Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NO x ) Emissions Using Deep Learning.

Pillai, Rinav; Triantopoulos, Vassilis; Berahas, Albert S; Brusstar, Matthew; Sun, Ruonan; Nevius, Tim; Boehman, André L

Modeling and Predicting Heavy-Duty Vehicle Engine-Out and Tailpipe Nitrogen Oxide (NO _x ) Emissions Using Deep Learning.

Pillai, Rinav; Triantopoulos, Vassilis; Berahas, Albert S; Brusstar, Matthew; Sun, Ruonan; Nevius, Tim; Boehman, André L.

Afiliação

Pillai R; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States.
Triantopoulos V; Department of Mechanical Engineering, University of Michigan, Ann Arbor, MI, United States.
Berahas AS; Plasma Science and Fusion Center, Massachusetts Institute of Technology, Cambridge, MA, United States.
Brusstar M; Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor, MI, United States.
Sun R; National Vehicle and Fuel Emissions Laboratory, U.S. EPA, Ann Arbor, MI, United States.
Nevius T; National Vehicle and Fuel Emissions Laboratory, U.S. EPA, Ann Arbor, MI, United States.
Boehman AL; Horiba Instruments Inc., Saline, MI, United States.

Front Mech Eng ; 82022.

Article em En | MEDLINE | ID: mdl-35445105

Palavras-chave

artificial neural networks; data-driven modelling; deep learning; heavy-duty vehicles; nitrogen oxide emissions; optimization

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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Tipo de estudo: Prognostic_studies / Risk_factors_studies Idioma: En Ano de publicação: 2022 Tipo de documento: Article

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