Search Results

The work examined the practicality of converting a modern production 6 cylinder 7.7 litre heavy-duty diesel engine for flex dual-fuel operation with ammonia as the main fuel. A small amount of diesel fuel (pilot) was used as an ignition source. Ammonia was injected into the intake ports during the intake stroke, while the original direct fuel injection equipment was retained and used for pilot diesel injection. A bespoke engine control unit was used to control the injection of both fuels and all other engine parameters. The aim was to provide a cost-effective retrofitting technology for existing heavy-duty engines, to enable eco-friendly operation with minimal carbon emissions. The tests were carried out at a baseline speed of 600 rpm for the load range of the engine (10-90%), with minimum pilot diesel quantity and as high as 90% substitution ratio of ammonia for diesel fuel.

Ammonia (NH3) is emerging as a potential fuel for longer range decarbonised heavy transport, predominantly due to favourable characteristics as an effective hydrogen carrier. This is despite generally unfavourable combustion and toxicity attributes, restricting end use to applications where robust health and safety protocols can always be upheld. In the currently reported work, a spark ignited thermodynamic single cylinder research engine was upgraded to include gaseous ammonia and hydrogen port injection fueling, with the aim of understanding maximum viable ammonia substitution ratios across the speed-load operating map. The work was conducted under stoichiometric conditions with the spark timing re-optimised for maximum brake torque at all stable logged sites. The experiments included industry standard measurements of combustion, performance and engine-out emissions.

The fusion of multi-modal perception in autonomous driving plays a pivotal role in vehicle behavior decision-making. However, much of the previous research has predominantly focused on the fusion of Lidar and cameras. Although Lidar offers an ample supply of point cloud data, its high cost and the substantial volume of point cloud data can lead to computational delays. Consequently, investigating perception fusion under the context of 4D millimeter-wave radar is of paramount importance for cost reduction and enhanced safety. Nevertheless, 4D millimeter-wave radar faces challenges including sparse point clouds, limited information content, and a lack of fusion strategies. In this paper, we introduce, for the first time, an approach that leverages Graph Neural Networks to assist in expressing features from 4D millimeter-wave radar point clouds. This approach effectively extracts unstructured point cloud features, addressing the loss of object detection due to sparsity.

Operating condition of rotor embedded magnet materials for permanent magnet synchronous motor (PMSM) critically affect electric vehicle (EV) range and dynamic characteristics. The rotor liquid cooling technique has a deep influence on PMSM performance improvement, and begin to be studied and applied increasingly in EV field. Here, the fluid, thermal, and electromagnetic characteristics of motor with and without hollow-shaft cooling are researched comprehensively based on 100 kW PMSM with housing water jacket (HWJ) and hollow-shaft rotor water jacket (SWJ). The solid models are constructed considering temperature-dependent power loss and anisotropic thermal conductivity. After the fluid models are set up by using Reynolds stress model (RSM), conjugate heat transfer is conducted through computational fluid dynamics (CFD) simulation, and is verified by real PMSM test bench experiments.

Porous wall permeability is one of the most critical factors for the estimation of backpressure, a key performance indicator in automotive particulate filters. Current experimental and analytical filter models could be calibrated to predict the permeability of a specific filter. However, they fail to provide a reliable estimation for the dependence of the permeability on key parameters such as wall porosity and pore size. This study presents a novel methodology for experimentally determining the permeability of filter walls. The results from four substrates with different porosities and pore sizes are compared with several popular permeability estimation methods (experimental and analytical), and their validity for this application is assessed. It is shown that none of the assessed methods predict all permeability trends for all substrates, for cold or hot flow, indicating that other wall properties besides porosity and pore size are important.

The measurement protocol of solid particle number with the lower detection limit (D50) at 10 nm (SPN10) is planned to be implemented in European emission regulations by means of laboratory-grade measurement systems. Furthermore, SPN10 measurement as the real driving emissions (RDE) regulations is under development by defining appropriate technical specifications for the portable emissions measurement system (PEMS). It is under discussion to implement SPN10 limits as one of additional pollutants to the new European emissions regulations, so-called “Euro 7”. As the Consortium for ultra LOw Vehicle Emissions (CLOVE) has proposed, RDE testing by means of PEMS will be the primary means of emissions determination for certification purposes. Measurement equivalency between laboratory-grade emissions measurement systems and PEMS is still important due to the necessity of validation in laboratories before on-road testing by comparing determined emissions by both.

To reduce the energy consumption level of electric vehicles, the working range of the regenerative braking system will gradually expand to the high state of charge of the battery. The time delay in the control signal transmission path of the high state of charge regenerative braking control process will affect the regenerative braking. At the same time, regenerative braking under a high state of charge puts forward higher requirements for the control accuracy of regenerative current. In the research of this paper, the motor model, battery model, and vehicle dynamics model are firstly established by using MATLAB/Simulink, and the dynamic relationship between regenerative current and regenerative braking torque is analyzed at the same time. Considering the system time delay, this paper proposes a high-charge regenerative braking control strategy (SPPC) that combines Smith prediction and prescribed performance control.

Gasoline-related factors that affect low-speed pre-ignition (LSPI) include the distillation properties of gasoline, manganese (Mn), ethanol, diesel fuel, detergent for aftermarket, and iron (Fe). The combined effect of Mn with ethanol or high calcium engine oil (high-Ca oil) has not been sufficiently clarified. Therefore, appropriate countermeasures for LSPI have not yet been implemented. To clarify the effect of the gasoline properties and additives on LSPI, engine tests were conducted using gasoline with different “PM Index” values, an indicator of distillation properties, different concentrations of Mn, ethanol, diesel fuel, detergent, Fe, and high-Ca oil. The results showed that the LSPI frequency tended to increase with the PM Index, Mn up to 60 ppm, diesel fuel up to 2 vol.%, and detergent up to three times the standard amount.

TWC exposure to extreme temperature could result in irreversible damage or thermal failure. Thus, a strategy embedded in the engine control unit (ECU) called catalyst overheating protection (COP) will be activated to prevent TWC overheating. When COP is activated, the command air-fuel ratio will be enriched to cool the catalyst monolith down. Fuel enrichment has been proven a main prerequisite for ammonia formation in hot TWCs as a by-product of NOx reduction. Hence, COP events could theoretically be a source of post-catalyst ammonia from petrol vehicles, but this theory is yet to be confirmed in published literature. This paper validated this hypothesis using a self-programmed chassis-level test. The speed of the test vehicle was set to constant while the TWC temperature was raised stepwise until a COP event was activated.

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.

Permanent magnet (PM) electrical machine has far-reaching impacts in aviation electrification due to the continuous development in high power density and high efficiency electrical drives. The primary barrier to acceptance of permanent magnet machines for safety-critical starter-generator systems is its low fault-tolerance capability and low reliability (for the conventional designs). This article investigates a modular triple three-phase PM starter-generator comprehensively, including the tradeoff of fault-tolerant topology, optimization design process, analysis of electromagnetic (highlight the post-fault analysis) and thermal behavior, respectively. The triple three-phase segmented topology proposed meet the fault-tolerant requirement along with complete electrical, magnetic, and thermal isolation. There would be cost penalty on the proposed topology, but it gets offset by the ease of manufacturing of coils and their insertion.

Electric machines in aerospace applications are subjected to extremely high operating temperatures. This increases coercivity or decreases saturation flux density of the electrical steel resulting in increased core loss. The need for high power density and increased operating speed favours the use of thin gauge Silicon Steel (Si-Fe) and Cobalt Iron (Co-Fe) laminations for aerospace applications. Therefore, the variation in iron loss is studied for three grades of Si-Fe laminations by subjecting them to controlled ageing in laboratory. The analysis is also provided over a range of flux density and frequency to generalize the phenomenon over the operating domain. The results of ageing the laminations are in turn used to predict the degradation in performance of a 1.15 MW, 16-pole 48-slot propulsion machine for aerospace application. The degradation is estimated in terms of variation in iron loss.

Evolving of aircraft design towards further electrification requires safe and fault-free operation of all the components. More electric aircraft are increasingly utilizing electro-mechanical actuators (EMA). EMAs are prone to jamming and subsequent failure due to large forces on the shaft. Large forces are generated due to the high reflected inertia of the electric machine rotor. To limit the force acting on the shaft, a torque limiting device is connected to the power train which can separate the rotating mass of the electric machine from the power train. In this paper, a concept of integration of torque limiter and the electric machine rotor is presented to reduce overall volume and mass. It is connected closely with the rotor, within the motor envelope. A commercially available torque limiter and an electric machine designed for actuator application are used to demonstrate the concept. While essential for safety, the torque limiter adds to the mass and size of the overall EMA.

Electric machines offering a high power density are required for aerospace applications. Soft magnetic material with a high saturation flux density is one of the key component which is required to realize these power density targets. The need for a high saturation flux density necessitates the use of cobalt iron lamination over the conventional silicon steel. However, cobalt iron is very expensive i.e. order of 10 in comparison to silicon steel. Stator segmentation is identified as an appropriate method to reduce the wastage and cost associated with lamination. Consequently, in this paper, stator segmentation is analyzed on a 1.35 MW, 16-pole 48-slot propulsion machine. The impact of manufacturing is accounted by controlling the resulting airgap between the segmented structures. Electromagnetic performance for various segmented topologies are compared in terms of torque, torque ripple, and iron loss.

The increased severity and prevalence of insoluble deposits formed on fuel injectors in gasoline direct injection (GDI) engines precipitates negative environmental, economic and healthcare impacts. A necessary step in mitigating deposits is to unravel the molecular compositions of these complex layered materials. But very little molecular data has been acquired. Mass spectrometry shows promise but most techniques require the use of solvents, making them unsuited for analyzing insoluble deposits. Here, we apply the high mass-resolving power and in-situ analysis capabilities of 3D OrbitrapTM secondary ion mass spectrometry (3D OrbiSIMS) to characterize deposits formed on the external tip and internal needle from a GDI injector. This is the first application of the technique to study internal GDI deposits. Polycyclic aromatic hydrocarbons (PAHs) are present up to higher maximum masses in the external deposit.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

The principal objective of the present work is to investigate the fundamental characteristics of a commercially available outwardly opening twin-fluid injector, which utilizes air-assisted atomization principle to attain pulse-type injection of fuel-air mixture. The electromagnetic characteristics of this injector were simulated and the effects of dominating parameters on the electromagnetic force to drive injector were ascertained. On that basis, this paper elaborates on the fundamental characteristics of air-assisted spray using gasoline and kerosene with the employment of two types of optical testing techniques. The spray morphological evolution under varied fuel injection durations and ambient pressures were captured with high-speed shadowgraph thus the corresponding external macroscopic characteristics were obtained and further compared. Spray droplet velocity and diameter at fixed monitoring location were measured by using PDPA (Phase Doppler Particle Analyzer).

Gear rattle noise is one of the important characteristics of manual and dual-clutch transmission，it is generated by the impact of unloaded meshing gear pairs in the transmission due to engine torsional vibration. Based on a front-drive manual transmission and a five dynos drivetrain NVH test bench with high-speed sine wave generator function, this paper designs an experimental program suitable for transmission rattle noise. By driving dynamometer to simulate the torque fluctuation of real engine, the main research is to study the characteristics of the transmission rattle noise under different excitation amplitudes and different excitation frequencies, and the sensitivity of rattle noise under different gears, different oil temperatures, different excitation amplitudes and excitation frequencies is analyzed. Finally, the transmission maps of rattle noise in different gears can be obtained.

Engine research has increasingly focused on emission of sub 23 nm particulates in recent years. Likewise, current legislative efforts are being made for particulate number (PN) emission limits to include this previously omitted size range. In Europe, PN measurement equipment and procedures for regulatory purposes are defined by the particle measurement programme (PMP). Latest regulation drafts for sub 23 nm measurements specify counting efficiencies with a 65% cut-off size at 10 nm (d65) and a minimum of 90% above 15 nm (d90). Even though alternative instruments, such as differential mobility spectrometers (DMS), are widely used in laboratory environments, the interpretation of their sub 23 nm measurements has not yet been widely discussed. For this study, particulate emissions of a 1.0L gasoline direct injection (GDI) engine have been measured with a DMS system for low to medium speeds with two load steps.

Due to the poor ability of millimeter wave radar in recognizing distant static objects, target loss and incomplete information will occur when it recognizes the static target in front, thus increasing the false alarm rate and missing alarm rate of the radar-dependent driving assistant system, which will reduce the driving safety and the acceptability of the assistant system. Aiming at the radar's poor ability to recognize static targets, this paper uses a model based on machine learning algorithm to recognize and track targets. The radar signals are collected and processed in different conditions, and the results show that the radar has a poor recognition effect when the distance is more than 100 meters and the speed is more than 19m/s.