Abstract

As in many engineering industries, production timelines for internal combustion engines are too strict to allow for full (multi-disciplinary) exploration of design permutations through large volumes of simulation and physical test. This study combines machine learning and CFD simulation for accelerated and intelligent design of an internal combustion engine (ICE) to accommodate such a challenge. The specimen investigated is a parameterized cylinder port design, in a 4-stroke gasoline engine, which whereby a number of simulations are generated to partially cover the design space. The focus is an inlet port design which creates favorable developments in the turbulent flow-field for more ideal combustion. With such simulation data generated, neural networks are created to capture the relationship between the design parameters and the performance results (in 1D, 2D, and 3D). For the one-dimensional data, predictions are made for the transient evolution of important scalar performance metrics over an engine cycle, such as turbulent kinetic energy, tumble, and other thermodynamic variables. For the two-dimensional data, predictions are made for local values, similar to the one-dimensional data predictions, in the center plane of the cylinder. Since the design objective is focused on the turbulent flow-field, the three-dimensional data predictions focus on predicting the turbulent kinetic energy in the highly turbulent sections of the flow-field. These predictions prove to be quite accurate and reveal that neural networks are effective at modeling simulation data for predictive design exploration. Their mathematical structure allows them to capture highly non-linear and multi-variable physical behavior. With such a simulation-machine learning approach, design exploration with a greater concentration on more attractive designs is possible. These trained neural networks can also be used in design cycles of subsequent similar products, which could expedite early-stage design via transfer learning. To evaluate the transfer learning capabilities for this problem, the simulation data was split for training and validation in such a way that both focused on different flow-field characteristics. Specifically, the training data was comprised of simulation data with port designs that were acute, which created ‘sharp’ angles that resulted in large flow separation upon entry to the cylinder. For the validation dataset, the simulations had intake port designs which were less steep and therefore significantly different in terms of the resulting flow patterns. Since these flow patterns greatly affect the resulting turbulence and therefore the combustion behavior, it is encouraging that the neural networks were able to accurately predict the ICE simulation results and additionally provides confidence that they can provide further value throughout the design process.