A5034373557- Carbon Nanotubes in Composites
- Machine Learning in Materials Science
- Aluminum Alloys Composites Properties
- Advanced ceramic materials synthesis
- Advanced machining processes and optimization
- Hydrogen embrittlement and corrosion behaviors in metals
- Metal Forming Simulation Techniques
- Metal and Thin Film Mechanics
- Fiber-reinforced polymer composites
- Material Properties and Applications
- Diamond and Carbon-based Materials Research
- Structural Behavior of Reinforced Concrete
- Probabilistic and Robust Engineering Design
- Advanced Sensor and Energy Harvesting Materials
- Nanotechnology research and applications
- Composite Material Mechanics
- Injection Molding Process and Properties
- Fluid Dynamics and Heat Transfer
- Structural Response to Dynamic Loads
- Advancements in Photolithography Techniques
- Advanced materials and composites
- Nuclear and radioactivity studies
- Dielectric materials and actuators
- Boron and Carbon Nanomaterials Research
- Additive Manufacturing and 3D Printing Technologies
China General Nuclear Power Corporation (China)
2020-2023
Beijing Institute of Technology
2017
Institut National des Sciences Appliquées Rouen Normandie
2014
Abstract Predicting the performance of mechanical properties is an important and current issue in field engineering materials science, but traditional experiments modeling calculations often consume large amounts time resources. Therefore, it imperative to use appropriate methods accelerate process material selection design. The artificial intelligence method, particularly deep learning models, has been verified as effective efficient method for handling computer vision neural language...
Using computer simulation tools such as finite element analysis (FEA) to perform material stress is a common design method in engineering practice. In order model more realistic real‐world systems, models have become complex, and calculation becomes expensive result. The rise of artificial intelligence technologies has made it possible integrate deep learning methods analysis. Herein, FEA software employed obtain large number cases training samples uses fully connected neural network...
This paper carries out a preliminary study for the elastic properties of single walled carbon nanotube (SWCNT) thin film. The SWCNT films (~250 nm) are prepared by simple and cost effective method spin-coating technology. Nanoindentation test with Berkovich indenter is used to determine hardness modulus It important note that film indirectly derived from information load displacement under certain assumptions, deviation 'test value' inevitable. In this regard, uncertainty analysis an process...
This paper proposes a 3D finite element method procedure to study the electrostrictive property of single-walled carbon nanotube (SWCNT)-based composite. The numerical model in nanoscale is developed investigate behavior SWCNT and polyvinylidene fluoride trifluoroethylene P(VDF-TrFE) A bond electrical contact adopted reproduce coupled electromechanical effects interface between P(VDP-TrFE). According proposed, mechanism enhanced electrostriction SWCNT/P(VDP-TrFE) composite intuitively...
The degassing tower liquid cooler is an important equipment for boron recovery system of HPR1000 nuclear power plant. It crucial to ensure the security function under earthquake. This report carried out a seismic analysis cooler. finite element (FE) model Degassing Tower Liquid Cooler was established by using ANSYS code. response spectrum method adopted and evaluation. Through analysis, design weakness identified. And modified meet requirement practical engineering according FE results...