Study on pollutants formation under knocking combustion conditions using an optical single cylinder SI research engine

The aim of this experimental study is to investigate the pollutants formation and cyclic emission variability under knocking combustion conditions. A great number of studies extensively describe the phenomenon of knock and its combustion characteristics as well as the effect of knock on engine performance; however the impact of knocking combustion on pollutants formation and how it affects cyclic emission variability has not been previously explored. In this study, an optical single cylinder SI research engine and fast response analyzers were employed to experimentally correlate knocking combustion characteristics with cyclic resolved emissions from cycle to cycle. High-speed natural light photography imaging and simultaneous in-cylinder pressure measurements were obtained from the optical research engine to interpret emissions formation under knocking combustion. The test protocol included the investigation of the effect of various engine parameters such as ignition timing and mixture air/fuel ratio on knocking combustion and pollutant formation. Results showed that at stoichiometric conditions by advancing spark timing from MBT to knock intensity equal to 6 bar, instantaneous NO and HC emissions are increased by up to 60% compared to the MBT operating conditions. A further increase of knock intensity at the limits of pre-ignition region was found to significantly drop NO emissions. Conversely, it was found that when knocking combustion occurs at lean conditions, NO emissions are enhanced as knock intensity is increased

UConn Baseball Batting Order Optimization

Challenging conventional wisdom is at the very core of baseball analytics. Using data and statistical analysis, the sets of rules by which coaches make decisions can be justified, or possibly refuted. One of those sets of rules relates to the construction of a batting order. Through data collection, data adjustment, the construction of a baseball simulator, and the use of a Monte Carlo Simulation, I have assessed thousands of possible batting orders to determine the roster-specific strategies that lead to optimal run production for the 2023 UConn baseball team. This paper details a repeatable process in which basic player statistics (available to almost any baseball program) can be used to evaluate and select batting orders that have never before been tested in live action

UConn Baseball Batting Order Optimization

Challenging conventional wisdom is at the very core of baseball analytics. Using data and statistical analysis, the sets of rules by which coaches make decisions can be justified, or possibly refuted. One of those sets of rules relates to the construction of a batting order. Through data collection, data adjustment, the construction of a baseball simulator, and the use of a Monte Carlo Simulation, I have assessed thousands of possible batting orders to determine the roster-specific strategies that lead to optimal run production for the 2023 UConn baseball team. This paper details a repeatable process in which basic player statistics (available to almost any baseball program) can be used to evaluate and select batting orders that have never before been tested in live action

Nitric oxide formation and thermodynamic modeling in spark ignition engines

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering, 2000.Includes bibliographical references (p. 139-141).by Matthew J. Rublewski.S.M