Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators

Probst, Daniel; Raju, Mandhapati; Senecal, P. K.; Kodavasal, Janardhan; Pal, Pinaki; Som, Sibendu; Moiz, Ahmed Abdul; Pei, Yuanjiang

doi:10.1115/1.4043964

Cited by 23 publications

(7 citation statements)

References 24 publications

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“…ML is a rapidly growing body of modular algorithms that can be used for the identification and modeling of data-driven systems. Unique aspects of data-driven modeling of fluid flows include the availability of partial prior knowledge of the equations, constraints, and symmetries that govern [18] [19] [20].…”

Section: Related Workmentioning

confidence: 99%

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Amroun¹,

Ammi

Hafid³

2021

IEEE Access

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In this paper, we present a new approach to use machine learning (ML) for the calibration of a physical model allowing the reproduction of the vibratory behavior of an overhead line conductor. This physical model known as Strip Theory (ST) has the advantage of being very precise but very complicated and cumbersome in its software operations and manipulations. A second model known as the Wake Oscillator (WO) has been implemented in order to meet the limitations of the ST model. In order to be able to use the WO model instead of the ST model, very heavy manual adjustments are required, which makes its use complicated.Precisely, the WO must be able to generate a time series similar to a time series generated by the ST model. In order to respond to this limitation, a machine learning model known as ENS has been proposed. The machine learning model will therefore take as input the data from the WO model and output the data from the ST model. A series of Machine learning models have been implemented and tested. The ENS algorithm was retained with a best Pearson's linear coefficient of determination (R2 Score) of almost 0.7 and a Root mean square deviation (RMSE) of 7.57e-09. This type of approach therefore makes it possible to calibrate the WO model so that simulations of the behavior of overhead line conductors are carried out only with the WO model.

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Section: Related Workmentioning

confidence: 99%

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Amroun¹,

Ammi

Hafid³

2021

IEEE Access

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“…Compared with experimental measurement and physical calculation models, machine learning has advantages such as fast response, high accuracy, and strong generalization ability [11,12]. In recent years, because of its powerful induction and reasoning capabilities, machine learning has been gradually applied to pattern recognition and performance optimization of engines [13][14][15][16][17]. Jihad A. Badra et al developed a Machine Learning Grid Gradient Ascent (ML-GGA) approach to optimize the performance of internal combustion engines and demonstrated the potential of ML-GGA to significantly reduce the time needed for optimization problems, without a loss of accuracy compared with traditional approaches [18].…”

Section: Introductionmentioning

confidence: 99%

Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm

Sun

Lee

et al. 2022

Applied Sciences

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In-cylinder pressure is one of the most important references in the process of diesel engine performance optimization. In order to acquire effective in-cylinder pressure value, many physical tests are required. The cost of physical testing is high; various uncertain factors will bring errors to test results, and the time of an engine test is so long that the test results cannot meet the real-time requirement. Therefore, it is necessary to develop technology with high accuracy and a fast response to predict the in-cylinder pressure of diesel engines. In this paper, the in-cylinder pressure values of a high-speed diesel engine under different conditions are used to train the extreme gradient boosting model, and the sparrow search algorithm—which belongs to the swarm intelligence optimization algorithm—is introduced to optimize the hyper parameters of the model. The research results show that the extreme gradient boosting model combined with the sparrow search algorithm can predict the in-cylinder pressure under each verification condition with high accuracy, and the proportion of the samples which prediction error is less than 10% in the validation set is 94%. In the process of model optimization, it is found that compared with the grid search method, the sparrow search algorithm has stronger hyper parameter optimization ability, which reduces the mean square error of the prediction model by 27.99%.

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“…Kodavasal et al (2018) used machine learning techniques to analyze the controlling factor of cycle-to-cycle variation in a gasoline spark-ignited engine. Probst et al (2019) used two machine learning techniques (Gaussian process and SuperLearner) in engine combustion predictions. Different optimization methods were compared.…”

Section: Introductionmentioning

confidence: 99%

CFD Optimization of the Pre-Chamber Geometry for a Gasoline Spark Ignition Engine

Bakir

Yadav

et al. 2021

Front. Mech. Eng.

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In the present paper, an efficient optimization method based on Bayesian updating strategy is developed for the design of a spark-ignition engine equipped with pre-chamber. 3D computational fluid dynamics (CFD) simulation coupled with strategies including design of experiment, genetic algorithm, and machine learning methods is used to optimize the pre-chamber with desired combustion phasing. The optimization process starts from a design of experiment matrix of 11 design parameters, which are used to analytically characterize the pre-chamber geometry and set up the 3D combustion CFD. Taking CA50 as the single objective, the CFD results are then used to train the machine learning models. Different machine learning models are evaluated based on their Root Mean Square Error. Five machine learning models from five different categories are selected for second round evaluation. The trained machine learning model is used in the genetic algorithm optimization, which yields the optimized configuration and is again justified by CFD. The new CFD results based on the optimized design are added into the database to further refine the machine learning model. After 24 iterations for each selected machine learning models, the medium Gaussian support vector machine model is identified as the best method for the present application. Iterations using the medium Gaussian support vector machine model continue until a satisfactory result is achieved. Detailed combustion analysis is conducted to investigate the physical mechanism about how the design of pre-chamber influences the engine's performance. It is found that larger volume of the upper part of the pre-chamber results in stronger jet flow and turbulent intensity which further accelerates the flame propagation inside the pre-chamber, dominating the contrary effects from reduced pressure and temperature. Regression analysis shows that the radius of the pre-chamber is the most influential design parameter. The current work not only sheds light on the optimization of engine design, but also has demonstrated a general strategy applicable to the purpose of arbitrary engine optimization and mechanical system design.

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Evaluating Optimization Strategies for Engine Simulations Using Machine Learning Emulators

Cited by 23 publications

References 24 publications

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Proof of Concept: Calibration of an Overhead Line Conductors’ Movements Simulation Model Using Ensemble-Based Machine Learning Model

Prediction of In-Cylinder Pressure of Diesel Engine Based on Extreme Gradient Boosting and Sparrow Search Algorithm

CFD Optimization of the Pre-Chamber Geometry for a Gasoline Spark Ignition Engine

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