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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 523 records · Page 29
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Thermodynamic Analysis of Thermo-vacuum Clothes Drying Operation

Clothes drying accounts for a significant amount of energy consumed in residential and commercial sectors. A thermal vacuum clothes drying technology (TVCD) is proposed as an advanced clothes dryer which can significantly reduce the energy requirements by expediting the drying process. In the conventional convective clothes dryer, hot dry air is introduced into the drum which gets in direct contact to dry the clothes. This process is energy inefficient since the significant amount of heat and the water carried out with the exhaust stream are wasted. In contrast to the conventional convective drying technique, the drying mechanism of TVCD is through nucleate boiling at low temperature due to reduced vessel pressure. The process is not only efficient but also reduces the required time for drying. The objective of this paper is to develop a comprehensive thermodynamic model to predict the transient drying process of TVCD. The three-stage system model including the detail analysis of individual components is capable of simulating the variation of the water content in the textile under various operational conditions. The preliminary results show that the drying time of 3 lb textile from 70% to 2.5% in TVCD is approximately four times less than the time required in the conventional clothes dryer. Parametric studies are carried out to understand the effect of operation conditions and component geometry on the system performance. Energy consumption of the system is also analyzed.

Yang, Cheng-Min↗
DOE OSTI2024

Technology Feasibility of High-Temperature Heat Pumps for Industrial Drying Process in the US

High-temperature heat pumps are a prospective technology for electrifying and decarbonizing industrial drying processes. This study investigates the technological potential of HTHPs in replacing fossil-fuel burners or steam boilers in industrial spray dryers. The performance of HTHPs with three configurations was investigated, including high high-temperature heat pump boosted with an electric air heater, a steam-generating high-temperature heat pump with multi-stage mechanical vapor compressions, and transcritical high-temperature heat pumps. R1336mzz(Z) is used as the refrigerant in both subcritical and transcritical high-temperature heat pumps, and R717 is used as the working fluid in the mechanical vapor recompression and the heat transfer fluids. Thermodynamic models are built to evaluate the energy efficiency of HTHPs. A case study was carried out on an industrial spray dryer integrated with a waste heat recuperator, where the HTHPs are used to recover precooled air with a dew point of 40℃ and supply heat for drying air at 200℃. For a high-temperature heat pump boosted with an electric air heater, a higher system-level COP is achieved with a high supply temperature from high-temperature heat pump. For a high-temperature heat pump with mechanical vapor recompressions, larger temperature drops in the flashing tank provide higher heat capacities but almost constant values of the coefficient of performance. For a transcritical high-temperature heat pump, an optimum discharge pressure exists for the temperature profiles within the gas cooler and the coefficient of performance. The transcritical high-temperature heat pump provides the best energy efficiency among the three configurations. The theoretical results prove the technical feasibility of high-temperature heat pumps in industrial air-drying applications. .

Wang, Pengtao↗
DOE OSTI2026

Impact of Molten Gallium on the Microstructure and Corrosion Behavior of Aluminum and Uranium-Aluminum Alloys for Used Nuclear Fuel Reprocessing

Test reactors around the world utilize highly enriched uranium fuel to achieve high neutron fluxes for materials testing. Once spent, the remaining uranium is a valuable resource for subsequent fuel fabrication. However, some of these test reactor cores consist of curved plate-type fuel elements, fabricated using aluminum alloy 6061 (AA6061) cladding to encapsulate a uranium-aluminum alloy (UAlx) fuel matrix. These assemblies require non-standard reprocessing approaches for uranium recovery, as aluminum dissolves readily in acidic solutions, generating large volumes of waste and complicating downstream chemical separations. In this work, we investigate a novel chemical decladding strategy based on the interaction between AA6061/UAlx and molten gallium (Ga). Ga is known to induce severe degradation of aluminum metal through liquid metal embrittlement (LME), even at relatively low Ga concentrations. By penetrating the aluminum crystal lattice, Ga disrupts grain cohesion and facilitates fracture or dissolution of the aluminum matrix. Thermodynamic analysis of the Al–Ga binary phase diagram suggests that Ga may offer a viable pathway to selectively weaken or dissolve the AA6061 cladding, and potentially the aluminum component of the UAlx fuel matrix within. To this end, parametric experiments were performed at 50 °C and 100 °C across a range of Al–Ga atomic fractions. At lower Al fractions, the AA6061 was completely molten after 2 hours of exposure to the Ga metal. In contrast, samples with higher Al fractions (0.9 Al, 0.1 Ga) contained residual solids after 2 hours, which were characterized by microstructural examination using electron backscatter diffraction (EBSD) and transmission electron microscopy (TEM). These Al-Ga compositions were also evaluated using FactSage thermodynamic modeling to further elucidate the relationship between phase diagram behavior and LME.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Case Study of Integrating High-Temperature Heat Pump with LiBr-H2O Absorption Chiller for Data Center Liquid Cooling

Data centers (DCs) are physical infrastructures that support artificial intelligence workloads. The rapid growth of artificial intelligence is putting substantial pressure on the US power grid. Most of electricity consumed by IT equipment, accounting for 50%-60% of total DC power, ultimately becomes waste heat. This heat is dissipated by DC’s cooling facilities, accounting for an additional 30%-40% of total DC power. Recovering and repurposing this waste heat offers a significant opportunity to enhance energy efficiency and reduce operating costs of DCs. One potential pathway is converting heat to cold using thermal-driven absorption chillers, therefore, reducing the power consumption in DC cooling facilities. Existing studies mainly demonstrate the technical and economic feasibility of repurposing DC’s waste heat for cooling applications but provide limited technical details on how to integrate the thermal-driven absorption chillers with DC cooling systems. In addition, the low-grade waste heat available from DCs must be upgraded to higher temperatures suitable for absorption chillers. This paper presents a case study on integrating high-temperature heat pumps with a LiBr-H2O absorption chiller to use DC waste heat for cooling. A thermodynamic model of single-effect, LiBr-H2O absorption chiller and an empirical model of high-temperature heat pumps were built. The case study considers ASHRAE W17 liquid-cooled DC, with facility service water supplied at 17.0℃ and returned at 25.3℃. The thermal behaviors of absorption chiller components were predicted for the generation temperature ranging from 75.0℃ to 115.0℃. Based on the available waste heat in the integrated system, two waste heat recovery strategies were evaluated: a facility service water-based strategy and cooling water-based strategy. Results indicated that the cooling water-based strategy achieves higher Coefficient of Performance (COPs) than the facility service water-based strategy. The relatively low cooling COPs of single-effect LiBr-H2O absorption chillers could be offset by high heating COP of high temperature heat pumps. The maximum cooling COP of absorption chiller and the overall COP of integrated systems occur at lower generation temperatures, but these conditions also yield lower cooling capacities. In practice, system operation should balance the trade-off between the COP and cooling capacity

Wang, Pengtao [ORNL] (ORCID:0000000214713429)↗
DOE OSTI2020

H2Fills™ [SWR-19-67]

H2FillS: Hydrogen Filling Simulation The Hydrogen Filling Simulation (H2FillS) software is a thermodynamic model designed to track and report on the transient change in hydrogen temperature, pressure, and mass flow when filling a fuel cell electric vehicle (FCEV). H2FillS simulates gas flow from the hydrogen station to the FCEV storage system. Using empirical fueling data sets, the model has been validated over a range of fueling conditions to match common light-duty FCEV fill profiles. Overall, it provides significant benefits to the light-duty fueling market and fill knowledge gaps of the interaction between a hydrogen station and an FCEV. Capabilities Use the comprehensive H2FillS model to: Safely design and operate a hydrogen fueling station Support code refinement with readily available data Develop system and operational improvements to reduce capital or operating costs at hydrogen stations or on-board FCEVs. How It Works H2FillS uses a "drag-and-drop" graphical user interface to simulate station and vehicle systems with preset parameters for common hydrogen station components as default values. When needed, users can define their own parameters to create their own station or vehicle components. Download H2FillS in Two Steps Read the terms of the end user software license agreement. Register to download the software. There are two versions of the model: A full-station model starts the simulation at high-pressure ground storage, runs through a dispenser, and ends at a vehicle storage system. A partial-station model starts at the dispenser breakaway and consists solely of the dispenser components and the vehicle storage system. H2FillS will automatically output fill performance data from the vehicle by tracking pressure and temperature throughout the fill. Users can input their own fill profiles into the model to run a variety of simulations.

Peters, Michael↗
DOE OSTI2022

Thermodynamically consistent versions of approximations used in modelling moist air

Abstract Some existing approaches to modelling the thermodynamics of moist air make approximations that break thermodynamic consistency , such that the resulting thermodynamics does not obey the first and second laws or has other inconsistencies. Recently, an approach to avoid such inconsistency has been suggested: the use of thermodynamic potentials in terms of their natural variables , from which all thermodynamic quantities and relationships (equations of state) are derived. In this article, we develop this approach for unapproximated moist‐air thermodynamics and two widely used approximations: the constant‐ approximation and the dry heat capacities approximation. The (consistent) constant‐ approximation is particularly attractive because it leads to, with the appropriate choice of thermodynamic variable, adiabatic dynamics that depend only on total mass and are independent of the breakdown between water forms. Additionally, a wide variety of material from different sources in the literature on thermodynamics in atmospheric modelling is brought together. It is hoped that this article provides a comprehensive reference for the use of thermodynamic potentials in atmospheric modelling, especially for the three systems considered here.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1986

Thermodynamics and combustion modeling

Modeling fluid phase phenomena blends the conservation equations of continuum mechanics with the property equations of thermodynamics. The thermodynamic contribution becomes especially important when the phenomena involve chemical reactions as they do in combustion systems. The successful study of combustion processes requires (1) the availability of accurate thermodynamic properties for both the reactants and the products of reaction and (2) the computational capabilities to use the properties. A discussion is given of some aspects of the problem of estimating accurate thermodynamic properties both for reactants and products of reaction. Also, some examples of the use of thermodynamic properties for modeling chemically reacting systems are presented. These examples include one-dimensional flow systems and the internal combustion engine.

Zeleznik, Frank J.↗
DOE OSTI2021

Thermodynamic and Kinetic Modeling of Co-utilization of Glucose and Xylose for 2,3-BDO Production by Zymomonas mobilis

Prior engineering of the ethanologen Zymomonas mobilis has enabled it to metabolize xylose and to produce 2,3-butanediol (2,3-BDO) as a dominant fermentation product. When co-fermenting with xylose, glucose is preferentially utilized, even though xylose metabolism generates ATP more efficiently during 2,3-BDO production on a BDO-mol basis. To gain a deeper understanding of Z. mobilis metabolism, we first estimated the kinetic parameters of the glucose facilitator protein of Z. mobilis by fitting a kinetic uptake model, which shows that the maximum transport capacity of glucose is seven times higher than that of xylose, and glucose is six times more affinitive to the transporter than xylose. With these estimated kinetic parameters, we further compared the thermodynamic driving force and enzyme protein cost of glucose and xylose metabolism. It is found that, although 20% more ATP can be yielded stoichiometrically during xylose utilization, glucose metabolism is thermodynamically more favorable with 6% greater cumulative Gibbs free energy change, more economical with 37% less enzyme cost required at the initial stage and sustains the advantage of the thermodynamic driving force and protein cost through the fermentation process until glucose is exhausted. Glucose-6-phosphate dehydrogenase (g6pdh), glyceraldehyde-3-phosphate dehydrogenase (gapdh) and phosphoglycerate mutase (pgm) are identified as thermodynamic bottlenecks in glucose utilization pathway, as well as two more enzymes of xylose isomerase and ribulose-5-phosphate epimerase in xylose metabolism. Acetolactate synthase is found as potential engineering target for optimized protein cost supporting unit metabolic flux. Pathway analysis was then extended to the core stoichiometric matrix of Z. mobilis metabolism. Growth was simulated by dynamic flux balance analysis and the model was validated showing good agreement with experimental data. Dynamic FBA simulations suggest that a high agitation is preferable to increase 2,3-BDO productivity while a moderate agitation will benefit the 2,3-BDO titer. Taken together, this work provides thermodynamic and kinetic insights of Z. mobilis metabolism on dual substrates, and guidance of bioengineering efforts to increase hydrocarbon fuel production.

23-butanediol↗
DOE OSTI2026

Thermodynamic evaluation and modeling of the CeF 3 -LaF 3 -LiF-NaF system

The thermodynamic properties of the LiF-NaF-CeF 3 -LaF 3 pseudo-quaternary system have been evaluated, and models for the relevant phases have been revised or newly developed. The solid solubility of NaF in LaF 3 and CeF 3 was described in the model based on reported phase-equilibria observations and results from density functional theory calculations. The Gibbs energy functions for the NaCeF 4 and NaLaF 4 phases have been updated with recently reported thermochemical measurements. The Gibbs energy functions and model relations for the LiF-NaF-LaF 3 and LiF-NaF-CeF 3 pseudo-ternary systems were successfully optimized based on thermochemical properties and phase equilibria observations.

CALPHAD↗
DOE OSTI2025

Classifying thermodynamic cloud phase using machine learning models

Vertically resolved thermodynamic cloud-phase classifications are essential for studies of atmospheric cloud and precipitation processes. The Department of Energy (DOE) Atmospheric Radiation Measurement (ARM) Thermodynamic Cloud Phase (THERMOCLDPHASE) value-added product (VAP) uses a multi-sensor approach to classify the thermodynamic cloud phase by combining lidar backscatter and depolarization, radar reflectivity, Doppler velocity, spectral width, microwave-radiometer-derived liquid water path, and radiosonde temperature measurements. The measured pixels are classified as ice, snow, mixed phase, liquid (cloud water), drizzle, rain, and liq_driz (liquid+drizzle). We use this product as the ground truth to train three machine learning (ML) models to predict the thermodynamic cloud phase from multi-sensor remote sensing measurements taken at the ARM North Slope of Alaska (NSA) observatory: a random forest (RF), a multi-layer perceptron (MLP), and a convolutional neural network (CNN) with a U-Net architecture. Evaluations against the outputs of the THERMOCLDPHASE VAP with 1 year of data show that the CNN outperforms the other two models, achieving the highest test accuracy, F1 score, and mean intersection over union (IOU). Analysis of ML confidence scores shows that ice, rain, and snow have higher confidence scores, followed by liquid, while mixed, drizzle, and liq_driz have lower scores. Feature importance analysis reveals that the mean Doppler velocity and vertically resolved temperature are the most influential data streams for ML thermodynamic cloud-phase predictions. Lidar measurements exhibit lower feature importance due to rapid signal attenuation caused by the frequent presence of persistent low-level clouds at the NSA site. The ML models' generalization capacity is further evaluated by applying them at another Arctic ARM site in Norway using data taken during the ARM Cold-Air Outbreaks in the Marine Boundary Layer Experiment (COMBLE) field campaign. The models demonstrated similar performance to that observed at the NSA site. Finally, we evaluate the ML models' response to simulated instrument outages and signal degradation and show that a CNN U-Net model trained with input channel dropouts performs better when input fields are missing.

ARM Aerial Facility↗
NASA NTRS2018

Evaluation of a Thermodynamic Ice Crystal Icing Model Using Experimental Ice Accretion Data

This paper presents the evaluation of a thermodynamic ice crystal icing model, previously presented to describe the possible mechanisms of icing within the core of a turbofan jet engine. It has been proposed that there are two types of distinct ice accretions based on a surface energy balance: freeze-dominated icing and melt-dominated icing. In the former, ice accretion occurs where a freeze fraction (0 to 1) of melted ice crystals freezes on a surface, along with the existing ice of the impinging water and ice mass. This freeze-dominated icing is characterized by having strong adhesion to the surface. In the latter, icing occurs from accumulated unmelted ice on a surface, where a melt fraction (0 to 1) dictates the amount of unmelted impinged ice. This melt-dominated icing is characterized by weakly bonded surface adhesion. The experimentally observed ice growth rates suggest that only a small fraction of the impinging ice remains on the surface, implying a mass loss mechanism such as splash, runback, bounce, or erosion. This mass loss parameter must be determined in conjunction with the fraction of freezing liquid water or fraction of melting ice on an icing surface. This loss parameter, however, along with the freeze and melt fraction, are the only experimental parameters that are currently not measured directly. Using reported icing growth rates from published ice crystal icing experiments, a methodology is proposed to determine these unknown parameters. This work takes reported ice accretion data from tests conducted by the National Aeronautics and Space Administration (NASA) in 2016 and tests NASA collaborated on with the National Research Council (NRC) of Canada in 2012 that examined the fundamental physics of ice crystal icing. Those research efforts sought to generate icing conditions representative of those that occur inside a jet engine when ingesting ice crystals. This paper presents the fundamental equations of the thermodynamic model, the methodology used to determine the aforementioned unknown icing parameters, and results from model evaluation using experimental data. In addition, this paper builds on the previously proposed model by adding a transient conduction term to explain ice growth behavior at the onset of experimental tests that was observed to be different from steady-state ice growth that occurred later in the test run.With the addition of this energy term, this becomes a quasi-steady model. A key finding from this work suggests that mass loss fractions can exceed 0.90 for steady ice growth periods. In addition, due to conductive heat fluxes when using a warmer-than-freezing airfoil, lower mass loss fraction values were calculated during the initial transient period.

Ice-Crystal Ice Accretion↗
NASA NTRS2021

Machine Learning the COSMO Model for Predicting Thermodynamics of Electrolyte Mixtures

Bottom-up design of electrolyte mixtures for battery systems requires predicting macro thermodynamic properties from molecular constituents. For instance, molten salt electrolyte batteries require conditions far above room temperature to operate. Therefore, discovering mixtures with increasingly lower eutectic melting points is desirable. A model that can approximate chemical activity is a valuable tool to search through the vast compositional design space. Machine learning can predict properties of materials such as vibrational free energies, electronic energy gaps, and thermal conductivities. Moreover, they can learn physical models such as interatomic potentials. The COSMO-SAC model uses theory and empirical parameterization to predict liquid-vapor and liquid-solid properties using first-principles calculations. However, obtaining activity coefficients required for parameterizing the COSMO-SAC model is costly and limited to a select chemical space. In this work, we explored if machine learning methods could improve the COSMO-SAC model and bridge density functional theory calculations to liquid phase thermodynamic properties. Our data-driven approach uses existing databases for sigma-profiles of organic solvents and reconciles their methodological differences via ensemble averaging. First, an optimal machine learning model is constructed for each dataset. Our machine learning algorithms use the sigma-profile as an input feature to predict binary mixtures' activity coefficients using multi-output regression. Each dataset uses different choices of functionals, methods, and basis sets. Therefore, our ensemble model attempts to predict corrected activity coefficients given the combination of all the model outputs. The activity coefficients used for training are generated using the COSMO-SAC model. This approach enables the extraction of meaningful information from the existing datasets to improve the COSMO-SAC model for obtaining thermodynamic properties of electrolyte mixtures. With the liquid phase activities, we can identify electrolyte mixtures that meet desired phase equilibria conditions.

Thermodynamics↗
NASA NTRS2014

T-MATS Toolbox for the Modeling and Analysis of Thermodynamic Systems

The Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS) is a MATLABSimulink (The MathWorks Inc.) plug-in for creating and simulating thermodynamic systems and controls. The package contains generic parameterized components that can be combined with a variable input iterative solver and optimization algorithm to create complex system models, such as gas turbines.

system modeling↗
NASA NTRS2014

Development of a Twin-spool Turbofan Engine Simulation Using the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS)

The Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS) is a tool that has been developed to allow a user to build custom models of systems governed by thermodynamic principles using a template to model each basic process. Validation of this tool in an engine model application was performed through reconstruction of the Commercial Modular Aero-Propulsion System Simulation (C-MAPSS) (v2) using the building blocks from the T-MATS (v1) library. In order to match the two engine models, it was necessary to address differences in several assumptions made in the two modeling approaches. After these modifications were made, validation of the engine model continued by integrating both a steady-state and dynamic iterative solver with the engine plant and comparing results from steady-state and transient simulation of the T-MATS and C-MAPSS models. The results show that the T-MATS engine model was accurate within 3 of the C-MAPSS model, with inaccuracy attributed to the increased dimension of the iterative solver solution space required by the engine model constructed using the T-MATS library. This demonstrates that, given an understanding of the modeling assumptions made in T-MATS and a baseline model, the T-MATS tool provides a viable option for constructing a computational model of a twin-spool turbofan engine that may be used in simulation studies.

thermodynamic simulation↗
DOE OSTI2022

Intertwined spin, charge, and pair correlations in the two-dimensional Hubbard model in the thermodynamic limit

Significance The high-temperature superconducting cuprates are governed by intertwined striped magnetic and charge orders, in addition to superconductivity. Remarkably similar behavior has also been seen in numerical calculations for the Hubbard model describing the copper–oxygen layers in these materials. Finite-cluster methods typically find that spin- and charge-stripe order dominates, while embedded quantum-cluster methods, which access the thermodynamic limit, often conclude that superconductivity does. Here, we report the observation of fluctuating spin and charge stripes in an embedded cluster calculation for the Hubbard model. This discovery demonstrates that striped states survive in the thermodynamic limit and allows us to study their influence on the model’s superconducting properties, where we find evidence for pair-density-wave correlations intertwined with the stripe correlations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS2014

Cantera Integration with the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS)

NASA Glenn Research Center (GRC) has recently developed a software package for modeling generic thermodynamic systems called the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). T-MATS is a library of building blocks that can be assembled to represent any thermodynamic system in the Simulink (The MathWorks, Inc.) environment. These elements, along with a Newton Raphson solver (also provided as part of the T-MATS package), enable users to create models of a wide variety of systems. The current version of T-MATS (v1.0.1) uses tabular data for providing information about a specific mixture of air, water (humidity), and hydrocarbon fuel in calculations of thermodynamic properties. The capabilities of T-MATS can be expanded by integrating it with the Cantera thermodynamic package. Cantera is an object-oriented analysis package that calculates thermodynamic solutions for any mixture defined by the user. Integration of Cantera with T-MATS extends the range of systems that may be modeled using the toolbox. In addition, the library of elements released with Cantera were developed using MATLAB native M-files, allowing for quicker prototyping of elements. This paper discusses how the new Cantera-based elements are created and provides examples for using T-MATS integrated with Cantera.

Thermodynamics↗
NASA NTRS2014

Cantera Integration with the Toolbox for Modeling and Analysis of Thermodynamic Systems (T-MATS)

NASA Glenn Research Center (GRC) has recently developed a software package for modeling generic thermodynamic systems called the Toolbox for the Modeling and Analysis of Thermodynamic Systems (T-MATS). T-MATS is a library of building blocks that can be assembled to represent any thermodynamic system in the Simulink(Registered TradeMark) (The MathWorks, Inc.) environment. These elements, along with a Newton Raphson solver (also provided as part of the T-MATS package), enable users to create models of a wide variety of systems. The current version of T-MATS (v1.0.1) uses tabular data for providing information about a specific mixture of air, water (humidity), and hydrocarbon fuel in calculations of thermodynamic properties. The capabilities of T-MATS can be expanded by integrating it with the Cantera thermodynamic package. Cantera is an object-oriented analysis package that calculates thermodynamic solutions for any mixture defined by the user. Integration of Cantera with T-MATS extends the range of systems that may be modeled using the toolbox. In addition, the library of elements released with Cantera were developed using MATLAB native M-files, allowing for quicker prototyping of elements. This paper discusses how the new Cantera-based elements are created and provides examples for using T-MATS integrated with Cantera.

Systems Analysis↗
NASA NTRS1986

Comparisons between thermodynamic and one-dimensional combustion models of spark-ignition engines

Results from a one-dimensional combustion model employing a constant eddy diffusivity and a one-step chemical reaction are compared with those of one-zone and two-zone thermodynamic models to study the flame propagation in a spark-ignition engine. One-dimensional model predictions are found to be very sensitive to the eddy diffusivity and reaction rate data. The average mixing temperature found using the one-zone thermodynamic model is higher than those of the two-zone and one-dimensional models during the compression stroke, and that of the one-dimensional model is higher than those predicted by both thermodynamic models during the expansion stroke. The one-dimensional model is shown to predict an accelerating flame even when the front approaches the cold cylinder wall.

Ramos, J. I.↗