A5091205564- High Entropy Alloys Studies
- Vacuum and Plasma Arcs
- High-Temperature Coating Behaviors
- Electrical Fault Detection and Protection
- High Temperature Alloys and Creep
- Complex Network Analysis Techniques
- Topological and Geometric Data Analysis
- Solidification and crystal growth phenomena
- Data Visualization and Analytics
- Text and Document Classification Technologies
- Domain Adaptation and Few-Shot Learning
- Thermodynamic and Structural Properties of Metals and Alloys
- Advanced Graph Neural Networks
- Antiplatelet Therapy and Cardiovascular Diseases
- nanoparticles nucleation surface interactions
- Acute Ischemic Stroke Management
- Additive Manufacturing Materials and Processes
- Metallurgical and Alloy Processes
- Semiconductor materials and devices
- Aluminum Alloy Microstructure Properties
- Venous Thromboembolism Diagnosis and Management
- Magnesium Alloys: Properties and Applications
- Machine Learning and Data Classification
- Topic Modeling
- Electrostatic Discharge in Electronics
KU Leuven
2021-2023
State Key Laboratory of Electrical Insulation and Power Equipment
2022-2023
National University of Defense Technology
2022-2023
Xi'an Jiaotong University
2022-2023
Considering its application in developing Raney-type Ni catalysts and metal surface coatings, the study on growth behavior of Al3Ni2 intermetallic compound (IMC) at Al/Ni material interface is utmost importance. The present work integrates nanoscale molecular dynamics (MD) calculation with mesoscale phase field model for studying interfacial phenomena associated 1173.15 K. energies computed from MD are range 0.9–1.2 J/m2 FCC/IMC featuring as largest value IMC/LIQUID one lowest value. Phase...
The study is to explore a relationship between vacuum arc characteristics and short-circuit current interrupting capability of fast circuit breaker (FVCB) at minimum arcing time. Both interruption test an mode transition observation were carried out the relationship. Firstly, FVCB with ceramic enclosure interrupter (VI), in which pair axial magnetic field (AMF) contacts arranged, was operated three different opening speeds interrupt currents. A Weil synthetic provided currents 20, 30, 40 kA,...
Abstract Microstructure simulations for quaternary alloys are still a challenge, although it is of high importance alloy development. This work presents Phase field (PF) approach capable resolving phase transformation in multicomponent system with simple and effective way to include the thermodynamic kinetic information such complex system. The microstructure evolution during diffusional between FCC BCC at 700 °C AlCrFeNi was simulated, accounting composition dependence off-diagonal terms...
Spark conditioning is an effective method to improve dielectric strength of vacuum gaps. The objective this paper study the influence breakdown energy on effect VIs during spark conditioning. An AC power source was adopted provide voltage necessary for a commercial 40.5 kV VI sat at 1 mm gap distance. A 100 k$\Omega$ current limiting resistor connected in series between and ensure that majorly adjusted by capacitors parallel with VI. When capacitor increased from 250 pF 750 pF, saturated 38...
On supervised learning tasks, introducing an information bottleneck can guide the model to focus on more discriminative features in input, which effectively prevent overfitting. The deep variational aims learn a global Gaussian latent variable using neural network, compresses mutual with input and retains most relevant output as much possible. However, for containing complex semantic information, such multi-label classification datasets, obeying simple distribution may not necessarily...
Abstract A phase-field approach is presented to simulate the evolution of a BCC precipitate in an FCC matrix for Al x CrFeNi based medium entropy systems. The CALPHAD composition dependence Gibbs energies and diffusion mobilities this quaternary alloy are introduced model using ThermoCalc with TCHEA2 MOBNI4 databases. Heterogeneous elastic properties assumed phase computed system. effect stresses due lattice mismatch on shape, single matrix. We find that effects considerable.
Embedding real-world networks presents challenges because it is not clear how to identify their latent geometries. some disassortative networks, such as scale-free the Euclidean space has been shown incur distortions. hyperbolic spaces offer an exciting alternative but incurs distortions when embedding assortative with geometries hyperbolic. We propose inductive model that leverages both expressiveness of GCNs and trivial bundle learn node representations for or without features. A a simple...