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

页数：10

共 50 条

[21] An MLOps Framework to Data-Driven Modelling of Digital Twins with an Application to Virtual Test Rigs
Kruschinski, Denis
Ngassam, Dylan Tchawou
Durak, Umut
Hartmann, Sven
ADVANCES IN CONCEPTUAL MODELING, ER 2024 WORKSHOPS, 2025, 14932 : 71 - 86
[22] Sustainable Business Models through Digital Twins
Werner, Andreas
Schuseil, Frauke
Zimmermann, Nikolas
Hämmerle, Moritz
Riedel, Oliver
WT Werkstattstechnik, 2024, 114 (06): : 276 - 284
[23] Civic technologies in data-driven societies
Jacobsson, Mattias
Hansson, Karin
Ho, Hayley
Normark, Maria
Lundmark, Sofia
Tholander, Jakob
ADJUNCT PROCEEDINGS OF THE 13TH NORDIC CONFERENCE ON HUMAN-COMPUTER INTERACTION, NORDICHI 2024, 2024,
[24] Dis/Trust and data-driven technologies
Duenas-Cid, David
Calzati, Stefano
INTERNET POLICY REVIEW, 2023, 12 (04):
[25] Numerical Weather Data-Driven Sensor Data Generation for PV Digital Twins: A Hybrid Model Approach
Lee, Jooseung
Kang, Jimyung
Son, Sangwoo
Oh, Hui-Myoung
IEEE ACCESS, 2025, 13 : 5009 - 5022
[26] Data-driven enabling technologies in soft sensors of modern internal combustion engines: Perspectives
Li, Ji
Zhou, Quan
He, Xu
Chen, Wan
Xu, Hongming
ENERGY, 2023, 272
[27] Data-driven physics-based digital twins via a library of component-based reduced-order models
Kapteyn, M. G.
Knezevic, D. J.
Huynh, D. B. P.
Tran, M.
Willcox, K. E.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, 2022, 123 (13) : 2986 - 3003
[28] Developing deep learning surrogate models for digital twins in mineral processing - A case study on data-driven multivariate multistep forecasting
Zeb, Akhtar
Linnosmaa, Joonas
Seppi, Mikko
Saarela, Olli
MINERALS ENGINEERING, 2024, 216
[29] Data-driven optimisation of leadership models during enterprise digital transformation
Guo, Yuntao
Applied Mathematics and Nonlinear Sciences, 2024, 9 (01)
[30] An Investigation of Unsupervised Data-Driven Models for Internal Combustion Engine Condition Monitoring
Liang, Xiaoxia
Fu, Chao
Sun, Xiuquan
Duan, Fang
Mba, David
Gu, Fengshou
Ball, Andrew D.
PROCEEDINGS OF INCOME-VI AND TEPEN 2021: PERFORMANCE ENGINEERING AND MAINTENANCE ENGINEERING, 2023, 117 : 463 - 475

← 1 2 3 4 5 →