This study investigated the effects of blending weight ratio of 5% and 10% ethanol (B7E5 and B7E10) with standard B7-diesel on the performance, combustion, and emission characteristics of a light-duty common-rail diesel engine. The engine was tested on a dynamometer across various speeds (1600–2000 rpm) and loads (84 and 112 Nm) to analyze in-cylinder pressure, thermal efficiencies, and exhaust emissions. Results indicated significant emission benefits, especially at high loads. The B7E10 blend reduced smoke intensity by approximately 75% and carbon dioxide emissions by 34% compared to the baseline B7. The performance analysis revealed a critical trade-off associated with the ethanol blends: while the inherent oxygen content in ethanol significantly improved the indicated thermal efficiency (ITE) through enhanced combustion, its lower viscosity simultaneously led to increased frictional losses. Consequently, these competing effects resulted in only a modest improvement in brake thermal efficiency (BTE) and comparable brake-specific energy consumption (BSEC) compared to the baseline B7. The primary objective is to identify the benefits and trade-offs associated with ethanol blending in biodiesel-based diesel fuels that are compatible with existing diesel vehicles.
{"title":"Influence of ethanol-blended B7-diesel on in-cylinder combustion characteristic, engine thermal efficiency and emission of a 3L-compression ignition engine","authors":"Teerapat Suteerapongpun , Poonnut Thaeviriyakul , Watanyoo Phairote , Peerawat Saisirirat , Watcharin Po-ngaen , Hidenori Kosaka , Preechar Karin","doi":"10.1016/j.jaecs.2026.100461","DOIUrl":"10.1016/j.jaecs.2026.100461","url":null,"abstract":"<div><div>This study investigated the effects of blending weight ratio of 5% and 10% ethanol (B7E5 and B7E10) with standard B7-diesel on the performance, combustion, and emission characteristics of a light-duty common-rail diesel engine. The engine was tested on a dynamometer across various speeds (1600–2000 rpm) and loads (84 and 112 Nm) to analyze in-cylinder pressure, thermal efficiencies, and exhaust emissions. Results indicated significant emission benefits, especially at high loads. The B7E10 blend reduced smoke intensity by approximately 75% and carbon dioxide emissions by 34% compared to the baseline B7. The performance analysis revealed a critical trade-off associated with the ethanol blends: while the inherent oxygen content in ethanol significantly improved the indicated thermal efficiency (ITE) through enhanced combustion, its lower viscosity simultaneously led to increased frictional losses. Consequently, these competing effects resulted in only a modest improvement in brake thermal efficiency (BTE) and comparable brake-specific energy consumption (BSEC) compared to the baseline B7. The primary objective is to identify the benefits and trade-offs associated with ethanol blending in biodiesel-based diesel fuels that are compatible with existing diesel vehicles.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100461"},"PeriodicalIF":5.0,"publicationDate":"2026-01-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977859","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.jaecs.2026.100460
Pourya Rahnama , Ricardo Novella , Bart Somers
This study combines a 0D Well Stirred Reactor (WSR), 1D counterflow flame, experimental data, and a 1D gas dynamic model to create an integrated modeling tool to study the dual fuel combustion behavior in engine-relevant conditions, fueled with E85 and diesel fuels. First, the performance of a reduced chemical kinetic mechanism is studied, and the most important reactions are identified. Subsequently, the validated mechanism is utilized to investigate ignition and flame propagation characteristics, and to analyze the combustion mode in different thermal and compositional stratification levels. The results reveal that under the studied operating conditions, auto-ignition of the background mixture was unlikely due to long ignition delay times compared to experimental combustion durations. Instead, the combustion mode is more of a partially premixed flame and diffusive combustion influenced by reactivity and thermal stratification. The effects of thermal stratification revealed that at higher temperatures of the background mixture, the location of the most reactive mixture fractions moves to the richer sides. Notably, low-temperature ignition behavior reflects the existence of cool flame chemistry near the stoichiometric zone, where intermediate species like formaldehyde form before full heat release occurs. When the oxidizer temperature increases further, a secondary, most reactive mixture fraction can be observed on the oxidizer (lean) side. Temperature and heat release rate profiles also revealed that at lower oxidizer temperatures, the heat release rate shows more traditional diffusive combustion behavior. However, at elevated temperatures, the secondary heat release rate, which corresponds to flame propagation, becomes more prominent. Increasing the ratio of E85 to diesel also influences the partially premixed flame propagation and its heat release. When the oxidizer temperature or E85 content is increased, the location of the secondary heat release moves further away to the oxidizer side, away from the stoichiometric region.
{"title":"A modeling study on the combustion characteristics of alcohol/diesel dual fuel counterflow flame","authors":"Pourya Rahnama , Ricardo Novella , Bart Somers","doi":"10.1016/j.jaecs.2026.100460","DOIUrl":"10.1016/j.jaecs.2026.100460","url":null,"abstract":"<div><div>This study combines a 0D Well Stirred Reactor (WSR), 1D counterflow flame, experimental data, and a 1D gas dynamic model to create an integrated modeling tool to study the dual fuel combustion behavior in engine-relevant conditions, fueled with E85 and diesel fuels. First, the performance of a reduced chemical kinetic mechanism is studied, and the most important reactions are identified. Subsequently, the validated mechanism is utilized to investigate ignition and flame propagation characteristics, and to analyze the combustion mode in different thermal and compositional stratification levels. The results reveal that under the studied operating conditions, auto-ignition of the background mixture was unlikely due to long ignition delay times compared to experimental combustion durations. Instead, the combustion mode is more of a partially premixed flame and diffusive combustion influenced by reactivity and thermal stratification. The effects of thermal stratification revealed that at higher temperatures of the background mixture, the location of the most reactive mixture fractions moves to the richer sides. Notably, low-temperature ignition behavior reflects the existence of cool flame chemistry near the stoichiometric zone, where intermediate species like formaldehyde form before full heat release occurs. When the oxidizer temperature increases further, a secondary, most reactive mixture fraction can be observed on the oxidizer (lean) side. Temperature and heat release rate profiles also revealed that at lower oxidizer temperatures, the heat release rate shows more traditional diffusive combustion behavior. However, at elevated temperatures, the secondary heat release rate, which corresponds to flame propagation, becomes more prominent. Increasing the ratio of E85 to diesel also influences the partially premixed flame propagation and its heat release. When the oxidizer temperature or E85 content is increased, the location of the secondary heat release moves further away to the oxidizer side, away from the stoichiometric region.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100460"},"PeriodicalIF":5.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-08DOI: 10.1016/j.jaecs.2026.100459
Jikang Wang , Yichen Wang, Yupeng Qin, Xuan Lv
This paper compares two data-driven frameworks for monitoring thermoacoustic instability (TAI) in a gas turbine combustor. A conventional machine learning approach using handcrafted features is contrasted with an end-to-end deep learning method employing a convolutional autoencoder (CNN-AE). Both frameworks generate a continuous stability index to quantify the transition from stable to unstable states. Using experimental data, both indices successfully track the entire dynamic evolution, including intermittent precursors. Critically, the CNN-AE autonomously learns a physically meaningful latent space. Visualizing this space reveals the system’s trajectory, showing a clear transition from a disordered attractor (combustion noise) to well-defined limit cycles (instability) through distinct topological shifts. The study demonstrates that deep representation learning not only automates monitoring but also provides a powerful tool for uncovering the underlying nonlinear dynamics of TAI.
{"title":"Monitoring thermoacoustic instability: A comparative analysis of feature-based and end-to-end deep learning approaches","authors":"Jikang Wang , Yichen Wang, Yupeng Qin, Xuan Lv","doi":"10.1016/j.jaecs.2026.100459","DOIUrl":"10.1016/j.jaecs.2026.100459","url":null,"abstract":"<div><div>This paper compares two data-driven frameworks for monitoring thermoacoustic instability (TAI) in a gas turbine combustor. A conventional machine learning approach using handcrafted features is contrasted with an end-to-end deep learning method employing a convolutional autoencoder (CNN-AE). Both frameworks generate a continuous stability index to quantify the transition from stable to unstable states. Using experimental data, both indices successfully track the entire dynamic evolution, including intermittent precursors. Critically, the CNN-AE autonomously learns a physically meaningful latent space. Visualizing this space reveals the system’s trajectory, showing a clear transition from a disordered attractor (combustion noise) to well-defined limit cycles (instability) through distinct topological shifts. The study demonstrates that deep representation learning not only automates monitoring but also provides a powerful tool for uncovering the underlying nonlinear dynamics of TAI.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100459"},"PeriodicalIF":5.0,"publicationDate":"2026-01-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-05DOI: 10.1016/j.jaecs.2026.100458
Mingyu Li , Ruixiao Li , Vladimir Zarko , Richard A. Yetter , Zhongyue Zhou , Weiqiang Pang
Nano-sized boron (nB)-based composite energetic materials (CEMs) are an emerging class of high-energy-density fuels with excellent combustion performance, offering broad potential applications in space propulsion and explosives. In this paper, we review the various preparation techniques for nB-based CEMs (comparing their respective advantages and limitations) and discuss the combustion characteristics and reaction mechanisms of these materials, while also surveying current development trends and future challenges. Recent findings show that incorporating nB significantly improves the ignition characteristics, burning rates, and overall energy release efficiency of B-based energetic formulations. In particular, nB-based composites exhibit faster reaction kinetics, higher energy release rates, and greater gas generation than their micro-sized boron (μB) counterparts. These enhancements underscore the promise of nB-based CEMs for next-generation propellants, explosives, and pyrotechnics, and existing research has already laid a solid foundation for further progress in designing such advanced energetic systems.
{"title":"Nano-sized boron composites energetic materials: Preparation, combustion and mechanism","authors":"Mingyu Li , Ruixiao Li , Vladimir Zarko , Richard A. Yetter , Zhongyue Zhou , Weiqiang Pang","doi":"10.1016/j.jaecs.2026.100458","DOIUrl":"10.1016/j.jaecs.2026.100458","url":null,"abstract":"<div><div>Nano-sized boron (nB)-based composite energetic materials (CEMs) are an emerging class of high-energy-density fuels with excellent combustion performance, offering broad potential applications in space propulsion and explosives. In this paper, we review the various preparation techniques for nB-based CEMs (comparing their respective advantages and limitations) and discuss the combustion characteristics and reaction mechanisms of these materials, while also surveying current development trends and future challenges. Recent findings show that incorporating nB significantly improves the ignition characteristics, burning rates, and overall energy release efficiency of B-based energetic formulations. In particular, nB-based composites exhibit faster reaction kinetics, higher energy release rates, and greater gas generation than their micro-sized boron (μB) counterparts. These enhancements underscore the promise of nB-based CEMs for next-generation propellants, explosives, and pyrotechnics, and existing research has already laid a solid foundation for further progress in designing such advanced energetic systems.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100458"},"PeriodicalIF":5.0,"publicationDate":"2026-01-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-01DOI: 10.1016/j.jaecs.2026.100457
Márton Kovács , Kaito Hirose , Koji Shimoyama , Hisashi Nakamura
A methodology is presented to develop compact, high-fidelity simplified reaction models for hydrocarbon combustion using virtual species and simplified reaction pathways, with rate parameters optimized via a genetic algorithm (GA). The method was applied to methane and natural gas combustion, targeting key combustion properties: ignition delay times (IDT) and laminar burning velocities (LBV). The approach combines a detailed H2/CO core with virtual reactions representing the main fuel oxidation pathways through fuel, fuel radical, and aldehyde virtual species. For natural gas, fuel components were lumped, and averaged thermodynamic properties were assigned to the virtual species. The optimization process produced simplified models with 14 species and 57 reactions, which could accurately reproduce the IDT and LBV simulation results of the AramcoMech 3.0 detailed model across a wide range of equivalence ratios and temperatures. The mean absolute deviations for all test conditions were 11.9% for IDT and 2.5% for LBV in methane, and 10.5% for IDT and 1.4% for LBV in natural gas simulations. The models could capture the tendency differences between methane/air and natural gas/air mixtures in ignition characteristics while preserving the similarities in flame propagation. The proposed method offers a practical alternative to conventional reduction techniques, enabling the generation of simple yet accurate reaction models suitable for CFD simulations in practical combustors with significantly reduced computational cost.
{"title":"Generating a simplified reaction model for methane and natural gas combustion using a genetic algorithm","authors":"Márton Kovács , Kaito Hirose , Koji Shimoyama , Hisashi Nakamura","doi":"10.1016/j.jaecs.2026.100457","DOIUrl":"10.1016/j.jaecs.2026.100457","url":null,"abstract":"<div><div>A methodology is presented to develop compact, high-fidelity simplified reaction models for hydrocarbon combustion using virtual species and simplified reaction pathways, with rate parameters optimized via a genetic algorithm (GA). The method was applied to methane and natural gas combustion, targeting key combustion properties: ignition delay times (IDT) and laminar burning velocities (LBV). The approach combines a detailed H<sub>2</sub>/CO core with virtual reactions representing the main fuel oxidation pathways through fuel, fuel radical, and aldehyde virtual species. For natural gas, fuel components were lumped, and averaged thermodynamic properties were assigned to the virtual species. The optimization process produced simplified models with 14 species and 57 reactions, which could accurately reproduce the IDT and LBV simulation results of the AramcoMech 3.0 detailed model across a wide range of equivalence ratios and temperatures. The mean absolute deviations for all test conditions were 11.9% for IDT and 2.5% for LBV in methane, and 10.5% for IDT and 1.4% for LBV in natural gas simulations. The models could capture the tendency differences between methane/air and natural gas/air mixtures in ignition characteristics while preserving the similarities in flame propagation. The proposed method offers a practical alternative to conventional reduction techniques, enabling the generation of simple yet accurate reaction models suitable for CFD simulations in practical combustors with significantly reduced computational cost.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100457"},"PeriodicalIF":5.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977860","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The rapid growth of municipal solid waste (MSW) has posed severe environmental challenges, making its safe and resource-efficient disposal crucial for urban sustainable development. Gasification technology offers a promising route for the high-value utilization of MSW by converting it into hydrogen-rich syngas that can be used for power generation, heating, or the production of high-purity H2 fuels. However, the heterogeneous and variable composition of MSW complicates gasification, while the coupled effects of operating parameters on H2 formation remain insufficiently understood. Moreover, a systematic understanding that integrates feedstock characteristics, process optimization, and emerging gasification technologies for efficient H2 generation is still lacking. Therefore, this review systematically summarizes the fundamental characteristics of MSW, the pyrolysis and gasification behaviors of MSW and products characteristics. Key process parameters affecting hydrogen production, including gasifying agents, reaction temperature, residence time, and catalyst type, are critically analyzed. In addition, recent advances in novel heating-assisted gasification technologies, including plasma, Joule heating, electromagnetic induction heating, and microwave heating, are reviewed, together with novel processes such as chemical looping gasification. Finally, large-scale industrial applications of MSW gasification and the recent syngas purification methods for pure H2 production are summarized, followed by an outlook on the future development trends and research priorities for MSW gasification toward sustainable hydrogen production. This review is expected to provide valuable guidance for the process optimization, development of novel gasification technologies, and engineering application of MSW gasification technology for hydrogen production.
{"title":"A review of municipal solid waste gasification for hydrogen production: Influencing factors, novel technologies, and engineering prospects","authors":"Yongfeng Jiang , Zixuan Yuan , Hao Jiang, Hao Song, Qiang Hu, Jiageng Xia, Haiping Yang, Hanping Chen","doi":"10.1016/j.jaecs.2026.100456","DOIUrl":"10.1016/j.jaecs.2026.100456","url":null,"abstract":"<div><div>The rapid growth of municipal solid waste (MSW) has posed severe environmental challenges, making its safe and resource-efficient disposal crucial for urban sustainable development. Gasification technology offers a promising route for the high-value utilization of MSW by converting it into hydrogen-rich syngas that can be used for power generation, heating, or the production of high-purity H<sub>2</sub> fuels. However, the heterogeneous and variable composition of MSW complicates gasification, while the coupled effects of operating parameters on H<sub>2</sub> formation remain insufficiently understood. Moreover, a systematic understanding that integrates feedstock characteristics, process optimization, and emerging gasification technologies for efficient H<sub>2</sub> generation is still lacking. Therefore, this review systematically summarizes the fundamental characteristics of MSW, the pyrolysis and gasification behaviors of MSW and products characteristics. Key process parameters affecting hydrogen production, including gasifying agents, reaction temperature, residence time, and catalyst type, are critically analyzed. In addition, recent advances in novel heating-assisted gasification technologies, including plasma, Joule heating, electromagnetic induction heating, and microwave heating, are reviewed, together with novel processes such as chemical looping gasification. Finally, large-scale industrial applications of MSW gasification and the recent syngas purification methods for pure H<sub>2</sub> production are summarized, followed by an outlook on the future development trends and research priorities for MSW gasification toward sustainable hydrogen production. This review is expected to provide valuable guidance for the process optimization, development of novel gasification technologies, and engineering application of MSW gasification technology for hydrogen production.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100456"},"PeriodicalIF":5.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145977862","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-29DOI: 10.1016/j.jaecs.2025.100455
Haowen Chen, Benjamin Böhm, Tao Li
Particle velocity is an essential parameter in solid fuel combustion studies, however, the accurate detection and tracking of particles in high particle number density (PND) combustion scenario remain challenging. The current study advances the machine-learning particle detection approaches for precise velocity measurements of solid particles. For visualizing particle locations, time-resolved laser Mie scattering experiments were performed for high-volatile bituminous (hvb) coal particles of different size burning in a high-temperature oxidizing laminar flow. The machine learning (ML) based object detection models you only look once (YOLO) and realtime detection transformer (RT-DETR) were trained on the conventional blob detection annotations (weak-label) from low-PND cases and evaluated against the manually labeled images from high-PND cases, which served as ground truth. Particle tracking was then performed using the simple online realtime tracking (SORT) algorithm. The results demonstrate that models trained on a limited set of weak-label data can achieve satisfactory prediction performance in complex environments that are difficult for traditional object detection methods. Slicing aided hyper inference (SAHI) algorithm is implemented for improving the performance of the used ML models. By evaluating the velocity statistics, it is found that the mean particle velocity decreases with increasing PND and particle size, primarily due to stronger particle–gas and particle–particle interactions. The particle dynamics are closely related to the position of volatile combustion zone.
{"title":"Machine learning enhanced time-resolved multi-particle tracking velocimetry in solid fuel particle group combustion","authors":"Haowen Chen, Benjamin Böhm, Tao Li","doi":"10.1016/j.jaecs.2025.100455","DOIUrl":"10.1016/j.jaecs.2025.100455","url":null,"abstract":"<div><div>Particle velocity is an essential parameter in solid fuel combustion studies, however, the accurate detection and tracking of particles in high particle number density (PND) combustion scenario remain challenging. The current study advances the machine-learning particle detection approaches for precise velocity measurements of solid particles. For visualizing particle locations, time-resolved laser Mie scattering experiments were performed for high-volatile bituminous (hvb) coal particles of different size burning in a high-temperature oxidizing laminar flow. The machine learning (ML) based object detection models you only look once (YOLO) and realtime detection transformer (RT-DETR) were trained on the conventional blob detection annotations (weak-label) from low-PND cases and evaluated against the manually labeled images from high-PND cases, which served as ground truth. Particle tracking was then performed using the simple online realtime tracking (SORT) algorithm. The results demonstrate that models trained on a limited set of weak-label data can achieve satisfactory prediction performance in complex environments that are difficult for traditional object detection methods. Slicing aided hyper inference (SAHI) algorithm is implemented for improving the performance of the used ML models. By evaluating the velocity statistics, it is found that the mean particle velocity decreases with increasing PND and particle size, primarily due to stronger particle–gas and particle–particle interactions. The particle dynamics are closely related to the position of volatile combustion zone.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100455"},"PeriodicalIF":5.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-24DOI: 10.1016/j.jaecs.2025.100453
Francesco Cenvinzo , Alberto Procacci , Alessandro Parente , Pascale Domingo , Luc Vervisch
Despite advances in computing power, a major limitation in the simulation of turbulent flame stems from the need to track all chemical species involved in the thin reaction zones throughout the flow field. This paper investigates how Reduced Order Models (ROMs), combining data-driven analysis and neural network training, can significantly reduce computational cost. Specifically, neural networks are employed to assist in solving , a thermochemical scalar representing species mass fractions, energy, or temperature. The evolution of over time steps is used as input to a ROM framework, in which dimensionality reduction is achieved using Proper Orthogonal Decomposition (POD), while temporal dynamics are modeled using a Long Short-Term Memory (LSTM) network, with ANN trained for each of the retained POD modes. The scalar field for the subsequent time steps is then predicted by the network, bypassing the need to solve the transport equation for these iterations. In this work the pair of values () and () are implemented. This approach is first validated on a non-reactive Large Eddy Simulation (LES) of a cavity flow, where air and H2 are injected separately and mix downstream. The methodology is then extended to a reactive Unsteady Reynolds-Averaged Navier–Stokes (URANS) simulation of a non-premixed H2-air flame stabilized downstream of the same cavity geometry, assuming infinitely fast chemistry. When skipping CFD iterations, the network can also predict the flow evolution over a time step that is ten times larger than the standard CFD time step. This leads to a reduction in computational cost to reach a given physical time. Results demonstrate that the ROM is capable of accurately predicting the unsteady dynamics of the turbulent system across testing sequences unseen during training. The approach yields a CPU time saving of the order of 27%.
尽管计算能力有所进步，但湍流火焰模拟的一个主要限制源于需要跟踪整个流场中薄反应区涉及的所有化学物质。本文研究了将数据驱动分析和神经网络训练相结合的降阶模型（ROMs）如何显著降低计算成本。具体来说，神经网络被用来帮助解决φ (x *,t)，一个热化学标量表示物种的质量分数，能量，或温度。无时间步长的ϕ（x *,t）演变被用作ROM框架的输入，其中使用适当的正交分解（POD）实现降维，而时间动态使用长短期记忆（LSTM）网络建模，并为每个保留的POD模式训练人工神经网络。然后由网络预测nrom后续时间步的标量场，而不需要为这些迭代求解传输方程。在这项工作中，实现了一对值（no=10,nrom=1）和（no=20,nrom=5）。该方法首先在空腔流的非反应性大涡模拟（LES）中得到验证，其中空气和H2分别注入并在下游混合。然后将该方法扩展到反应非定常reynolds - average Navier-Stokes （URANS）模拟中，该模拟是在相同腔体几何形状的下游稳定的非预混h2 -空气火焰，假设化学反应无限快。当跳过CFD迭代时，该网络还可以预测比标准CFD时间步长10倍的流动演变。这可以减少达到给定物理时间的计算成本。结果表明，ROM能够准确地预测湍流系统在训练过程中未见的测试序列的非定常动力学。这种方法可以节省大约27%的CPU时间。
{"title":"Accelerating mixing controlled turbulent combustion simulations with hybrid Navier–Stokes/ANN scalar-solvers","authors":"Francesco Cenvinzo , Alberto Procacci , Alessandro Parente , Pascale Domingo , Luc Vervisch","doi":"10.1016/j.jaecs.2025.100453","DOIUrl":"10.1016/j.jaecs.2025.100453","url":null,"abstract":"<div><div>Despite advances in computing power, a major limitation in the simulation of turbulent flame stems from the need to track all chemical species involved in the thin reaction zones throughout the flow field. This paper investigates how Reduced Order Models (ROMs), combining data-driven analysis and neural network training, can significantly reduce computational cost. Specifically, neural networks are employed to assist in solving <span><math><mrow><mi>ϕ</mi><mrow><mo>(</mo><munder><mrow><mi>x</mi></mrow><mo>̲</mo></munder><mo>,</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span>, a thermochemical scalar representing species mass fractions, energy, or temperature. The evolution of <span><math><mrow><mi>ϕ</mi><mrow><mo>(</mo><munder><mrow><mi>x</mi></mrow><mo>̲</mo></munder><mo>,</mo><mi>t</mi><mo>)</mo></mrow></mrow></math></span> over <span><math><msup><mrow><mi>n</mi></mrow><mrow><mi>o</mi></mrow></msup></math></span> time steps is used as input to a ROM framework, in which dimensionality reduction is achieved using Proper Orthogonal Decomposition (POD), while temporal dynamics are modeled using a Long Short-Term Memory (LSTM) network, with ANN trained for each of the retained POD modes. The scalar field for the <span><math><msup><mrow><mi>n</mi></mrow><mrow><mi>r</mi><mi>o</mi><mi>m</mi></mrow></msup></math></span> subsequent time steps is then predicted by the network, bypassing the need to solve the transport equation for these iterations. In this work the pair of values (<span><math><mrow><msup><mrow><mi>n</mi></mrow><mrow><mi>o</mi></mrow></msup><mo>=</mo><mn>10</mn><mo>,</mo><msup><mrow><mi>n</mi></mrow><mrow><mi>r</mi><mi>o</mi><mi>m</mi></mrow></msup><mo>=</mo><mn>1</mn></mrow></math></span>) and (<span><math><mrow><msup><mrow><mi>n</mi></mrow><mrow><mi>o</mi></mrow></msup><mo>=</mo><mn>20</mn><mo>,</mo><msup><mrow><mi>n</mi></mrow><mrow><mi>r</mi><mi>o</mi><mi>m</mi></mrow></msup><mo>=</mo><mn>5</mn></mrow></math></span>) are implemented. This approach is first validated on a non-reactive Large Eddy Simulation (LES) of a cavity flow, where air and H<sub>2</sub> are injected separately and mix downstream. The methodology is then extended to a reactive Unsteady Reynolds-Averaged Navier–Stokes (URANS) simulation of a non-premixed H<sub>2</sub>-air flame stabilized downstream of the same cavity geometry, assuming infinitely fast chemistry. When skipping CFD iterations, the network can also predict the flow evolution over a time step that is ten times larger than the standard CFD time step. This leads to a reduction in computational cost to reach a given physical time. Results demonstrate that the ROM is capable of accurately predicting the unsteady dynamics of the turbulent system across testing sequences unseen during training. The approach yields a CPU time saving of the order of 27%.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100453"},"PeriodicalIF":5.0,"publicationDate":"2025-12-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925454","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-20DOI: 10.1016/j.jaecs.2025.100450
Patrick A. Meagher , Samuel Calello III , Xinyu Zhao
Despite extensive studies of local and global extinction, it remains unclear how local flame dynamics contribute to the global evolution of a turbulent flame at large Karlovitz numbers. Furthermore, the extent to which canonical laminar flame properties, such as laminar flame speed and extinction strain rate, govern the behavior of highly turbulent flames is not yet well understood. Unique to these conditions is the formation of isolated Product-in-Fuel (PiF) pockets from an initially continuous flame surface. The present study proposes that local extinctions are coupled to the global ignition or extinction of a turbulent flame through the PiF pocket mechanism. Direct numerical simulations of a methane/air flame kernel subject to homogeneous isotropic turbulence at Karlovitz numbers larger than 100 are conducted. A 17-species reduced mechanism derived from FFCM-1 is employed and later perturbed to conduct the parametric studies. The topology and evolution of the PiF pockets are quantified. The distribution of thermochemical states in spatial coordinates relative to the flame surface demonstrates that the quenching pockets serve to deposit sensible enthalpy, combustion products, and radicals (particularly CHO) into the fresh mixture ahead of the flame. To evaluate the role of canonical flame properties on the pocket formation and flame evolution, the baseline methane/air chemical kinetics model is perturbed using a Monte Carlo method to generate modified reaction mechanisms that individually alter the laminar flame speed and extinction strain rate. The laminar flame speed is found to be the most relevant parameter for the current configuration, counteracting the turbulent deformation of the flame and ensuring the necessary product support for flame propagation.
{"title":"Role of pocket formation in the extinction of methane flames subject to strong turbulence","authors":"Patrick A. Meagher , Samuel Calello III , Xinyu Zhao","doi":"10.1016/j.jaecs.2025.100450","DOIUrl":"10.1016/j.jaecs.2025.100450","url":null,"abstract":"<div><div>Despite extensive studies of local and global extinction, it remains unclear how local flame dynamics contribute to the global evolution of a turbulent flame at large Karlovitz numbers. Furthermore, the extent to which canonical laminar flame properties, such as laminar flame speed and extinction strain rate, govern the behavior of highly turbulent flames is not yet well understood. Unique to these conditions is the formation of isolated Product-in-Fuel (PiF) pockets from an initially continuous flame surface. The present study proposes that local extinctions are coupled to the global ignition or extinction of a turbulent flame through the PiF pocket mechanism. Direct numerical simulations of a methane/air flame kernel subject to homogeneous isotropic turbulence at Karlovitz numbers larger than 100 are conducted. A 17-species reduced mechanism derived from FFCM-1 is employed and later perturbed to conduct the parametric studies. The topology and evolution of the PiF pockets are quantified. The distribution of thermochemical states in spatial coordinates relative to the flame surface demonstrates that the quenching pockets serve to deposit sensible enthalpy, combustion products, and radicals (particularly CH<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>O) into the fresh mixture ahead of the flame. To evaluate the role of canonical flame properties on the pocket formation and flame evolution, the baseline methane/air chemical kinetics model is perturbed using a Monte Carlo method to generate modified reaction mechanisms that individually alter the laminar flame speed and extinction strain rate. The laminar flame speed is found to be the most relevant parameter for the current configuration, counteracting the turbulent deformation of the flame and ensuring the necessary product support for flame propagation.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100450"},"PeriodicalIF":5.0,"publicationDate":"2025-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925586","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-18DOI: 10.1016/j.jaecs.2025.100451
Jiahui Zhu , Ning Cai , Chuanwen Zhao , Haiping Yang
Thermochemical conversion of waste plastics presents a pivotal strategy for simultaneously addressing global plastic pollution and clean hydrogen demand. This review provides a systematic synthesis of established pathways, including catalytic pyrolysis and gasification, alongside emerging electrification technologies such as microwave-assisted conversion and Flash Joule Heating (FJH). Beyond descriptive summaries, we strictly benchmark these routes regarding hydrogen yield, energy efficiency, and techno-economic viability. The analysis reveals distinct trade-offs: while gasification currently offers the lowest levelized cost due to economies of scale, emerging electrified pathways demonstrate superior specific energy efficiency and potential negative production costs via high-value co-products. Critical technical bottlenecks are critically examined, with a focus on deciphering catalyst deactivation mechanisms—specifically coking and heteroatom poisoning—and evaluating scale-up constraints based on industrial pilot cases. Furthermore, by integrating insights from Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA), we clarify the divergence between economic drivers and environmental benefits. The review concludes by proposing a forward-looking strategic framework that prioritizes impurity-tolerant reactor designs and continuous process integration, aiming to bridge the gap between laboratory technical potential and sustainable industrial realization.
{"title":"Research progress on thermochemical conversion technologies for hydrogen production from waste plastics","authors":"Jiahui Zhu , Ning Cai , Chuanwen Zhao , Haiping Yang","doi":"10.1016/j.jaecs.2025.100451","DOIUrl":"10.1016/j.jaecs.2025.100451","url":null,"abstract":"<div><div>Thermochemical conversion of waste plastics presents a pivotal strategy for simultaneously addressing global plastic pollution and clean hydrogen demand. This review provides a systematic synthesis of established pathways, including catalytic pyrolysis and gasification, alongside emerging electrification technologies such as microwave-assisted conversion and Flash Joule Heating (FJH). Beyond descriptive summaries, we strictly benchmark these routes regarding hydrogen yield, energy efficiency, and techno-economic viability. The analysis reveals distinct trade-offs: while gasification currently offers the lowest levelized cost due to economies of scale, emerging electrified pathways demonstrate superior specific energy efficiency and potential negative production costs via high-value co-products. Critical technical bottlenecks are critically examined, with a focus on deciphering catalyst deactivation mechanisms—specifically coking and heteroatom poisoning—and evaluating scale-up constraints based on industrial pilot cases. Furthermore, by integrating insights from Techno-Economic Analysis (TEA) and Life Cycle Assessment (LCA), we clarify the divergence between economic drivers and environmental benefits. The review concludes by proposing a forward-looking strategic framework that prioritizes impurity-tolerant reactor designs and continuous process integration, aiming to bridge the gap between laboratory technical potential and sustainable industrial realization.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100451"},"PeriodicalIF":5.0,"publicationDate":"2025-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925585","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
扫码关注我们
求助内容：
应助结果提醒方式：
京公网安备 11010802042870号