Grey-box modeling of HCCI engines

Bidarvatan, Mehran; Thakkar, V.; Shahbakhti, Mahdi; Bahri, Bahram; Aziz, Azhar Abdul

doi:10.1016/j.applthermaleng.2014.05.031

Cited by 42 publications

(8 citation statements)

References 32 publications

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“…Authors of Bidarvatan et al (2014) developed an HM to predict several performance parameters of Homegeneous Charge Compression Ignition (HCCI) engines. Namely, the the 50% mass fraction burnt crank angle, the indicated mean effective pressure, exhaust temperature, and concentration of CO, total unburned hydrocarbons and NO x .…”

Section: Hybrid Modelsmentioning

confidence: 99%

Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

Coraddu

Kalikatzarakis

Theotokatos

et al. 2021

Energy, Environment, and Sustainability

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Accurate, reliable, and computationally inexpensive models of the dynamic state of combustion engines are a fundamental tool to investigate new engine designs, develop optimal control strategies, and monitor their performance. The use of those models would allow to improve the engine cost-efficiency trade-off, operational robustness, and environmental impact. To address this challenge, two state-of-the-art alternatives in literature exist. The first one is to develop high fidelity physical models (e.g., mean value engine, zero-dimensional, and one-dimensional models) exploiting the physical principles that regulate engine behaviour. The second one is to exploit historical data produced by the modern engine control and automation systems or by high-fidelity simulators to feed data-driven models (e.g., shallow and deep machine learning models) able to learn an accurate digital twin of the system without any prior knowledge. The main issues of the former approach are its complexity and the high (in some case prohibitive) computational require-

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Section: Hybrid Modelsmentioning

confidence: 99%

Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

Coraddu

Kalikatzarakis

Theotokatos

et al. 2021

Energy, Environment, and Sustainability

View full text Add to dashboard Cite

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“…It is critical to have computationally-efficient predictive HCCI models to control HCCI combustion [51]. However, no such model exists in the literature for predicting HCCI emissions, while HCCI suffers from high HC and CO emissions.…”

Section: Introductionmentioning

confidence: 98%

Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks

et al. 2015

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“…An R 2 above 0.93 was achieved for all of the output predictions. A final example can be seen in [24], where a multizone thermokinetic engine model was coupled with an ANN and a genetic algorithm to predict CA50, IMEP, CO, and total unburned hydrocarbons (THC); average errors of 1.2 CAD, 0.4 bar, 10 PPM, 0.8%, and 394 PPM were obtained for the predictions, respectively.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Physics-Based and Stochastic Neural Network Model Structure for Diesel Engine Combustion Events

Ankobea-Ansah

Hall

2022

Vehicles

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Estimation of combustion phasing and power production is essential to ensuring proper combustion and load control. However, archetypal control-oriented physics-based combustion models can become computationally expensive if highly accurate predictive capabilities are achieved. Artificial neural network (ANN) models, on the other hand, may provide superior predictive and computational capabilities. However, using classical ANNs for model-based prediction and control can be challenging, since their heuristic and deterministic black-box nature may make them intractable or create instabilities. In this paper, a hybridized modeling framework that leverages the advantages of both physics-based and stochastic neural network modeling approaches is utilized to capture CA50 (the timing when 50% of the fuel energy has been released) along with indicated mean effective pressure (IMEP). The performance of the hybridized framework is compared to a classical ANN and a physics-based-only framework in a stochastic environment. To ensure high robustness and low computational burden in the hybrid framework, the CA50 input parameters along with IMEP are captured with a Bayesian regularized ANN (BRANN) and then integrated into an overall physics-based 0D Wiebe model. The outputs of the hybridized CA50 and IMEP models are then successively fine-tuned with BRANN transfer learning models (TLMs). The study shows that in the presence of a Gaussian-distributed model uncertainty, the proposed hybridized model framework can achieve an RMSE of 1.3 × 10−5 CAD and 4.37 kPa with a 45.4 and 3.6 s total model runtime for CA50 and IMEP, respectively, for over 200 steady-state engine operating conditions. As such, this model framework may be a useful tool for real-time combustion control where in-cylinder feedback is limited.

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Grey-box modeling of HCCI engines

Cited by 42 publications

References 32 publications

Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

Physical and Data-Driven Models Hybridisation for Modelling the Dynamic State of a Four-Stroke Marine Diesel Engine

Performance prediction of HCCI engines with oxygenated fuels using artificial neural networks

A Hybrid Physics-Based and Stochastic Neural Network Model Structure for Diesel Engine Combustion Events

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