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.
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页数：10
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