Applications in Energy and Combustion Science最新文献

{"title":"One-step flame-synthesized catalysts enable high-value sugar production from cellulose fast pyrolysis","authors":"Heqing Zhang ,&nbsp;Xing Jin ,&nbsp;Mingxun Zeng ,&nbsp;Xiaoya Zhang ,&nbsp;Chao Liu ,&nbsp;Yujie Tao ,&nbsp;Huiyan Zhang","doi":"10.1016/j.jaecs.2025.100416","DOIUrl":"10.1016/j.jaecs.2025.100416","url":null,"abstract":"<div><div>D-allose is a novel, low-calorie monosaccharide with strong potential as a substitute for traditional high-calorie sweeteners, offering wide-ranging applications in both the food and pharmaceutical industries. However, producing d-allose from renewable biomass remains challenging, and there is an urgent need for a simple, scalable method to enable industrial production. In this study, we present a straightforward approach for producing d-allose using alumina-based catalysts prepared via flame spray pyrolysis (FSP). This one-step method allows precise control over catalyst morphology by adjusting the precursor solution concentration and injection flow rate. To enhance catalytic performance, the alumina was doped with metal oxides Fe₂O₃, NiO, and MgO. Catalysts produced under 0.5 M precursor concentration and 1000 mL/h flow rate exhibited high specific surface areas of up to 188.5 m²/g. Among the various formulations tested, the Fe₂O₃-doped alumina catalyst delivered the highest selectivity for d-allose (up to 46.3 %). This improvement is attributed to the synergistic interaction between γ-Al₂O₃ and Fe₂O₃ acid sites, which promoted selective isomerization of C₆ sugars while minimizing unwanted degradation. Characterization using pore structure and NH₃-TPD analysis showed that both the number and strength of acid sites—closely linked to surface area and composition—play a crucial role in the catalytic conversion process. This method avoids the need for biomass pretreatment and complex catalyst fabrication, highlighting its potential as a scalable, sustainable solution for producing high-value sugars from lignocellulosic biomass.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100416"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145416154","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}

{"title":"Modeling combustion chemistry using C3MechV4.0: An extension to mixtures of hydrogen, ammonia, alkanes, and cycloalkanes","authors":"Luna Pratali Maffei ,&nbsp;Raymond Langer ,&nbsp;Yuki Murakami ,&nbsp;Scott W. Wagnon ,&nbsp;Pengzhi Wang ,&nbsp;Jiaxin Liu ,&nbsp;Mohsin Raza ,&nbsp;Yuxiang Zhu ,&nbsp;Sanket Girhe ,&nbsp;Christian Schwenzer ,&nbsp;Joachim Beeckmann ,&nbsp;Stephen J. Klippenstein ,&nbsp;Tiziano Faravelli ,&nbsp;Heinz Pitsch ,&nbsp;Peter Kelly Senecal ,&nbsp;Henry J. Curran","doi":"10.1016/j.jaecs.2025.100385","DOIUrl":"10.1016/j.jaecs.2025.100385","url":null,"abstract":"<div><div>The design of novel renewable fuel mixtures (such as hydrogen, ammonia, methanol, ethanol, and other blends) compatible with existing engine infrastructure can be greatly aided by accurate kinetic modeling of fuel surrogate mixtures. With this objective, a robust kinetic model should accurately describe the kinetics of the pure components and their mixtures at engine-relevant conditions. This work presents the results of the continued work of the Computational Chemistry Consortium (C3) to build a “universal” chemical kinetic mechanism to describe the oxidation of fuel surrogate mixtures. Our previous model, C3MechV3.3, focused on conventional fuel mixtures, i.e., the ignition behavior of <span><math><mi>n</mi></math></span>-alkanes up to C₁₂, as well as pollutant formation, including polycyclic aromatic hydrocarbons (PAHs) and nitrogen oxides (NO<span><math><msub><mrow></mrow><mrow><mi>x</mi></mrow></msub></math></span>). To meet the needs of fast renewable fuel mixture screening, the updated model, C3MechV4.0, now also includes the combustion of carbon-free fuels, including hydrogen and ammonia; dimethyl carbonate, and ethylene carbonate, which are useful to investigate battery fires in hybrid vehicles; cyclopentane, cyclohexane, and xylene, which enrich the palette of useful conventional fuel surrogate components. Each kinetic subset was updated according to the most recent literature findings and, if needed, tuned to improve the prediction of experimental target data. The model was tested against a wide range of experimental data (some of which is new) for fuel mixtures, focusing on hydrogen, methane, and ammonia, proving the model’s predictive capabilities. A hierarchical and modular mechanism structure was enforced, enabling the automatic assembly of smaller subsets of mechanisms, which can accelerate kinetic simulations of fuel mixtures.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100385"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220380","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}

{"title":"Large eddy simulation of turbulent premixed combustion with a data-driven filtered density function model","authors":"Hanying Yang ,&nbsp;James C. Massey ,&nbsp;Nedunchezhian Swaminathan","doi":"10.1016/j.jaecs.2025.100393","DOIUrl":"10.1016/j.jaecs.2025.100393","url":null,"abstract":"<div><div>Recent data-driven approaches have demonstrated promising predictive capabilities in many <em>a priori</em> assessments. However, their performance evaluation in <em>a posteriori</em> large eddy simulation (LES) assessments is limited. This study implements an artificial neural network (ANN)-based sub-grid filtered density function (FDF) model in LES of turbulent premixed combustion. The ANN model is trained on DNS data from moderate or intense low-oxygen dilution (MILD) combustion and a premixed twin-V flame, predicting sub-grid marginal FDFs of the progress variable to model the filter reaction rate through on-the-fly inference. Simulations with the ANN models are 2 to 3 times longer than those for the presumed FDF-based tabulation method because of its complex architecture and reduced parallel scalability. However, its over 300-fold lower memory usage offsets the computational expenses. The ANN model is evaluated using two cases, a bluff-body stabilised methane–air flame and the well-known Volvo afterburner propane–air flame. The simulations demonstrate improved prediction of key flow and flame characteristics, including recirculation zone length, velocity recovery, and downstream temperature distributions, with lower sensitivity to mesh resolution. These findings highlight the potentials of the ANN-based sub-grid FDF modelling approach as a better alternative to conventional tabulation methods.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100393"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220376","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}

{"title":"Impact of initial pressure and heat flux on hydrogen and carbon monoxide production in supercritical water gasification of biomass: A molecular dynamics study","authors":"Mohammed Al-Asadi ,&nbsp;Ali B. M. Ali ,&nbsp;Dheyaa J. Jasim ,&nbsp;Narinderjit Singh Sawaran Singh ,&nbsp;Soheil Salahshour ,&nbsp;S. Mohammad Sajadi ,&nbsp;Kamkar Vahedi","doi":"10.1016/j.jaecs.2025.100395","DOIUrl":"10.1016/j.jaecs.2025.100395","url":null,"abstract":"<div><div>This study investigates the effects of initial pressure and external heat flux on hydrogen and carbon monoxide production during the supercritical water gasification of biomass. According to the results, after one nanosecond of equilibration, the system reached thermal equilibrium and structural stability, with potential and total energies stabilizing at –83.84 and –83.77 kcal/mol, respectively. The results show that increasing the initial pressure from 0 to 2.5 bar caused a decrease in the number of CO molecules from 86 to 71 and H₂ molecules from 574 to 543, indicating that higher pressure suppressed gas formation. Combustion efficiency also declined from 32 % to 25 % with increasing pressure, suggesting more complete reactions under elevated pressure conditions. Conversely, heat flux slightly increases from 3.92 to 4.06 W/m², likely due to enhanced gas production, while thermal conductivity rose from 0.30 to 0.37 W/m·K, reflecting improved heat transfer resulting from denser atomic packing. Furthermore, increasing the external heat flux from 0.001 to 0.005 W/m² intensified molecular dissociation, raising CO and H₂ counts from 93 to 112 and 605 to 692, respectively, which corresponded with an improvement in combustion efficiency from 49 % to 69 %. However, the heat flux decreases from 3.89 to 3.76 W/m², and thermal conductivity dropped from 0.28 to 0.19 W/m·K with higher heat flux, attributed to structural degradation and disrupted conductive pathways. Overall, these findings demonstrate the complex interplay between pressure and heat flux on gasification efficiency, molecular product distribution, and thermal properties, providing valuable insights for optimizing hydrogen production through supercritical water gasification of biomass.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100395"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145220375","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}

{"title":"In-situ CO2 capture by DFMs to enhance hydrogen production and regeneration performance of biomass-H2O gasification","authors":"Qi Shang ,&nbsp;Dongdong Feng ,&nbsp;Peicheng Yan ,&nbsp;Yunzhi Li ,&nbsp;Linhan Dong ,&nbsp;Shizhang Wang ,&nbsp;Xiaowei Zhang ,&nbsp;Yijun Zhao ,&nbsp;Shaozeng Sun","doi":"10.1016/j.jaecs.2025.100420","DOIUrl":"10.1016/j.jaecs.2025.100420","url":null,"abstract":"<div><div>Developing green hydrogen energy can alleviate the problem of CO₂ emissions caused by excessive use of fossil fuels. In-situ capture of CO₂ for enhanced H₂ production in zero-carbon energy biomass-H₂O gasification can achieve the dual effects of green H₂ production and negative carbon. The study used red mud (RM) to modify CaO and prepare dual-functional materials (DFMs). And the in-situ CO₂ capture enhanced H₂ production and regeneration cycle performance of DFMs in biomass-H₂O gasification were studied, and the influence of biomass ash on the H₂ production and low-temperature (650 °C) regeneration performance of DFMs in the cycle was analyzed. The results are as follows: In DFMs catalyzed biomass-H₂O gasification, due to the continuous deposition of alkali and alkaline earth metals (AAEMs) in biomass ash with increasing cycle times, its catalytic effect increased H₂ production by 27 % after twenty cycles, and the pore structure degradation and cycle stability of DFMs decreased by 44.71 %. DFMs have demonstrated excellent catalytic performance and cycling stability in the catalytic removal of ash from biomass. After twenty cycles, the production of H₂ only decreased by 20.59 % and the performance of CaO decreased by 26.67 %, demonstrating the enormous potential of DFMs for in-situ CO₂ capture and enhanced H₂ production.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100420"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474155","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}

{"title":"A hybrid AI-kinetic framework for predicting the pyrolysis of food packaging plastic waste: Integrating TGA, model-free kinetics, and artificial neural networks","authors":"Mohamed Koraiem M. Handawy ,&nbsp;Tamer M.M. Abdellatief ,&nbsp;Xiongbo Duan ,&nbsp;Muhammad Tawalbeh ,&nbsp;Tareq Salameh ,&nbsp;Abdul-Kadir Hamid ,&nbsp;Mousa Hussein ,&nbsp;Hamada Mohamed Abdelmotalib","doi":"10.1016/j.jaecs.2025.100418","DOIUrl":"10.1016/j.jaecs.2025.100418","url":null,"abstract":"<div><div>The increase in the accumulation of plastic waste, such as polypropylene (PP) used in food packaging, causes serious environmental problems because of its non-biodegradable nature. The pyrolysis process is presented as an efficient thermochemical technique to convert such waste and other wastes into valuable products such as bio-gas, bio-oil, and biochar. Studying the degradation and kinetics of PP is crucial for process optimization and design. In the present study, both different kinetic models and an artificial neural network (ANN) approach were used to study and predict the PP pyrolysis behavior. The kinetic parameters were calculated using different model-free methods with Thermogravimetric analysis (TGA) data at different heating rates of 15, 30, and 60 °C/min. The activation energy results showed a range between 192.5 and 238.5 kJ/mol, depending on the calculation method used. The Vyazovkin method recorded the most stable values, with an average of 205.5 ± 8.1 kJ/mol, reflecting its reliability in representing the thermal decomposition mechanism of polypropylene. In parallel, the ANN model was developed to predict thermal degradation and other parameters. The ANN model was trained using the Levenberg–Marquardt algorithm with three hidden layers (20–20–10 neurons). The developed ANN model exhibited high prediction accuracy with RMSE values lower than 2.7 % and R² greater than 0.96 at all heating rates. The predicted derivative thermogravimetric (DTG) curves affirm the ability of the ANN model to reproduce kinetics and degradation peak trends. The model could also predict the pyrolysis product yields at different temperatures of 400, 500, and 600 °C, indicating that a temperature of 500 °C at moderate heating rates is the optimum conditions to achieve the highest bio-oil yield. The integration of experimental TGA data with kinetic analysis and ANN modeling represents the key novelty of this study, presenting a hybrid and robust predictive approach to estimate the thermal degradation behavior of PP plastic waste, guiding the design of an effective scalable pyrolysis reactor used for sustainable valorization of plastic wastes.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100418"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145474163","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}

{"title":"Spray combustion of ammonia–hydrocarbon blended fuels under high-pressure dual-fuel conditions in large engines: Numerical investigation","authors":"Takuya Wakasugi ,&nbsp;Daisuke Tsuru ,&nbsp;Hiroaki Watanabe","doi":"10.1016/j.jaecs.2025.100425","DOIUrl":"10.1016/j.jaecs.2025.100425","url":null,"abstract":"<div><div>Ammonia is a promising zero-carbon fuel for high-power internal combustion engines, but its inherently low reactivity poses significant challenges for stable and efficient combustion. To address these challenges, this study conducted three-dimensional computational fluid dynamics (CFD) of pure ammonia and ammonia–hydrocarbon blended fuels under high-pressure dual-fuel (HPDF) conditions, using a setup that replicates the configuration of the Rapid Compression Expansion Machine used in our previous experiments. The CFD model incorporated detailed chemical kinetics (112 species, 641 reactions) and was validated against experimentally measured heat release rate profiles. The simulations successfully captured key combustion phenomena, including ignition delay, combustion duration, flame structure evolution, and post-injection afterburning behavior. Hydrocarbon blending improved combustion stability by modifying the spray flame structure, with methanol–ammonia blending achieving a favorable balance of high combustion efficiency, shorter combustion duration, and moderate emission levels without severely increasing local equivalence ratios. In contrast, propane–ammonia blending broadened the high-equivalence-ratio region along the spray, degrading diffusion combustion and leading to undesirable afterburning. This resulted in enhanced oxidation of residual ammonia but also introduced potential drawbacks in thermal efficiency and emission behavior. These findings elucidate the strong influence of fuel composition on spray flame structure, combustion sustainability, and nitrogen-based emissions in ammonia-fueled HPDF systems, providing critical insights for optimizing ammonia-based fuel strategies for large engines.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100425"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528777","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}

{"title":"Modeling local extinction and differential diffusion in KAUST ammonia flames D and F using principal component transport","authors":"Suliman Abdelwahid ,&nbsp;Mohammad Rafi Malik ,&nbsp;Junjun Guo ,&nbsp;Hao Tang ,&nbsp;Francisco E. Hernández–Pérez ,&nbsp;Gaetano Magnotti ,&nbsp;Hong G. Im","doi":"10.1016/j.jaecs.2025.100424","DOIUrl":"10.1016/j.jaecs.2025.100424","url":null,"abstract":"<div><div>Partial cracking of ammonia (NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>) into hydrogen (H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>) and nitrogen (N<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>) enhances flame stability in carbon-free combustion by increasing fuel reactivity. However, modeling turbulent jet flames fueled by such multicomponent mixtures remains challenging due to complex turbulence-chemistry interactions (TCI), localized extinction, and the role of differential diffusion. This study develops a reduced-order large eddy simulation (LES) framework that couples principal component (PC) transport with deep neural networks (DNNs), incorporating differential diffusion and subgrid-scale (SGS) closures for efficient and accurate simulation of NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span>/H<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>/N<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>-air jet flames. A mixture-averaged (MA) transport model is formulated using a Varimax-rotated PC basis, resulting in a diagonally dominant diffusion matrix that enables species-specific transport. SGS closures for PC variance and scalar fluxes are integrated to improve representation of unresolved turbulence. The framework is validated against experimental data for two piloted jet flames from the KAUST NH<span><math><msub><mrow></mrow><mrow><mn>3</mn></mrow></msub></math></span> flame series, referred to as flames D and F, with Reynolds numbers of 24,000 and 36,000, corresponding to 59% and 89% of the blowoff threshold, respectively. The proposed MA-Varimax-SG model captures key features including mixture fraction decay, temperature profiles, and the distributions of major and intermediate species (e.g., OH, NH<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span>), while accurately predicting localized flame holes and extinction-reignition dynamics. Compared to the PC-DNN model with unity Lewis number, the proposed approach improves extinction prediction in highly strained regions with less than 10% additional computational cost. These findings highlight the critical role of differential diffusion and SGS modeling in partially cracked ammonia flame simulations and demonstrate the scalability and robustness of the proposed framework for complex combustion systems.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100424"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145528776","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}

{"title":"Iterative experimental design for model uncertainty quantification with a novel likelihood function incorporating error dependence on condition parameters","authors":"Yuxi Zhao,&nbsp;Liming Cai","doi":"10.1016/j.jaecs.2025.100413","DOIUrl":"10.1016/j.jaecs.2025.100413","url":null,"abstract":"<div><div>Automatic optimization and uncertainty quantification (UQ) methods are often applied in the development of chemical mechanisms to calibrate models against a number of experiments. For the reduction of experimental effort, design of experiments (DOE) methods can be used to find most valuable data. In each iteration of model-based DOE, the most informative experiment is supposed to be selected for the improvement of the current model performance. The iteration repeats until the desired prediction accuracy is achieved. The likelihood function, which can be regarded as the objective function of UQ, determines the posterior distributions of model parameters, is expected to affect the results of iterative DOE. Often, the likelihood function is derived based on the assumption that error of each measurement is independent. However, the experimental observations and their errors are dependent on various physical condition parameters, such as temperature. Recently, surface-matching (SM) method is proposed in our previous work to quantify the dissimilarity between surface shapes and values of simulation and experiment. By minimizing the SM-based objective function, the joint dependence of model predictions on various physical conditions is optimized. Thus, in this work, an iterative experimental design framework with Bayesian UQ method and SM-based likelihood function is proposed. The SM-based function is expected to incorporate error dependence on physical conditions into UQ. Based on this framework, the most informative experiments can be selected iteratively to improve the model performance in terms of joint dependence on physical conditions. The application of this framework is demonstrated by designing ignition delay experiments for the optimization of a dimethyl ether mechanism. For comparison, iterative DOEs are implemented with the point-wise and curve matching (CM)-based likelihood functions. Different results on the selection of experiments and development of model improvement provided by the iterative DOEs with various likelihood functions demonstrate the significant impact of likelihood function definition.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"24 ","pages":"Article 100413"},"PeriodicalIF":5.0,"publicationDate":"2025-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145361618","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}

{"title":"Numerical study of non-premixed hydrogen combustion in an argon power-cycle engine","authors":"N. Diepstraten,&nbsp;L.M.T. Somers,&nbsp;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}