A5040203793- Landslides and related hazards
- Dam Engineering and Safety
- Enhanced Oil Recovery Techniques
- Machine Learning in Materials Science
- Thermochemical Biomass Conversion Processes
- Thermal and Kinetic Analysis
- Geotechnical Engineering and Analysis
- Zeolite Catalysis and Synthesis
- Remote Sensing and Land Use
- Energetic Materials and Combustion
- Photopolymerization techniques and applications
- Geotechnical Engineering and Underground Structures
- Hydraulic flow and structures
- Flood Risk Assessment and Management
- Catalysis and Oxidation Reactions
- Numerical methods in engineering
- Heat and Mass Transfer in Porous Media
- Carbon Nanotubes in Composites
- Aerogels and thermal insulation
- Antimicrobial agents and applications
- Seismic Imaging and Inversion Techniques
- Climate change and permafrost
- Computational Drug Discovery Methods
- Flame retardant materials and properties
- Cryospheric studies and observations
PowerChina (China)
2024
East China University of Science and Technology
2022-2024
State Key Laboratory of Chemical Engineering
2023-2024
The impoundment of a hydropower station can cause water levels in reservoir areas to rise, potentially triggering nearby landslides and generating surge waves that pose significant threats navigation infrastructure. Traditional methods for predicting landslide-induced often struggle accurately capture peak wave heights their evolving trends. To address this challenge, study employs machine learning approaches enhance the prediction characteristics by integrating insight from physical model...
Phenol-formaldehyde (PF) resin, a significant synthetic thermosetting polymer, is extensively utilized across diverse engineering domains. The exploration of the high-temperature oxidation mechanism PF resin pivotal to enhancing its thermal stability. However, current research lacks comprehensive study on pyrolysis under different oxidizing conditions. Herein, this work systematically explores various environments by using experiments and molecular dynamics simulations. oxidative cracking...
Surge is a common secondary disaster caused by reservoir landslides. The study of its spatial and temporal distribution characteristics important since it affects not only the normal operation reservoirs but also safety people residing along river. This paper presents large-scale three-dimensional physical modeling experiment using near-dam high-position landslide project as prototype. It investigated relationships between river course characteristics, volume, head wave velocity surge,...
A precise three-dimensional (3D) microstructure is crucial in porous material-based sciences and technologies, whereas accurate efficient reconstruction using two-dimensional (2D) images remains challenging. Here, the strategy of ascertaining nanopore boundaries focused ion beam-scanning electron microscopy experimentally guided image segmentation deep learning-based proposed for heat transfer performance prediction. We demonstrate that uncertain 2D pore characteristics reconstructed 3D...
Landslide-generated surge waves are significant natural hazards, posing severe risks to engineering safety. Despite extensive research on the dynamics of landslide-generated waves, studies analyzing controlling factors and their mechanisms remain limited, leaving key influencing processes inadequately understood. This study utilizes computational fluid (CFD) perform a numerical simulation semi-submerged landslide in hydropower station reservoir area. The systematically investigated effects...
Abstract A Physics-Informed Neural Network (PINN) provides a distinct advantage by synergizing neural networks' capabilities with the problem's governing physical laws. In this study, we introduce an innovative approach for solving seepage problems utilizing PINN, harnessing of Deep Networks (DNNs) to approximate hydraulic head distributions in analysis. To effectively train PINN model, comprehensive loss function comprising three components: one evaluating differential operators, another...
Large-scale and long-term simulation of chemical reactions are key research topics in computational chemistry. However, there still difficulties simulating high-temperature reactions, such as polymer thermal decomposition. Herein, we introduce an adaptive potential parameter optimization framework designed to automatically fine-tune parameters, the application it optimize ReaxFF parameters enhances accuracy reaction simulations conducted at experimental temperatures. To achieve this,...
A Physics-Informed Neural Network (PINN) provides a distinct advantage by synergizing neural networks' capabilities with the problem's governing physical laws. In this study, we introduce an innovative approach for solving seepage problems utilizing PINN, harnessing of Deep Networks (DNNs) to approximate hydraulic head distributions in analysis. To effectively train PINN model, comprehensive loss function comprising three components: one evaluating differential operators, another assessing...