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Machine Learning Applications to Combustion Engines

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Dr. Ahfaz Ahmed

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Mechanics and Maritime Sciences, Divison of Combustion and Propulsion Systems, Chalmers University Technology, Chalmersplatsen 4, 412 96 Göteborg, Sweden
Interests: engine combustion; diesel engines; renewable fuels; chemical kinetics

Special Issue Information

Dear Colleagues,

Applications of machine learning and artificial intelligence is growing and leading to tremendous advancements in areas of energy and transportation. This emergent trend is also visible in combustion engines research as it continues to advance focus areas such as combustion strategy, performance, fuel efficiency, emissions. Such applications have led to new possibilities combining expertise of engine researchers with data driven methods to make transportation more sustainable.

To bring such research efforts on one platform, this special issue seeks to invite submissions in form of case studies, review articles, short communications, original research articles in machine learning applications to combustion engines.

This special issue will focus on applications of machine learning and artificial intelligence methods to combustion engines in various areas including:

Engine controls
Fuel properties detection
Emissions prediction
Knock and preignition detection and/or mitigation
Advanced combustion strategies

Keywords

Deep learning
Machine learning and artificial intelligence for combustion engines

Published Papers (2 papers)

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Research

16 pages, 883 KiB

Open AccessArticle

Modeling Cycle-to-Cycle Variations of a Spark-Ignited Gas Engine Using Artificial Flow Fields Generated by a Variational Autoencoder

by Stefan Posch, Clemens Gößnitzer, Andreas B. Ofner, Gerhard Pirker and Andreas Wimmer

Energies 2022, 15(7), 2325; https://doi.org/10.3390/en15072325 - 23 Mar 2022

Cited by 4 | Viewed by 1556

Abstract

A deeper understanding of the physical nature of cycle-to-cycle variations (CCV) in internal combustion engines (ICE) as well as reliable simulation strategies to predict these CCV are indispensable for the development of modern highly efficient combustion engines. Since the combustion process in ICE strongly depends on the turbulent flow field in the cylinder and, for spark-ignited engines, especially around the spark plug, the prediction of CCV using computational fluid dynamics (CFD) is limited to the modeling of turbulent flows. One possible way to determine CCV is by applying large eddy simulation (LES), whose potential in this field has already been shown despite its drawback of requiring considerable computational time and resources. This paper presents a novel strategy based on unsteady Reynolds-averaged Navier–Stokes (uRANS) CFD in combination with variational autoencoders (VAEs). A VAE is trained with flow field data from presimulated cycles at a specific crank angle. Then, the VAE can be used to generate artificial flow fields that serve to initialize new CFD simulations of the combustion process. With this novel approach, a high number of individual cycles can be simulated in a fraction of the time that LES needs for the same amount of cycles. Since the VAE is trained on data from presimulated cycles, the physical information of the cycles is transferred to the generated artificial cycles. Full article

(This article belongs to the Special Issue Machine Learning Applications to Combustion Engines)

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18 pages, 1593 KiB

Open AccessArticle

A Long Short-Term Memory Neural Network for the Low-Cost Prediction of Soot Concentration in a Time-Dependent Flame

by Mehdi Jadidi, Luke Di Liddo and Seth B. Dworkin

Energies 2021, 14(5), 1394; https://doi.org/10.3390/en14051394 - 3 Mar 2021

Cited by 6 | Viewed by 1859

Abstract

Particulate matter (soot) emissions from combustion processes have damaging health and environmental effects. Numerical techniques with varying levels of accuracy and computational time have been developed to model soot formation in flames. High-fidelity soot models come with a significant computational cost and as a result, accurate soot modelling becomes numerically prohibitive for simulations of industrial combustion devices. In the present study, an accurate and computationally inexpensive soot-estimating tool has been developed using a long short-term memory (LSTM) neural network. The LSTM network is used to estimate the soot volume fraction (

f_{v}

) in a time-varying, laminar, ethylene/air coflow diffusion flame with 20 Hz periodic fluctuation on the fuel velocity and a 50% amplitude of modulation. The LSTM neural network is trained using data from CFD, where the network inputs are gas properties that are known to impact soot formation (such as temperature) and the network output is

f_{v}

. The LSTM is shown to give accurate estimations of

f_{v}

, achieving an average error (relative to CFD) in the peak

f_{v}

of approximately 30% for the training data and 22% for the test data, all in a computational time that is orders-of-magnitude less than that of high-fidelity CFD modelling. The neural network approach shows great potential to be applied in industrial applications because it can accurately estimate the soot characteristics without the need to solve the soot-related terms and equations. Full article

(This article belongs to the Special Issue Machine Learning Applications to Combustion Engines)

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