83 research outputs found
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
- …