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Processes
Special Issues
Data-Driven Modeling and Applications for Flow, Heat Transfer, and Combustion
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Data-Driven Modeling and Applications for Flow, Heat Transfer, and Combustion

A special issue of Processes (ISSN 2227-9717). This special issue belongs to the section "Energy Systems".

Deadline for manuscript submissions: 31 December 2025 | Viewed by 3918

Special Issue Editors

Dr. Songbai Yao

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Guest Editor
Ningbo Institute of Materials Technology and Engineering, Chinese Academy of Sciences, Ningbo, China
Interests: combustion; multi-phase flows; computational fluid dynamics; detonation propulsion; machine learning
Prof. Dr. Ping Wang

E-Mail Website
Guest Editor
Energy Research Institue, Jiangsu University, Zhenjiang, China
Interests: turbulent combustion; large eddy simulation; gas turbines combustion technology; hydrogen and ammonia combustion
Special Issues, Collections and Topics in MDPI journals
Dr. Bosen Wang

E-Mail Website
Guest Editor
Research Institute of Aero-Engine, Beihang University, Beijing, China
Interests: numerical combustion; spray; PINN
Dr. Weijia Qian

E-Mail Website
Guest Editor
Institute for Energy Research, Jiangsu University, Zhenjiang, China
Interests: application of artificial intelligence in fluid flow and heat transfer; multiphase flow; spray combustion

Special Issue Information

Dear Colleagues,

Data-driven methodologies have become a crucial tool for understanding flow dynamics, heat transfer phenomena, and reacting flows across various domains of applications. This Special Issue explores computational approaches and experimental diagnoses combined with machine learning methods for single- and multi-phase flows, heat and mass transfer processes, and reacting flows, with applications pertinent to combustion engines, turbomachinery, and power generation systems.

We invite original research articles, review articles, and technical notes that contribute to the advancement of knowledge in this interdisciplinary field.

Topics include, but are not limited to, the following:

  • Data-driven turbulence modeling and closures;
  • Data-driven models for turbulence/chemistry interaction;
  • Data-driven models for mass and heat transfer processes;
  • Machine learning for combustion chemistry acceleration;
  • Machine learning for fluid dynamics data analysis;
  • Machine learning for flow control and detection.

Dr. Songbai Yao
Prof. Ping Wang
Dr. Bosen Wang
Dr. Weijia Qian
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Processes is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • fluid dynamics
  • heat transfer
  • reacting flows
  • numerical modeling
  • experimental diagnosis

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  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
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Further information on MDPI's Special Issue policies can be found here.

Published Papers (3 papers)

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Research

21 pages, 6542 KiB  
Article
An Automated System for Constructing a Database of Leidenfrost Evaporation Curves Using Image Processing Techniques
by Chun-Yu Tsai, Hsiu-Ju Cheng, Po-Lun Lai and Chen-Kang Huang
Processes 2025, 13(2), 586; https://doi.org/10.3390/pr13020586 - 19 Feb 2025
Viewed by 349
Abstract
To analyze the progression of Leidenfrost evaporation, traditional experiments were conducted manually to generate a complete evaporation curve. However, physical constraints render Leidenfrost evaporation experiments inherently time-consuming and susceptible to uncertainty. To address these challenges, this study aimed to develop an automated system [...] Read more.
To analyze the progression of Leidenfrost evaporation, traditional experiments were conducted manually to generate a complete evaporation curve. However, physical constraints render Leidenfrost evaporation experiments inherently time-consuming and susceptible to uncertainty. To address these challenges, this study aimed to develop an automated system using webcams for real-time image acquisition and processing, as well as a syringe pump constructed using an Arduino microcontroller, a stepper motor, and 3D-printed components. In the domain of real-time image processing, the radii of levitated droplets were determined using circular detection techniques. By fitting the droplet radii over hundreds of consecutive frames, it was concluded that the shrinking rate of levitated droplet radii remain constant when the radius exceeds 0.6 mm, and the evaporation time is accurately derived. A moving average algorithm was employed to identify the heat transfer area as well as the evaporation time between the boiling droplet and the hot surface, enabling simultaneous calculation of the heat flux. The automated system was then used to perform Leidenfrost experiments under varying experimental parameters, and was compared to manual methods to demonstrate its superior precision in both the film boiling and nucleate boiling regimes. For example, the automated system was utilized to perform a series of experiments as the Weber number increased from 7.01 to 23.18. The detected Leidenfrost temperature rose from 154 °C to 192 °C, while the evaporation time decreased from 85.2 s to 78.9 s. These findings were consistent with previous studies and aligned with physical expectations, reinforcing the reliability of the system and its results. Full article
(This article belongs to the Special Issue Data-Driven Modeling and Applications for Flow, Heat Transfer, and Combustion)
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22 pages, 13800 KiB  
Article
Research on Flow Field Prediction in a Multi-Swirl Combustor Using Artificial Neural Network
by Weijia Qian, Siheng Yang, Weijie Liu, Quanhong Xu and Wenbin Zhu
Processes 2024, 12(11), 2435; https://doi.org/10.3390/pr12112435 - 4 Nov 2024
Viewed by 1153
Abstract
In aero-engine combustion research, the pursuit of cost-effective and rapid methods for acquiring precise flow fields across various operating conditions remains a significant challenge. This study offers novel insights into the rapid modeling of complex multi-swirling flows, introducing flow-field-based analytical methods to evaluate [...] Read more.
In aero-engine combustion research, the pursuit of cost-effective and rapid methods for acquiring precise flow fields across various operating conditions remains a significant challenge. This study offers novel insights into the rapid modeling of complex multi-swirling flows, introducing flow-field-based analytical methods to evaluate flow topologies, spray dispersion, ignition dynamics, and flame propagation patterns. A data-driven model is proposed to predict the swirling velocity field inside a multi-swirl combustor, using spatial coordinates and air pressure drops as input features. Particle Image Velocimetry (PIV) experiments under different air pressure drops are performed to generate the necessary flow field dataset. A fully connected deep neural network is designed and optimized with a focus on prediction accuracy, training efficiency, and mitigation of over-fitting. The predicted flow characteristics, including swirling jets, shear layers, recirculation zones, and velocity profiles, align closely with the PIV experimental results. This demonstrates the model’s capability to effectively capture the intricate multi-swirling flow structures and the complex relationships between input parameters and the resulting flow field. Furthermore, the trained model shows excellent generalization capability, accurately predicting flow fields under previously unseen operating conditions. Finally, combustion-relevant characteristics, such as ignition and flame propagation, are successfully extracted and analyzed from the predicted flow fields using the proposed deep learning framework. Full article
(This article belongs to the Special Issue Data-Driven Modeling and Applications for Flow, Heat Transfer, and Combustion)
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20 pages, 3850 KiB  
Article
Fouling Prediction of a Heat Exchanger Based on Wavelet Neural Network Optimized by Improved Particle Swarm Optimization Algorithm
by Yandong Liang, Lipeng Zhu, Yang Wang, Hao Wu, Junwei Zhang, Jing Guan and Jianguo Wang
Processes 2024, 12(11), 2412; https://doi.org/10.3390/pr12112412 - 1 Nov 2024
Cited by 3 | Viewed by 1318
Abstract
The relevant experimental data of the fouling formation process of a heat exchanger were obtained through the fouling monitoring experimental platform. Whereafter, with regard to the conventional particle swarm optimization (PSO) algorithm, this study commenced from the iteration formula and innovatively presented an [...] Read more.
The relevant experimental data of the fouling formation process of a heat exchanger were obtained through the fouling monitoring experimental platform. Whereafter, with regard to the conventional particle swarm optimization (PSO) algorithm, this study commenced from the iteration formula and innovatively presented an optimization approach for improving the inertia weight, thereby obtaining the improved particle swarm optimization (IPSO) algorithm. The wavelet neural network (WNN) was optimized through the application of the IPSO–WNN algorithm, resulting in the development of the IPSO–WNN model. Utilizing this model, a predictive model for fouling thermal resistance was constructed, incorporating input variables such as conductivity, pH, dissolved oxygen, average wall temperature, and bulk temperature, while the output variable represented fouling thermal resistance. Comparative analyses demonstrated that the IPSO–WNN model exhibited superior prediction accuracy and robust generalization capabilities to that of the conventional WNN and PSO–WNN models, as evidenced by significantly lower values across all indicators, including MAPE, MAE, and RMSE. The IPSO algorithm effectively optimized the initial parameters of the WNN, addressing the challenge of local minimum and enhancing the model’s overall capacity to identify optimal solutions. This model effectively captures the dynamic trends of fouling thermal resistance during its growth stage and approaches the asymptotic value in the stable stage. Precise prediction models for heat exchanger fouling contribute valuable insights for its prediction in practical industrial applications. Full article
(This article belongs to the Special Issue Data-Driven Modeling and Applications for Flow, Heat Transfer, and Combustion)
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