Výsledky vyhledávání - "Raju, Mandhapati"

Report

Skeletal mechanism generation for surrogate fuels using directed relation graph with error propagation and sensitivity analysis

Autor: Niemeyer, Kyle E., Sung, Chih-Jen, Raju, Mandhapati P.

Publikováno v: Combust. Flame 157 (2010) 1760-1770

A novel implementation for the skeletal reduction of large detailed reaction mechanisms using the directed relation graph with error propagation and sensitivity analysis (DRGEPSA) is developed and presented with examples for three hydrocarbon compone

Externí odkaz: http://arxiv.org/abs/1607.05079

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Dissertation/ Thesis

HEAT AND MASS TRANSPORT INSIDE A CANDLE WICK

Autor: Raju, Mandhapati P.

Candle flames are typical examples of wick stabilized diffusion flames and are important from the point of view of understanding the fundamentals of diffusion flames. Past modeling work on candle flames has assumed a wick with coated liquid fuel on i

Externí odkaz: http://rave.ohiolink.edu/etdc/view?acc_num=case1157564736

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Domain Decomposition Based High Performance Parallel Computing

Autor: Raju, Mandhapati P., Khaitan, Siddhartha

Publikováno v: M. P. Raju and S. Khaitan, "Domain Decomposition Based High Performance Parallel Computing", International Journal of Computer Science Issues,IJCSI, Volume 5, pp27-32, October 2009

The study deals with the parallelization of finite element based Navier-Stokes codes using domain decomposition and state-ofart sparse direct solvers. There has been significant improvement in the performance of sparse direct solvers. Parallel sparse

Externí odkaz: http://arxiv.org/abs/0911.0910

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Report

Parallel Computation of Finite Element Navier-Stokes codes using MUMPS Solver

Autor: Raju, Mandhapati P.

Publikováno v: M. P. Raju," Parallel Computation of Finite Element Navier-Stokes codes using MUMPS Solver",International Journal of Computer Science Issues, IJCSI, Volume 4, Issue 2, pp20-24, September 2009"

The study deals with the parallelization of 2D and 3D finite element based Navier-Stokes codes using direct solvers. Development of sparse direct solvers using multifrontal solvers has significantly reduced the computational time of direct solution m

Externí odkaz: http://arxiv.org/abs/0910.1845

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Predicting Combustion Variability Using Machine Learning From the Flow Field Data at Spark Timing for a Gasoline Direct Injection Engine

Autor: Daniel Probst, Nitesh Attal, Raju Mandhapati, Oshin Avanessian

Publikováno v: ASME 2022 ICE Forward Conference.

Machine learning was used to predict three combustion metrics based on the flow field data at spark angle (SA), obtained from large eddy simulations (LES). These metrics were peak cylinder pressure (PCP), crank angle of PCP (CA of PCP), and indicated

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::45fdf7aab2d2cd2a46393ccc73e5f64f
https://doi.org/10.1115/icef2022-91016

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