Extensive study on the detonation cell size of biogas-oxygen mixtures

Siatkowski, Stanisław; Wacko, Krzysztof; Kindracki, Jan

doi:10.1016/j.fuel.2023.128016

Cited by 4 publications

(5 citation statements)

References 31 publications

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“…The model was created and trained using the training dataset, with the intent of minimizing the loss The ANN model was used to predict the cell sizes for different mixtures, and compare them with the corresponding experimental cell sizes. These experimental cell sizes were mainly sourced from studies in the literature [7,17,18,34,39,40]. In addition to those, detonation cell sizes from two series of experiments for a stoichiometric C 2 H 2 -N 2 O mixture at different pressures were obtained as part of this study, and subsequently compared against the ANN predictions.…”

Section: Methodsmentioning

confidence: 99%

“…As a first step of this process, the experimental cell sizes of biogas-oxygen mixtures [7] were chosen to be compared with the ANN predictions. This fuel-oxidizer combination (CH 4 -O 2 ) had been used in the ANN's training process, but was now highly diluted with CO 2 at different initial conditions.…”

Section: Biogas-oxygen Mixtures (Ch 4 -Co 2 -O 2 )mentioning

confidence: 99%

“…Detonation research is generally focused on the safety of engineering applications and industrial processes in the chemical and energy sectors [2,3], and also on the development of hypersonic propulsion systems [4][5][6]. In recent years, there has been a strong focus, especially in the transportation and aerospace industry, to explore greener aviation fuels, that are either carbon-neutral, like biogas [7][8][9], or carbon-free, like hydrogen (H 2 ) [10] and ammonia (NH 3 ) or its blend [11][12][13][14][15], which can also be used as a carrier of green hydrogen. While hydrogen/nitrogen/ammonia/air mixtures have been shown to have similar combustion characteristics as gasoline, they are also susceptible to auto-ignition or detonation phenomena.…”

Section: Introductionmentioning

confidence: 99%

“…Indeed, a better predictive model [27] has been developed based on machine learning, and more specifically by creating Artificial Neural Networks (ANNs), suitable for a wide range of combustible mixtures and initial conditions. Developing ANN models to predict detonation cell sizes has also been used in other studies [28,29], but in both cases, it was limited only to the few mixtures that were used to create these models. In contrast, our recently developed model [27] was trained on a wide range of mixtures and conditions, using chemical kinetic parameters as input features, meaning that it could technically be used for any mixture and initial condition, as long as the reaction zone structure described by the required chemical kinetic parameters could be computed.…”

Section: Introductionmentioning

confidence: 99%

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Detonation Cell Size Prediction Using Artificial Neural Networks (ANNs) for Hydrogen/Hydrocarbon/Ammonia/Nitrous Oxide Mixtures

Bakalis,

2024

Energies

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In this work, a previously developed three-feature Artificial Neural Network (ANN) model with dimensional inputs is directly applied to predict the cell size of hydrocarbon/ammonia/nitrous oxide mixtures and compare these to experimental data. This model uses as inputs three ZND parameters (MCJ, ΔI, and σ˙max), which are mainly calculated using Konnov’s and Mével’s mechanisms. A similar prediction is obtained with the two mechanisms for the biogas–O2, H2–O2, H2–N2O, and NH3–O2 mixtures, indicating that the model is not only limited to Konnov’s chemical kinetic mechanism which was used for its training. The overall good agreement between the ANN predictions and the actual experimental values for the aforementioned mixtures, which are not used in the original training of the ANN model, is promising and shows its potential for application and extension to other mixtures and initial conditions, provided that the chemical kinetic parameters describing the ideal reaction zone structure could be computed. The model is then used to compare experimental cell size data from two detonation tube facilities, and also different chemical kinetic mechanisms for NH3-N2O mixtures. In the end, the original ANN model is expanded with the inclusion of additional cell size data, showing a slightly lower mean error for the predicted cell sizes if the data for the mixtures considered in this study are taken into account for the training of the new ANN model.

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Section: Methodsmentioning

confidence: 99%

Section: Biogas-oxygen Mixtures (Ch 4 -Co 2 -O 2 )mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Detonation Cell Size Prediction Using Artificial Neural Networks (ANNs) for Hydrogen/Hydrocarbon/Ammonia/Nitrous Oxide Mixtures

Bakalis,

2024

Energies

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“…Case A uses the specific heat compensation method, while case B uses the dissociation reactions. The dissociation reaction rates are Energies 2023, 16, 5043 4 of 15 from the UCSD mechanism [15], which is extensively used and validated [16][17][18][19][20][21]. The geometry and boundary setup are shown in Figure 2.…”

Section: Iedm Modelmentioning

confidence: 99%

Further Development of Eddy Dissipation Model for Turbulent Non-Premixed Combustion Simulation

Chen

Wang

2023

Energies

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In view of the application limits of the modified eddy dissipation model (MEDM) in simulations of weakly turbulent flow, compressible flow, and internal flow, an improved eddy dissipation model (IEDM) is proposed. The IEDM model uses the dissociation reactions to obtain the correct combustion temperature instead of the specific heat compensation used in the MEDM model. This extends the application in compressible flow simulation. The simulation accuracy of the IEDM model for weakly turbulent flow is improved by using the accurate transport property and model. The maximum ε/k is limited to give a reasonable reaction rate near walls, and the expression for the model parameter A is also updated. Nine turbulent flames including seven jet flames and two opposed jet flames are simulated with the improved model. Compared with the experimental data of the jet flames, the peak temperature differences with the MEDM model and the IEDM model are 189 and 161 K, respectively, indicating the minor accuracy improvement of the IEDM model. Compared with the experimental data of the opposed flames, the peak temperature differences with the MEDM model and the IEDM model are 131 and 7 K, respectively, indicating the significant accuracy improvement of the IEDM model.

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Predicting detonation cell size of biogas–oxygen mixtures using machine learning models

Siatkowski,

Wacko,

Kindracki

2024

Shock Waves

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Detonation cell size is a very important parameter describing the detonation process, used both for explosion safety analysis and for the design of detonation combustion chambers. Typically it has been studied either experimentally or by CFD simulations; both options are costly in terms of money and time. However, progress in the machine learning (ML) methods opened a third way of obtaining cell size. When trained properly, such models are capable of giving rapid, accurate predictions. Utilization of machine learning in the combustion field is gaining more attention from the research community. In this study, the process of training, testing, and evaluation of three different machine learning models for predicting biogas–oxygen mixture detonation cell size is presented. The models include: linear regression (LR), support vector regression (SVR), and neural network (NN). The dataset used for training and testing comes from the experimental studies conducted previously by the authors. It was shown that all the models give very good results with support vector regression proving to be the best.

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Extensive study on the detonation cell size of biogas-oxygen mixtures

Cited by 4 publications

References 31 publications

Detonation Cell Size Prediction Using Artificial Neural Networks (ANNs) for Hydrogen/Hydrocarbon/Ammonia/Nitrous Oxide Mixtures

Detonation Cell Size Prediction Using Artificial Neural Networks (ANNs) for Hydrogen/Hydrocarbon/Ammonia/Nitrous Oxide Mixtures

Further Development of Eddy Dissipation Model for Turbulent Non-Premixed Combustion Simulation

Predicting detonation cell size of biogas–oxygen mixtures using machine learning models

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