Determining the Effects of Hailstone Impact on Flat Cold-Reduced Steel Roof Sheeting

Hail damage is responsible for significant economic losses in Australia, and the damage will likely be greater in the future due to increased incidence of severe hailstorms. One of the major costs comes from damaged roofs. Compared to the conventional asphalt shingle roofing and concrete tiled roofing, steel roofing is becoming popular due to its long service life, low maintenance cost, and better resistance to natural disasters. However, there is a lack of knowledge regarding the dent resistance of steel sheet to natural hailstone impact as a function of its yield stress and thickness. In the literature, either steel projectiles or ice balls are used as the artificial hailstones in the hail impact tests. However, there has been no study to correlate the indentation caused by a steel ball to that caused by a natural hailstone. On the other hand, the ice balls used by previous researchers shattered on impacting steel sheeting at velocities close to the terminal velocities of the natural hailstones, while some natural hailstones remain intact after impacting steel roof sheeting at their terminal velocities. When a hailstone remains intact on impact, more energy is available to damage the steel sheet. In this thesis, a new method to make water based artificial hailstones that remain intact after impacting flat steel roof sheeting at terminal velocities has been successfully developed with a combination of 88% water and 12% PVA (Polyvinyl alcohol). The indentation results of the present artificial hailstones have been validated against those of pure clear ice balls that happened to remain intact after impact at similar velocities. Five sheet thicknesses (0.35 mm, 0.42 mm, 0.55 mm, 0.75 mm and 1.00 mm) and two sheet steel grades (G300 and G550) are tested under the impact of five sizes of artificial hailstones (25 mm, 33 mm, 38 mm, 45 mm and 50.8 mm) at three designated impact velocities (20 m/s, 30 m/s, 40 m/s). Each sheeting is screwed to timber battens spaced at 600 mm from each other, and the projectile is aimed perpendicularly at the middle between the two battens. The dent depths caused by the PVA ice balls that remained intact after impact are significantly greater than those caused by the PVA ice balls that disintegrated upon impact. For the case involving intact artificial hailstones, the dent depth varies linearly with the square root of the impact energy, and is inversely proportional to the square roots of the sheet thickness and the yield stress. The findings (based on experimental observations and theoretical derivations) regarding the effects of the sheet thickness and the yield stress are believed to be original. Additional experimental findings are that the rebound energy of hailstones impacting steel roof sheeting is negligible (less than 1% of the impact energy), and that most energy loss of the impact energy is in the form of flexural vibration of the flat steel sheeting. Provided that denting takes place, the energy lost to flexural vibration is a function of the elastic flexural stiffness of the steel sheeting, and varies linearly with the impact energy. An empirical equation is proposed in this thesis to determine the proportion of impact energy that is lost to flexural vibration of the steel sheeting, based on the sheet thickness and the spacing between the battens. Once the flexural vibration energy and therefore the net impact energy is determined, the dent depth can be estimated from the sheet thickness and the yield stress under the assumption of a (partly) spherical dent

Autonomous pointing control of a large satellite antenna subject to parametric uncertainty

With the development of satellite mobile communications, large antennas are now widely used. The precise pointing of the antenna’s optical axis is essential for many space missions. This paper addresses the challenging problem of high-precision autonomous pointing control of a large satellite antenna. The pointing dynamics are firstly proposed. The proportional–derivative feedback and structural filter to perform pointing maneuvers and suppress antenna vibrations are then presented. An adaptive controller to estimate actual system frequencies in the presence of modal parameters uncertainty is proposed. In order to reduce periodic errors, the modified controllers, which include the proposed adaptive controller and an active disturbance rejection filter, are then developed. The system stability and robustness are analyzed and discussed in the frequency domain. Numerical results are finally provided, and the results have demonstrated that the proposed controllers have good autonomy and robustness

Enhancing the SST Turbulence Model with Symbolic Regression: A Generalizable and Interpretable Data-Driven Approach

Turbulence modeling within the RANS equations' framework is essential in engineering due to its high efficiency. Field inversion and machine learning (FIML) techniques have improved RANS models' predictive capabilities for separated flows. However, FIML-generated models often lack interpretability, limiting physical understanding and manual improvements based on prior knowledge. Additionally, these models may struggle with generalization in flow fields distinct from the training set. This study addresses these issues by employing symbolic regression (SR) to derive an analytical relationship between the correction factor of the baseline turbulence model and local flow variables, enhancing the baseline model's ability to predict separated flow across diverse test cases. The shear-stress-transport (SST) model undergoes field inversion on a curved backward-facing step (CBFS) case to obtain the corrective factor field beta, and SR is used to derive a symbolic map between local flow features and beata. The SR-derived analytical function is integrated into the original SST model, resulting in the SST-SR model. The SST-SR model's generalization capabilities are demonstrated by its successful predictions of separated flow on various test cases, including 2D-bump cases with varying heights, periodic hill case where separation is dominated by geometric features, and the three-dimensional Ahmed-body case. In these tests, the model accurately predicts flow fields, showing its effectiveness in cases completely different from the training set. The Ahmed-body case, in particular, highlights the model's ability to predict the three-dimensional massively separated flows. When applied to a turbulent boundary layer with Re_L=1.0E7, the SST-SR model predicts wall friction coefficient and log layer comparably to the original SST model, maintaining the attached boundary layer prediction performance.Comment: 37 pages, 46 figure

Data-driven Analysis of Remote Work in China during the COVID-19 Pandemic

This paper leverages online content to investigate teleworking forced due to the COVID-19 pandemic -- using China as a primary case study. Telecommuting has become popular since February 2020 primarily due to the pandemic, and people have been slowly returning to their office from May 2020. This study focuses on two time windows in the year 2020 to calculate the growth of different job sectors. Our results indicate the negative impact of teleworking in manufacturing industry, but shows that information technology-related industries are less affected by working from home. This paper also investigates the impact of COVID-19 on the stock market and discussed what plan of action the policy-makers should take to provide a good economic environment. In addition to the overall economic situation, the psychological situation of employees will affect the development of a given industry. Therefore, misinformation in certain Chinese social media channels is also a concern studied in this paper specifically examining the rumors and their latent topics. We hope that our work will initiate a dialogue and collaboration between scientists, policy makers and government officials to use these lessons and engage effectively for the betterment of society

A general construction of regular complete permutation polynomials

Let $r\geq 3$ be a positive integer and $\mathbb{F}_q$ the finite field with $q$ elements. In this paper, we consider the $r$-regular complete permutation property of maps with the form $f=\tau\circ\sigma_M\circ\tau^{-1}$ where $\tau$ is a PP over an extension field $\mathbb{F}_{q^d}$ and $\sigma_M$ is an invertible linear map over $\mathbb{F}_{q^d}$. We give a general construction of $r$-regular PPs for any positive integer $r$. When $\tau$ is additive, we give a general construction of $r$-regular CPPs for any positive integer $r$. When $\tau$ is not additive, we give many examples of regular CPPs over the extension fields for $r=3,4,5,6,7$ and for arbitrary odd positive integer $r$. These examples are the generalization of the first class of $r$-regular CPPs constructed by Xu, Zeng and Zhang (Des. Codes Cryptogr. 90, 545-575 (2022)).Comment: 24 page

Regular complete permutation polynomials over quadratic extension fields

Let $r\geq 3$ be any positive integer which is relatively prime to $p$ and $q^2\equiv 1 \pmod r$. Let $\tau_1, \tau_2$ be any permutation polynomials over $\mathbb{F}_{q^2},$ $\sigma_M$ is an invertible linear map over $\mathbb{F}_{q^2}$ and $\sigma=\tau_1\circ\sigma_M\circ\tau_2$. In this paper, we prove that, for suitable $\tau_1, \tau_2$ and $\sigma_M$, the map $\sigma$ could be $r$-regular complete permutation polynomials over quadratic extension fields.Comment: 10 pages. arXiv admin note: substantial text overlap with arXiv:2212.1286

Regular graphs with a complete bipartite graph as a star complement

Let $G$ be a graph of order $n$ and $\mu$ be an adjacency eigenvalue of $G$ with multiplicity $k\geq 1$. A star complement $H$ for $\mu$ in $G$ is an induced subgraph of $G$ of order $n-k$ with no eigenvalue $\mu$, and the vertex subset $X=V(G-H)$ is called a star set for $\mu$ in $G$. The study of star complements and star sets provides a strong link between graph structure and linear algebra. In this paper, we study the regular graphs with $K_{t,s}\ (s\geq t\geq 2)$as a star complement for an eigenvalue$\mu$, especially, characterize the case of$t=3$completely, obtain some properties when$t=s$, and propose some problems for further study

Quantifying the Knowledge in GNNs for Reliable Distillation into MLPs

To bridge the gaps between topology-aware Graph Neural Networks (GNNs) and inference-efficient Multi-Layer Perceptron (MLPs), GLNN proposes to distill knowledge from a well-trained teacher GNN into a student MLP. Despite their great progress, comparatively little work has been done to explore the reliability of different knowledge points (nodes) in GNNs, especially their roles played during distillation. In this paper, we first quantify the knowledge reliability in GNN by measuring the invariance of their information entropy to noise perturbations, from which we observe that different knowledge points (1) show different distillation speeds (temporally); (2) are differentially distributed in the graph (spatially). To achieve reliable distillation, we propose an effective approach, namely Knowledge-inspired Reliable Distillation (KRD), that models the probability of each node being an informative and reliable knowledge point, based on which we sample a set of additional reliable knowledge points as supervision for training student MLPs. Extensive experiments show that KRD improves over the vanilla MLPs by 12.62% and outperforms its corresponding teacher GNNs by 2.16% averaged over 7 datasets and 3 GNN architectures

Does My Dog ''Speak'' Like Me? The Acoustic Correlation between Pet Dogs and Their Human Owners

How hosts language influence their pets' vocalization is an interesting yet underexplored problem. This paper presents a preliminary investigation into the possible correlation between domestic dog vocal expressions and their human host's language environment. We first present a new dataset of Shiba Inu dog vocals from YouTube, which provides 7500 clean sound clips, including their contextual information of these vocals and their owner's speech clips with a carefully-designed data processing pipeline. The contextual information includes the scene category in which the vocal was recorded, the dog's location and activity. With a classification task and prominent factor analysis, we discover significant acoustic differences in the dog vocals from the two language environments. We further identify some acoustic features from dog vocalizations that are potentially correlated to their host language patterns

Towards Lexical Analysis of Dog Vocalizations via Online Videos

Deciphering the semantics of animal language has been a grand challenge. This study presents a data-driven investigation into the semantics of dog vocalizations via correlating different sound types with consistent semantics. We first present a new dataset of Shiba Inu sounds, along with contextual information such as location and activity, collected from YouTube with a well-constructed pipeline. The framework is also applicable to other animal species. Based on the analysis of conditioned probability between dog vocalizations and corresponding location and activity, we discover supporting evidence for previous heuristic research on the semantic meaning of various dog sounds. For instance, growls can signify interactions. Furthermore, our study yields new insights that existing word types can be subdivided into finer-grained subtypes and minimal semantic unit for Shiba Inu is word-related. For example, whimper can be subdivided into two types, attention-seeking and discomfort