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- Ghanaati, Ali, 1979, et al.
(författare)
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A Comparative Study on Knock Occurrence for Different Fuel Octane Number
- 2018
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Ingår i: SAE Technical Papers. - 400 Commonwealth Drive, Warrendale, PA, United States : SAE International. - 0148-7191 .- 2688-3627. ; 2018-September:September
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Tidskriftsartikel (refereegranskat)abstract
- Combustion with knock is an abnormal phenomenon which constrains the engine performance, thermal efficiency and longevity. The advance timing of the ignition system requires it to be updated with respect to fuel octane number variation. The production series engines are calibrated by the manufacturer to run with a special fuel octane number. In the experiment, the engine was operated at different speeds, loads, spark advance timings and consumed commercial gasoline with research octane numbers (RON) 95, 97 and 100. A 1-dimensional validated engine combustion model was run in the GT-Power software to simulate the engine conditions required to define the knock envelope at the same engine operation conditions as experiment. The knock intensity investigation due to spark advance sweep shows that combustion with noise was started after a specific advance ignition timing and the audible knock occur by increasing the advance timing. Therefore, the engine operation was divided into three regions; knock-free, light knock and heavy knock. The results for heavy-knock were well suited to audible knock detected by knock sensor. The simulation results from knock model divide the engine operation into two regions; normal combustion and knock region. The knock region was well suited to light-knock and heavy-knock which has been defined using experimental results. Next, an artificial neural network (ANN) model has been designed to classify the different RONs using engine rotational speed signal. The model classified different RONs accurately after starting point of noisy combustion (light-knock). This point defined from experimental results and was well suited with starting point of knock index increment from simulation results. The simulation tool ability to predict the knock envelope will reduce the experimental cost and time to generate the spark timing look-up table.
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| 3. |
- Ghanaati, Ali, 1979, et al.
(författare)
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Design of a virtual test cell based on GMDH-type neural network for a heavy-duty diesel engine
- 2021
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Ingår i: Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering. - : SAGE Publications. - 2041-2991 .- 0954-4070. ; 235:2-3, s. 436-445
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Tidskriftsartikel (refereegranskat)abstract
- The engine development process faces big challenges from new strict emission regulations in addition to the need for fuel efficiency improvements. The Software-in-the-Loop (SiL) and Hardware-in-the-Loop (HiL) environments decreases the required time during engine development, calibration, verification, and validation of the product. An accurate and easy to build dyno-engine model with real-time operational ability is required for this purpose. Artificial Neural Networks (ANN) have shown ability to model dynamic and complex systems like internal combustion engines. In this paper, the Group Method of Data Handling (GMDH) algorithm was utilized to build an ANN model of a heavy-duty diesel engine. One objective is to reduce the amount of manual labor on the results during the ANN model development process. The GMDH algorithm is a self-organizing process that will find the system laws and optimize the model structure automatically in one iteration. The GMDH model results were compared with a model developed by Levenberg-Marquardt Backpropagation (LM-BP) algorithm. The ANN models used actuator signals from an Engine Management System (EMS) to simulate the engine operation parameters. As revealed by the simulation results, the ANN models successfully predicted engine torque, fuel flow, and NOx concentration. The GMDH model as a self-organized model reduced lead time, had slightly higher transient cycle accuracy, had fewer inconsistent predictions, and demonstrated better extrapolation capability. The prediction accuracy for transient operation was improved by shifting the predicted value by calculating time delay and a decrease of 76.66% for fuel flow and 66.51% for NOX concentration in model accuracy were achieved. The GMDH dyno-engine model can be effectively applied as a virtual test cell instrument for testing, calibration, and optimization purposes.
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