3 citations · 6 across the 16 of their papers we have counts for
16 papers
Physics Transformer: Tailoring Transformer for General PDE Prediction
Guoze Sun, Rui Zhang, Jiankai Tang +4
Transformer architectures have attracted increasing attention for solving partial differential equations (PDEs), owing to their flexibility in handling irregular discretizations an…
Thermodynamics-Informed Input Reparameterization for Neural Prediction of Real-Fluid Thermodynamic Properties in Supercritical Combustion
Haoze Zhang, Han Li, Ke Xiao +3
Real-fluid thermodynamic property evaluation is a major computational cost in supercritical combustion simulations. In the enthalpy-based pressure-correction formulation, the closu…
NPSolver: Neural Poisson Solver with Iterative Physics Supervision
Bocheng Zeng, Rui Zhang, Runze Mao +5
Efficiently solving Poisson equations on complex, irregular domains remains a fundamental challenge in scientific computing, as classical iterative solvers often suffer from prohib…
Full-Stack Domain Enhancement for Combustion LLMs: Construction and Optimization
Quanjia Xiao, Weimin Ouyang, Zonglin Yang +4
Large language models (LLMs) in the direction of task adaptation and capability enhancement for professional fields demonstrate significant application potential. Nevertheless, for…
A unified foundational framework for knowledge injection and evaluation of Large Language Models in Combustion Science
Zonglin Yang, Runze Mao, Tianhao Wu +3
To advance foundation Large Language Models (LLMs) for combustion science, this study presents the first end-to-end framework for developing domain-specialized models for the combu…
A Preliminary Assessment of Coding Agents for CFD Workflows
Ke Xiao, Haoze Zhang, Yangchen Xu +3
We investigate the use of tool-using coding agents to automate end-to-end workflows in the open-source CFD package OpenFOAM. Building on general-purpose coding agent interfaces, we…
Towards LLM-enabled autonomous combustion research: A literature-aware agent for self-corrective modeling workflows
Ke Xiao, Haoze Zhang, Runze Mao +2
The rapid evolution of large language models (LLMs) is transforming artificial intelligence into autonomous research partners, yet a critical gap persists in complex scientific dom…
Benchmarking neural surrogates on realistic spatiotemporal multiphysics flows
Runze Mao, Rui Zhang, Xuan Bai +21
Predicting multiphysics dynamics is computationally expensive and challenging due to the severe coupling of multi-scale, heterogeneous physical processes. While neural surrogates p…
Deep Learning-Enabled Supercritical Flame Simulation at Detailed Chemistry and Real-Fluid Accuracy Towards Trillion-Cell Scale
Zhuoqiang Guo, Runze Mao, Lijun Liu +3
For decades, supercritical flame simulations incorporating detailed chemistry and real-fluid transport have been limited to millions of cells, constraining the resolved spatial and…
Enhancing deep learning of ammonia/natural gas combustion kinetics via physics-aware data augmentation and scale separation
Ke Xiao, Yangchen Xu, Han Li +1
Accurate and efficient numerical simulation of ammonia combustion is critical for advancing ammonia-based energy systems, where turbulent flame dynamics and pollutant formation str…
Graphics Processing Unit/Artificial Neural Network-accelerated large-eddy simulation of turbulent combustion: Application to swirling premixed flames
Min Zhang, Runze Mao, Han Li +2
Within the scope of reacting flow simulations, the real-time direct integration (DI) of stiff ordinary differential equations (ODE) for the computation of chemical kinetics stands…
Large-eddy simulation of turbulent spray flames: Effects of scalar correlation and enthalpy reduction in flamelet modeling
Dong Wang, Min Zhang, Ruixin Yang +1
Numerical modeling of turbulent spray combustion provides a promising tool for advanced engine design. In spray flames, the droplet evaporation not only reduces the ambient gas tem…
A comprehensive study on the accuracy and generalization of deep learning-generated chemical ODE integrators
Han Li, Ruixin Yang, Min Zhang +2
The application of deep neural networks (DNNs) holds considerable promise as a substitute for the direct integration of chemical source terms in combustion simulations. However, ch…
An integrated framework for accelerating reactive flow simulation using GPU and machine learning models
Runze Mao, Yingrui Wang, Min Zhang +5
Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations.…
GPU-accelerated Large Eddy Simulation of turbulent stratified flames with machine learning chemistry
Min Zhang, Runze Mao, Han Li +2
Stratified premixed combustion, known for its capability to expand flammability limits and reduce overall-lean combustion instability, has been widely adopted to comply with increa…
Detailed simulation of LOX/GCH4 flame-vortex interaction in supercritical Taylor-Green flows with machine learning
Jiayang Xu, Yifan Xu, Zifeng Weng +4
Accurate and affordable simulation of supercritical reacting flow is of practical importance for developing advanced engine systems for liquid rockets, heavy-duty powertrains, and…