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4. Machine Learning Approaches to Learn HyChem Models

Author

Ji, Weiqi, Zanders, Julian, Park, Ji-Woong, and Deng, Sili

Subjects
Physics - Chemical Physics
Abstract

The HyChem approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than high-temperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can cover both high-temperature and low-temperature regimes. Specifically, we develop a HyChem model using the experimental datasets of ignition delay times covering a wide range of temperatures and equivalence ratios. The chemical kinetic model is treated as a neural network model, and we then employ stochastic gradient descent (SGD), a technique that was developed for deep learning, for the training. We demonstrate the approach in learning the HyChem model for F-24, which is a Jet-A derived fuel, and compare the results with previous work employing genetic algorithms. The results show that the SGD approach can achieve comparable model performance with genetic algorithms but the computational cost is reduced by 1000 times. In addition, with regularization in SGD, the SGD approach changes the kinetic parameters from their original values much less than genetic algorithm and is thus more likely to retrain mechanistic meanings. Finally, our approach is built upon open-source packages and can be applied to the development and optimization of chemical kinetic models for internal combustion engine simulations.

Published
2021
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25. Data-Driven Approaches to Learn HyChem Models

Author

Massachusetts Institute of Technology. Department of Mechanical Engineering, Ji, Weiqi, Zanders, Julian, Park, Ji-Woong, Deng, Sili, Massachusetts Institute of Technology. Department of Mechanical Engineering, Ji, Weiqi, Zanders, Julian, Park, Ji-Woong, and Deng, Sili

Abstract

Abstract The HyChem (Hybrid Chemistry) approach has recently been proposed for modeling high-temperature combustion of real, multi-component fuels. The approach combines lumped reaction steps for fuel thermal and oxidative pyrolysis with detailed chemistry for the oxidation of the resulting pyrolysis products. Determining the pyrolysis submodel requires extensive experimentation on speciation measurements. Recent work has been directed to learn HyChem from an existing HyChem model for a similar fuel, which requires less data. However, the approach usually shows substantial discrepancies with experimental data within the Negative Temperature Coefficient (NTC) regime, as the low-temperature chemistry is more fuel-specific than high-temperature chemistry. This paper proposes a machine learning approach to learn the HyChem models that can cover both high-temperature and low-temperature regimes. Specifically, we develop a HyChem model using the experimental datasets of ignition delay times covering a wide range of temperatures and equivalence ratios. The chemical kinetic model is treated as a neural network model, and we then employ stochastic gradient descent (SGD), a technique that was developed for deep learning, for the training. We demonstrate the approach in learning the HyChem model for F-24, which is a Jet-A derived fuel, and compare the results with previous work employing genetic algorithms. The results show that the SGD approach can achieve comparable model performance with genetic algorithms but the computational cost is reduced by 1000 times. In addition, with regularization in SGD, the SGD approach changes the kinetic parameters from their original values much less than genetic algorithm and is thus more likely to retrain mechanistic meanings. Finally, our approach is built upon open-source packages and can be applied to the development and optimization of chemical kinetic models for internal combustion engine simulations.

Published
2023

28. Analysis of heat diffusion considering driving images on 6-inch flexible AMOLED display.

Author

Jeon, Chang Hoon, Park, Ji Woong, Kang, Byung Wook, Jang, Su Hyuk, Kwon, Kyung Joon, Hong, Soon Kwang, Ha, Yong Min, and Jang, Jin

Subjects
FLEXIBLE display systems, FINITE difference method, TEMPERATURE distribution, HEAT convection, LIGHT emitting diodes
Abstract

We report the heat diffusion on flexible active-matrix organic light-emitting diode (AMOLED) displays. Two-dimensional heat diffusion is used for the heat conduction and convection analysis, generated on the surface of a flexible AMOLED display. The heat diffusion parameters and the time constant are studied in terms of the driving conditions of a 6-inch flexible AMOLED display. The temperature distribution on the screen is obtained by applying heat diffusion using the finite difference method (FDM) with an FPGA driving board. We can predict temperature more precisely by calculating temperature changes according to the images. The proposed method improves the accuracy of predicting the temperature. The average and standard deviation of the temperature error (TER) are ∼0.5°C and ∼0.5°C for the proposed method, respectively. For the conventional method, the values are ∼4.5°C and ∼0.7°C, respectively, neglecting the increase in temperature based on the images. This method can be used in compensation technology for OLEDs and thin film transistors (TFTs) that require accurate temperature distribution on the screen. [ABSTRACT FROM AUTHOR]

Published
2023
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32. Experimental diesel spray characterization of the medium-duty injector with single- and multi-hole nozzle configurations under non-reacting, non-vaporizing conditions

Author

Park, Ji-Woong, Jouzdani, Shirin, Tzanetakis, Tom, Schmidt, Henry, Atkinson, William, Naber, Jeffrey, Pei, Yuanjiang, Tao, Feng, Garg, Rajesh, Langenderfer, David, Zhang, Yu, and Som, Sibendu

Subjects
Mechanical Engineering, General Materials Science, Industrial and Manufacturing Engineering, Computer Science Applications
Abstract

Characteristics of diesel sprays injected through Cummins medium-duty ISB injectors were studied experimentally in an optically accessible constant-volume combustion vessel. The experiments were performed with ultra-low-sulfur diesel (ULSD) under non-reacting and non-vaporizing conditions, including different ambient gas densities (23–65 kg/m3), injection pressures (500–1,500 bar), and injection duration times (0.5–1.5 ms). The ambient temperature of the vessel was maintained at a room temperature of 313 K for all the tests. A systematic comparison was made between single-hole (SH) and multi-hole (MH) injector configurations. A plume-to-plume variation in spray penetration length was observed for various operating conditions. A substantial deviation was observed for a specific hole against the averaged plume, indicating that arbitrary selection of the plume index may result in inaccurate spray characterization of the MH injector. The penetration length of the MH injector was shorter than that of the SH injector under the same operating conditions, indicating that a spray model calibrated on SH injector data may not accurately predict the transient spray behavior of the MH injector in practical engine simulations. A square-root correlation of the spray penetration length was applied for both the SH and MH injectors. The spray penetration length and dispersion angles of the ISB SH injector were also compared with those of the heavy-duty Cummins ISX SH injector. While the ISX SH injector showed a faster penetration than the ISB SH injector, the dispersion angle was similar. The differences in spray penetration between ISB and ISX injectors followed the expected trend based on their nozzle hole diameters.

Published
2022

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