Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Wai Tong Chung"'
Publikováno v:
Applications in Energy and Combustion Science, Vol 12, Iss , Pp 100087- (2022)
Many state-of-the-art machine learning (ML) fields rely on large datasets and massive deep learning models (with O(109) trainable parameters) to predict target variables accurately without overfitting. Within combustion, a wealth of data exists in th
Externí odkaz:
https://doaj.org/article/abb66e0b52ed4056a8616fe487e2a0c2
Publikováno v:
Energies, Vol 16, Iss 5, p 2343 (2023)
Physics-informed machine-learning (PIML) enables the integration of domain knowledge with machine learning (ML) algorithms, which results in higher data efficiency and more stable predictions. This provides opportunities for augmenting—and even rep
Externí odkaz:
https://doaj.org/article/52c3da0c378c456f8a60d507fa99fc9d
Publikováno v:
Proceedings of the Combustion Institute.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e13efe4815ab0488fea33476237f07e0
https://doi.org/10.1016/j.proci.2022.07.233
https://doi.org/10.1016/j.proci.2022.07.233
Publikováno v:
International Journal of Engine Research. 21:122-133
High-pressure conditions in diesel engines can often surpass the thermodynamic critical limit of the working fluid. Consequently, the injection of fuel at these conditions can lead to complex behaviors that remain only incompletely understood. This s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3b8ba86459d44281148a4df4c4cd122f
https://doi.org/10.1177/1468087419868388
https://doi.org/10.1177/1468087419868388
Publikováno v:
Progress in Energy and Combustion Science. 91:101010
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bff29052683075ed9c2632e7c9ac735c
https://doi.org/10.1016/j.pecs.2022.101010
https://doi.org/10.1016/j.pecs.2022.101010
Many practical combustion systems such as those in rockets, gas turbines, and internal combustion engines operate under high pressures that surpass the thermodynamic critical limit of fuel-oxidizer mixtures. These conditions require the consideration
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::630d4a6f77d5d7d838056775c998fcf7
http://arxiv.org/abs/2103.06397
http://arxiv.org/abs/2103.06397
In this investigation, we outline a data-assisted approach that employs random forest classifiers for local and dynamic combustion submodel assignment in turbulent-combustion simulations. This method is applied in simulations of a single-element GOX/
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fbdbe5ea390525c6dcdba02dc60f8bd