Pub Date : 2025-12-10DOI: 10.1016/j.jaecs.2025.100446
Abdul Basyir , Ho Sung Kim , Jung Keun Cha , Soo Hyung Kim
In this study, the effects of particle size variation (micro- and nano-scale) in fullerene (C60) on the safety, ignition, combustion, and propulsion performances of Al/CuO-based energetic materials (EMs) were systematically investigated. Micro- (mC60) and nano- (nC60) C60 were incorporated into Al/CuO composites with various contents, and their ignition–combustion behaviors, combustion pressure, burning rate, energy density, and propulsion performance were quantitatively compared. Safety tests indicated that mC60 improved mechanical insensitivity acting as a partial binder that mitigates friction and impact sensitivity. Increasing C60 content further enhanced this insensitivity. In contrast, nC60 addition markedly increased combustion pressure, burning rate, and energy density. Optimum performance was achieved at 1 wt.% mC60 and 5 wt.% nC60, while excessive addition reduced reactivity by hindering Al–CuO interfacial contact. In both bullet-type and rocket-type propulsion tests, nC60 incorporation significantly enhanced thrust, projectile velocity, and specific impulse. These results demonstrate that the particle size of C60 critically governs the safety and energetic performance of Al/CuO-based EMs, offering a strategy to design high-safety, high-performance propellants.
{"title":"Ignition, combustion, and energy release characteristics of Al/CuO energetic composites incorporated with micro- and nano-sized fullerene additives","authors":"Abdul Basyir , Ho Sung Kim , Jung Keun Cha , Soo Hyung Kim","doi":"10.1016/j.jaecs.2025.100446","DOIUrl":"10.1016/j.jaecs.2025.100446","url":null,"abstract":"<div><div>In this study, the effects of particle size variation (micro- and nano-scale) in fullerene (C<sub>60</sub>) on the safety, ignition, combustion, and propulsion performances of Al/CuO-based energetic materials (EMs) were systematically investigated. Micro- (mC<sub>60</sub>) and nano- (nC<sub>60</sub>) C<sub>60</sub> were incorporated into Al/CuO composites with various contents, and their ignition–combustion behaviors, combustion pressure, burning rate, energy density, and propulsion performance were quantitatively compared. Safety tests indicated that mC<sub>60</sub> improved mechanical insensitivity acting as a partial binder that mitigates friction and impact sensitivity. Increasing C<sub>60</sub> content further enhanced this insensitivity. In contrast, nC<sub>60</sub> addition markedly increased combustion pressure, burning rate, and energy density. Optimum performance was achieved at 1 wt.% mC<sub>60</sub> and 5 wt.% nC<sub>60</sub>, while excessive addition reduced reactivity by hindering Al–CuO interfacial contact. In both bullet-type and rocket-type propulsion tests, nC<sub>60</sub> incorporation significantly enhanced thrust, projectile velocity, and specific impulse. These results demonstrate that the particle size of C<sub>60</sub> critically governs the safety and energetic performance of Al/CuO-based EMs, offering a strategy to design high-safety, high-performance propellants.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100446"},"PeriodicalIF":5.0,"publicationDate":"2025-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737615","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 : 2025-12-04DOI: 10.1016/j.jaecs.2025.100443
Anna L. Stevenson, Chloe E. Dedic
Coherent control for the selective spectral suppression of combustion species using femtosecond/picosecond coherent anti-Stokes Raman scattering (fs/ps CARS) is presented. Phase modulation of one of the excitation pulses was accomplished using a 4-f pulse shaper with a spatial light modulator at the Fourier plane. A feedback-controlled genetic algorithm was used to search for the appropriate modulated waveform in a non-reacting gas jet of known composition; the selected phase function was subsequently applied in a H/air flat-flame diluted with CO. Q-branch transitions of O and CO were targeted. To our knowledge, this is the first demonstration of coherent control using fs/ps CARS for spectral species selectivity in a combustion environment.
{"title":"Coherent control for species selectivity in a flat-flame using femtosecond/picosecond coherent Raman scattering","authors":"Anna L. Stevenson, Chloe E. Dedic","doi":"10.1016/j.jaecs.2025.100443","DOIUrl":"10.1016/j.jaecs.2025.100443","url":null,"abstract":"<div><div>Coherent control for the selective spectral suppression of combustion species using femtosecond/picosecond coherent anti-Stokes Raman scattering (fs/ps CARS) is presented. Phase modulation of one of the excitation pulses was accomplished using a 4-<em>f</em> pulse shaper with a spatial light modulator at the Fourier plane. A feedback-controlled genetic algorithm was used to search for the appropriate modulated waveform in a non-reacting gas jet of known composition; the selected phase function was subsequently applied in a H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>/air flat-flame diluted with CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>. Q-branch transitions of O<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> and CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> were targeted. To our knowledge, this is the first demonstration of coherent control using fs/ps CARS for spectral species selectivity in a combustion environment.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100443"},"PeriodicalIF":5.0,"publicationDate":"2025-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737620","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 : 2025-12-03DOI: 10.1016/j.jaecs.2025.100440
Ning Guo , Erik Jansson , Mattias Johansson , Ronny Lindgren , Andreas Nyman , Jonas Sjöblom
Predictive maintenance in internal combustion engines can be enhanced by accurately identifying the fuel type based on data collected from sensors or electronic control units (ECUs). This paper presents a study that aims to predict the fuel type (HVO100 or EN590) using machine learning techniques, specifically based only on the engine's rotational speed. The rotational speed data of a heavy-duty 6-cylinder diesel engine is measured and downsampled to frequencies of 100, 1000, and 10,000 Hz. To extract relevant features from the time series data, hundreds of features are extracted using hypothesis tests via the tsfresh library. Subsequently, selected features are trained using Databricks' automated machine learning (AutoML) platform. The study explores the relationships between the number of features, downsampling frequency, and the choice of machine learning models. The results indicate that, under the current configuration, the best test F1 score of 0.995 is achieved using logistic regression with 20 features and a downsampling frequency of 10,000 Hz. The analysis of SHAP values and p-values revealed that components of the Fourier transform and wavelet transform of the rotational speed play crucial roles in distinguishing between the fuel types. It is our hypothesis that the differences observed in the frequency domain are related to variations in fuel characteristics. Overall, this study presents a simple, interpretable, and computationally cost-efficient machine learning solution for predicting fuel type in industrial engines. The findings demonstrate the potential of applying this approach in real-world production environments.
{"title":"Classification of fuel type for predictive maintenance in marine and industrial engines using time series feature extraction based on hypothesis tests and automated machine learning","authors":"Ning Guo , Erik Jansson , Mattias Johansson , Ronny Lindgren , Andreas Nyman , Jonas Sjöblom","doi":"10.1016/j.jaecs.2025.100440","DOIUrl":"10.1016/j.jaecs.2025.100440","url":null,"abstract":"<div><div>Predictive maintenance in internal combustion engines can be enhanced by accurately identifying the fuel type based on data collected from sensors or electronic control units (ECUs). This paper presents a study that aims to predict the fuel type (HVO100 or EN590) using machine learning techniques, specifically based only on the engine's rotational speed. The rotational speed data of a heavy-duty 6-cylinder diesel engine is measured and downsampled to frequencies of 100, 1000, and 10,000 Hz. To extract relevant features from the time series data, hundreds of features are extracted using hypothesis tests via the tsfresh library. Subsequently, selected features are trained using Databricks' automated machine learning (AutoML) platform. The study explores the relationships between the number of features, downsampling frequency, and the choice of machine learning models. The results indicate that, under the current configuration, the best test F1 score of 0.995 is achieved using logistic regression with 20 features and a downsampling frequency of 10,000 Hz. The analysis of SHAP values and p-values revealed that components of the Fourier transform and wavelet transform of the rotational speed play crucial roles in distinguishing between the fuel types. It is our hypothesis that the differences observed in the frequency domain are related to variations in fuel characteristics. Overall, this study presents a simple, interpretable, and computationally cost-efficient machine learning solution for predicting fuel type in industrial engines. The findings demonstrate the potential of applying this approach in real-world production environments.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100440"},"PeriodicalIF":5.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737616","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 : 2025-12-03DOI: 10.1016/j.jaecs.2025.100442
Xinrui Ren , Huixin Yang , Molly Meng-Jung Li , Shao-Yuan Leu , Carol Sze Ki Lin , Xiaoge Zhang , Chih-yung Wen , Christopher Yu Hang Chao , Song Cheng
Sustainable aviation fuel (SAF) is considered the primary technological pathway to decarbonize the global civil aviation in the short to medium term. However, the scale-up of SAFs has been hampered by the high risk and high cost associated with assessing SAFs. Therefore, this study aims to develop an innovative framework to enable streamlined assessment and optimization of SAFs. A multi-scheme semi-supervised few-shot learning (MSSFSL) framework is first proposed and demonstrated to reconstruct a limited jet fuel database with respect to the information on ASTM-specified jet fuel properties. The database is further expanded via a novel synthetic data generation framework, where the database is enriched from only 48 jet fuel samples to over 2000 jet fuel samples. With the reconstructed and enriched database in conjunction with stacking ensemble learning, an accurate and reliable reciprocating mapping between jet fuel compositions and 14 ASTM-specified jet fuel properties (including density, derived cetane number, six distillation temperatures, flash point, freezing point, heat of combustion, surface tension, and viscosities) is established, based on which excellent prediction accuracy is achieved for practical petroleum-derived jet fuels, SAFs and their blends, with R2 values above 0.8 achieved for all properties and relative prediction error reduced by approximately 2 orders of magnitude. Correlation analysis between jet fuel properties and compositions is subsequently conducted using a case study of ATJ, which further reveals the commendable contribution of iso-paraffins and di-cycloparaffins to the performance of SAFs (e.g., heat of combustion and freezing point). A genetic algorithm-based optimization framework is further developed and demonstrated through a case study where the 14 ASTM-specified jet fuel properties of three SAF candidates (i.e., one 100 % SAF and two 50 %/50 % SAF/Jet A blends) are optimized to fall within the ASTM tolerance and the respective property ranges of Jet A and Jet A-1. The proposed framework enables real-time, risk-lean and cost-effective SAF assessment and fine-tuning at lab-scale based on jet fuel standards and feedback from end-users before platform scale-up, which is expected to facilitate SAF development considerably. The developed model is released as a module of the SAFRA_Master_1.0 software package, hosted free of charge at the UHPC laboratory website (https://uhpc-lab.org/downloads/).
{"title":"Streamlined assessment and optimization of sustainable aviation fuels via few-shot learning and tailored genetic algorithm","authors":"Xinrui Ren , Huixin Yang , Molly Meng-Jung Li , Shao-Yuan Leu , Carol Sze Ki Lin , Xiaoge Zhang , Chih-yung Wen , Christopher Yu Hang Chao , Song Cheng","doi":"10.1016/j.jaecs.2025.100442","DOIUrl":"10.1016/j.jaecs.2025.100442","url":null,"abstract":"<div><div>Sustainable aviation fuel (SAF) is considered the primary technological pathway to decarbonize the global civil aviation in the short to medium term. However, the scale-up of SAFs has been hampered by the high risk and high cost associated with assessing SAFs. Therefore, this study aims to develop an innovative framework to enable streamlined assessment and optimization of SAFs. A multi-scheme semi-supervised few-shot learning (MSSFSL) framework is first proposed and demonstrated to reconstruct a limited jet fuel database with respect to the information on ASTM-specified jet fuel properties. The database is further expanded via a novel synthetic data generation framework, where the database is enriched from only 48 jet fuel samples to over 2000 jet fuel samples. With the reconstructed and enriched database in conjunction with stacking ensemble learning, an accurate and reliable reciprocating mapping between jet fuel compositions and 14 ASTM-specified jet fuel properties (including density, derived cetane number, six distillation temperatures, flash point, freezing point, heat of combustion, surface tension, and viscosities) is established, based on which excellent prediction accuracy is achieved for practical petroleum-derived jet fuels, SAFs and their blends, with R<sup>2</sup> values above 0.8 achieved for all properties and relative prediction error reduced by approximately 2 orders of magnitude. Correlation analysis between jet fuel properties and compositions is subsequently conducted using a case study of ATJ, which further reveals the commendable contribution of iso-paraffins and di-cycloparaffins to the performance of SAFs (e.g., heat of combustion and freezing point). A genetic algorithm-based optimization framework is further developed and demonstrated through a case study where the 14 ASTM-specified jet fuel properties of three SAF candidates (i.e., one 100 % SAF and two 50 %/50 % SAF/Jet A blends) are optimized to fall within the ASTM tolerance and the respective property ranges of Jet A and Jet A-1. The proposed framework enables real-time, risk-lean and cost-effective SAF assessment and fine-tuning at lab-scale based on jet fuel standards and feedback from end-users before platform scale-up, which is expected to facilitate SAF development considerably. The developed model is released as a module of the SAFRA_Master_1.0 software package, hosted free of charge at the UHPC laboratory website (<span><span>https://uhpc-lab.org/downloads/</span><svg><path></path></svg></span>).</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100442"},"PeriodicalIF":5.0,"publicationDate":"2025-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737618","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 : 2025-12-01DOI: 10.1016/j.jaecs.2025.100433
Hao Zhang , Shengda Cao , Haixia Zhang , Chen Ren , Rundong Hu , Zhou Chen
The widespread adoption of high-energy-density NCM 811 lithium-ion batteries in electric vehicles is hampered by significant safety risks associated with thermal runaway and its propagation. This study addresses this critical issue by developing and validating a hybrid thermal management strategy that synergistically combines passive insulation with active liquid cooling. A fiber-reinforced fumed silica composite was fabricated via hot-pressing and subsequently encapsulated into vacuum insulation panels. This material exhibited an ultralow thermal conductivity of 4.42 mW/(m·K) and demonstrated exceptional flame retardancy and thermal stability in flame burn and hot plate tests. Numerical simulations revealed that a mere 3 mm thickness of this composite could completely block TRP within an NCM 811 battery module. Furthermore, the conventional single-serpentine liquid cooling plate was optimized into dual-inlet and quadruple-inlet configurations. Simulation results demonstrated that the hybrid system, integrating the 3 mm composite with the optimized quadruple-inlet cooling plate, successfully suppressed thermal runaway propagation and limited the peak temperature of adjacent cells to 162 °C. This work provides a practical and highly effective solution for enhancing the safety of high-energy battery systems, thereby contributing to the development of more reliable electric vehicles.
{"title":"Synergy of high-efficiency passive protective nanocomposite materials and active liquid cooling for suppressing thermal diffusion in lithium-ion power batteries","authors":"Hao Zhang , Shengda Cao , Haixia Zhang , Chen Ren , Rundong Hu , Zhou Chen","doi":"10.1016/j.jaecs.2025.100433","DOIUrl":"10.1016/j.jaecs.2025.100433","url":null,"abstract":"<div><div>The widespread adoption of high-energy-density NCM 811 lithium-ion batteries in electric vehicles is hampered by significant safety risks associated with thermal runaway and its propagation. This study addresses this critical issue by developing and validating a hybrid thermal management strategy that synergistically combines passive insulation with active liquid cooling. A fiber-reinforced fumed silica composite was fabricated via hot-pressing and subsequently encapsulated into vacuum insulation panels. This material exhibited an ultralow thermal conductivity of 4.42 mW/(m·K) and demonstrated exceptional flame retardancy and thermal stability in flame burn and hot plate tests. Numerical simulations revealed that a mere 3 mm thickness of this composite could completely block TRP within an NCM 811 battery module. Furthermore, the conventional single-serpentine liquid cooling plate was optimized into dual-inlet and quadruple-inlet configurations. Simulation results demonstrated that the hybrid system, integrating the 3 mm composite with the optimized quadruple-inlet cooling plate, successfully suppressed thermal runaway propagation and limited the peak temperature of adjacent cells to 162 °C. This work provides a practical and highly effective solution for enhancing the safety of high-energy battery systems, thereby contributing to the development of more reliable electric vehicles.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100433"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624093","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 : 2025-12-01DOI: 10.1016/j.jaecs.2025.100429
N. Diepstraten, L.M.T. Somers, J.A. van Oijen
The hydrogen-fueled direct-injection (DI) compression-ignition (CI) argon power cycle (APC) is a highly efficient and emission-free energy conversion system that relies on the low specific heat capacity of the working fluid argon. Compared to conventional internal combustion engines, the DI CI APC allows to easily adjust the oxygen level of the charge. Besides, the engine operating pressure can be increased at virtually no cost assuming the mechanics of the engine are suitable. In this study, we systematically investigate how to take advantage of these two benefits by varying the intake pressure and oxygen mole fraction using a validated Reynolds-averaged numerical simulation environment. It is found that the optimal amount of oxygen is a trade-off between burn duration and specific heat ratio of the charge. Provided that the oxygen mole fraction is high enough to achieve complete combustion, the sensitivity of thermal efficiency to oxygen mole fraction is relatively low. Increasing the intake pressure for a fixed oxygen level leads to shorter burn durations and less heat loss, thereby significantly increasing thermal efficiency. Flame–wall interaction is a dominant factor that negatively impacts the engine performance, and should therefore be minimized. The highest obtained thermal efficiency is 70 %, which is comparable to but mostly higher than efficiencies of state-of-the-art hydrogen fuel cells. The DI CI APC has therefore potential to overcome challenges of conventional engines, without penalizing its strengths.
氢燃料直喷（DI）压缩点火（CI）氩气动力循环（APC）是一种高效、无排放的能量转换系统，它依赖于工作流体氩气的低比热容。与传统内燃机相比，DI CI APC可以轻松调整充电的氧气水平。此外，如果发动机的机械结构合适，可以在几乎没有成本的情况下提高发动机的工作压力。在本研究中，我们系统地研究了如何通过改变进气压力和氧摩尔分数来利用这两种优势，并使用经过验证的reynolds平均数值模拟环境。研究发现，最佳的氧气用量是燃烧时间和电荷比热比之间的权衡。如果氧摩尔分数足够高，可以实现完全燃烧，热效率对氧摩尔分数的敏感性相对较低。在固定的氧气水平下，增加进气压力可以缩短燃烧持续时间，减少热量损失，从而显著提高热效率。火墙相互作用是对发动机性能产生负面影响的主要因素，因此应尽量减少。获得的最高热效率为70%，与最先进的氢燃料电池的效率相当，但大多高于效率。因此，DI CI APC有潜力克服传统发动机的挑战，而不会损害其优势。
{"title":"Numerical study of non-premixed hydrogen combustion in an argon power-cycle engine","authors":"N. Diepstraten, L.M.T. Somers, J.A. van Oijen","doi":"10.1016/j.jaecs.2025.100429","DOIUrl":"10.1016/j.jaecs.2025.100429","url":null,"abstract":"<div><div>The hydrogen-fueled direct-injection (DI) compression-ignition (CI) argon power cycle (APC) is a highly efficient and emission-free energy conversion system that relies on the low specific heat capacity of the working fluid argon. Compared to conventional internal combustion engines, the DI CI APC allows to easily adjust the oxygen level of the charge. Besides, the engine operating pressure can be increased at virtually no cost assuming the mechanics of the engine are suitable. In this study, we systematically investigate how to take advantage of these two benefits by varying the intake pressure and oxygen mole fraction using a validated Reynolds-averaged numerical simulation environment. It is found that the optimal amount of oxygen is a trade-off between burn duration and specific heat ratio of the charge. Provided that the oxygen mole fraction is high enough to achieve complete combustion, the sensitivity of thermal efficiency to oxygen mole fraction is relatively low. Increasing the intake pressure for a fixed oxygen level leads to shorter burn durations and less heat loss, thereby significantly increasing thermal efficiency. Flame–wall interaction is a dominant factor that negatively impacts the engine performance, and should therefore be minimized. The highest obtained thermal efficiency is 70<!--> <!-->%, which is comparable to but mostly higher than efficiencies of state-of-the-art hydrogen fuel cells. The DI CI APC has therefore potential to overcome challenges of conventional engines, without penalizing its strengths.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100429"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624091","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 : 2025-12-01DOI: 10.1016/j.jaecs.2025.100428
Réka Anna Kardos, Gyöngyvér Tóthpálné Hidegh, Erika Rácz, Viktor Józsa
Commercial digital cameras are versatile and low-cost non-invasive diagnostic tools for flame imaging with easy implementation for both industrial and laboratory flames. To quantify various flame metrics, flame image binarization is the first critical step in processing. Therefore, it is essential to know which binarization techniques perform well on different flame types, encompassing various operating conditions. This paper evaluates sixteen binarization methods for processing the chemiluminescent emission of eight natural gas and diesel oil flames captured by a commercial digital camera using three spectral filters at central wavelengths of 430, 515, and 590 nm. Proper binarization is challenging due to the blurry edges of the flame, which result from its finite thickness. If online data is required, processing time becomes critical, which is the first basis of the performance assessment of the sixteen binarization techniques. The second basis is a novel method for checking consistency, indicating the sensitivity of pixel assignment to foreground or background in a similar, yet new intensity neighborhood, which is critical for turbulent flames. The third metric was accuracy. It was defined as the fraction of the flame edge obtained from the binary image that was contained in the boundary region, determined based on the local standard deviations to avoid all biases associated with the use of a ground truth, making this basis novel and robust. The results were summarized for four different flame categories, concluding, for example, that the widely used Otsu method performs well in all cases.
{"title":"Assessment and ranking of 16 binarization methods for turbulent swirl flame images of various flame types","authors":"Réka Anna Kardos, Gyöngyvér Tóthpálné Hidegh, Erika Rácz, Viktor Józsa","doi":"10.1016/j.jaecs.2025.100428","DOIUrl":"10.1016/j.jaecs.2025.100428","url":null,"abstract":"<div><div>Commercial digital cameras are versatile and low-cost non-invasive diagnostic tools for flame imaging with easy implementation for both industrial and laboratory flames. To quantify various flame metrics, flame image binarization is the first critical step in processing. Therefore, it is essential to know which binarization techniques perform well on different flame types, encompassing various operating conditions. This paper evaluates sixteen binarization methods for processing the chemiluminescent emission of eight natural gas and diesel oil flames captured by a commercial digital camera using three spectral filters at central wavelengths of 430, 515, and 590 nm. Proper binarization is challenging due to the blurry edges of the flame, which result from its finite thickness. If online data is required, processing time becomes critical, which is the first basis of the performance assessment of the sixteen binarization techniques. The second basis is a novel method for checking consistency, indicating the sensitivity of pixel assignment to foreground or background in a similar, yet new intensity neighborhood, which is critical for turbulent flames. The third metric was accuracy. It was defined as the fraction of the flame edge obtained from the binary image that was contained in the boundary region, determined based on the local standard deviations to avoid all biases associated with the use of a ground truth, making this basis novel and robust. The results were summarized for four different flame categories, concluding, for example, that the widely used Otsu method performs well in all cases.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100428"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624131","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 : 2025-12-01DOI: 10.1016/j.jaecs.2025.100427
Tao Yang , Jie Niu , Xuren Zhu , Yujing Wei , Gani Issayev , Peng Zhang
The heat release rate (HRR) is a fundamental parameter in combustion science and technology, critical for both performance monitoring and device optimization. However, its direct experimental determination remains challenging, even for well-defined one-dimensional (1-D) laminar flames. This study develops a computationally efficient method, termed the Fast Temperature-based HRR Prediction (FT-HRRP), to accurately reconstruct HRR profiles in 1-D counterflow flames using solely temperature data, derived through rigorous analysis of the energy equation. A comprehensive validation database was established by using flame simulations of hydrogen-ammonia fuel mixtures with both non-premixed and premixed configurations, systematically examining the method's robustness across various strain rates (40–300 ), ammonia blending ratios (0–80 %), and equivalence ratios (0.8–1.2 for premixed cases). The reconstructed HRR profiles show excellent agreement with reference calculations, capturing all essential characteristics, including profile shapes, peak locations, and half widths of profiles. The accuracy and robustness have been confirmed by the experimental data from a published paper. Incorporating the additional physics of species mass transfer of key components (H₂, NH₃, O₂, N₂, etc.) in the present method, some subtle albeit nonessential features of HRR profiles can be well reproduced. The proposed method has the potential of being a useful and efficient analysis tool for 1-D counterflow flames.
{"title":"Reconstruction of heat release rate in one-dimensional counterflow flames","authors":"Tao Yang , Jie Niu , Xuren Zhu , Yujing Wei , Gani Issayev , Peng Zhang","doi":"10.1016/j.jaecs.2025.100427","DOIUrl":"10.1016/j.jaecs.2025.100427","url":null,"abstract":"<div><div>The heat release rate (HRR) is a fundamental parameter in combustion science and technology, critical for both performance monitoring and device optimization. However, its direct experimental determination remains challenging, even for well-defined one-dimensional (1-D) laminar flames. This study develops a computationally efficient method, termed the Fast Temperature-based HRR Prediction (FT-HRRP), to accurately reconstruct HRR profiles in 1-D counterflow flames using solely temperature data, derived through rigorous analysis of the energy equation. A comprehensive validation database was established by using flame simulations of hydrogen-ammonia fuel mixtures with both non-premixed and premixed configurations, systematically examining the method's robustness across various strain rates (40–300 <span><math><mover><mi>s</mi><mo>¯</mo></mover></math></span>), ammonia blending ratios (0–80 %), and equivalence ratios (0.8–1.2 for premixed cases). The reconstructed HRR profiles show excellent agreement with reference calculations, capturing all essential characteristics, including profile shapes, peak locations, and half widths of profiles. The accuracy and robustness have been confirmed by the experimental data from a published paper. Incorporating the additional physics of species mass transfer of key components (H₂, NH₃, O₂, N₂, etc.) in the present method, some subtle albeit nonessential features of HRR profiles can be well reproduced. The proposed method has the potential of being a useful and efficient analysis tool for 1-D counterflow flames.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100427"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145624092","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 : 2025-11-29DOI: 10.1016/j.jaecs.2025.100436
Yihua Qian , Yaohong Zhao , Zhi Li , Qing Wang , Nian Tang , Chao Wang
With the increasing demand for high battery energy density and thermal management, immersion liquid cooling has emerged as a promising strategy, while the safety of coolants remains an area requiring further investigation. The study systematically compares the physicochemical properties, thermal stability, combustion behavior, and char residuals of mineral oil-based coolant (MIC), silicone oil-based coolant (SIC), and ester-based coolant (EIC). The results indicated that MIC exhibited a low flash point (199.2 °C), poor thermal stability, and underwent dramatic combustion after ignition. Although SIC formed a protective SiO2 layer during combustion that inhibited further combustion, it released tremendous smoke particles. In contrast, EIC demonstrated the highest initial decomposition temperature and flash point (261.3 °C), combining low flammability with excellent thermal stability. A comprehensive safety evaluation revealed that EIC offered the best overall safety. This study provides valuable insights for coolant selection in safety-critical energy storage systems.
{"title":"Ester-based immersion coolant demonstrates superior safety for energy storage systems: a comprehensive comparative analysis","authors":"Yihua Qian , Yaohong Zhao , Zhi Li , Qing Wang , Nian Tang , Chao Wang","doi":"10.1016/j.jaecs.2025.100436","DOIUrl":"10.1016/j.jaecs.2025.100436","url":null,"abstract":"<div><div>With the increasing demand for high battery energy density and thermal management, immersion liquid cooling has emerged as a promising strategy, while the safety of coolants remains an area requiring further investigation. The study systematically compares the physicochemical properties, thermal stability, combustion behavior, and char residuals of mineral oil-based coolant (MIC), silicone oil-based coolant (SIC), and ester-based coolant (EIC). The results indicated that MIC exhibited a low flash point (199.2 °C), poor thermal stability, and underwent dramatic combustion after ignition. Although SIC formed a protective SiO<sub>2</sub> layer during combustion that inhibited further combustion, it released tremendous smoke particles. In contrast, EIC demonstrated the highest initial decomposition temperature and flash point (261.3 °C), combining low flammability with excellent thermal stability. A comprehensive safety evaluation revealed that EIC offered the best overall safety. This study provides valuable insights for coolant selection in safety-critical energy storage systems.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100436"},"PeriodicalIF":5.0,"publicationDate":"2025-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737617","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 : 2025-11-28DOI: 10.1016/j.jaecs.2025.100434
Xuren Zhu , Daniel Vigarinho de Campos , Yujing Wei , Thibault F. Guiberti
This paper proposes a non-intrusive method for quantitatively measuring heat release rate (HRR) through regression of chemiluminescence signals. The method is demonstrated using premixed ammonia-methane-air and ammonia-hydrogen-air counterflow twin laminar flames, with chemiluminescence data previously collected across a wide range of ammonia fractions, equivalence ratios, and strain rates. Opposed-flow 1-D simulations are employed to determine the true HRR of these twin flames, providing the complete dataset necessary to train an algorithm that correlates HRR with chemiluminescence intensities via Gaussian Process Regression (GPR). The accuracy of HRR predictions from the trained algorithms is evaluated using various input combinations, from including all recorded excited species (such as NO*, OH*, NH*, CN*, CO2*, and CH* for ammonia-methane-air flames, and NO*, OH*, NH* along with violet, blue, green, yellow, orange, and red colors for ammonia-hydrogen-air flames) to only one pair of species. Results indicate that UV–visible chemiluminescence alone can reliably infer HRR with high accuracy, better than 6.7 % for ammonia-methane and 7.5 % for ammonia-hydrogen flames, when all species are considered in the GPR, even if ammonia fraction, equivalence ratio, and strain rate are unknown. Further data show that chemiluminescence intensities from just two excited species, specifically OH*-CN* for ammonia-methane-air and OH*-NH* for ammonia-hydrogen-air, can provide average accuracies of 2.8 % and 4.7 %, respectively. The method is also applied to ammonia-hydrogen-nitrogen-air swirling turbulent flames, which better represent practical scenarios, using a new dataset of NH*, OH*, and NO* chemiluminescence intensities. It is demonstrated that, with proper data processing, this approach can accurately predict HRR for these more complex swirling turbulent flames.
{"title":"Measurement of heat release rate using regression of chemiluminescence signals in premixed ammonia-methane/hydrogen-air flames","authors":"Xuren Zhu , Daniel Vigarinho de Campos , Yujing Wei , Thibault F. Guiberti","doi":"10.1016/j.jaecs.2025.100434","DOIUrl":"10.1016/j.jaecs.2025.100434","url":null,"abstract":"<div><div>This paper proposes a non-intrusive method for quantitatively measuring heat release rate (HRR) through regression of chemiluminescence signals. The method is demonstrated using premixed ammonia-methane-air and ammonia-hydrogen-air counterflow twin laminar flames, with chemiluminescence data previously collected across a wide range of ammonia fractions, equivalence ratios, and strain rates. Opposed-flow 1-D simulations are employed to determine the true HRR of these twin flames, providing the complete dataset necessary to train an algorithm that correlates HRR with chemiluminescence intensities via Gaussian Process Regression (GPR). The accuracy of HRR predictions from the trained algorithms is evaluated using various input combinations, from including all recorded excited species (such as NO*, OH*, NH*, CN*, CO<sub>2</sub>*, and CH* for ammonia-methane-air flames, and NO*, OH*, NH* along with <em>violet, blue, green, yellow, orange</em>, and <em>red</em> colors for ammonia-hydrogen-air flames) to only one pair of species. Results indicate that UV–visible chemiluminescence alone can reliably infer HRR with high accuracy, better than 6.7 % for ammonia-methane and 7.5 % for ammonia-hydrogen flames, when all species are considered in the GPR, even if ammonia fraction, equivalence ratio, and strain rate are unknown. Further data show that chemiluminescence intensities from just two excited species, specifically OH*-CN* for ammonia-methane-air and OH*-NH* for ammonia-hydrogen-air, can provide average accuracies of 2.8 % and 4.7 %, respectively. The method is also applied to ammonia-hydrogen-nitrogen-air swirling turbulent flames, which better represent practical scenarios, using a new dataset of NH*, OH*, and NO* chemiluminescence intensities. It is demonstrated that, with proper data processing, this approach can accurately predict HRR for these more complex swirling turbulent flames.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100434"},"PeriodicalIF":5.0,"publicationDate":"2025-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145737619","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}
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