K
Krithika Narayanaswamy
Researcher at Indian Institute of Technology Madras
Publications - 20
Citations - 705
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.
Papers
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Experimental and chemical kinetic modeling investigation of methyl butanoate as a component of biodiesel surrogate
Aditya Lele,Sonal K. Vallabhuni,Kai Moshammer,Ravi Fernandes,Anand Krishnasamy,Krithika Narayanaswamy +5 more
TL;DR: Narayanaswamy et al. as mentioned in this paper derived a compact reaction scheme for methyl butanoate, which is a potentially important candidate for biodiesel surrogates, from a detailed reference mechanism.
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A comprehensively validated compact mechanism for dimethyl ether oxidation: an experimental and computational study
TL;DR: In this article, a short kinetic mechanism consisting of 23 species and 89 reactions is proposed to describe the oxidation of DME, which accurately reproduces the available experimental data for ignition delays, laminar flame speeds, and species profiles in flow reactors as well as jet-stirred reactors.
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Simulation-driven formulation of transportation fuel surrogates
TL;DR: In this article, an alternative way to formulate transportation fuel surrogates using model predictions of gas-phase combustion targets is explored and compared to conventional approaches, given a selection of indi...
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Experimental and kinetic modeling studies on the auto-ignition of methyl crotonate at high pressures and intermediate temperatures
TL;DR: In this paper, Gail et al. used a short unsaturated fatty acid methyl ester (FAME) as a model biodiesel fuel to study low-temperature combustion engine applications.
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An accurate, fast, mathematically robust, universal, non-iterative algorithm for computing multi-component diffusion velocities
TL;DR: This is the first provably accurate algorithm scaling at a computational complexity of $\mathcal{O}(N)$ in finite precision and it is proposed 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.