Search Results

This paper presents a method for analysing the characteristics of nano-scale particles emitted from a 1.6 Litre, 4-stroke, gasoline direct injection (GDI) and turbocharged spark ignition engine fitted with a three-way catalytic converter. Ensemble Empirical Mode Decomposition (EEMD) is employed in this work to decompose the nano-scale particle size spectrums obtained using a differential mobility spectrometer (DMS) into Intrinsic Mode Functions (IMF). Fast Fourier Transform (FFT) is then applied to each IMF to compute its frequency content. The results show a strong correlation between the IMFs of specific particle ranges and the IMFs of the total particle count at various speed and load operating conditions. Hence, it is possible to characterise the influence of specific nano-scale particle ranges on the total particulate matter signal by analysing the frequency components of its IMFs using the EEMD-FFT method.

The number of electric vehicles is increasing in line with the global carbon reduction targets. More households are installing electric charging points to complement the existing charging infrastructure. The increasing electricity prices affected by the global energy/economic crisis however pushed more households towards coupling their charging points with renewable energy generation and storage systems to manage the supply and demand of energy more effectively. In this study, an electric charging station equipped with Photovoltaic panels and an electric storage system utilising second-life Electric Vehicles (EV) batteries is designed and analysed. Various electricity generation capacities are considered to be installed on the roof of the case study building ranging from 5m2 and 20m2. The second-life batteries are disposed from EVs with an 80% state of health. MATLAB Simulink is used for mathematical modelling of system.

Formula (1) vehicles have transitioned from E5 to E10 fuel for the 2022 season to reduce carbon emissions and by 2026 the vehicles are required to use 100% sustainable fuels. The aim of this paper is to identify the operating envelope of the F1 power unit for E10-E100 fuel and the resulting emission levels for these fuel compositions using numerical simulations. To achieve this aim an F1 engine model has been developed in GT-Suite with reference to the FIA 2022 Technical Regulations. The combustion model has been validated using data obtained from literature relating to laminar and turbulent flame speed, friction and heat transfer characteristics within the combustion chamber. One of the main challenges of using ethanol-based fuels is the increased levels of formaldehyde in the tailpipe.

Internal combustion (IC) engines are the most common power unit technology found in road vehicles. The process of combustion within IC engines is linked to the output torque and overall powertrain performance. This work presents a method of analysing the parameters of cylinder pressure and crankshaft instantaneous speed signals obtained from a turbocharged, 4-stroke, 4-cylinder, 1.6 Litre, spark ignition, gasoline direct injection engine at various speed and load operating conditions. Whereas cepstrum analysis is used in the present work to extract critical features characterising the combustion process. Cepstrum analysis showed that the location of maximum heat release can be directly obtained from the quefrency of the instantaneous crank speed. This paper presents a systematic scheme for applying cepstrum for obtaining combustion features from the instantaneous crank speed signal.

The computer-aided engineering (CAE) automation study requires a large disk space and a premium processor. If all finite element (FE) models run locally, it may crash the local machine, and if the FE model runs on high-performance computing (HPC), transferring data from the server to the local machine to do the optimization may cause latency issues. This automation study provides a unique road map to optimize the design by working efficiently using the initial setup on the local machine, running an analysis of a large number of FE models on HPC, and performing optimization on the server. CAE Automation process has been demonstrated using a case study on a driveline component, crush spacer. Crush spacer is a very critical engineering design because, first, it provides the minimum required preload to the bearing inner races to keep them in position and, second, it endures a number of duty cycles.

The COVID-19 pandemic has forced governments to implement rigorous containment measures on reduction or cessation of human mobility, transportation and economic activities, to control the spread of the virus. This is considered as a unique opportunity to study the impact of local lockdowns periods, especially, on the vehicle emission levels, and urban air quality in cities with high pollution levels, such as Bogota, Colombia. The first case was confirmed in Colombia on March 6, 2020, since then to prevent its propagation, the government declared a national lockdown starting from March 20 until August 31, 2020. Therefore, this study attempted to analyse the air quality in Bogota by assessing the concentrations of the atmospheric pollutants NO₂, SO₂, O₃, CO, PM₂.₅ and PM₁₀ during the lockdown period and the corresponding concentrations levels during the same period in 2018 and 2019. The data for this pilot study was obtained from the air quality monitoring stations of Bogota.

Internal combustion (IC) engines are the current dominant power source used in the automotive industry for hybrid vehicles. The combustion process of IC engines involves various parameters, which are linked to the overall performance of the driveline. Therefore, finding the frequency coupling between the manifold pressure, in-cylinder pressure and output crankshaft speed will provide an insight into the reasons for torque fluctuations and its effect on driveline performance. The present work introduces a methodology to analyze cylinder pressure, manifold pressure and instantaneous crank speed signals measured from a 4 cylinder, 1.6 Litre, Gasoline Direct Injection Engine at different speed conditions to identify the frequency coupling between these signals. This work uses Ensemble Empirical Mode Decomposition (EEMD) as a de-noising method and Bispectral analysis for examining the presence of a frequency coupling from the signals.

Electric Vehicles (EVs) are considered to be a worthy alternative to automobiles powered by internal combustion engines to achieve the goal of sustainable transportation. For their many known advantages, Li-ion cells are considered to be the most practical energy storage solution for the purpose of EVs propulsion currently. The capability of Li-ion cells to store energy in extreme cold operating temperatures is significantly lower than that at nominal operating temperatures due to greater power losses at cold temperatures. Therefore, it leads to degradation of performance of EVs in sub-zero temperatures. The present work proposes a novel approach to use numerical simulation technique to build an EV model based on BMW i3 using GT Suite at sub-zero temperatures. The model is validated against experimental data obtained from Argonne National Laboratory for US06, HWY and UDDS legislative drive cycles.

A novel surrogate model is presented, which predicts the engine-out Particle Number (PN) emissions of a light-duty, spray-guided, turbo-charged, GDI engine. The model is developed through extensive CFD analysis, carried out using the Siemens Simcenter STAR-CD, and considers a range of part-load operating conditions and single-variable sweeps where control parameters such as start of injection and injection pressure are varied in isolation. The work is attached to the Ford-led APC6 DYNAMO project, which aims to improve efficiency and reduce harmful emissions from the next generation of gasoline engines. The CFD work focused on the air exchange, fuel spray and mixture preparation stages of the engine cycle. A combined Rosin-Rammler and Reitz-Diwakar model, calibrated over a wide range of injection pressure, is used to model fuel atomization and secondary droplets break-up.

This study analyzes the performance of the Energy Recovery System (ERS) of a Formula One car (F1) based on the qualification performance of 19 drivers for the first calendar race of the 2019 FIA Formula One World Championship®. In this study, the race circuit analysed was split into different sectors to examine the energy transfer between the Motor Generator Unit-Kinetic (MGU-K) and the Energy Storage (ES) systems. Positive Kinetic Energy (PKE) concept was used for estimating the energy deployment potential of the ERS along with numerical simulations for estimating the energy recovering potential. This investigation highlights the strategies used by different drivers and the effect of driver to driver variation on their ERS performance during qualification. The methodology demonstrated in this study is able to identify the correlation between the unique driving style of individual drivers and the ERS strategies used by the teams for maximizing the performance of their car.

Autonomous vehicles in formula student competition is a relatively new competition, with most of the teams testing new concepts every year with their challenger for the season. A background search conducted reflects the various concept changes offered by the FS teams in Formula student Germany each year. Hence, it can be concluded that the teams are uncertain about many concepts in an autonomous vehicle. This paper explores one such aspect; the choice of controller governs the steering capabilities of the autonomous vehicle. An MPC controller is used to build a basic controller model for the autonomous vehicle in the formula student competition. A bicycle model representative of the Oxford Brookes Racing team's electric vehicle is modeled, and the MPC controller is used to check various vehicle dynamic parameters in Simulink.

The aim of the present work is to propose a control strategy for maximizing the performance of a Formula One (F1) car through numerical simulation for 2021 regulations taking 2019 regulations as a benchmark. This study has developed an engine-powertrain model of an F1 car with real world driver data for estimating the vehicle’s full throttle performance. The maximum possible energy recovered, stored and deployed by the Energy Recovery System (ERS) was estimated for the first 10 circuits in the 2019 FIA Formula One World Championship® Race Calendar. A 1.6L V6 Internal Combustion Engine (ICE), as well as, a full vehicle was modelled according to the 2019 Federation Internationale de l'Automobile (FIA) Formula One technical regulations using GT-Suite software. The model was validated against the experimental data. The data for validation was extracted from On- Board videos using Optical Character Recognition (OCR) and FIA regulations.

Computer programs and models are playing an increasing role in simulating vehicle crashworthiness, dynamic, and fuel efficiency. To maximize the effectiveness of these models, the validity and predictive capabilities of these models need to be assessed quantitatively. For a successful implementation of Computer Aided Engineering (CAE) models as an integrated part of the current vehicle development process, it is necessary to develop objective validation metric that has the desirable metric properties to quantify the discrepancy between multiple tests and simulation results. However, most of the outputs of dynamic systems are multiple functional responses, such as time history series. This calls for the development of an objective metric that can evaluate the differences of the multiple time histories as well as the key features under uncertainty.

Simulation based design optimization has become the common practice in automotive product development. Increasing computer models are developed to simulate various dynamic systems. Before applying these models for product development, model validation needs to be conducted to assess their validity. In model validation, for the purpose of obtaining results successfully, it is vital to select or develop appropriate metrics for specific applications. For dynamic systems, one of the key obstacles of model validation is that most of the responses are functional, such as time history curves. This calls for the development of a metric that can evaluate the differences in terms of phase shift, magnitude and shape, which requires information from both time and frequency domain. And by representing time histories in frequency domain, more intuitive information can be obtained, such as magnitude-frequency and phase-frequency characteristics.

Current legform impact test methods using the FlexPLI have been developed to protect pedestrians from lower limb injuries in collisions with low-bumper vehicles. For this type of vehicles, the influence of the upper body on the bending load generated in the lower limb is compensated by setting the impact height of the FlexPLI 50 mm above that of pedestrians. However, neither the effectiveness of the compensation method of the FlexPLI nor the influence of the upper body on the bending load generated in the lower limb of a pedestrian has been clarified with high-bumper vehicles. In this study, therefore, two computer simulation analyses were conducted in order to analyze: (1) The influence of the upper body on the bending load generated in the lower limb of a pedestrian when impacted by high-bumper vehicles and (2) The effectiveness of the compensation method for the lack of the upper body by increasing impact height of the FlexPLI for high-bumper vehicles.

One of the passive methods to reduce drag on the unshielded underbody of a passenger road vehicle is to use a vertical deflectors commonly called air dams or chin spoilers. These deflectors reduce the flow rate through the non-streamlined underbody and thus reduce the drag caused by underbody components protruding in to the high speed underbody flow. Air dams or chin spoilers have traditionally been manufactured from hard plastics which could break upon impact with a curb or any solid object on the road. To alleviate this failure mode vehicle manufacturers are resorting to using soft plastics which deflect and deform under aerodynamic loading or when hit against a solid object without breaking in most cases. This report is on predicting the deflection of soft chin spoiler under aerodynamic loads. The aerodynamic loads deflect the chin spoiler and the deflected chin spoiler changes the fluid pressure field resulting in a drag change.

The heat rejection rates and skin temperatures of a liquid cooled exhaust manifold on a 3.5 L Gasoline Turbocharged Direct Injection (GTDI) engine are determined experimentally using an external cooling circuit, which is capable of controlling the manifold coolant inlet temperature, outlet pressure, and flow rate. The manifold is equipped with a jacket that surrounds the collector region and is cooled with an aqueous solution of ethylene-glycol-based antifreeze to reduce skin temperatures. Results were obtained by sweeping the manifold coolant flow rate from 2.0 to 0.2 gpm at 12 different engine operating points of increasing brake power up to 220 hp. The nominal coolant inlet temperature and outlet pressure were 85 °C and 13 psig, respectively. Data were collected under steady conditions and time averaged. For the majority of operating conditions, the manifold heat rejection rate is shown to be relatively insensitive to changes in manifold coolant flow rate.

This work investigates the effect of a three-way catalytic converter and sampling dilution ratio on nano-scale exhaust particulate matter emissions from a gasoline direct-injection engine during cold-start and warm-up transients. Experimental results are presented from a four cylinder in-line, four stroke, wall-guided direct-injection, turbo-charged and inter-cooled 1.6 litre gasoline engine. A fast-response particulate spectrometer for exhaust nano-particle measurement up to 1000 nm was utilised. It was observed that the three-way catalytic converter had a significant effect on particle number density, reducing the total particle number by up to 65 % over the duration of the cold-start test. The greatest change in particle number density occurred for particles less than 23 nm diameter, with reductions of up to 95 % being observed, whilst the number density for particles above 50 nm diameter exhibited a significant increase.

Model validation is a process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended uses of the model. In reliability based design, the intended use of the model is to identify an optimal design with the minimum cost function while satisfying all reliability constraints. It is pivotal that computational models should be validated before conducting the reliability based design. This paper presents an ensemble approach for model bias prediction in order to correct predictions of computational models. The basic idea is to first characterize the model bias of computational models, then correct the model prediction by adding the characterized model bias. The ensemble approach is composed of two prediction mechanisms: 1) response surface of model bias, and 2) Copula modeling of a series of relationships between design variables and the model bias, between model prediction and the model bias.

Pending reductions in light duty vehicle PM emissions standards from 10 to 3 mg/mi and below will push the limits of the gravimetric measurement method. At these levels the PM mass collected approaches the mass of non-particle gaseous species that adsorb onto the filter from exhaust and ambient air. This introduces an intrinsic lower limit to filter based measurement that is independent of improvements achieved in weighing metrology. The statistical variability of back-up filter measurements at these levels makes them an ineffective means for correcting the adsorption artifact. The proposed subtraction of a facility based estimate of the artifact will partially alleviate the mass bias from adsorption, but its impact on weighing variability remains a problem that can reach a significant fraction of the upcoming 3 and future 1 mg/mi standards. This paper proposes an improved PM mass method that combines the gravimetric filter approach with real time aerosol measurement.