Development of simulation metamodels to predict the performance and exhaust emission parameters of a spark ignition engine

Abstract

Developing more energy-efficient and environmentally friendly transportation technologies, that can enable to use significantly less petroleum and to reduce regulated emissions while meeting or exceeding drivers' performance expectations, has always been one of the main challenges in automotive technology. Therefore, based on an experimental dataset, metamodels were generated using design of computer experiments and central composite design technique in order to accurately predict carbon monoxide (CO), oxides of nitrogen (NO x ), hydrocarbon (HC) and carbon dioxide (CO 2 ) emissions, mean effective pressure and exergy destruction due to heat transfer and combustion process. Combustion metamodels was evaluated varying air-fuel ratio, ignition timing [( • CAD) Crank Angle Degrees], compression ratio, and combustion duration ( • ) on the performance of a Spark Ignition (SI) engine at constant speed of 750 rpm. Because SI gasoline engines always encounter the decreased thermal efficiency and increased toxic emissions at idle (Jurgen in Automotive electronics handbook, McGraw-Hill, New York, 1995). The Akaike information criterion was applied to automatically select the best metamodel for each case.

Automated and interactive evaluation of welding producibility in an multidisciplinary design optimization environment for aircraft components

“…Furthermore, the incorporation of statistical analysis and Design of Experiments (DoE) techniques for design space exploration has enabled building metamodels to predict product performance [5,[15][16][17]. Metamodels are prediction models based on simulations that allow the estimation of the functional performance of parametric combinations (design variants) within the design space that have not been simulated [5,16].…”