Artículos con órdenes de acceso público - Janardhan KodavasalMás información
No disponibles en ningún lugar: 6
Achieving stable engine operation of gasoline compression ignition using 87 AKI gasoline down to idle
C Kolodziej, J Kodavasal, S Ciatti, S Som, N Shidore, J Delhom
SAE Technical Paper, 2015
Órdenes: US Department of Energy
Development of a stiffness-based chemistry load balancing scheme, and optimization of I/O and communication, to enable massively parallel high-fidelity internal combustion …
J Kodavasal, K Harms, P Srivastava, S Som, S Quan, K Richards, ...
Internal Combustion Engine Division Fall Technical Conference 57281, V002T06A009, 2015
Órdenes: US Department of Energy
Global sensitivity analysis of a gasoline compression ignition engine simulation with multiple targets on an IBM Blue Gene/Q Supercomputer
J Kodavasal, Y Pei, K Harms, S Ciatti, A Wagner, P Senecal, M García, ...
SAE Technical Paper, 2016
Órdenes: US Department of Energy
Computational Fluid Dynamics Simulation of an Opposed-Piston Two-Stroke Gasoline Compression Ignition Engine
AA Moiz, J Kodavasal, S Som, R Hanson, F Redon, R Zermeno
Internal Combustion Engine Division Fall Technical Conference 51999, V002T06A021, 2018
Órdenes: US Department of Energy
Gasoline compression ignition—a simulation-based perspective
J Kodavasal, S Som
Advances in Internal Combustion Engine Research, 227-249, 2018
Órdenes: US Department of Energy
Analysis of the Impact of Uncertainties in Inputs on CFD Predictions of Gasoline Compression Ignition
J Kodavasal, S Ciatti, S Som
Internal Combustion Engine Division Fall Technical Conference 50503, V001T06A006, 2016
Órdenes: US Department of Energy
Disponibles en algún lugar: 12
A machine learning-genetic algorithm (ML-GA) approach for rapid optimization using high-performance computing
AA Moiz, P Pal, D Probst, Y Pei, Y Zhang, S Som, J Kodavasal
SAE International journal of commercial vehicles 11 (2018-01-0190), 291-306, 2018
Órdenes: US Department of Energy
Computational Fluid Dynamics Simulation of Gasoline Compression Ignition
J Kodavasal, CP Kolodziej, SA Ciatti, S Som
Journal of Energy Resources Technology 137 (3), 032212, 2015
Órdenes: US Department of Energy
Engine combustion system optimization using computational fluid dynamics and machine learning: a methodological approach
JA Badra, F Khaled, M Tang, Y Pei, J Kodavasal, P Pal, O Owoyele, ...
Journal of Energy Resources Technology 143 (2), 022306, 2021
Órdenes: US Department of Energy
Examining the role of flame topologies and in-cylinder flow fields on cyclic variability in spark-ignited engines using large-eddy simulation
L Zhao, AA Moiz, S Som, N Fogla, M Bybee, S Wahiduzzaman, ...
International Journal of Engine Research 19 (8), 886-904, 2018
Órdenes: US Department of Energy
Using machine learning to analyze factors determining cycle-to-cycle variation in a spark-ignited gasoline engine
J Kodavasal, A Abdul Moiz, M Ameen, S Som
Journal of Energy Resources Technology 140 (10), 102204, 2018
Órdenes: US Department of Energy
Effects of injection parameters, boost, and swirl ratio on gasoline compression ignition operation at idle and low-load conditions
J Kodavasal, CP Kolodziej, SA Ciatti, S Som
International Journal of Engine Research 18 (8), 824-836, 2017
Órdenes: US Department of Energy
Evaluating optimization strategies for engine simulations using machine learning emulators
DM Probst, M Raju, PK Senecal, J Kodavasal, P Pal, S Som, AA Moiz, ...
Journal of Engineering for Gas Turbines and Power 141 (9), 091011, 2019
Órdenes: US Department of Energy
The effect of diluent composition on homogeneous charge compression ignition auto-ignition and combustion duration
J Kodavasal, GA Lavoie, DN Assanis, JB Martz
Proceedings of the Combustion Institute 35 (3), 3019-3026, 2015
Órdenes: US Department of Energy
Predicting cycle-to-cycle variation with concurrent cycles in a gasoline direct injected engine with large eddy simulations
DM Probst, S Wijeyakulasuriya, E Pomraning, J Kodavasal, R Scarcelli, ...
Journal of Energy Resources Technology 142 (4), 042202, 2020
Órdenes: US Department of Energy
Machine learning analysis of factors impacting cycle-to-cycle variation in a gasoline spark-ignited engine
J Kodavasal, AA Moiz, M Ameen, S Som
Internal Combustion Engine Division Fall Technical Conference 58325, V002T06A020, 2017
Órdenes: US Department of Energy
Reaction-space analysis of homogeneous charge compression ignition combustion with varying levels of fuel stratification under positive and negative valve overlap conditions
J Kodavasal, GA Lavoie, DN Assanis, JB Martz
International Journal of Engine Research 17 (7), 776-794, 2016
Órdenes: US Department of Energy
Development of predictive capability of cycle-to-cycle variation in dual-fuel engines using supercomputing-based computational fluid dynamics
SR Gubba, RS Jupudi, J Kodavasal, S Som, RJ Primus, AE Klingbeil, ...
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States), 2018
Órdenes: US Department of Energy
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