Development of Innovative Fire Suppression Systems and Risk Mitigation Approaches with Multiphase Flow Techniques

Fire suppression and risk mitigation approaches are critical in modern life to ensure a safe living and working environment. Water-based fire suppression systems for fire suppression and waste heat powered steam ejector as a part of battery thermal management systems for fire risk mitigation were investigated. The aims of this thesis are: Developing innovative numerical tools using computational fluid dynamics (CFD) techniques to investigate the complex multiphase flow behaviour appeared and evaluate the feasibility and performance of the systems; gaining more insight into the water-based fire suppression systems by introducing a new statistical evaluation criterion and intuitive visualization of quantitative results. Exploring a potential solution for fire risk mitigation with waste heat recovering by innovative usage of steam. A novel battery thermal management design that uniquely uses recycled combustion waste heat with the steam ejector was proposed and targeted for hybrid electric vehicles (HEVs). An in-house wet-steam model considering the spontaneous condensation effect has been developed to accurately capture the complex multiphase flow behaviours. This thesis identified the key fire suppression mechanisms between conventional fire sprinkler systems and water mist systems, along with different fire suppression behaviours. Latent cooling and volumetric displacement were the major suppression mechanisms for water mist systems, and direct heat extraction dominates fire suppression for conventional sprinkler systems. The concept of water utilization rate is raised for water-based fire suppression systems due to self-developed droplet tracking and analyzing algorithms. This provided a new systematic approach for evaluating the performance of water-based fire suppression systems in any fire suppression scenario. Additionally, quantitative information such as spray pattern, accumulative mass fluxes, penetrability and number counts of water droplets were presented with an intuitive 3D visualization method. A fire risk mitigation approach for HEVs was proposed with a novel battery thermal management system. The battery management system proposed in the current thesis utilizes a steam ejector with engine waste heat as the power source; the complex transonic flow with spontaneous condensation inside the ejector was accurately captured and described by coupling an in-house wet steam model to the Eulerian-Eulerian multiphase CFD framework through user-defined functions. The current research made a successful attempt in both fire suppression and fire risk mitigation by developing and implementing numerical simulation tools

Hybrid Models for Mixed Variables in Bayesian Optimization

This paper presents a new type of hybrid models for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models merge Monte Carlo Tree Search structure (MCTS) for categorical variables with Gaussian Processes (GP) for continuous ones. Addressing efficiency in searching phase, we juxtapose the original (frequentist) upper confidence bound tree search (UCTS) and the Bayesian Dirichlet search strategies, showcasing the tree architecture's integration into Bayesian optimization. Central to our innovation in surrogate modeling phase is online kernel selection for mixed-variable BO. Our innovations, including dynamic kernel selection, unique UCTS (hybridM) and Bayesian update strategies (hybridD), position our hybrid models as an advancement in mixed-variable surrogate models. Numerical experiments underscore the hybrid models' superiority, highlighting their potential in Bayesian optimization.Comment: 32 pages, 8 Figure

PokeMQA: Programmable knowledge editing for Multi-hop Question Answering

Multi-hop question answering (MQA) is one of the challenging tasks to evaluate machine's comprehension and reasoning abilities, where large language models (LLMs) have widely achieved the human-comparable performance. Due to the dynamics of knowledge facts in real world, knowledge editing has been explored to update model with the up-to-date facts while avoiding expensive re-training or fine-tuning. Starting from the edited fact, the updated model needs to provide cascading changes in the chain of MQA. The previous art simply adopts a mix-up prompt to instruct LLMs conducting multiple reasoning tasks sequentially, including question decomposition, answer generation, and conflict checking via comparing with edited facts. However, the coupling of these functionally-diverse reasoning tasks inhibits LLMs' advantages in comprehending and answering questions while disturbing them with the unskilled task of conflict checking. We thus propose a framework, Programmable knowledge editing for Multi-hop Question Answering (PokeMQA), to decouple the jobs. Specifically, we prompt LLMs to decompose knowledge-augmented multi-hop question, while interacting with a detached trainable scope detector to modulate LLMs behavior depending on external conflict signal. The experiments on three LLM backbones and two benchmark datasets validate our superiority in knowledge editing of MQA, outperforming all competitors by a large margin in almost all settings and consistently producing reliable reasoning process.Comment: Our code is available at https://github.com/Hengrui-Gu/PokeMQ

Classification of knee osteoarthritis based on quantum-to-classical transfer learning

Quantum machine learning takes advantage of features such as quantum computing superposition and entanglement to enable better performance of machine learning models. In this paper, we first propose an improved hybrid quantum convolutional neural network (HQCNN) model. The HQCNN model was used to pre-train brain tumor dataset (MRI) images. Next, the quantum classical transfer learning (QCTL) approach is used to fine-tune and extract features based on pre-trained weights. A hybrid quantum convolutional network structure was used to test the osteoarthritis of the knee dataset (OAI) and to quantitatively evaluate standard metrics to verify the robustness of the classifier. The final experimental results show that the QCTL method can effectively classify knee osteoarthritis with a classification accuracy of 98.36%. The quantum-to-classical transfer learning method improves classification accuracy by 1.08%. How to use different coding techniques in HQCNN models applied to medical image analysis is also a future research direction

Object Detection for Caries or Pit and Fissure Sealing Requirement in Children's First Permanent Molars

Dental caries is one of the most common oral diseases that, if left untreated, can lead to a variety of oral problems. It mainly occurs inside the pits and fissures on the occlusal/buccal/palatal surfaces of molars and children are a high-risk group for pit and fissure caries in permanent molars. Pit and fissure sealing is one of the most effective methods that is widely used in prevention of pit and fissure caries. However, current detection of pits and fissures or caries depends primarily on the experienced dentists, which ordinary parents do not have, and children may miss the remedial treatment without timely detection. To address this issue, we present a method to autodetect caries and pit and fissure sealing requirements using oral photos taken by smartphones. We use the YOLOv5 and YOLOX models and adopt a tiling strategy to reduce information loss during image pre-processing. The best result for YOLOXs model with tiling strategy is 72.3 mAP.5, while the best result without tiling strategy is 71.2. YOLOv5s6 model with/without tiling attains 70.9/67.9 mAP.5, respectively. We deploy the pre-trained network to mobile devices as a WeChat applet, allowing in-home detection by parents or children guardian

Si3AlP: A new promising material for solar cell absorber

First-principles calculations are performed to study the structural and optoelectronic properties of the newly synthesized nonisovalent and lattice-matched (Si2)0.6(AlP)0.4 alloy [T. Watkins et al., J. Am. Chem. Soc. 2011, 133, 16212.] We find that the ordered CC-Si3AlP with a basic unit of one P atom surrounded by three Si atoms and one Al atom is the most stable one within the experimentally observed unit cell.1 Si3AlP has a larger fundamental band gap and a smaller direct band gap than Si, thus it has much higher absorption in the visible light region. The calculated properties of Si3AlP suggest that it is a promising candidate for improving the performance of the existing Si-based solar cells. The understanding on the stability and band structure engineering obtained in this study is general and can be applied for future study of other nonisovalent and lattice-matched semiconductor alloys