree into text representations. Besides the text encoder, HiTIN only contains a few multi-layer perceptions and linear transformations, which greatly saves memory. We conduct experiments on three commonly used datasets and the results demonstrate that HiTIN could achieve better test performance and less memory consumption than state-of-the-art (SOTA) methods.Comment: Accepted by ACL'2