V. Soloiu, Lily H. Parker, Rick Smith, Amanda Weaver, Austin Brant, Aidan Rowell, M. Ilie
Investigations were conducted using mass blends of Iso-Paraffinic Kerosene (IPK) and Fischer-Tropsch Synthetic Kerosene (S8) to produce a synthetic surrogate for aerospace F-24. Due to the fossil fuel origin of F-24, the introduction of a synthetic surrogate would create a sustainable aviation fuel (SAF) with sources obtained from within the United States. An analysis of ignition delay (ID), combustion delay (CD), derived cetane number (DCN), negative temperature coefficient (NTC) region, Low-Temperature Heat Release region (LTHR) and High-Temperature Heat Release (HTHR) was conducted using a PAC CID 510 Constant Volume Combustion Chamber (CVCC). The fuels examined in this study are neat IPK, neat S8, neat F-24, and by mass percentages, as follows: 75IPK 25S8, 52IPK 48S8, 51IPK 49S8, 50IPK 50S8 and 25IPK 75S8. The DCN values determined for IPK, S8, and F-24 were 26.92, 59.56 and 44.35 respectively. The influence of IPK present in the blends increases CD, thus reducing the DCN significantly. The fuel blend of 50IPK 50S8 was observed to be the closest match to F-24 when comparing DCN, ID and CD. The surrogate blends were determined to have a lower magnitude of peak pressure ringing compared to that of the neat S8 and F-24, this is due to the extended NTC region caused by the IPK present in the blend. During further refinement of the surrogate blend, the Apparent Heat Release Rate (AHRR) curve for the 51IPK 49S8 fuel blend was found to have the closest match to the AHRR of F24. The surrogate blend 50IPK 50S8 was shown to have the smallest percent difference and best match during the LTHR stage, compared to F-24, while 52IPK 48S8 had the smallest percent difference for the energy released during LTHR. The ID and CD of the 25/75% blends were too dissimilar from the F-24 target to be considered as a surrogate. A Noise Vibration Harshness (NVH) analysis was also conducted during the combustion of the three neat fuels in the CVCC. This analysis was conducted to relate the ID, CD, HTHR and ringing to the vibrations that occur during combustion. Neat S8 was observed to have the most vibrations occurring during the combustion process. Additionally, the HTHR was observed to have a distinct pattern for the three neat fuels and the combustion of these fuels was quieter overall.
{"title":"Development of a Synthetic Surrogate for F-24 From Blends of Iso-Paraffinic Kerosene (IPK) and Fischer-Tropsch Synthetic Kerosene (S8) in a Constant Volume Combustion Chamber (CVCC)","authors":"V. Soloiu, Lily H. Parker, Rick Smith, Amanda Weaver, Austin Brant, Aidan Rowell, M. Ilie","doi":"10.1115/icef2022-91028","DOIUrl":"https://doi.org/10.1115/icef2022-91028","url":null,"abstract":"\u0000 Investigations were conducted using mass blends of Iso-Paraffinic Kerosene (IPK) and Fischer-Tropsch Synthetic Kerosene (S8) to produce a synthetic surrogate for aerospace F-24. Due to the fossil fuel origin of F-24, the introduction of a synthetic surrogate would create a sustainable aviation fuel (SAF) with sources obtained from within the United States. An analysis of ignition delay (ID), combustion delay (CD), derived cetane number (DCN), negative temperature coefficient (NTC) region, Low-Temperature Heat Release region (LTHR) and High-Temperature Heat Release (HTHR) was conducted using a PAC CID 510 Constant Volume Combustion Chamber (CVCC). The fuels examined in this study are neat IPK, neat S8, neat F-24, and by mass percentages, as follows: 75IPK 25S8, 52IPK 48S8, 51IPK 49S8, 50IPK 50S8 and 25IPK 75S8.\u0000 The DCN values determined for IPK, S8, and F-24 were 26.92, 59.56 and 44.35 respectively. The influence of IPK present in the blends increases CD, thus reducing the DCN significantly. The fuel blend of 50IPK 50S8 was observed to be the closest match to F-24 when comparing DCN, ID and CD.\u0000 The surrogate blends were determined to have a lower magnitude of peak pressure ringing compared to that of the neat S8 and F-24, this is due to the extended NTC region caused by the IPK present in the blend. During further refinement of the surrogate blend, the Apparent Heat Release Rate (AHRR) curve for the 51IPK 49S8 fuel blend was found to have the closest match to the AHRR of F24. The surrogate blend 50IPK 50S8 was shown to have the smallest percent difference and best match during the LTHR stage, compared to F-24, while 52IPK 48S8 had the smallest percent difference for the energy released during LTHR. The ID and CD of the 25/75% blends were too dissimilar from the F-24 target to be considered as a surrogate.\u0000 A Noise Vibration Harshness (NVH) analysis was also conducted during the combustion of the three neat fuels in the CVCC. This analysis was conducted to relate the ID, CD, HTHR and ringing to the vibrations that occur during combustion. Neat S8 was observed to have the most vibrations occurring during the combustion process. Additionally, the HTHR was observed to have a distinct pattern for the three neat fuels and the combustion of these fuels was quieter overall.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"42 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120868616","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A. J. Susa, Lingzhi Zheng, Zach D. Nygaard, A. Ferris, Ronald K. Hanson
Experimentally measured values of the laminar flame speed (SL) are reported for the primary reference fuels over a range of unburned-gas temperatures (Tu) spanning from room temperature to above 1,000 K, providing the highest-temperature SL measurements ever reported for gasoline-relevant fuels. Measurements were performed using expanding flames ignited within a shock tube and recorded using side-wall schlieren imaging. The recently introduced area-averaged linear curvature (AA-LC) model is used to extrapolate stretch-free flame speeds from the aspherical flames. High-temperature SL measurements are compared to values simulated using different kinetic mechanisms and are used to assess three functional forms of empirical SL–Tu relationships: the ubiquitous power-law model, an exponential relation, and a non-Arrhenius form. This work demonstrates the significantly enhanced capability of the shock-tube flame speed method to provide engine-relevant SL measurements with the potential to meaningfully improve accuracy and reduce uncertainty of kinetic mechanisms when used to predict global combustion behaviors most relevant to practical engine applications.
{"title":"Laminar Flame Speed Measurements of Primary Reference Fuels at Extreme Temperatures","authors":"A. J. Susa, Lingzhi Zheng, Zach D. Nygaard, A. Ferris, Ronald K. Hanson","doi":"10.1115/icef2022-90501","DOIUrl":"https://doi.org/10.1115/icef2022-90501","url":null,"abstract":"\u0000 Experimentally measured values of the laminar flame speed (SL) are reported for the primary reference fuels over a range of unburned-gas temperatures (Tu) spanning from room temperature to above 1,000 K, providing the highest-temperature SL measurements ever reported for gasoline-relevant fuels. Measurements were performed using expanding flames ignited within a shock tube and recorded using side-wall schlieren imaging. The recently introduced area-averaged linear curvature (AA-LC) model is used to extrapolate stretch-free flame speeds from the aspherical flames. High-temperature SL measurements are compared to values simulated using different kinetic mechanisms and are used to assess three functional forms of empirical SL–Tu relationships: the ubiquitous power-law model, an exponential relation, and a non-Arrhenius form. This work demonstrates the significantly enhanced capability of the shock-tube flame speed method to provide engine-relevant SL measurements with the potential to meaningfully improve accuracy and reduce uncertainty of kinetic mechanisms when used to predict global combustion behaviors most relevant to practical engine applications.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125897203","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Kevin K. Schwarm, N. Minesi, Barathan Jeevaretanam, Sarah Enayati, T. Tsao, R. Spearrin
A high-speed in-situ laser absorption sensor has been developed for cycle-resolved emissions analysis in the exhaust manifold of production-scale internal combustion engines. An inline sensor module, using optical fiber-coupling of interband and quantum cascade lasers, targets the fundamental rovibrational absorption lines of carbon monoxide and nitric oxide near 5 μm in wavelength. The sensor module was integrated into a commercial EPA-certified natural gas spark-ignition generator operated at 3,300 rpm for measurements of exhaust pulse temperature, CO, and NO concentrations at a rate of 10 kHz. Novel high-temperature optomechanical design enabled in-stream sensor coupling near the exhaust valve with local gas temperatures up to ∼1200 K and valve to sensor gas transit times on the order of milliseconds. Measurement results reveal high degrees of intra-cycle and cycle-to-cycle variations which are otherwise undetectable with standard emission gas analyzers. Sensor response to variations in fuel composition was evaluated by introduction of 1–10% NH3 or H2 into the natural gas fuel system. The effects of fuel blending on exhaust emissions of CO and NO were well-distinguished even at 1% volume fraction, and the sensor captured both intra-cycle and cycle-averaged emissions differences between the three fuel types. Measured concentrations of CO and NO ranged from 0.1–2.8% and 30–3500 ppm with detection limits of 0.07% and 26 ppm, respectively. The exhaust sensor presented here has potential for integration with real-time control systems to enable adaptive optimization of polyfuel internal combustion engines to meet the need for flexible, low-carbon, on-demand energy conversion.
{"title":"Cycle-Resolved Emissions Analysis of Polyfuel Reciprocating Engines via In-Situ Laser Absorption Spectroscopy","authors":"Kevin K. Schwarm, N. Minesi, Barathan Jeevaretanam, Sarah Enayati, T. Tsao, R. Spearrin","doi":"10.1115/icef2022-88543","DOIUrl":"https://doi.org/10.1115/icef2022-88543","url":null,"abstract":"\u0000 A high-speed in-situ laser absorption sensor has been developed for cycle-resolved emissions analysis in the exhaust manifold of production-scale internal combustion engines. An inline sensor module, using optical fiber-coupling of interband and quantum cascade lasers, targets the fundamental rovibrational absorption lines of carbon monoxide and nitric oxide near 5 μm in wavelength. The sensor module was integrated into a commercial EPA-certified natural gas spark-ignition generator operated at 3,300 rpm for measurements of exhaust pulse temperature, CO, and NO concentrations at a rate of 10 kHz. Novel high-temperature optomechanical design enabled in-stream sensor coupling near the exhaust valve with local gas temperatures up to ∼1200 K and valve to sensor gas transit times on the order of milliseconds. Measurement results reveal high degrees of intra-cycle and cycle-to-cycle variations which are otherwise undetectable with standard emission gas analyzers. Sensor response to variations in fuel composition was evaluated by introduction of 1–10% NH3 or H2 into the natural gas fuel system. The effects of fuel blending on exhaust emissions of CO and NO were well-distinguished even at 1% volume fraction, and the sensor captured both intra-cycle and cycle-averaged emissions differences between the three fuel types. Measured concentrations of CO and NO ranged from 0.1–2.8% and 30–3500 ppm with detection limits of 0.07% and 26 ppm, respectively. The exhaust sensor presented here has potential for integration with real-time control systems to enable adaptive optimization of polyfuel internal combustion engines to meet the need for flexible, low-carbon, on-demand energy conversion.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122151906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
J. Cowart, T. Dickerson, Andy McDaniel, D. L. Luning Prak
Nearly four hundred different samples of jet and diesel fuels were used to train and test Machine Learning (ML) models for Derived Cetane Number (DCN – ASTM D6890) prediction using eight of the fuels’ physical properties as model inputs. Linear Regression (LR), Artificial Neural Networks (ANNs) and Gaussian based models all showed good performance predicting DCN with nominal prediction errors of 1 to 1.7 cetane numbers (CN). Shallow ANNs showed comparable prediction results as compared to LR, with the Gaussian Exponential Model yielding the best results overall. The DCN prediction models were exercised to observe the most critical-sensitive properties in the DCN prediction. Fuel density and T50 were seen to be the most important for both jet and diesel fuels. This result supports the usage of these two properties in cetane number prediction via the Cetane Index (CI) calculation (ASTM D976). Flash point and Tend of the distillation curve were of secondary importance. Additionally, jet fuel chemical composition data from 8 chemical fuel classes were applied to predict DCN. Adding the chemical composition data to the physical property data did not provide for improved DCN prediction. This result supports the coupling and connection between a fuel’s physical and chemical properties. An analysis of the most important (to DCN) fuel classes shows alkanes (high cetane) and alkyl-benzene (low cetane) components to be the most influential. Finally, fuel similarity was characterized using Self Organizing Maps (SOMs). The SOM map was trained for both jet and diesel fuels using physical properties alone. Different fuels (e.g. alternative Alcohol-to-Jet) were then applied to the SOM to test similarity. SOM Position and Quantization Error are shown to accurately characterize these fuels as significantly different than the conventional jet and diesel fuels used to establish the SOM.
{"title":"Using Machine Learning to Predict Derived Cetane Number and Fuel Similarity","authors":"J. Cowart, T. Dickerson, Andy McDaniel, D. L. Luning Prak","doi":"10.1115/icef2022-89295","DOIUrl":"https://doi.org/10.1115/icef2022-89295","url":null,"abstract":"\u0000 Nearly four hundred different samples of jet and diesel fuels were used to train and test Machine Learning (ML) models for Derived Cetane Number (DCN – ASTM D6890) prediction using eight of the fuels’ physical properties as model inputs. Linear Regression (LR), Artificial Neural Networks (ANNs) and Gaussian based models all showed good performance predicting DCN with nominal prediction errors of 1 to 1.7 cetane numbers (CN). Shallow ANNs showed comparable prediction results as compared to LR, with the Gaussian Exponential Model yielding the best results overall. The DCN prediction models were exercised to observe the most critical-sensitive properties in the DCN prediction. Fuel density and T50 were seen to be the most important for both jet and diesel fuels. This result supports the usage of these two properties in cetane number prediction via the Cetane Index (CI) calculation (ASTM D976). Flash point and Tend of the distillation curve were of secondary importance. Additionally, jet fuel chemical composition data from 8 chemical fuel classes were applied to predict DCN. Adding the chemical composition data to the physical property data did not provide for improved DCN prediction. This result supports the coupling and connection between a fuel’s physical and chemical properties. An analysis of the most important (to DCN) fuel classes shows alkanes (high cetane) and alkyl-benzene (low cetane) components to be the most influential. Finally, fuel similarity was characterized using Self Organizing Maps (SOMs). The SOM map was trained for both jet and diesel fuels using physical properties alone. Different fuels (e.g. alternative Alcohol-to-Jet) were then applied to the SOM to test similarity. SOM Position and Quantization Error are shown to accurately characterize these fuels as significantly different than the conventional jet and diesel fuels used to establish the SOM.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130612007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Joern Alexander Judith, M. Kettner, Danny Schwarz, M. Klaissle, T. Koch
Homogeneous charge compression ignition (HCCI) promises low NOx emission and high efficiency, though showing a limited operating range and difficult-to-control combustion timing. In recent years, spark assisted compression ignition (SACI) was shown to be an efficacious technique to extend the operating range and to control combustion timing in HCCI engines within certain limits. As an alternative to spark assist, a hot surface ignition system (HSI) was demonstrated in a previous work to enable hot surface assisted compression ignition (HSACI) featuring similar combustion characteristics compared to SACI. The scope of this work is the comparison of both types of ignition assistance at various levels of dilution and intake temperatures with regard to the ability to control combustion timing, similarities in the course of combustion, the strength of the ignition systems and the susceptibility to cycle-by-cycle variations (CCV). Engine trials were conducted at a single-cylinder test-bench under steady state conditions at a constant engine speed of 1400 1/min. The engine operated naturally aspirated under full load conditions using natural gas as the fuel and conditioned intake pressures in the range of 993–995 mbar. Experimental conditions cover relative air-fuel ratios (λ) in the range of λ = 2.1–3.1 and intake temperatures in between 140–170°C. The earliest applicable combustion timing was used as the target variable for the evaluation of the strength of the ignition systems. Results show similar capabilities of SACI and HSACI to control combustion timing by means of spark timing in SACI and hot surface temperature in HSACI. Heat release analyses of individual combustion cycles at same crank angle timing of center of combustion (CA50) in SACI and HSACI show high agreement of the course of heat release and point out the similarity of both combustion processes. The evaluation of the strength of the ignition systems reveals that HSACI extends the lean limit by Δλ = 0.05–0.10 and the early ignition limit by ΔMinCA50 = 1.0–4.5°CA towards earlier CA50 depending on intake temperature and provided that ringing is not of concern. Comparison of CCV in HCCI, SACI and HSACI at given levels of CA50 show highest combustion stability for HCCI, followed by SACI. HSACI evinces highest CCV due to a larger variation in the start of combustion compared to HCCI and SACI.
{"title":"Experimental Study on Spark Assisted and Hot Surface Assisted Compression Ignition (SACI, HSACI) in a Naturally Aspirated Single-Cylinder Gas Engine","authors":"Joern Alexander Judith, M. Kettner, Danny Schwarz, M. Klaissle, T. Koch","doi":"10.1115/icef2022-89494","DOIUrl":"https://doi.org/10.1115/icef2022-89494","url":null,"abstract":"\u0000 Homogeneous charge compression ignition (HCCI) promises low NOx emission and high efficiency, though showing a limited operating range and difficult-to-control combustion timing. In recent years, spark assisted compression ignition (SACI) was shown to be an efficacious technique to extend the operating range and to control combustion timing in HCCI engines within certain limits. As an alternative to spark assist, a hot surface ignition system (HSI) was demonstrated in a previous work to enable hot surface assisted compression ignition (HSACI) featuring similar combustion characteristics compared to SACI. The scope of this work is the comparison of both types of ignition assistance at various levels of dilution and intake temperatures with regard to the ability to control combustion timing, similarities in the course of combustion, the strength of the ignition systems and the susceptibility to cycle-by-cycle variations (CCV).\u0000 Engine trials were conducted at a single-cylinder test-bench under steady state conditions at a constant engine speed of 1400 1/min. The engine operated naturally aspirated under full load conditions using natural gas as the fuel and conditioned intake pressures in the range of 993–995 mbar. Experimental conditions cover relative air-fuel ratios (λ) in the range of λ = 2.1–3.1 and intake temperatures in between 140–170°C. The earliest applicable combustion timing was used as the target variable for the evaluation of the strength of the ignition systems.\u0000 Results show similar capabilities of SACI and HSACI to control combustion timing by means of spark timing in SACI and hot surface temperature in HSACI. Heat release analyses of individual combustion cycles at same crank angle timing of center of combustion (CA50) in SACI and HSACI show high agreement of the course of heat release and point out the similarity of both combustion processes. The evaluation of the strength of the ignition systems reveals that HSACI extends the lean limit by Δλ = 0.05–0.10 and the early ignition limit by ΔMinCA50 = 1.0–4.5°CA towards earlier CA50 depending on intake temperature and provided that ringing is not of concern. Comparison of CCV in HCCI, SACI and HSACI at given levels of CA50 show highest combustion stability for HCCI, followed by SACI. HSACI evinces highest CCV due to a larger variation in the start of combustion compared to HCCI and SACI.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125612662","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Fahad Almatrafi, Kalim Uddeen, Moez Ben Houidi, E. Cenker, J. Turner
Lean operation increases the efficiency of the Otto-cycle internal combustion engine and decreases its emissions. However, increasing the air-fuel ratio beyond stoichiometry requires higher ignition energy to maintain the stable operation of the engine. The pre-chamber emerges as one of the promising enablers of lean operation, providing much larger energy into the main combustion chamber than simple a spark plug at multiple sites to increase combustion stability. Pre-chambers are classified into two categories based on their fuel input; active pre-chambers, with a dedicated fuel injection system, and passive pre-chambers, which are solely charged with the main chamber air-fuel mixture through nozzle holes. Therefore, the passive pre-chamber type is favorable for existing engines because of its compact design and limited modification requirements. Nevertheless, passive pre-chambers have issues with igniting very lean mixtures. In this study, a single-cylinder light-duty engine is used to study the possibility of extending the lean limit of the passive pre-chamber using a split direct injection (DI) strategy and indirect enrichment of the pre-chamber mixture. The results of the split injection method were then compared to port fuel injection (PFI) measurements. Also, another set of experiments was performed with a standard spark plug using PFI and split DI for comparison. The results showed an increase in the lean limit of passive pre-chamber operation when using the split DI strategy compared to PFI, from λ = 1.5 to 1.7. However, increased soot production was observed when using the split injection strategy.
{"title":"Direct Injection Strategy to Extend the Lean Limit of a Passive Pre-Chamber","authors":"Fahad Almatrafi, Kalim Uddeen, Moez Ben Houidi, E. Cenker, J. Turner","doi":"10.1115/icef2022-89021","DOIUrl":"https://doi.org/10.1115/icef2022-89021","url":null,"abstract":"\u0000 Lean operation increases the efficiency of the Otto-cycle internal combustion engine and decreases its emissions. However, increasing the air-fuel ratio beyond stoichiometry requires higher ignition energy to maintain the stable operation of the engine. The pre-chamber emerges as one of the promising enablers of lean operation, providing much larger energy into the main combustion chamber than simple a spark plug at multiple sites to increase combustion stability. Pre-chambers are classified into two categories based on their fuel input; active pre-chambers, with a dedicated fuel injection system, and passive pre-chambers, which are solely charged with the main chamber air-fuel mixture through nozzle holes. Therefore, the passive pre-chamber type is favorable for existing engines because of its compact design and limited modification requirements. Nevertheless, passive pre-chambers have issues with igniting very lean mixtures. In this study, a single-cylinder light-duty engine is used to study the possibility of extending the lean limit of the passive pre-chamber using a split direct injection (DI) strategy and indirect enrichment of the pre-chamber mixture. The results of the split injection method were then compared to port fuel injection (PFI) measurements. Also, another set of experiments was performed with a standard spark plug using PFI and split DI for comparison. The results showed an increase in the lean limit of passive pre-chamber operation when using the split DI strategy compared to PFI, from λ = 1.5 to 1.7. However, increased soot production was observed when using the split injection strategy.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127900991","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Hosna Geraei, Essam Seddik, G. Neame, Elliot (Yixin) Huangfu, S. Habibi
Fault Detection and Diagnosis (FDD) in internal combustion engines is an important tool for better performance, safety, reliability, and instrument to reduce maintenance costs. Early detection of engine faults can help avoid abnormal event progression to failure. This study is carried out to develop two FDD algorithms to detect and diagnose internal combustion engine faults using an optical crank angle encoder. Experiments were carried out on a 2018 Ford Gen 3, 5.0L, V8, Coyote engine to achieve these goals. The engine head was modified to access the combustion chamber of specific cylinders for in-cylinder pressure measurement and, subsequently, combustion analysis. During this project, three engine faults were introduced: EGR valve failure, cylinder leakage, and spark plug degradation. In the first method, Fast Fourier Transform (FFT) is applied to the data collected using the optical crank angle encoder. FFT converts the crank angle domain data to the frequency domain. Then, the data dimension is reduced using Principal Component Analysis (PCA). The dataset with reduced dimensions is used as Multi-layer Perceptron (MLP) inputs. 10-fold cross-validation is used to determine the number of hidden layers in the MLP. The MLP model detects and diagnoses severities of cylinder leaks and EGR faults with a relatively high success rate (92%). The second method developed a classification model using the Random Forest (RF) classifier and Curve Descriptive (CD) Features. The performance of the MLP model and the Curve Descriptive features with Random Forest (CD-RF) models for detecting and diagnosing misfire faults are compared. Results show that the MLP model and CD-RF model accuracy for classifying misfire faults are 86.67% and 88,89%, respectively.
{"title":"Machine Learning-Based Fault Detection and Diagnosis of Internal Combustion Engines Using an Optical Crank Angle Encoder","authors":"Hosna Geraei, Essam Seddik, G. Neame, Elliot (Yixin) Huangfu, S. Habibi","doi":"10.1115/icef2022-88851","DOIUrl":"https://doi.org/10.1115/icef2022-88851","url":null,"abstract":"\u0000 Fault Detection and Diagnosis (FDD) in internal combustion engines is an important tool for better performance, safety, reliability, and instrument to reduce maintenance costs. Early detection of engine faults can help avoid abnormal event progression to failure. This study is carried out to develop two FDD algorithms to detect and diagnose internal combustion engine faults using an optical crank angle encoder. Experiments were carried out on a 2018 Ford Gen 3, 5.0L, V8, Coyote engine to achieve these goals. The engine head was modified to access the combustion chamber of specific cylinders for in-cylinder pressure measurement and, subsequently, combustion analysis. During this project, three engine faults were introduced: EGR valve failure, cylinder leakage, and spark plug degradation. In the first method, Fast Fourier Transform (FFT) is applied to the data collected using the optical crank angle encoder. FFT converts the crank angle domain data to the frequency domain. Then, the data dimension is reduced using Principal Component Analysis (PCA). The dataset with reduced dimensions is used as Multi-layer Perceptron (MLP) inputs. 10-fold cross-validation is used to determine the number of hidden layers in the MLP. The MLP model detects and diagnoses severities of cylinder leaks and EGR faults with a relatively high success rate (92%). The second method developed a classification model using the Random Forest (RF) classifier and Curve Descriptive (CD) Features. The performance of the MLP model and the Curve Descriptive features with Random Forest (CD-RF) models for detecting and diagnosing misfire faults are compared. Results show that the MLP model and CD-RF model accuracy for classifying misfire faults are 86.67% and 88,89%, respectively.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132756822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
L. Jin, Simon Leblanc, Xiaoxi Zhang, Alex Bastable, J. Tjong, M. Zheng
For future SI engines, the ignition processes of an air-fuel mixture are often subjected to a fuel-lean mixture of considerably higher density, high intake boost, and high compression ratio to further improve engine efficiency. The ignition systems for future gasoline engines should effectively ignite the mixture and secure the flame kernel until it develops into self-sustainable propagation. In this paper, the impact of discharge current profile on flame kernel formation and development processes of methane-hydrogen/air mixtures under engine-like conditions are experimentally investigated in a rapid compression machine. The discharge current during the glow phase is modulated to change the energy discharge profiles. A Field-programmable gate array based multi-task control system is established to effectively control and stabilize the discharge current amplitude and duration for different ignition strategies. The ignition and combustion process are characterized via simultaneous high-speed direct imaging and in-cylinder pressure measurement. The ignition delay is analyzed with respect to the in-cylinder pressure under various boundary conditions such as fuel blending ratio and spark discharge parameters, with a focus on the efficacy of ignition strategies under various hydrogen/methane blending ratios.
{"title":"Impact of Discharge Current Profiling on Ignition Characteristics of Hydrogen/Methane Blends","authors":"L. Jin, Simon Leblanc, Xiaoxi Zhang, Alex Bastable, J. Tjong, M. Zheng","doi":"10.1115/icef2022-88393","DOIUrl":"https://doi.org/10.1115/icef2022-88393","url":null,"abstract":"\u0000 For future SI engines, the ignition processes of an air-fuel mixture are often subjected to a fuel-lean mixture of considerably higher density, high intake boost, and high compression ratio to further improve engine efficiency. The ignition systems for future gasoline engines should effectively ignite the mixture and secure the flame kernel until it develops into self-sustainable propagation. In this paper, the impact of discharge current profile on flame kernel formation and development processes of methane-hydrogen/air mixtures under engine-like conditions are experimentally investigated in a rapid compression machine.\u0000 The discharge current during the glow phase is modulated to change the energy discharge profiles. A Field-programmable gate array based multi-task control system is established to effectively control and stabilize the discharge current amplitude and duration for different ignition strategies. The ignition and combustion process are characterized via simultaneous high-speed direct imaging and in-cylinder pressure measurement. The ignition delay is analyzed with respect to the in-cylinder pressure under various boundary conditions such as fuel blending ratio and spark discharge parameters, with a focus on the efficacy of ignition strategies under various hydrogen/methane blending ratios.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131280075","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Zachary L. Williams, Prathik Meruva, Daniel Christopher Bitsis
Meeting regulatory and customer demands requires detailed powertrain calibration which can be expensive and time-consuming. There is often a reliance on mathematical optimization tools to convert experimental learnings into a final calibration. This work focuses on developing multiple neural network machine learning (ML) models which were trained on different test-train data splits of test-cell recorded steady-state medium-duty (MD) diesel engine data. The output data was used to develop engine actuator maps by utilizing a genetic algorithm (GA). The genetic algorithm contains a fitness function which was varied to target different combinations of low NOx and CO2 emissions. The input variables used for the ML model were engine speed, engine torque, fuel rail pressure, exhaust gas recirculation (EGR) valve command, main injection timing, and wastegate valve command. The output variables predicted were NOx mass flow rate, exhaust temperature, fuel flow rate, and dry intake mass flow rate. The ML models were used to predict cycle-averaged engine-out emissions and time-series predictions of all output variables for different transient drive cycles. The drive cycles used for this case were the Heavy-Duty Federal Test Procedure (HDFTP) transient cycle, the Non-Road Transient Cycle (NRTC), the Ramped Mode Cycle (RMC) and the newly proposed on-road Low-Load Cycle (LLC).
{"title":"Machine Learning and Genetic Algorithm Method for Powertrain Development: Rapid Generation of Engine Calibration Maps","authors":"Zachary L. Williams, Prathik Meruva, Daniel Christopher Bitsis","doi":"10.1115/icef2022-91169","DOIUrl":"https://doi.org/10.1115/icef2022-91169","url":null,"abstract":"\u0000 Meeting regulatory and customer demands requires detailed powertrain calibration which can be expensive and time-consuming. There is often a reliance on mathematical optimization tools to convert experimental learnings into a final calibration. This work focuses on developing multiple neural network machine learning (ML) models which were trained on different test-train data splits of test-cell recorded steady-state medium-duty (MD) diesel engine data. The output data was used to develop engine actuator maps by utilizing a genetic algorithm (GA). The genetic algorithm contains a fitness function which was varied to target different combinations of low NOx and CO2 emissions. The input variables used for the ML model were engine speed, engine torque, fuel rail pressure, exhaust gas recirculation (EGR) valve command, main injection timing, and wastegate valve command. The output variables predicted were NOx mass flow rate, exhaust temperature, fuel flow rate, and dry intake mass flow rate. The ML models were used to predict cycle-averaged engine-out emissions and time-series predictions of all output variables for different transient drive cycles. The drive cycles used for this case were the Heavy-Duty Federal Test Procedure (HDFTP) transient cycle, the Non-Road Transient Cycle (NRTC), the Ramped Mode Cycle (RMC) and the newly proposed on-road Low-Load Cycle (LLC).","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131301095","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Md Nayer Nasim, Behlol Nawaz, Oliver A. Dyakov, J. H. Mack
Lean-burn spark ignition engines can reduce emissions, increase efficiencies, and mitigate knocking conditions. Several factors can affect the lean flammability limit of natural gas engines, including the fuel composition, temperature, pressure, and spark characteristics. It has recently been shown that spark plugs with a nanostructured central electrode, treated using pulsed laser irradiation and effectively increasing the surface area, extend the lean flammability limit (LFL) of methane/air mixtures in a constant volume combustion chamber (CVCC). In this study, the effect of varying levels of surface modifications is experimentally examined for two different power configurations of femtosecond laser. These spark plugs are tested by igniting methane/air mixtures at different equivalence ratios in a CVCC coupled with high-speed Z-type Schlieren visualization. The durability of the nanostructures on the electrode surfaces is tested by repeating the evaluations after 6,000, 66,000 and 666,000 spark events. Scanning Electron Microscope (SEM) images at different magnification rates and the root mean square (RMS) surface roughness derived from optical profilometry are used to examine the degradation of the electrode surfaces. The results point towards the existence of an optimized value of surface roughness in terms of the LFL (phi = 0.55 for 5.89 μm and phi = 0.58 for 13.68 μm). Performance degradation was particularly pronounced for electrodes with a high level of initial surface roughness (13.68 μm) whereas the electrode with a lower initial surface roughness (5.89 μm) held a superior LFL (phi = 0.57) compared to the standard spark plug (phi = 0.61) even after going through 666,000 sparks.
{"title":"An Experimental Study on the Performance and Durability of Nanostructured Spark Plugs","authors":"Md Nayer Nasim, Behlol Nawaz, Oliver A. Dyakov, J. H. Mack","doi":"10.1115/icef2022-90609","DOIUrl":"https://doi.org/10.1115/icef2022-90609","url":null,"abstract":"\u0000 Lean-burn spark ignition engines can reduce emissions, increase efficiencies, and mitigate knocking conditions. Several factors can affect the lean flammability limit of natural gas engines, including the fuel composition, temperature, pressure, and spark characteristics. It has recently been shown that spark plugs with a nanostructured central electrode, treated using pulsed laser irradiation and effectively increasing the surface area, extend the lean flammability limit (LFL) of methane/air mixtures in a constant volume combustion chamber (CVCC). In this study, the effect of varying levels of surface modifications is experimentally examined for two different power configurations of femtosecond laser. These spark plugs are tested by igniting methane/air mixtures at different equivalence ratios in a CVCC coupled with high-speed Z-type Schlieren visualization. The durability of the nanostructures on the electrode surfaces is tested by repeating the evaluations after 6,000, 66,000 and 666,000 spark events. Scanning Electron Microscope (SEM) images at different magnification rates and the root mean square (RMS) surface roughness derived from optical profilometry are used to examine the degradation of the electrode surfaces. The results point towards the existence of an optimized value of surface roughness in terms of the LFL (phi = 0.55 for 5.89 μm and phi = 0.58 for 13.68 μm). Performance degradation was particularly pronounced for electrodes with a high level of initial surface roughness (13.68 μm) whereas the electrode with a lower initial surface roughness (5.89 μm) held a superior LFL (phi = 0.57) compared to the standard spark plug (phi = 0.61) even after going through 666,000 sparks.","PeriodicalId":257981,"journal":{"name":"ASME 2022 ICE Forward Conference","volume":"15 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117135555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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