Modeling Thermal Engine Behavior Using Artificial Neural Network

Jander, Bojan; Baar, Roland

doi:10.4271/2017-01-0534

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…Also, the optimum number of neurons will be determined during this research. The used inputs and their varying ranges are given at Table 2.2 and parameters are selected according to [20,21]. In this context a 2400 point DOE data is created with varying of these parameters.…”

Section: Ann Model Designmentioning

confidence: 99%

Modeling of Peak value in Cylinder Firing Pressure in Diesel Engines using Artificial Neural Network Methodology

Bozdağ¹,

Özener²

2019

IJERAT

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From the past to the present, restrictions have been imposed on emissions due to the widespread use of internal combustion engines and the environmental damage caused by emissions. Engine manufacturers perform calibration studies by affecting the fuel-air parameters through the engine control unit to ensure emission limits. The maximum pressure due to the inside of the cylinder is called the maximum pressure inside the cylinder. In order for the engine to work properly, there is a cylindrical pressure resistance due to the material strength. When performing calibration work, the maximum pressure inside the cylinder should be monitored and maintained within the strength limits. In-cylinder pressure sensors are used in the in-cylinder pressure monitoring. Because the pressure sensors are exposed to high pressure (more than 200 bar or more in heavily heated), the values they read can be reduced or distorted. In this study, 6 different the artificial neural network (ANN) model design was created and trained with 2400 test points obtained from engine dynamometers system. Then these networks are tested again with 60 unused test points which are not used during training phases. Then the results are analyzed in terms of peak firing pressure difference. The research results showed that 2 neurons ANN system is best ANN system capable of predicting peak firing pressure within 1.7 bar average difference to measured data.

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Section: Ann Model Designmentioning

confidence: 99%

Modeling of Peak value in Cylinder Firing Pressure in Diesel Engines using Artificial Neural Network Methodology

Bozdağ¹,

Özener²

2019

IJERAT

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“…Aktif bileşenlerin soğutma sisteminde kullanılmasıyla bunların denetlenmesi amacıyla farklı kontrol stratejileri uygulanmıştır (Jander and Baar, 2017). Bunlar arasından model öngörü kontrol (Castiglione, Pizzonia, and Bova, 2016;Hakariya, Toda, and Sakai, 2017) (Pizzonia, Castiglione, and Bova 2016).…”

Section: Introductionunclassified

Kesir Dereceli PID Denetleyici Kullanarak İçten Yanmalı Motorlarda Otonom Termal Yönetim Sistemi Tasarımı

Kaleli

2019

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi

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Öz Bu çalışmada, kesir dereceli PID (FOPID) tipi kontrolcülerin içten yanmalı motorlarda bağımsız soğutma sistemi için tasarımı gerçekleştirilmiştir. Önerilen kontrol stratejisinde kontrolcü parametrelerinin belirlenmesinde doğrusal olamayan optimizasyon tekniği kullanılmıştır. Otonom motor soğutma şeklinde isimlendirilen bu sistem, geleneksel sisteme göre optimal ısı yönetimi bakımından üstün bir performans sağlar. Tasarımda öncelikle soğutma sisteminin matematiksel modeli elde edilmiştir. Sonrasında soğutma sistemi bileşenleri için önerilen kontrol stratejisi hem sabit ve hem de geçici motor çalışma koşulları için test edilmiştir. Sabit çalışma koşullarında kontolcü sayesinde motor çıkış sıcaklığını izeleme hatası 0,0248℃ ve değişken koşullarda ise sıcaklık izleme hatası ise 2,108℃ elde edilmiştir.

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Improving prediction accuracy of ignition model by weighting using machine learning

Nishii

Yamasaki

2022

Transactions of the JSME (In Japanese)

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Compression auto-ignition engines have a high potential to achieve both high thermal efficiency and low emission of harmful exhaust gases, but they have a problem of difficult control of ignition and combustion. To realize such control, model-based control has attracted much attention, which requires an ignition model with high prediction accuracy and light computational load. In this study, we developed a machine learning method to enhance the prediction accuracy of an existing empirical model of ignition delay period. The method has multiple existing empirical models, each with different model parameter values, and a neural network that weights the prediction values of them considering the driving conditions and in-cylinder gas states. To obtain data for training and evaluation, transient operation experiments of a diesel engine were conducted under conditions that relatively premixed combustion occurred. The neural network and the model parameters were trained by the stochastic gradient descendant algorithm using the training data, and the proposed method showed much better prediction accuracy for the test data than the single empirical ignition delay period model adapted using the training data. Investigation on each ignition delay period model of the proposed method after training revealed that the prediction values of each model are highly correlated, indicating that the proposed method does not take advantage of the availability of multiple models. However, the proposed method successfully realized the state that multiple ignition delay period models make different predictions, and the neural network weights them based on the driving conditions and in-cylinder gas states.

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Modeling Thermal Engine Behavior Using Artificial Neural Network

Cited by 7 publications

References 9 publications

Modeling of Peak value in Cylinder Firing Pressure in Diesel Engines using Artificial Neural Network Methodology

Modeling of Peak value in Cylinder Firing Pressure in Diesel Engines using Artificial Neural Network Methodology

Kesir Dereceli PID Denetleyici Kullanarak İçten Yanmalı Motorlarda Otonom Termal Yönetim Sistemi Tasarımı

Improving prediction accuracy of ignition model by weighting using machine learning

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