Computational singular perturbation analysis of brain lactate metabolism

Lactate in the brain is considered an important fuel and signalling molecule for neuronal activity, especially during neuronal activation. Whether lactate is shuttled from astrocytes to neurons or from neurons to astrocytes leads to the contradictory Astrocyte to Neuron Lactate Shuttle (ANLS) or Neuron to Astrocyte Lactate Shuttle (NALS) hypotheses, both of which are supported by extensive, but indirect, experimental evidence. This work explores the conditions favouring development of ANLS or NALS phenomenon on the basis of a model that can simulate both by employing the two parameter sets proposed by Simpson et al. (J Cereb. Blood Flow Metab., 27:1766, 2007) and Mangia et al. (J of Neurochemistry, 109:55, 2009). As most mathematical models governing brain metabolism processes, this model is multi-scale in character due to the wide range of time scales characterizing its dynamics. Therefore, we utilize the Computational Singular Perturbation (CSP) algorithm, which has been used extensively in multi-scale systems of reactive flows and biological systems, to identify components of the system that (i) generate the characteristic time scale and the fast/slow dynamics, (ii) participate to the expressions that approximate the surfaces of equilibria that develop in phase space and (iii) control the evolution of the process within the established surfaces of equilibria. It is shown that a decisive factor on whether the ANLS or NALS configuration will develop during neuronal activation is whether the lactate transport between astrocytes and interstitium contributes to the fast dynamics or not. When it does, lactate is mainly generated in astrocytes and the ANLS hypothesis is realised, while when it doesn’t, lactate is mainly generated in neurons and the NALS hypothesis is realised. This scenario was tested in exercise conditions

Chemical Ignition Characteristics of Ethanol Blending with Primary Reference Fuels

Synergistic octane blending behavior of ethanol with gasoline and its surrogates has been observed by many researchers. The nonlinear octane boosting tendency is observed at mid and high molar blends of ethanol in primary reference fuels. The present work aims to provide chemical insight into this nonlinear blending behavior of ignition processes when ethanol is blended with primary reference fuels. To this end, ignition delay time (IDT) calculations, using a well-validated mechanism, were performed for several fuel blends of iso-octane, n-heptane, and ethanol. Temperature and pressure values were found, correlating experimentally measured octane numbers and simulated homogenous batch reactor IDTs. The temperature and pressure conditions obtained, were then used to study the evolution of heat release and reactivity before the onset of auto-ignition in a homogeneous premixed reactor. Markers of low- and high-temperature reactivity (OH and HO2) were analyzed for various molar blends of n-heptane with ethanol–iso-octane. Ethanol was observed to be better at radical scavenging than iso-octane at a higher mole fraction. A computational singular perturbation analysis was conducted for a selection of blends to clarify the reactions responsible for the synergistic blending behavior of ethanol in n-heptane. The role of the H-abstraction reactions was highlighted during the first ignition stage; reactions related to n-heptane were found to compete with the H-abstraction reactions of iso-octane or ethanol. Notably, the H-abstraction path of ethanol was more favored than that of the iso-octane, as a result of the smaller activation energies of the related reactions in ethanol. The competition of the H-abstraction paths resulted in a smaller radical pool in the n-heptane–iso-octane–air case, and an even smaller pool in n-heptane–ethanol–air. In all the cases considered, the second stage was dominated mainly by hydrogen-related reactions, regardless of the initial mixture, with H2O2 (+M) → 2OH (+M) and H + O2 → O + OH playing the most important roles. This work employed a novel approach to examine specific reactions responsible for auto-ignition in ethanol blends, which can be used for fuel design, primarily around the generation/consumption of radical pool intermediates by interaction with fuel components

Screening gas‐phase chemical kinetic models: Collision limit compliance and ultrafast timescales

Detailed gas‐phase chemical kinetic models are widely used in combustion research, and many new mechanisms for different fuels and reacting conditions are developed each year. Recent works have highlighted the need for error checking when preparing such models, but a useful community tool to perform such analysis is missing. In this work, we present a simple online tool to screen chemical kinetic mechanisms for bimolecular reactions exceeding collision limits. The tool is implemented on a user‐friendly website, cloudflame.kaust.edu.sa, and checks three different classes of bimolecular reactions; (ie, pressure independent, pressure‐dependent falloff, and pressure‐dependent PLOG). In addition, two other online modules are provided to check thermodynamic properties and transport parameters to help kinetic model developers determine the sources of errors for reactions that are not collision limit compliant. Furthermore, issues related to unphysically fast timescales can remain an issue even if all bimolecular reactions are within collision limits. Therefore, we also present a procedure to screen ultrafast reaction timescales using computational singular perturbation. For demonstration purposes only, three versions of the rigorously developed AramcoMech are screened for collision limit compliance and ultrafast timescales, and recommendations are made for improving the models. Larger models for biodiesel surrogates, tetrahydropyran, and gasoline surrogates are also analyzed for exemplary purposes. Numerical simulations with updated kinetic parameters are presented to show improvements in wall‐clock time when resolving ultrafast timescales

Chemical kinetic insights into the ignition dynamics of n-hexane

Normal alkanes constitute a significant fraction of transportation fuels, and are the primary drivers of ignition processes in gasoline and diesel fuels. Low temperature ignition of n-alkanes is driven by a complex sequence of oxidation reactions, for which detailed mechanisms are still being developed. The current study explores the dynamics of low-temperature ignition of n-hexane/air mixtures, and identifies chemical pathways that characterize the combustion process. Two chemical kinetic mechanisms were selected as a comparative study in order to better understand the role of specific reaction sequences in ignition dynamics: one mechanism including a new third sequential O2 addition reaction pathways (recently proposed by Wang et al. 2017), while the other without (Zhang et al. 2015). The analysis is conducted by applying tools generated from the computational singular perturbation (CSP) approach to two distinct ignition phenomena: constant volume and compression ignition. In both cases, the role of the third sequential O2 addition reactions proves to be significant, although it is found to be much more pronounced in the constant volume cases compared to the HCCI. In particular, in the constant volume ignition case, reactions present in the third sequential O2 addition reaction pathways (e.g., KDHP  →  products + OH) contribute significantly to the explosivity of the mixture; when accounted for along with reactions P(OOH)2 + O2  →  OOP(OOH)2 and OOP(OOH)2  →  KDHP + OH, they decrease ignition delay time of the mixture by up to 40%. Under HCCI conditions, in the first-stage ignition, the third-O2 addition reactions contribute to the process, although their role decays with time and becomes negligible at the end of the first stage. The second ignition stage is dominated almost exclusively by hydrogen-related chemistry

Development of a reduced four-component (toluene/n-heptane/iso-octane/ethanol) gasoline surrogate model

The prospect of blending gasoline fuel with ethanol is being investigated as a potential way to improve the knock residence of the base gasoline. However, one of the drawbacks is a lack of proper understanding of the reason for the non-linear response of blending ethanol and gasoline. This non-linearity could be better understood by an improved knowledge of the interactions of these fuel components at a molecular level. This study proposed a highly reduced four-component (toluene/n-heptane/iso-octane/ethanol) gasoline surrogate model containing 59 species and 270 reactions. The model was reduced using the direct relation graph with expert knowledge (DRG-X) (Lu and Law, 20015; Lu et al., 2011) and isomer lumping method. The computational singular perturbation (CSP) analysis were performed to reduce the potential stiffness issues by accordingly adjusting the Arrhenius coefficients of the proper reactions. The model has been comprehensively validated against wide range of ignition delay times (IDT) and flame speed (FS) measurement data as well as compared against two representative literature models from Liu et al. (2013) and Wang et al. (2015). Overall, good agreements were observed between model predictions and experimental data across the entire research octane number (RON), equivalence ratio, pressure and temperature range. In addition, the model has also been coupled with the computational fluid dynamic (CFD) models to simulate the experimental data of constant volume reacting spray of a low-octane gasoline (Haltermann straight-run naphtha), and in-cylinder pressures and temperatures of a high-octane gasoline (Haltermann Gasoline) combustion in a heavy duty compression ignition engine. The coupled model can qualitatively predict the experimentally obtained data with an improved performance for PRF, TPRF, and TPRF-ethanol surrogates