Krithika Narayanaswamy

Bio: Krithika Narayanaswamy is an academic researcher from Indian Institute of Technology Madras. The author has contributed to research in topics: Combustion & Biodiesel. The author has an hindex of 6, co-authored 18 publications receiving 574 citations. Previous affiliations of Krithika Narayanaswamy include Stanford University & Indian Institutes of Technology.

Analytical, experimental and computational investigation of the influence of stoichiometric mixture fraction on structure and extinction of laminar, nonpremixed methane flames and ethane flames

Abstract: Fundamental studies on combustion in laminar, nonpremixed flames are often carried out using conserved scalar quantities. These conserved scalar quantities, represented here by mixture fraction, E, are used as independent variables in activation-energy asymptotic analysis and in rate-ratio asymptotic analysis. These analyses are carried out in the asymptotic limit of large Damkohler number, with chemical reactions presumed to take place in a thin reaction zone, that is located at E = Est. The quantity Est is the stoichiometric mixture fraction. A characteristic diffusion time is given by the reciprocal of the scalar dibipation rate, X. Previous computational studies have shown that the scalar dibipation rate at extinction depends on Est and the maximum flame temperature, Tst. Here a rate-ratio asymptotic analysis is carried out using reduced chemistry to elucidate the influence of Est on critical conditions of extinction. The scalar dibipation rate at extinction was predicted as a function of Est with the mab fractions of reactants so chosen that the adiabatic flame temperature, Tst, is fixed. The predictions of the analysis show that with increasing values of Est, the scalar dibipation rate at extinction first increases and then decreases. To test the predictions of the asymptotic analysis critical conditions of extinction are measured on nonpremixed methane flames stabilized in the counterflow configuration. With increasing values of stoichiometric mixture fraction, the strain rate at extinction was found to increase and the scalar dibipation rate at extinction was found to first increase and then decrease. The predictions of the asymptotic analysis agreed with experiments. A key outcome of the analysis is that with increasing Est the thickneb of the regions where oxygen and fuel are consumed first increase and the decrease. This is responsible for the observed non-monotonic changes in the values of the scalar dibipation rate at extinction with changes in Est. (Less)

Performance and emission characteristics of a diesel engine using biodiesel blended with nano additives

Abstract: Economic uncertainty, price escalation and depletion levels of fossil fuels increases the day-to-day need to look for alternative fuel to meet the world’s energy needs. Biodiesel provides the best choice for energy in new world. Biodiesel can be a diesel fuel replacement. In this research, Biodiesel is made with the combination of cottonseed oil and diesel. To stabilize biodiesel and boost its properties, nano particles are added to improve fuel quality. In this study, Silicon oxide is mixed with biodiesel, evaluated and compared to existing diesel with its engine performance, and emission characteristics. Cottonseed oil biodiesel blends improved in properties high calorific value fuel attainment and elimination of toxic exhaust emission forming to the atmosphere by added silicon oxide nano particle promising technique for biodiesel/diesel use.

A data-driven framework to predict ignition delays of straight-chain alkanes

Abstract: Ignition delay time (IDT) is an important global combustion property that affects the thermal efficiency of the engine and emissions (particularly NO ). IDT is generally measured by performing experiments using Shock-tube and Rapid Compression Machine (RCM). The numerical calculation of IDT is a computationally expensive and time-consuming process. Arrhenius type empirical correlations offer an inexpensive alternative to obtain IDT. However, such correlations have limitations as these typically involve ad-hoc parameters and are valid only for a specific fuel and particular range of temperature/pressure conditions. This study aims to formulate a data-driven scientific way to obtain IDT for new fuels without performing detailed numerical calculations or relying on ad-hoc empirical correlations. We propose a rigorous, well-validated data-driven study to obtain IDT for new fuels using a regression-based clustering algorithm. In our model, we bring in the fuel structure through the overall activation energy ( ) by expressing it in terms of the different bonds present in the molecule. Gaussian Mixture Model (GMM) is used for the formation of clusters, and regression is applied over each cluster to generate models. The proposed algorithm is used on the ignition delay dataset of straight-chain alkanes (C H for n = 4 to 16). The high level of accuracy achieved demonstrates the applicability of the proposed algorithm over IDT data. The algorithm and framework discussed in this article are implemented in python and made available at https://doi.org/10.5281/zenodo.5774617.

Experimental and computational investigation of extinction and autoignition of propane and n-heptane in nonpremixed flows

Abstract: An experimental and computational investigation is carried out to characterize the influence of reactants on critical conditions for extinction and for autoignition of propane and n-heptane in nonpremixed counterflow configurations. Propane or vaporized n-heptane mixed with nitrogen is transported in one stream while the other stream is made up of air mixed with nitrogen. Measurements of the oxidizer stream temperature needed for autoignition are made at fixed values of the strain rate, either with the fuel mass fraction varied at a fixed oxygen mass fraction or with the oxygen mass fraction varied at a fixed fuel mass fraction. Extinction strain rates for propane are measured as a function of the oxygen mass fraction with room-temperature feed streams and the fuel mass fraction fixed and for n-heptane as a function of the fuel mass fraction with the oxygen mass fraction and feed-stream temperatures fixed. Predictions of critical conditions for extinction and autoignition are made employing detailed kinetic mechanisms. Predictions of critical conditions for extinction are in reasonable agreement with measurements, but there are significant discrepancies for autoignition. Measurements show that increasing the mass fraction of either fuel or oxygen increases the overall reactivity thereby reducing the autoignition temperature. The kinetic models predict the increase in reactivity of the mixing layer with increasing mass fraction of fuel but predict very little change in reactivity of the mixing layer with increasing mass fraction of oxygen, thus failing to predict the influence of oxygen on autoignition. It is concluded that there may exist kinetic pathways responsible for this disagreement that are yet to be discovered, and paths that fail to explain the results are identified.

An accurate, fast, mathematically robust, universal, non-iterative algorithm for computing multi-component diffusion velocities

Abstract: Using accurate multi-component diffusion treatment in numerical combustion studies remains formidable due to the computational cost associated with solving for diffusion velocities. To obtain the diffusion velocities, for low density gases, one needs to solve the Stefan-Maxwell equations along with the zero diffusion flux criteria, which scales as $\mathcal{O}(N^3)$, when solved exactly. In this article, we propose an accurate, fast, direct and robust algorithm to compute multi-component diffusion velocities. To our knowledge, this is the first provably accurate algorithm (the solution can be obtained up to an arbitrary degree of precision) scaling at a computational complexity of $\mathcal{O}(N)$ in finite precision. The key idea involves leveraging the fact that the matrix of the reciprocal of the binary diffusivities, $V$, is low rank, with its rank being independent of the number of species involved. The low rank representation of matrix $V$ is computed in a fast manner at a computational complexity of $\mathcal{O}(N)$ and the Sherman-Morrison-Woodbury formula is used to solve for the diffusion velocities at a computational complexity of $\mathcal{O}(N)$. Rigorous proofs and numerical benchmarks illustrate the low rank property of the matrix $V$ and scaling of the algorithm.

Wide range modeling study of dimethyl ether oxidation

Abstract: A detailed chemical kinetic model has been used to study dimethyl ether (DME) oxidation over a wide range of conditions. Experimental results obtained in a jet-stirred reactor (JSR) at I and 10 atm, 0.2 < 0 < 2.5, and 800 < T < 1300 K were modeled, in addition to those generated in a shock tube at 13 and 40 bar, 0 = 1.0 and 650 :5 T :5 1300 K. The JSR results are particularly valuable as they include concentration profiles of reactants, intermediates and products pertinent to the oxidation of DME. These data test the Idnetic model severely, as it must be able to predict the correct distribution and concentrations of intermediate and final products formed in the oxidation process. Additionally, the shock tube results are very useful, as they were taken at low temperatures and at high pressures, and thus undergo negative temperature dependence (NTC) behavior. This behavior is characteristic of the oxidation of saturated hydrocarbon fuels, (e.g. the primary reference fuels, n-heptane and iso- octane) under similar conditions. The numerical model consists of 78 chemical species and 336 chemical reactions. The thermodynamic properties of unknown species pertaining to DME oxidation were calculated using THERM.