Zobrazeno 1 - 10
of 152
pro vyhledávání: '"Mao, Runze"'
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 as the primary demand on computational resources. Meanwhile, as the nu
Externí odkaz:
http://arxiv.org/abs/2402.18858
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, challenges persist in ensuring high precision and generalisation across
Externí odkaz:
http://arxiv.org/abs/2312.16387
Autor:
Mao, Runze, Wang, Yingrui, Zhang, Min, Li, Han, Xu, Jiayang, Dong, Xinyu, Zhang, Yan, Chen, Zhi X.
Recent progress in artificial intelligence (AI) and high-performance computing (HPC) have brought potentially game-changing opportunities in accelerating reactive flow simulations. In this study, we introduce an open-source computational fluid dynami
Externí odkaz:
http://arxiv.org/abs/2312.13513
GPU-accelerated Large Eddy Simulation of turbulent stratified flames with machine learning chemistry
Stratified premixed combustion, known for its capability to expand flammability limits and reduce overall-lean combustion instability, has been widely adopted to comply with increasingly stringent environmental regulations. Numerous numerical simulat
Externí odkaz:
http://arxiv.org/abs/2312.05800
Accurate and affordable simulation of supercritical reacting flow is of practical importance for developing advanced engine systems for liquid rockets, heavy-duty powertrains, and next-generation gas turbines. In this work, we present detailed numeri
Externí odkaz:
http://arxiv.org/abs/2312.04830
Autor:
Mao, Runze
甲第24227号
工博第5055号
新制||工||1789(附属図書館)
学位規則第4条第1項該当
Doctor of Philosophy (Engineering)
Kyoto University
DGAM
工博第5055号
新制||工||1789(附属図書館)
学位規則第4条第1項該当
Doctor of Philosophy (Engineering)
Kyoto University
DGAM
Externí odkaz:
http://hdl.handle.net/2433/277370
In this work, we introduce DeepFlame, an open-source C++ platform with the capabilities of utilising machine learning algorithms and pre-trained models to solve for reactive flows. We combine the individual strengths of the computational fluid dynami
Externí odkaz:
http://arxiv.org/abs/2210.07094
Publikováno v:
In Energy 15 November 2024 309
Autor:
Gao, YunFei, Zhang, Quanbi, Mao, Runze, Duan, Jiaxin, Wang, Huiyong, Xu, Guogang, Wang, Xinzhen, Xiong, Ya, Tian, Jian
Publikováno v:
In Sensors and Actuators: B. Chemical 15 November 2024 419
Autor:
Li, Yuanjiang, Zhu, Ying, Yu, Yang, Mao, Runze, Ye, Linchang, Liu, Yun, Liu, Ruochen, Lang, Tao, Zhang, Jinglin
Publikováno v:
In Expert Systems With Applications 5 December 2024 256