Applications in Energy and Combustion Science最新文献

{"title":"Investigation of ethanol blending on soot particle evolution in counterflow diffusion flames of a gasoline surrogate","authors":"Fenja Ahrendt ,&nbsp;Robert Schmitz ,&nbsp;Fabian P. Hagen ,&nbsp;Petros Vlavakis ,&nbsp;Dimosthenis Trimis ,&nbsp;Federica Ferraro","doi":"10.1016/j.jaecs.2025.100435","DOIUrl":"10.1016/j.jaecs.2025.100435","url":null,"abstract":"<div><div>Across all transport sectors, there is an increasing recognition of the importance of reducing not only CO₂ emissions but also other climate-impacting pollutants, such as soot particulate matter. Ethanol, a widely utilized biofuel, has the potential to significantly reduce CO₂ emissions. However, its influence on soot formation when blended with complex hydrocarbon fuels remains unclear. Previous studies showed that blending ethanol above 40<!--> <!-->vol% reduces both the total soot volume fraction and particle diameter, while at lower blending ratios, 20<!--> <!-->vol% ethanol or less, discrepant trends can be found in the literature. In some studies, a synergistic effect has been observed, where ethanol blending with various hydrocarbon fuels promotes soot formation. To address these uncertainties, this study investigates the effects of varying ethanol blending ratios with a gasoline surrogate (90<!--> <!-->vol% iso-octane and 10<!--> <!-->vol% toluene) on soot formation characteristics in a laminar counterflow diffusion flame for ethanol ratios up to 60<!--> <!-->vol%. Experimental and numerical investigations are conducted to understand the effects of ethanol blending on gas-phase products, soot volume fraction and particle size distribution. Gas-Chromatography and two-color Time-Resolved Laser Induced Incandescence (TR-LII) were employed to measure gas species, soot volume fraction and primary particle size distribution, respectively. Numerical simulations are carried out using a detailed soot model based on the Split-based Extended Quadrature Method of Moments (S-EQMOM). Both experimental and numerical results consistently show a non-linear impact of ethanol blending on soot precursors and a non-monotonic effect on particle quantities. Small amounts of ethanol, up to 10<!--> <!-->vol%, have little effect on soot formation. However, higher levels of ethanol significantly reduce soot formation and the diameter of large particles. Analysis of individual soot processes reveals that, when ethanol is added, PAH-related soot processes, such as inception and PAH-deposition, are the limiting steps, while the HACA and soot oxidation mechanisms are less affected.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100435"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145685971","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":"On the application of planar laser Rayleigh scattering imaging to Ethylene-air detonations at normal atmospheric conditions","authors":"Nicholas Rock ,&nbsp;Mateo Gomez ,&nbsp;Aaron W. Skiba","doi":"10.1016/j.jaecs.2025.100454","DOIUrl":"10.1016/j.jaecs.2025.100454","url":null,"abstract":"<div><div>Detonation-based technologies have been developed primarily by evaluating different design concepts using global performance metrics (e.g., wave speeds and thrust output). However, several technical challenges, which may require a physics-based development approach to mitigate or resolve, continue to hinder these systems. Unfortunately, the most commonly used tools for characterizing the physics of detonations (i.e., cell size measurements, empirical correlations, and predictive models) do not typically provide enough detailed information to make a significant impact on the development of detonation-based technologies. Therefore, this study demonstrates the use of planar laser Rayleigh scattering (PLRS) imaging to acquire highly resolved data from ethylene-air detonations produced at atmospheric conditions. In particular, the PLRS images enable rare measurements of induction zone regions and allow the details of a detonation’s underlying structure to be visualized in great detail. The accuracy of the induction zone length measurements is assessed via a systematic numerical analysis involving the simulation of laser Rayleigh scattering (LRS) signals from one-dimensional (1-D) ZND results. The numerical results were quantitatively compared to measured LRS signal levels, their spatial gradients, and induction zone lengths at local wave speeds estimated from high-speed (MHz) chemiluminescence imaging. Varying levels of agreement between the measured and calculated results are observed, with the best agreement achieved for detonations propagating at or faster than the CJ speed. Beyond the measured results, this manuscript clearly highlights the benefits, challenges, and future potential of applying PLRS imaging to detonations.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100454"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146022802","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":"Radiative characteristics of premixed ammonia-hydrogen and cracked ammonia swirling flames","authors":"Daisuke Sato ,&nbsp;Jordan Davies ,&nbsp;Syed Mashruk ,&nbsp;Agustin Valera-Medina ,&nbsp;Ryoichi Kurose","doi":"10.1016/j.jaecs.2026.100465","DOIUrl":"10.1016/j.jaecs.2026.100465","url":null,"abstract":"<div><div>Renewably produced ammonia does not emit CO₂ with combustion. Therefore, numerous studies have been conducted in recent years to utilise ammonia as a carbon free fuel. However, there is limited previous knowledge regarding the radiation characteristics of ammonia blend combustion. In this study, radiation characteristics are investigated for 15 kW premixed swirling flames of NH₃/H₂ (70/30) and 20% cracked NH₃, which have been frequent targets of recent research, with equivalence ratios varying from 0.6 ≤ Φ ≤ 1.4. Specifically, water radiation (wavelength 2.7 μm), which is the main radiation source, is measured using an infrared spectrometer. Additionally, radiation emission and absorption in the combustor are evaluated theoretically using H₂O exhaust gas concentrations and temperature measurement data. The results suggest that radiation changes due to equivalence ratio variations are gradual on the rich side, showing different trends compared to the lean side. Furthermore, radiation attenuation in the combustor become active around Φ = 0.9. This suggests that when considering radiation in ammonia blend combustion, not only the blend composition but also equivalence ratio conditions must be carefully considered. In addition, radiative heat fluxes were analysed for three blends (NH₃/H₂, pure NH₃, and cracked NH₃) at Φ = 1.0, suggesting no significant differences in radiative heat flux among these blends. These research findings provide valuable insights for future combustor designs using ammonia blend fuels.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100465"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146077722","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":"Experimental study on the effect of fire source height on the flame merging of two rectangular fire sources","authors":"Shaojie Zhang ,&nbsp;Wei Peng ,&nbsp;Kuibin Zhou ,&nbsp;Honglu Zheng ,&nbsp;Xiaodong Xie","doi":"10.1016/j.jaecs.2025.100447","DOIUrl":"10.1016/j.jaecs.2025.100447","url":null,"abstract":"<div><div>In large-scale forest and wildland-urban interface (WUI) fires, the simultaneous ignition of multiple elevated fuels often leads to complex flame interactions. However, the influence of fire source height on flame merging remains poorly understood. This study experimentally investigates the flame merging behavior of two rectangular fire sources under different fire source heights. By combining theoretical analysis and experimental observations, it is demonstrated that the fire source height <em>h</em> significantly affects flame merging. As <em>h</em> increases, the interaction between the two fire sources diminishes, leading to a transition from merged to unmerged flames. At the same time, the sensitivity of flame merging to <em>h</em> gradually decreases with increasing <em>h</em>. The influence mechanism of <em>h</em> on flame merging is found to be analogous to that of fire spacing <em>s</em>, both enhancing the air entrainment area at the gap ends. A dimensionless spacing considering <em>s, h</em>, and heat release rate per unit area <em>Q\"</em> is proposed, and a relationship between flame merging probability and the dimensionless spacing is established for two rectangular fire sources. Furthermore, <em>h</em> affects the flame height <em>Z_f</em> through two mechanisms: altering the flame merging state and modifying the turbulence intensity at the flame base. Based on these mechanisms, an expression for predicting the flame height of two rectangular fire sources is developed.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100447"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145791041","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":"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 ,&nbsp;Erik Jansson ,&nbsp;Mattias Johansson ,&nbsp;Ronny Lindgren ,&nbsp;Andreas Nyman ,&nbsp;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":"2026-03-01","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}

{"title":"Ignition, combustion, and energy release characteristics of Al/CuO energetic composites incorporated with micro- and nano-sized fullerene additives","authors":"Abdul Basyir ,&nbsp;Ho Sung Kim ,&nbsp;Jung Keun Cha ,&nbsp;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 (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.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100446"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","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}

{"title":"A randomization and unrolling framework to improve deep neural networks modelling chemistry integration across multiple combustion regimes","authors":"Weitao Liu,&nbsp;Andreas Kronenburg,&nbsp;Thorsten Zirwes","doi":"10.1016/j.jaecs.2025.100445","DOIUrl":"10.1016/j.jaecs.2025.100445","url":null,"abstract":"<div><div>Solving finite-rate chemical kinetics through direct integration is computationally expensive and often becomes a performance bottleneck in high-fidelity combustion simulations. Deep neural networks (DNNs) offer a promising alternative by directly modelling integrated chemical source terms. However, conventional training approaches often suffer from error accumulation, especially when modelling detailed chemistry, leading to unreliable performance in <em>a posteriori</em> simulations. In this work, we propose an unrolling training strategy based on randomized states that exposes the network to accumulated errors during training and enables it to observe the evolution of species across varying chemical time scales. The effectiveness of this approach is systematically evaluated in a two-dimensional laminar counterflow flame, which spans ignition, propagation and extinction regimes using the detailed GRI 3.0 mechanism. The influence of different unrolling step numbers on model performance as well as computationally efficiency on both CPU and GPU is also analysed. To demonstrate the method’s generalizability, the trained networks are further assessed in a three-dimensional turbulent planar jet flame with strong turbulence–chemistry interactions.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100445"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145925451","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":"Accelerating mixing controlled turbulent combustion simulations with hybrid Navier–Stokes/ANN scalar-solvers","authors":"Francesco Cenvinzo ,&nbsp;Alberto Procacci ,&nbsp;Alessandro Parente ,&nbsp;Pascale Domingo ,&nbsp;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 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%.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100453"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","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}

{"title":"Research progress on thermochemical conversion technologies for hydrogen production from waste plastics","authors":"Jiahui Zhu ,&nbsp;Ning Cai ,&nbsp;Chuanwen Zhao ,&nbsp;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":"2026-03-01","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}

{"title":"A latent variable framework for handling detailed chemistry in reacting flows","authors":"Eva Muñoz ,&nbsp;Mohammad Rafi Malik ,&nbsp;Alberto Cuoci ,&nbsp;Hong G. Im ,&nbsp;Alessandro Parente","doi":"10.1016/j.jaecs.2025.100430","DOIUrl":"10.1016/j.jaecs.2025.100430","url":null,"abstract":"<div><div>Combustion involves multiple scales and intricate physical processes that make its simulation extremely demanding in terms of computational resources. To overcome this challenge, reduced-order models are essential for improving our understanding of the process and speeding up simulations. This work introduces a latent variable (LV) transport framework and demonstrates its compression capabilities through 0D batch reactor simulations. To improve solver efficiency and numerical stability, we introduce <em>projected tolerances</em>, which scale the ODE solver tolerances in the latent space, significantly improving numerical stability and computational efficiency compared to traditional <em>fixed tolerance</em> approaches.</div><div>The methodology is validated on 0D simulations using methane, propane, and n-heptane combustion with detailed and lumped kinetic mechanisms. Results show that the LV solver achieves substantial computational cost reductions (up to 50%) while preserving solution accuracy. We further analyse the influence of kinetic mechanisms, demonstrating that detailed mechanisms benefit the most from latent space compression due to their higher dimensionality.</div><div>The proposed LV framework offers a robust and efficient alternative to conventional solvers for detailed kinetics and establishes a foundation for its extension to multidimensional reactive flow simulations.</div></div>","PeriodicalId":100104,"journal":{"name":"Applications in Energy and Combustion Science","volume":"25 ","pages":"Article 100430"},"PeriodicalIF":5.0,"publicationDate":"2026-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145651982","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}