Numerical modeling of hydrogen catalytic reactions over a circular bluff body

Wu, Chia Ching

doi:10.1016/j.ijhydene.2021.12.006

Cited by 8 publications

(2 citation statements)

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“…We retrieve the data from the WO to train the model and the model data from ST as test data. We apply this procedure by first concatenating the time series and then separating the simulation and reference data with 80% of the data for training and 20% for validation [102][103][104][105][106][107][108][109][110][111][112][113]. In some cases, we get non-exploitable values in the time series of ST such as missing values (NA).…”

Section: Discussionmentioning

confidence: 99%

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

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One of the applications that machine learning can offer to the world of Engineering and Fluid Mechanics in particular is the calibration of models making it possible to approximate the representation of a particular phenomenon. Indeed, the computational cost generated by some fluid mechanics models pushes scientists to use other models close to the original models but less computationally intensive in order to facilitate their handling. Among the different approaches used: machine learning coupled with some optimization methods and algorithms in order to reduce the computation cost induced. This paper proposes a new framework called OPTI-ENS: a new flexible, optimized and improved method, to calibrate a physical model, called the wake oscillator (WO), which simulates the vibratory behaviors of overhead line conductors. An approximation of a heavy and complex model called the strip theory (ST) model. OPTI-ENS is composed of an ensemble machine learning algorithm (ENS) and an optimization algorithm of the WO model so that the WO model can generate the adequate training data as input to the ENS model. ENS model will therefore take as input the data from the WO model and output the data from the ST model. As a benchmark, a series of Machine learning models have been implemented and tested. The OPTI-ENS algorithm was retained with a best Coefficient of determination (R2 Score) of almost 0.7 and a Root mean square error (RMSE) of 7.57e-09. In addition, this model is approximately 170 times faster (in terms of calculation time) than an ENS model without optimization of the generation of training data by the WO model. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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Section: Discussionmentioning

confidence: 99%

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Amroun,

Hafid,

Ammi

2022

IJICS

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“…It provides a powerful tool that enables engineers to evaluate and analyze various engineering systems and processes more quickly and effectively [31,32]. CFD is commonly utilized to optimize the design and operating parameters of practical engineering problems [33][34][35]. However, the difficulties in CFD optimization are high computational costs and time constraints due to the need to consider the complexity of the geometry, the number of meshes, the applicability of the model, and the complexity of the chemical reaction mechanism.…”

Section: Introductionmentioning

confidence: 99%

Prediction and Optimization of Heat Transfer Performance of Premixed Methane Impinging Flame Jet Using the Kriging Model and Genetic Algorithm

Chen,

2024

Applied Sciences

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In practical applications, rapid prediction and optimization of heat transfer performance are essential for premixed methane impinging flame jets (PMIFJs). This study uses computational fluid dynamics (CFD) combined with a methane detailed chemical reaction mechanism (GRI–Mech 3.0) to study the equivalence ratio (Φ), Reynolds number (Re) of the mixture, and the normalized nozzle–to–plate distance (H/d) on the heat transfer performance of PMIFJs. Moreover, the Kriging model (KM) was used to construct a prediction model of PMIFJ heat transfer performance. A genetic algorithm (GA) was used to determine the maximum likelihood function (MLE) of the model parameters for constructing KM and identify the points with the maximum root mean square error (RMSE) as the new infilled points for surrogate–based optimization (SBO). Combining these methods to analyze the simulation results, the results show that the global heat transfer performance of PMIFJs is enhanced with the increase in Φ, the increase in Re, and the decrease in H/d. Sensitivity analysis points out that Re and Φ significantly affect enhanced heat transfer, while H/d has a relatively small effect. In addition, GA was also used to search for the optimal heat transfer performance, and the global heat transfer performance at specific conditions was significantly enhanced. This study deepens the understanding of the heat transfer mechanism of impinging flame jets and provides an efficient method framework for practical applications.

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Performance optimizing and entropy generation analysis of a platinum–stainless-steel segmented microreactor

Kao

Wang

et al. 2023

Chemical Engineering Journal

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Numerical modeling of hydrogen catalytic reactions over a circular bluff body

Cited by 8 publications

References 50 publications

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Optimization of the Creation of a Training Set for the Calibration of a Model Reproducing the Vibration Behavior of an Overhead Line Conductor

Prediction and Optimization of Heat Transfer Performance of Premixed Methane Impinging Flame Jet Using the Kriging Model and Genetic Algorithm

Performance optimizing and entropy generation analysis of a platinum–stainless-steel segmented microreactor

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