Characterization of performance-emission indices of a diesel engine using ANFIS operating in dual-fuel mode with LPG

Chakraborty, Amitav; Roy, Sumit; Banerjee, Rahul

doi:10.1007/s00231-018-2312-8

Cited by 11 publications

(3 citation statements)

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“…Hence, different optimization and machine learning (ML) methods are taking place and are coupled with the numerical engine models to easily find the optimal values of different parameters and to fulfill the objective and the purpose of the studies [20,21]. However, before performing any In most of these models, the combustion process and the injection system require careful attention in order to achieve the required power, to ensure the reliability and the safety of the engine and to reduce the brake specific fuel consumption (BSFC) of the engine.…”

Section: Figurementioning

confidence: 99%

“…Hence, different optimization and machine learning (ML) methods are taking place and are coupled with the numerical engine models to easily find the optimal values of different parameters and to fulfill the objective and the purpose of the studies [20,21]. However, before performing any simulations, the engine models must be calibrated using real data at different operating points such as the compression ratio of the turbocharger, pressure before and after the intercooler, firing pressure, exhaust temperature before and after the turbine, amount of mass flow rate and BSFC to ensure the accuracy of the computed results [22,23].…”

Section: Figurementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Data Driven In-Cylinder Pressure Diagram Based Optimization Procedure

Tadros

Ventura

Soares

2020

JMSE

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An engine optimization model is developed to fit the calculated in-cylinder pressure diagram to the experimental data by finding the optimal values of the start angle of injection and the amount of injected fuel for different engine loads. Firstly, the engine model is built in Ricardo Wave software and some parts are calibrated using data collected from the manufacturer. Then, an optimization process is performed based on the fitness function that includes the objective of the study and the penalty functions to express constraints. This optimization environment simulates the performance of a marine generator system for three different loads by minimizing the mean absolute percentage error (MAPE) between the in-cylinder pressure simulated data and the measured data along 40 degrees of the combustion process and by verifying the firing pressure and the engine brake power. The percentage of error between the calculated and the real thermodynamic data does not exceed 3.4% and the MAPE between the calculated and the real in-cylinder pressure diagram along the combustion process does not exceed 5.7% for the different loads. The proposed method can be further used to find the optimal value of different input parameters during the calibration process of different engine numerical models.used and the required details in thermodynamics, combustion, heat transfer, fluid and chemical parts. Figure 2 presents a general overview of the model's structure to simulate the performance of different ICEs.

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Section: Figurementioning

confidence: 99%

“…Hence, different optimization and machine learning (ML) methods are taking place and are coupled with the numerical engine models to easily find the optimal values of different parameters and to fulfill the objective and the purpose of the studies [20,21]. However, before performing any simulations, the engine models must be calibrated using real data at different operating points such as the compression ratio of the turbocharger, pressure before and after the intercooler, firing pressure, exhaust temperature before and after the turbine, amount of mass flow rate and BSFC to ensure the accuracy of the computed results [22,23].…”

Section: Figurementioning

confidence: 99%