Data-driven models and digital twins for sustainable combustion technologies

被引：7
|
作者：
Parente, Alessandro [1 ,2 ,3 ,4 ]
Swaminathan, Nedunchezhian [5 ]
机构：
[1] Univ Libre Bruxelles, Ecole Polytech Bruxelles, Aerothermo Mech Dept, Ave Franklin D,Roosevelt 50, B-1050 Brussels, Belgium
[2] WEL Res Inst, Ave Pasteur 6, B-1300 Wavre, Belgium
[3] Univ Libre Bruxelles, Brussels Inst Thermal Fluid Syst & Clean Energy B, B-1050 Ixelles, Belgium
[4] Vrije Univ Brussel, B-1050 Ixelles, Belgium
[5] Univ Cambridge, Dept Engn, Hopkinson Lab, Cambridge CB2 1PZ, England
来源：
ISCIENCE | 2024年 / 27卷 / 04期
基金：
英国工程与自然科学研究理事会;
关键词：
PRINCIPAL COMPONENT ANALYSIS; DIRECT NUMERICAL-SIMULATION; GENERATIVE ADVERSARIAL NETWORKS; PROPER ORTHOGONAL DECOMPOSITION; CONVOLUTIONAL NEURAL-NETWORKS; NOX EMISSIONS; TURBULENT; LES; IDENTIFICATION; FRAMEWORK;
D O I：
10.1016/j.isci.2024.109349
中图分类号：
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
We highlight the critical role of data in developing sustainable combustion technologies for industries requiring high -density and localized energy sources. Combustion systems are complex and difficult to predict, and high-fidelity simulations are out of reach for practical systems because of computational cost. Data -driven approaches and artificial intelligence offer promising solutions, enabling renewable synthetic fuels to meet decarbonization goals. We discuss open challenges associated with the availability and fidelity of data, physics -based numerical simulations, and machine learning, focusing on developing digital twins capable of mirroring the behavior of industrial combustion systems and continuously updating based on newly available information.
引用
收藏
页数：10
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