A novel design optimization approach (ActivO) that employs an ensemble of machine
learning algorithms is presented. The proposed approach is a surrogate-based scheme …

In recent years, the use of machine learning-based surrogate models for computational fluid
dynamics (CFD) simulations has emerged as a promising technique for reducing the …

A computational fluid dynamics (CFD) guided combustion system optimization was
conducted for a heavy-duty diesel engine running with a gasoline fuel that has a research …

The use of machine learning (ML)-based surrogate models is a promising technique to
significantly accelerate simulation-driven design optimization of internal combustion (IC) …

In this study, a numerical methodology for the optimization of the combustion chamber in
compression ignited engines using OME as fuel is presented. The objective is to obtain a …

In this work, a novel design optimization technique based on active learning, which involves
dynamic exploration and exploitation of the design space of interest using an ensemble of …

Adverse effects of global warming due to the greenhouse gas emissions is changing the
actual paradigm for the use energy resources. In the absence of a mid-term solution for …

Conventional compression-ignition (CI) engines have long offered high thermal efficiencies
and torque across a wide range of loads, but often require extensive exhaust gas treatment …

As computational capabilities continue to grow, exploring the limits of computational fluid
dynamics to capture complex and elusive phenomena, which are otherwise difficult to study …

Abstract Design exploration in engineering applications often requires a meticulous
experimental or numerical study to evaluate performance (Y) of each design, which may …