Algorithmic Analysis of Chemical Dynamics of the Autoignition of NH3–H2O2/Air Mixtures

Abstract

The dynamics of a homogeneous adiabatic autoignition of an ammonia/air mixture at constant volume was studied, using the algorithmic tools of Computational Singular Perturbation. Since ammonia combustion is characterized by both unrealistically long ignition delays and elevated NO x emissions, the time frame of action of the modes that are responsible for ignition was analyzed by calculating the developing time scales throughout the process and by studying their possible relation to NO x emissions. The reactions that support or oppose the explosive time scale were identified, along with the variables that are related the most to the dynamics that drive the system to an explosion. It is shown that reaction H 2 O 2 (+M) → OH + OH (+M) is the one contributing the most to the time scale that characterizes ignition and that its reactant H 2 O 2 is the species related the most to this time scale. These findings suggested that addition of H 2 O 2 in the initial mixture will influence strongly the evolution of the process. It was shown that ignition of pure ammonia advanced as a slow thermal explosion with very limited chemical runaway. The ignition delay could be reduced by more than two orders of magnitude through H 2 O 2 addition, which causes only a minor increase in NO x emissions.

The chemical dynamics of hydrogen/hydrogen peroxide blends diluted with steam at compression ignition relevant conditions

“…Moreover, the addition of hydrogen peroxide in fuel/air mixtures for conventional transport-related applications is a well-documented method of effective ignition promotion. Injected as a pure substance or as emulsified fuel into the engine cylinder, hydrogen peroxide has been successfully tested with fuels like n-decane [100], natural gas [101,102], methane [103][104][105], diesel [106][107][108][109][110][111][112], gasoline [113], n-heptane [114], iso-octane [115], jatropha oil [116], n-butanol [117], ethanol [118,119], ethanol-diesel [120] and ammonia [121].…”

“…• the timescale participation index (TPI), which identifies the reactions mostly related to each mode's timescale [129][130][131][132]; thus, for each CSP mode, the TPIs of all reactions are calculated and the largest ones (in absolute value) are selected; when positive, the related reaction favors the explosive character of the mode's timescale, while when negative the associated reaction tends to dissipate the related timescale [133]. This tool will be mainly used to identify the reactions mostly related to the system's fast explosive timescale (τ e,f ), i.e., the system's characteristic timescale, which controls the ignition delay time [121,[134][135][136][137][138][139]. It is noted that the system's characteristic mode must meet the following two conditions: (i) it has to be the fastest of the slow timescales, thus ensuring that the timeframe of action of that mode will be relevant to the system's slow evolution and (ii) its amplitude must be among the largest [133].…”

Asymptotic Analysis of Detonation Development at SI Engine Conditions Using Computational Singular Perturbation

“…The sign of the eigenvalue (positive or negative) determines the nature of the respective CSP mode; a negative eigenvalue indicates a dissipative mode that tends to drive the system towards equilibrium, while a positive eigenvalue is called explosive [2,93,94] that drives the system away from the equilibrium. Explosive modes have been extensively investigated in reacting flows because they are inherently related to limit phenomena and flames [95][96][97]. As previously discussed, however, their mere existence does not qualify them as dominant ones for the system's slow evolution.…”

Dynamics Analysis of a Jet-Fuel Surrogate and Development of a Skeletal Mechanism for Computational Fluid Dynamic Applications

“…The proposed method has showcased its success the last 30 years in providing reduced models of increased accuracy and analysing in detail highly complex mathematical models, in a wide range of different fields, such as chemical kinetics (e.g., (Tingas et al 2018c;Yalamanchi et al 2020)), reacting flows (e.g., (Manias et al 2019b;Prager et al 2011)), atmospheric environment (e.g., (Neophytou et al 2004;Neophytou et al 2005)), applied mathematics (e.g., (Goussis and Valorani 2006;Maris and Goussis 2015)), biological modelling (e.g., and pharmacokinetics (e.g., (Patsatzis et al 2016;Michalaki and Goussis 2018)). In the field of combustion, CSP has been used in the analysis of a range of different applications such as zerodimensional autoigniting systems (e.g., (Tingas et al 2015;Khalil et al 2019)), one-dimensional laminar flames/igniting systems (e.g., (Massias et al 1999;Song et al 2018)), two-dimensional turbulent igniting systems (e.g., (Pal et al 2017)) and three-dimensional turbulent flames (e.g., (Manias et al 2019c;Manias et al 2019a)). For a detailed description of the CSP method and the tools used in the current study, the reader is referred to (Lam and Goussis 1994;Hadjinicolaou and Goussis 1998;Valorani et al 2006b;Valorani et al 2020).…”