Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption

This paper presents a machine learning based model for control of local bioaerosol concentration via a forced corner flow with optimal energy efficiency in an indoor environment. A recirculation zone determined by the inlet flow rate traps particles partially with one or more vortices around the cor...

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Main Authors: Zhang, Xingyu, Li, Hua
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160945
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1609452022-08-08T05:41:18Z Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption Zhang, Xingyu Li, Hua School of Mechanical and Aerospace Engineering Engineering::Mechanical engineering Neural Network Machine Learning This paper presents a machine learning based model for control of local bioaerosol concentration via a forced corner flow with optimal energy efficiency in an indoor environment. A recirculation zone determined by the inlet flow rate traps particles partially with one or more vortices around the corner. The profile of the recirculation zone is then determined mathematically by the minimum net mass flux principle with a grid search technique. Subsequently, the variation of the recirculation zone profile is then learned through a neural network (NN), in which data is collected from the simulation by the Eulerian–Lagrangian scheme. Moreover, a model predictive control (MPC) algorithm is implemented to achieve an optimal profile of the recirculation zone with optimal energy consumption, based on the linearized NN model. Finally, the proposed NN-MPC is implemented for simulation of removing the local bioaerosol from an indoor corner through a flow-rate-controllable airflow from ventilation outlet located on the ceiling. Nanyang Technological University The authors gratefully acknowledge the financial support from Nanyang Technological University through NTU Research Scholarships. 2022-08-08T05:41:18Z 2022-08-08T05:41:18Z 2020 Journal Article Zhang, X. & Li, H. (2020). Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption. Computers and Mathematics With Applications, 80(5), 1360-1374. https://dx.doi.org/10.1016/j.camwa.2020.07.002 0898-1221 https://hdl.handle.net/10356/160945 10.1016/j.camwa.2020.07.002 2-s2.0-85087784807 5 80 1360 1374 en Computers and Mathematics with Applications © 2020 Elsevier Ltd. All rights reserved.
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
Neural Network
Machine Learning
spellingShingle Engineering::Mechanical engineering
Neural Network
Machine Learning
Zhang, Xingyu
Li, Hua
Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption
description This paper presents a machine learning based model for control of local bioaerosol concentration via a forced corner flow with optimal energy efficiency in an indoor environment. A recirculation zone determined by the inlet flow rate traps particles partially with one or more vortices around the corner. The profile of the recirculation zone is then determined mathematically by the minimum net mass flux principle with a grid search technique. Subsequently, the variation of the recirculation zone profile is then learned through a neural network (NN), in which data is collected from the simulation by the Eulerian–Lagrangian scheme. Moreover, a model predictive control (MPC) algorithm is implemented to achieve an optimal profile of the recirculation zone with optimal energy consumption, based on the linearized NN model. Finally, the proposed NN-MPC is implemented for simulation of removing the local bioaerosol from an indoor corner through a flow-rate-controllable airflow from ventilation outlet located on the ceiling.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Zhang, Xingyu
Li, Hua
format Article
author Zhang, Xingyu
Li, Hua
author_sort Zhang, Xingyu
title Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption
title_short Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption
title_full Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption
title_fullStr Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption
title_full_unstemmed Neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption
title_sort neural-network-based control of discrete-phase concentration in a gas-particle corner flow with optimal energy consumption
publishDate 2022
url https://hdl.handle.net/10356/160945
_version_ 1743119608350507008