Aerodynamics of Low Pressure Steam Turbine Exhaust Systems

The low pressure (LP) exhaust system presents a promising avenue for improving the performance of large steam turbines. For this reason, LP exhaust systems have attracted the attention of the research community for decades. Nevertheless, we still lack understanding of the flow physics and loss mechanisms in the exhaust system, especially at part-load conditions. It is also unclear how the exhaust system should be designed when its required operating range widens. This thesis provides solutions to these aerodynamic issues through experimental and numerical investigations, and provides tools that could contribute to better designs of LP exhaust systems. Firstly, the Computational Fluid Dynamics (CFD) solver ANSYS CFX was validated against experiments performed on a scaled test rig under representative part-load flow conditions. This validation exposed the weakness of Reynolds-averaged Navier–Stokes (RANS) CFD when there is a highly swirling flow and large separation regions in the exhaust diffuser. To facilitate the numerical studies, a series of tools were also developed. A design suite, ExhaustGen, was used to automate the pre- and post-processing of CFD calculations. The exhaust diffuser was parametrised using "Minimum Energy Curves", which reduce the dimension of parameter space. Further, a suitable stage-hood interface treatment (Multiple Mixing Planes) was chosen to predict the circumferentially non-uniform flow in the exhaust hood at low computational cost. Numerical investigation of the baseline geometry provided insights into the key flow features and loss mechanisms in the exhaust system, over a wide range of operating conditions. In particular, the bearing cone separation was identified as a key source of loss at part-load conditions. The effect of stage-hood interaction on the performance and design of the exhaust system was studied by varying the rotor blade design, which can positively influence system performance. Finally, a global sensitivity study was performed to identify the most influential design parameters of the exhaust hood. These findings allow, for the first time, LP exhaust hood performance maps to be constructed, so that the benefits of choosing a suitable hood geometry and blade design can be revealed. The thesis also offers contribution towards formulating LP exhaust system design guidance for a wide operating range

How would Stance Detection Techniques Evolve after the Launch of ChatGPT?

Stance detection refers to the task of extracting the standpoint (Favor, Against or Neither) towards a target in given texts. Such research gains increasing attention with the proliferation of social media contents. The conventional framework of handling stance detection is converting it into text classification tasks. Deep learning models have already replaced rule-based models and traditional machine learning models in solving such problems. Current deep neural networks are facing two main challenges which are insufficient labeled data and information in social media posts and the unexplainable nature of deep learning models. A new pre-trained language model chatGPT was launched on Nov 30, 2022. For the stance detection tasks, our experiments show that ChatGPT can achieve SOTA or similar performance for commonly used datasets including SemEval-2016 and P-Stance. At the same time, ChatGPT can provide explanation for its own prediction, which is beyond the capability of any existing model. The explanations for the cases it cannot provide classification results are especially useful. ChatGPT has the potential to be the best AI model for stance detection tasks in NLP, or at least change the research paradigm of this field. ChatGPT also opens up the possibility of building explanatory AI for stance detection

Multi-modal Facial Affective Analysis based on Masked Autoencoder

Human affective behavior analysis focuses on analyzing human expressions or other behaviors to enhance the understanding of human psychology. The CVPR 2023 Competition on Affective Behavior Analysis in-the-wild (ABAW) is dedicated to providing high-quality and large-scale Aff-wild2 for the recognition of commonly used emotion representations, such as Action Units (AU), basic expression categories(EXPR), and Valence-Arousal (VA). The competition is committed to making significant strides in improving the accuracy and practicality of affective analysis research in real-world scenarios. In this paper, we introduce our submission to the CVPR 2023: ABAW5. Our approach involves several key components. First, we utilize the visual information from a Masked Autoencoder(MAE) model that has been pre-trained on a large-scale face image dataset in a self-supervised manner. Next, we finetune the MAE encoder on the image frames from the Aff-wild2 for AU, EXPR and VA tasks, which can be regarded as a static and uni-modal training. Additionally, we leverage the multi-modal and temporal information from the videos and implement a transformer-based framework to fuse the multi-modal features. Our approach achieves impressive results in the ABAW5 competition, with an average F1 score of 55.49\% and 41.21\% in the AU and EXPR tracks, respectively, and an average CCC of 0.6372 in the VA track. Our approach ranks first in the EXPR and AU tracks, and second in the VA track. Extensive quantitative experiments and ablation studies demonstrate the effectiveness of our proposed method

Multiscale characterization of thermoacoustic response and fatigue failure of aerospace structures

Sustained hypersonic flight has presented a complex problem to researchers and structural designers in recent decades as it has been seen to induce failure of thin aerospace panels in modes that had not been previously accounted for. These new and unaccounted for failure modes are attributed to the extreme and coupled loading conditions of the thermoacoustic environment prevalent in hypersonic flight. Prior research has highlighted the resonant behavior of simple structures in a combined loading environment of vibration and heating. The effects of various heating distributions on pre-thermally and post-thermally buckled plates have been evaluated in theoretical and experimental work. However, this understanding has not yet found its way into advanced thermomechanical coupled simulations, in part because fatigue failure caused by in-plane thermal gradients from localized heating, vibration, and mechanical boundary conditions has not been sufficiently addressed in the laboratory setting to validate such complex simulations. The present work seeks to add to our current understanding of this topic with a series of experiments which investigate structural response and failure at multiple length scales. Non-contact optical methods for displacement and strain measurement were used to study the resonance, broadband excitation response, and thermal loading response of structures with varying boundary conditions. Thin aerospace-type beams and plates made of a nickel super-alloy, Hastelloy X, Al 1100-O, and Al 1100-H14 were subjected to forced vibration initially at room temperature and subsequently with localized heating to examine the effects of thermal stress gradients on structural response. Coarse-grained specimens were then produced by annealing aluminum Al 1100-O (commercially pure Al) to explore the role of microstructural phenomena in the thermoacoustic environment and their influences on global behavior. Using oligocrystal samples in this fashion made the grain scale effects occur at the same scale as the sample size and thus both effects could be investigated simultaneously. The microstructural heterogeneity of coarse-grained beams was shown to have significant effect on plastic hinging behavior at the beam root. Finally, fatigue experiments were performed in a combined loading environment to assess behavior beyond the linear elastic regime and promote plasticity and failure. Although fatigue failure was suppressed in thin beams and panels, adding a stress concentrator, such as a notch near the beam root, promoted fatigue crack nucleation

TrTr: A Versatile Pre-Trained Large Traffic Model based on Transformer for Capturing Trajectory Diversity in Vehicle Population

Understanding trajectory diversity is a fundamental aspect of addressing practical traffic tasks. However, capturing the diversity of trajectories presents challenges, particularly with traditional machine learning and recurrent neural networks due to the requirement of large-scale parameters. The emerging Transformer technology, renowned for its parallel computation capabilities enabling the utilization of models with hundreds of millions of parameters, offers a promising solution. In this study, we apply the Transformer architecture to traffic tasks, aiming to learn the diversity of trajectories within vehicle populations. We analyze the Transformer's attention mechanism and its adaptability to the goals of traffic tasks, and subsequently, design specific pre-training tasks. To achieve this, we create a data structure tailored to the attention mechanism and introduce a set of noises that correspond to spatio-temporal demands, which are incorporated into the structured data during the pre-training process. The designed pre-training model demonstrates excellent performance in capturing the spatial distribution of the vehicle population, with no instances of vehicle overlap and an RMSE of 0.6059 when compared to the ground truth values. In the context of time series prediction, approximately 95% of the predicted trajectories' speeds closely align with the true speeds, within a deviation of 7.5144m/s. Furthermore, in the stability test, the model exhibits robustness by continuously predicting a time series ten times longer than the input sequence, delivering smooth trajectories and showcasing diverse driving behaviors. The pre-trained model also provides a good basis for downstream fine-tuning tasks. The number of parameters of our model is over 50 million.Comment: 16 pages, 6 figures, under reviewed by Transportation Research Board Annual Meeting, work in updat

Correlation Filters for Unmanned Aerial Vehicle-Based Aerial Tracking: A Review and Experimental Evaluation

Aerial tracking, which has exhibited its omnipresent dedication and splendid performance, is one of the most active applications in the remote sensing field. Especially, unmanned aerial vehicle (UAV)-based remote sensing system, equipped with a visual tracking approach, has been widely used in aviation, navigation, agriculture,transportation, and public security, etc. As is mentioned above, the UAV-based aerial tracking platform has been gradually developed from research to practical application stage, reaching one of the main aerial remote sensing technologies in the future. However, due to the real-world onerous situations, e.g., harsh external challenges, the vibration of the UAV mechanical structure (especially under strong wind conditions), the maneuvering flight in complex environment, and the limited computation resources onboard, accuracy, robustness, and high efficiency are all crucial for the onboard tracking methods. Recently, the discriminative correlation filter (DCF)-based trackers have stood out for their high computational efficiency and appealing robustness on a single CPU, and have flourished in the UAV visual tracking community. In this work, the basic framework of the DCF-based trackers is firstly generalized, based on which, 23 state-of-the-art DCF-based trackers are orderly summarized according to their innovations for solving various issues. Besides, exhaustive and quantitative experiments have been extended on various prevailing UAV tracking benchmarks, i.e., UAV123, UAV123@10fps, UAV20L, UAVDT, DTB70, and VisDrone2019-SOT, which contain 371,903 frames in total. The experiments show the performance, verify the feasibility, and demonstrate the current challenges of DCF-based trackers onboard UAV tracking.Comment: 28 pages, 10 figures, submitted to GRS