Testing and modelling of butt-welded connections in thin-walled aluminium structures

The present paper experimentally investigated the mechanical behaviour of butt-welded joints and evaluated suitable numerical approaches for modelling them in thin-walled structures with large shell-based models. Welded connections of both similar and dissimilar materials were first experimentally investigated. Two extruded plates in 6060 and 7003 in temper T6 were used as parent materials for Metal Inert Gas (MIG) welding. Three welded joints were made by combining the two parent materials. Extensive testing was carried out to investigate microstructure, hardness and mechanical stress–strain behaviour of the base materials, heat-affected zones (HAZ) and weld metals. Cross-weld tensile tests with two weld orientations (with respect to the loading direction) were performed to study the load–displacement and fracture behaviour of the welded joints. The experimental results were also used to provide inputs to calibrate and validate shell element-based models simulating the response of welded aluminium structures. Two modelling approaches were investigated. The first approach, which is a conventional “mechanical analysis”, used material model inputs from the experimental testing, assuming uniform HAZ strength. The second modelling approach, which is proposed in this study for engineering applications, relies on inverse modelling of the load–displacement behaviour of similar material cross-weld tension tests to optimize the HAZ and weld properties. The newly proposed modelling approach was further verified based on a set of verification tests of cross-weld tension, using shell-based models with different mesh sizes. A good agreement between numerical and experimental results both in terms of load–displacement and fracture behaviour was obtained, suggesting that the novel modelling approach could be a reliable and efficient method for designing butt-welded aluminium structures.publishedVersio

A Combined Strain Element in Static, Frequency and Buckling Analyses of Laminated Composite Plates and Shells

This paper deals with numerical analyses of laminated composite plate and shell structures using a new four-node quadrilateral flat shell element, namely SQ4C, based on the first-order shear deformation theory (FSDT) and a combined strain strategy. The main notion of the combined strain strategy is based on the combination of the membrane strain and shear strain related to tying points as well as bending strain with respect to cell-based smoothed finite element method. Many desirable characteristics and the enforcement of the SQ4C element are verified and proved through various numerical examples in static, frequency and buckling analyses of laminated composite plate and shell structures. Numerical results and comparison with other reference solutions suggest that the present element is accuracy, efficiency and removal of shear and membrane locking

Fabrication of TiO2 nanofibre photoelectrode for photoelectrochemical cells

The TiO2 nanofibres (NFs), prepared with the electrospinning method, acted as the photoanode in a photoelectrochemical cell (PEC) for hydrogen generation. The fabrication parameters of Ti/PVP (polyvinylpyrrolidone) fibres were determined with the field-emission scanning electron microscopy (FE-SEM) method. The structure and morphology of the TiO2 fibres were characterized by using X-ray diffraction (XRD), FE-SEM, transmission electron microscopy (TEM), and high-resolution transmission electron microscopy (HR-TEM). The average diameter of the TiO2 fibre is 132 ± 16 nm. A three-electrode potentiostat was used to study the photoelectrochemical properties of the photoanode. The density photocurrent reached the saturation value of 80 mA·cm–2 at 0.2 V under the irradiation of a Xenon lamp

Toward BCI-enabled Metaverse: A Joint Learning and Resource Allocation Approach

Toward user-driven Metaverse applications with fast wireless connectivity and tremendous computing demand through future 6G infrastructures, we propose a Brain-Computer Interface (BCI) enabled framework that paves the way for the creation of intelligent human-like avatars. Our approach takes a first step toward the Metaverse systems in which the digital avatars are envisioned to be more intelligent by collecting and analyzing brain signals through cellular networks. In our proposed system, Metaverse users experience Metaverse applications while sending their brain signals via uplink wireless channels in order to create intelligent human-like avatars at the base station. As such, the digital avatars can not only give useful recommendations for the users but also enable the system to create user-driven applications. Our proposed framework involves a mixed decision-making and classification problem in which the base station has to allocate its computing and radio resources to the users and classify the brain signals of users in an efficient manner. To this end, we propose a hybrid training algorithm that utilizes recent advances in deep reinforcement learning to address the problem. Specifically, our hybrid training algorithm contains three deep neural networks cooperating with each other to enable better realization of the mixed decision-making and classification problem. Simulation results show that our proposed framework can jointly address resource allocation for the system and classify brain signals of the users with highly accurate predictions

When Virtual Reality Meets Rate Splitting Multiple Access: A Joint Communication and Computation Approach

Rate Splitting Multiple Access (RSMA) has emerged as an effective interference management scheme for applications that require high data rates. Although RSMA has shown advantages in rate enhancement and spectral efficiency, it has yet not to be ready for latency-sensitive applications such as virtual reality streaming, which is an essential building block of future 6G networks. Unlike conventional High-Definition streaming applications, streaming virtual reality applications requires not only stringent latency requirements but also the computation capability of the transmitter to quickly respond to dynamic users' demands. Thus, conventional RSMA approaches usually fail to address the challenges caused by computational demands at the transmitter, let alone the dynamic nature of the virtual reality streaming applications. To overcome the aforementioned challenges, we first formulate the virtual reality streaming problem assisted by RSMA as a joint communication and computation optimization problem. A novel multicast approach is then proposed to cluster users into different groups based on a Field-of-View metric and transmit multicast streams in a hierarchical manner. After that, we propose a deep reinforcement learning approach to obtain the solution for the optimization problem. Extensive simulations show that our framework can achieve the millisecond-latency requirement, which is much lower than other baseline schemes

Optimizing Boiler Efficiency by Data Mining Teciques: A Case Study

In a fertilizer plant, the steam boiler is the most important component. In order to keep the plant operating in the effective mode, the boiler efficiency must be observed continuously by several operators. When the trend of the boiler efficiency is going down, they may adjust the controlling parameters of the boiler to increase its efficiency. Since manual operation usually leads to unex-pectedly mistakes and hurts the efficiency of the system, we build an information system that plays the role of the operators in observing the boiler and adjusting the controlling parameters to stabilize the boiler efficiency. In this paper, we first introduce the architecture of the information system. We then present how to apply K-means and Fuzzy C-means algorithms to derive a knowledge base from the historical operational data of the boiler. Next, recurrent fuzzy neural network is employed to build a boiler simulator for evaluating which tuple of input values is the best optimal and then automatically adjusting controlling inputs of the boiler by the optimal val-ues. In order to prove the effectiveness of our system, we deployed it at Phu My Fertilizer Plant equipped with MARCHI boiler having capacity of 76-84 ton/h. We found that our system have improved the boiler efficiency about 0.28-1.12% in average and brought benefit about 57.000 USD/year to the Phu My Fertilizer Plant

Unsupervised Detection of Anomalous Sound for Machine Condition Monitoring using Fully Connected U-Net

Anomaly detection in the sound from machines is an important task in machine monitoring. An autoencoder architecture based on the reconstruction error using a log-Mel spectrogram feature is a conventional approach for this domain. However, because of the non-stationary nature of some sounds from the target machine, such a conventional approach does not perform well in those circumstances. In this paper, we propose a novel approach regarding the choice of used features and a new auto-encoder architecture. We created the Mixed Feature, which is a mixture of different sound representations, and a new deep learning method called Fully-Connected U-Net, a form of autoencoder architecture. With experiments on the same dataset as the baseline system, using the same architecture for all types of machines, the experimental results showed that our methods outperformed the baseline system in terms of the AUC and pAUC evaluation metrics. The optimized model achieved 83.38% AUC and 64.51% pAUC on average overall machine types on the developed dataset and outperformed the published baseline by 13.43% AUC and 8.13% pAUC

Reconstructing Human Pose from Inertial Measurements: A Generative Model-based Compressive Sensing Approach

The ability to sense, localize, and estimate the 3D position and orientation of the human body is critical in virtual reality (VR) and extended reality (XR) applications. This becomes more important and challenging with the deployment of VR/XR applications over the next generation of wireless systems such as 5G and beyond. In this paper, we propose a novel framework that can reconstruct the 3D human body pose of the user given sparse measurements from Inertial Measurement Unit (IMU) sensors over a noisy wireless environment. Specifically, our framework enables reliable transmission of compressed IMU signals through noisy wireless channels and effective recovery of such signals at the receiver, e.g., an edge server. This task is very challenging due to the constraints of transmit power, recovery accuracy, and recovery latency. To address these challenges, we first develop a deep generative model at the receiver to recover the data from linear measurements of IMU signals. The linear measurements of the IMU signals are obtained by a linear projection with a measurement matrix based on the compressive sensing theory. The key to the success of our framework lies in the novel design of the measurement matrix at the transmitter, which can not only satisfy power constraints for the IMU devices but also obtain a highly accurate recovery for the IMU signals at the receiver. This can be achieved by extending the set-restricted eigenvalue condition of the measurement matrix and combining it with an upper bound for the power transmission constraint. Our framework can achieve robust performance for recovering 3D human poses from noisy compressed IMU signals. Additionally, our pre-trained deep generative model achieves signal reconstruction accuracy comparable to an optimization-based approach, i.e., Lasso, but is an order of magnitude faster