Dr Stephen Samuel

The search for alternative fuel for transport vehicles and also replacement of internal combustion engines in order to reduce the harmful emissions have been forcing the vehicle manufacturers to develop, design and propose technology solutions for meeting the stringent legislative targets. Mexico’s commitment for de-carbonisation of transportation sector and meeting the environmental goals is shaping its policy towards this sector and favours the move towards electrification of the vehicles. Therefore, as an interim solution, the presence of hybrid vehicle is increasing in Mexico City. The aim of the present work is to numerically evaluate the possibility of replacing the internal combustion engines in the existing hybrid vehicles with the Hydrogen fuel cell. This work numerically modelled a Hydrogen fuel cell vehicle based on Toyota MIRAI and validated the fuel economy performance of the vehicle using experimental data. This validated model was used to estimate the fuel economy for real-world drive cycles generated in 2019 from Mexico City. It considered three different drive cycles representing real-world driving in the Metropolitan Area of the Valley of Mexico. This study estimated the amount of reduction in CO2 and other pollutants for the year 2020, and up to 2050 if the internal combustion engine in the electric hybrid vehicles are replaced with the Hydrogen fuel cell. The study also estimated the amount of Hydrogen fuel required for replacing the internal combustion engines with the Hydrogen fuel cell for moving towards electrification of light duty vehicles. Acknowledgments The development of driving cycles in Mexico City was supported by UNAM, with the PAPIIT IG 181010 project.

With the introduction of the V6 engines in Formula 1, in 2014, the sport aimed to close the gap between the automotive engine and high-performance motorsport engines in the area of fuel economy. A set of very challenging engineering regulations were introduced by the FIA to restrict the power from the Internal Combustion Engine (ICE), while allowing for more power to be harvested through energy recovery systems. Although progress has been made in developing a highly efficient powertrain, the limit to which this system can be pushed to is still unknown due to a significant gap between the technological choices available and the optimal control strategy used. This study investigated an engine-powertrain model of an F1 car with real world driver data for estimating the vehicle’s full throttle performance. The work used engine and drive-cycle simulation-modeling tools to build a representative car model which complied with the 2019 FIA regulations, in conjunction with real world data to identify the most critical parameter such as the gear shift strategy and the maximum energy recovered, stored and deployed that decides which car wins the race. This work identified the different strategies used by drivers and their respective teams for achieving the best possible vehicle performance, to finish the race and win. Based on real world driver data, a comparative analysis is done between drivers finishing at 1st Place, 10th Place and the last driver to successfully complete the race. A suitable strategy is proposed in this work for all the analyzed races in order to maximize the vehicle’s performance. The work provides an insight towards the direction in which the F1 industry could possibly be progressing for 2021.

This study aims to determine the most suitable flow metering device for the characterisation of heavy duty diesel injector behaviour. The study focuses on three commercially available metering devices and the main principles they employ. An experiment was carried out to benchmark the performance of each device’s measurement repeatability in the characterisation of fuel injector behaviour. This study then compares the capabilities and suitability of each for use in a production environment. The comparison was carried out for Delphi Technologies using the new DFi21 heavy duty diesel injector which uses the miniaturised hydraulic three way control valve technology.

The aim of this study was to implement Economic Order Quantity method (EOQ) together with the Lambert W function in a 1-D engine simulation model in order to develop a fuel control strategy for a Gasoline direct injection (GDI) engine. Previous work of the co-author demonstrated the possibility of optimizing fuel injection quantity in GDI engine using the EOQ that is commonly used in supply chain of perishable products. This work extends the previous work and implements it in a 1-D, crank angle resolved, engine simulation model for the application of model based calibration process. The present work uses a validated engine simulation model, which is based on predictive combustion modelling approach, and couples the 1-D engine simulation model with SIMULINK to add the evaporation, wall- wetting and heat transfer models. It employs FORTRAN subroutines to modify the internal code of the 1-D simulation software in order to add crank angle resolved evaporation model. Finally, EOQ with Lambert W function was added to the model using MATLAB with special attention to the decimal control for the solution. This study demonstrated that EOQ and Lambert W functions together are a suitable method to develop fuel control strategy for a model based calibration procedure when implemented in crank angle resolved 1-D simulation model.

The present work evaluated the suitability of Economic Order Quantity (EOQ), commonly used in supply  chain management and process optimization, for combustion in Gasoline Direct Injected (GDI) engines. It  identified appropriate sub-models to draw an analogy between the EOQ for melon picking and fuel  injection in GDI engines. It used experimental data from in-cylinder combustion processes for validating  the model. It used peak cylinder pressure and indicative mean effective pressure for validating the model; the R2  value for linear correlation between the experimental value and estimated value is 0.98. This work  proposes that the EOQ based on Lambert W function could be employed for optimizing the fuel quantity in  GDI engines for real-world fuel economy.

A phenomenological model based on the Lambert  Function is presented which is capable of describing the variations in nano-scale Particulate Matter (PM) number concentrations across the turbine stage of a Turbocharged Gasoline Direct Injected (TGDI) engine over a wide range of engine operating conditions with a Coefficient of Determination ( ) of 0.917. The model predictors are variables which are available to the Engine Control Unit (ECU) either by means of stored maps or on‑board measurements. The suitability of the proposed model for real-time usage within the ECU for vehicle calibration is discussed.

The influence of the exhaust gas turbocharger on nano-scale Particulate Matter (PM) number emissions from a Gasoline Direct Injected (GDI) engine is investigated at fixed exhaust gas dilution ratio for a matrix of three engine speeds and four engine load operating points. Experimental repeatability is assessed by means of the Coefficient of Variation (CoV) from three independent measurements for every test point. A hypothesis test on the difference between total number count before and after the turbine shows that there are statistically relevant variations for most operating points. A reduction in PM total number count at low load is observed, and an increment at high load. It is conjectured that as fuel injection pressure and duration increase with load, a larger share of volatile particulate matter is produced, which then undergoes nucleation as the exhaust gas expands through the turbine. At the same time, the centrifugal action within the turbocharger is believed to promote particle agglomeration and growth, and fragmentation of micro-scale particles. Experiments with variable dilution ratio at a fixed engine test point show that changes in dilution ratio affect repeatability of the emissions measurements only marginally. Yet, a hypothesis test on the variation of total number count with dilution shows that PM number counts are systematically affected by changes in dilution ratio. Furthermore, a hypothesis test also shows that the impact of the turbocharger on total number emissions is statistically relevant regardless of the dilution ratio adopted.

This work investigates the effect of engine operating conditions and exhaust sampling conditions (i.e. dilution ratio) on engine-out, nano-scale, particulate matter emissions from a gasoline direct-injection engine during cold-start and warm-up transients. The engine used for this research was an in-line four cylinder, four stroke, wall-guided direct-injection, turbo-charged and inter-cooled 1.6 l gasoline engine. A fast-response particulate spectrometer for exhaust nano-particle measurement up to 1000 nm was utilized, along with a spark-plug mounted pressure transducer for combustion analysis. It was observed that the total particle count decreases during the cold-start transient, and has a distinct relationship with the engine body temperature. Tests have shown that the engine body temperature may be used as a control strategy for engine-out particulate emissions. This work has identified that up to 95 % of particles emitted during the cold-start transient are in the 5-23 nm size range. It is also evident that the dilution ratio of the exhaust sample has a significant effect on the particulate matter number and size distribution.

The main challenge facing the concept of gasoline direct injection is the unfavourable physical conditions at which the premixed charge is prepared and burned. These conditions include the short time available for gasoline to be sprayed, evaporated, and homogeneously mixed with air. These conditions most probably affect the combustion process and the cycle-by-cycle variation and may be reflected in overall engine operation. The aim of this research is to analyze the combustion characteristics and cycle-by-cycle variation including engine-out nanoparticulates of a turbocharged, gasoline direct injected spark ignition (DISI) engine at a wide range of operating conditions. Gasoline DISI, turbo-intercooled, 1.6L, 4 cylinder engine has been used in the study. In-cylinder pressure has been measured using spark plug mounted piezoelectric transducer along with a PC based data acquisition. A single zone heat release model has been used to analyze the in-cylinder pressure data. The analysis of the combustion characteristics includes the flame development (0-10% burned mass fraction) and rapid burn (10-90% burned mass fraction) durations at different engine conditions. The cycle-by-cycle variations have been characterized by the coefficient of variations (COV) in the peak cylinder pressure, the indicated mean effective pressure (IMEP), burn durations, and particle number density. The combustion characteristics and cyclic variability of the DISI engine are compared with data from throttle body injected (TBI) engine and conclusions are developed.

This study aims to identify the factors that control particulate matter (PM) formation and size distribution in direct-injection spark-ignition (DISI) engines. The test engine used for this research was a 1.6 litre, wall-guided DISI, turbocharged, intercooled, in-line 4 cylinder, Euro IV engine. The exhaust sampling point was before the catalytic converter, i.e. engine-out emissions were measured. The first part of this paper investigates the characteristics of PM number and size distribution of DISI and throttle body injected (TBI) engines. The second part investigates the effect of combustion characteristics of DISI engines on the number of 5nm and 10nm (nucleation) and 200nm (accumulation) PM. A statistical analysis of the coefficient of variance (COV) of the maximum rate of pressure rise (RPmax) over 100 cycles was performed against the COV of 5nm, 10nm and 200nm total particle number. The degree of asymmetry of the COV of RPmax around its mean and the relative peakedness or flatness of the distribution were analysed using Skewness and Kurtosis. It was found that the more positive the skewness and more negative the kurtosis of the COV of RPmax, i.e. a distribution with an asymmetric tail extending towards more positive values, higher RPmax, and a relatively flat distribution, lead to the lowest COV of 5nm (nucleation) particulates. For the 200nm (accumulation) particulates, it was found that a relatively flat distribution and consistent RPmax lead to the lowest COV of particulate number.

This work experimentally investigated the effect of fuel temperature on nano-scale particulate matter (PM) from a Direct-Injection Spark-Ignition (DISI) engine. The first part of the investigation focused on the effect of fuel temperature on combustion characteristics and the second part focused on the engine-out particulate matter. This study found that the injection of chilled fuel into the combustion chamber led to higher in-cylinder pressure, longer duration of combustion (DOC), lower PM number and lower Geometric Mean Diameter (GMD) of the PM than the injection of fuel at ambient temperature.

Recent developments in measurement techniques enabled researchers to measure ultra-fine particulates of nano-scale range and provided more evidence that the smaller particulates typically emitted from gasoline engines may have more severe impacts on human respiratory system than the bigger particulates from diesel engines. The knowledge of the characteristics of particulates from gasoline engines, especially, the effect of fuel borne additives is sparse. This work presents the findings from a study into the effect of aftermarket additives on nano-scale particulates. Four commercially available fuel borne additives used in gasoline engines mainly by private vehicle owners in the United Kingdom were selected for this study. The combustion and emission performance of the additive fuels were compared against that of commercially available gasoline fuel using a 4-stroke, throttle body injected gasoline engine. The engine-out particulates in the range of 5 to 1000 nm were measured using a fast particle spectrometer along with the in-cylinder pressure trace. The work identified that the total particulate count for certain types of additives are two orders of magnitude greater than that of base fuel at the same engine operating condition. In contrast, other types of additives produce significantly lower levels of particulate when compared with the base fuel especially in the range of 10 nm size.

The work presented here had two main aims: first, it investigated the relationship between in-cylinder combustion performance and particulate size distribution and number count; second, it examined the possibility of reconstructing transient events using steady state test data relating to particulate size distribution and number count. It identified a strong correlation between the geometric mean diameter (GMD) of the engine-out particulate matter and the rate of burning during the early stage of combustion. In addition, it identified correlations between the coefficient of variance for 50 per cent mass fraction burned and the coefficient of variance for particulate GMD. The tests relating to transient events identified that steady state data can be used to reconstruct acceleration events, but not deceleration events. The methodology followed to obtain these findings is detailed in this paper.