T
Thompson Lanzanova
Researcher at Universidade Federal de Santa Maria
Publications - 37
Citations - 460
Thompson Lanzanova is an academic researcher from Universidade Federal de Santa Maria. The author has contributed to research in topics: Combustion & Ignition system. The author has an hindex of 11, co-authored 31 publications receiving 344 citations. Previous affiliations of Thompson Lanzanova include Universidade Federal do Rio Grande do Sul & Brunel University London.
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Journal ArticleDOI
Potential of internal EGR and throttled operation for low load extension of ethanol-diesel dual-fuel reactivity controlled compression ignition combustion on a heavy-duty engine
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Performance and economic analysis of a direct injection spark ignition engine fueled with wet ethanol
TL;DR: The Brazilian Council for Scientific and Technological Development (CNPq) supported the PhD studies of Mr. Lanzanova and Mr. Dalla Nora at Brunel University London.
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Full-load Miller cycle with ethanol and EGR: Potential benefits and challenges
TL;DR: In this article, a detailed 1D simulation analysis of a full-load Miller cycle spark-ignited engine when running on hydrous ethanol in different configurations of valve train and supercharging is presented.
Journal ArticleDOI
Effects of valve timing, valve lift and exhaust backpressure on performance and gas exchanging of a two-stroke GDI engine with overhead valves
TL;DR: In this paper, an overhead four-valve spark-ignition gasoline engine was modified to run in the two-stroke cycle, and the scavenging process took place during a long valve overlap period around bottom dead centre at each crankshaft revolution.
Journal ArticleDOI
Experimental analysis and modeling of internal combustion engine operating with wet ethanol
Weslei Monteiro Ambrós,Thompson Lanzanova,J.L.S. Fagundez,Rafael Lago Sari,Damaris Kirsch Pinheiro,Mario Eduardo Santos Martins,Nina P.G. Salau +6 more
TL;DR: In this paper, a mathematical model was proposed to predict the effect that the use of wet ethanol has on the performance of internal combustion engines, along with experimental data, which showed good ability to predict engine performance based on the variations of power, torque, conversion efficiency and specific fuel consumption.