A novel machine learning-based optimization algorithm (ActivO) for accelerating simulation-driven engine design. (1st March 2021)

• Record Type: Journal Article
• Authors: Owoyele, Opeoluwa; Pal, Pinaki
• Is Part Of: Applied energy. Volume 285(2021)
• Publication Date: 2021-03-01
• Subjects: Design optimization -- Machine learning -- Surrogate models -- Active learning -- Computational fluid dynamics -- Internal combustion engines -- Energy efficiency -- Simulation; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.116455
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: A novel ensemble machine learning-based optimizer is introduced. The novel optimizer works by combining weak and strong learner surrogates. Quick identification of promising regions coupled with effective exploitation of design space lead to fast convergence. Time-to-convergence and required computing resources is reduced by 80%. Engine optimization leads to savings of 1.9% in energy consumption. Abstract: A novel design optimization approach (ActivO) that employs an ensemble of machine learning algorithms is presented. The proposed approach is a surrogate-based scheme, where the predictions of a weak leaner and a strong learner are utilized within an active learning loop. The weak learner is used to identify promising regions within the design space to explore, while the strong learner is used to determine the exact location of the optimum within promising regions. For each design iteration, exploration is done by randomly selecting evaluation points within regions where the weak learner-predicted fitness is high. The global optimum obtained by using the strong learner as a surrogate is also evaluated to enable rapid convergence once the most promising region has been identified. First, the performance of ActivO was compared against five other optimizers on a cosine mixture function with 25 local optima and one global optimum. In the second problem, the objective was to minimize indicated specific fuel consumption of a compression-ignition internal combustion (IC) engine while adhering to desired constraints associated with in-cylinder pressure and emissions. Here, the efficacy of the proposed approach is compared to that of a genetic algorithm, which is widely used within the internal combustion engine community for engine optimization, showing that ActivO reduces the number of function evaluations needed to reach the global optimum, and thereby time-to-design by 80%. Furthermore, the optimization of engine design parameters leads to savings of around 1.9% in energy consumption, while maintaining operability and acceptable pollutant emissions.