A5037932717- Phase Equilibria and Thermodynamics
- Heat transfer and supercritical fluids
- Combustion and flame dynamics
- Gaussian Processes and Bayesian Inference
- Probabilistic and Robust Engineering Design
- Refrigeration and Air Conditioning Technologies
- Additive Manufacturing Materials and Processes
- High-Temperature Coating Behaviors
- High Entropy Alloys Studies
- Advanced Data Processing Techniques
- Advanced Multi-Objective Optimization Algorithms
- Nuclear reactor physics and engineering
- Field-Flow Fractionation Techniques
- Advanced Combustion Engine Technologies
- Process Optimization and Integration
Tsinghua University
2022-2024
Hebei University of Engineering
2024
Abstract Supercritical fluids (SCFs) hold potential in the fields of energy and advanced propulsion, highlighting significance comprehensively investigating SCF flow heat transfer characteristics. The intricate nonlinear thermophysical property variations SCFs coupled with primitive variables conservation equations pose several challenges effectively modeling simulating flows transfer. This paper conducts a thorough assessment commonly used state look-up tables for describing properties...
Deep learning-based surrogate models have received wide attention for efficient and cost-effective predictions of fluid flows combustion, while their hyperparameter settings often lack generalizable guidelines. This study examines two different types models, convolutional autoencoder (CAE)-based reduced order (ROMs) fully connected (FCAE)-based ROMs, emulating hydrogen-enriched combustion from a triple-coaxial nozzle jet. The performances these ROMs are discussed in detail, with an emphasis...
Numerical simulations of fluid flows and combustion in advanced propulsion power-generation systems heavily rely on precise thermophysical property evaluation schemes. Despite the availability tools like NIST REFPROP, cubic equations state (EOSs), deep neural network (DNN) models, inaccuracies persist within specific thermodynamic regions when compared to experimental data. This paper aims address this challenge by introducing data-driven models based Gaussian process regression (GPR). The...
The phases of high-entropy alloys (HEAs) are crucial to their material properties. Although meta-learning can recommend a desirable algorithm for materials designers, it does not utilize the optimal solution information similar historical problems in HEA field. To address this issue, transferable model (MTL-AMWO) based on an adaptive migration walrus optimizer is proposed predict HEAs. Firstly, frame proposed, which consists optimizer, balanced-relative density peaks clustering, and transfer...
Carbon dioxide has important usage in many engineering applications, such as regenerative cooling scramjets and supercritical power cycles, thermophysical properties are essential numerical simulation. For the typical cubic real-fluid equation of states (EOSs), Soave-Redlich-Kwong (SRK) Peng-Robinson (PR) models, their reliability accuracy relatively poor high-pressure compressed-liquid, pseudo-boiling, near-critical regions. In region around critical point, renormalization group theory was...
Abstract The parameter optimization of a multi-component isotope separation cascade is multi-dimensional problem, involving many variables such as cut for stages, feed stage and factors. In this paper, novel metaheuristic algorithm called butterfly (BOA) applied the first time in field. Different objective functions are introduced like D function combination total flow rates. BOA method solves problem 20-stage square to separate Xenon isotopes with aim getting greatest value or least results...