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The European Commission is going to publish the new Euro7 standard shortly, with the target of reducing the impact on pollutant emissions due to transportation systems. Besides forcing internal combustion engines to operate cleaner in a wider range of operating conditions, the incoming regulation will point out the role of On-Board Monitoring (OBM) as a key enabler to ensure limited emissions over the whole vehicle lifetime, necessarily taking into account the natural aging of involved systems and possible electronic/mechanical faults and malfunctions. In this scenario, this work aims to study the potential of data-driven approaches in detecting emission-relevant engine faults, supporting standard On-Board Diagnostics (OBD) in pinpointing faulty components, which is part of the main challenges introduced by Euro7 OBM requirements.

During the development of an Internal Combustion Engine-based powertrain, traditional procedures for control strategies calibration and validation produce huge amount of data, that can be used to develop innovative data-driven applications, such as emission virtual sensing. One of the main criticalities is related to the data quality, that cannot be easily assessed for such a big amount of data. This work focuses on an emission modeling activity, using an enhanced Light Gradient Boosting Regressor and a dedicated data pre-processing pipeline to improve data quality. First thing, a software tool is developed to access a database containing data coming from emissions tests. The tool performs a data cleaning procedure to exclude corrupted data or invalid parts of the test. Moreover, it automatically tunes model hyperparameters, it chooses the best set of features, and it validates the procedure by comparing the estimation and the experimental measurement.

The challenge of industrial carbon footprint reduction is led by the engine manufacturers that are developing new technologies and fuels to lower CO2 emissions. Although the deployment of relevant investments for the development of battery electric vehicles, diesel, and gasoline cars are still widely used, especially for their longer operating range, faster refueling, and lower cost. For this reason, more efficient traditional internal combustion engines can guide the transition towards new propulsion systems. In this document, the innovative piston damage and exhaust gas temperature models previously developed by the authors are reversed and coupled to manage the combustion process, increasing the overall energy conversion efficiency. The instantaneous piston erosion and the exhaust gas temperature at the turbine inlet are evaluated according to the models’ estimation which manages both the spark advance, and the target lambda.

The increasing environmental concern is leading to the need for innovation in the field of internal combustion engines, in order to reduce the carbon footprint. In this context, hydrogen is a possible mid-term solution to be used both in conventional-like internal combustion engines and in fuel cells (for hybridization purposes), thus, hydrogen combustion characteristics must be considered. In particular, the flame of a hydrogen combustion is less subjected to the quenching effect caused by the engine walls in the combustion chamber. Thus, the significant heating up of the thin lubricant layer upon the cylinder liner may lead to its evaporation, possibly and negatively affecting the combustion process, soot production. The authors propose an analysis which aims to address the behavior of different typical engine oils, (SAE0W30, SAE5W30, SAE5W40) under engine thermo-physical conditions considering a large hydrogen-fuelled engine.

Nowadays, in the perspective of a full electric automotive scenario, internal combustion engines can still play a central role in the fulfilment of different needs if the efficiency will be improved, and the tailpipe emission will be further limited. Gasoline Compression Ignition engines can offer a favourable balance between NOx, particulate, operating range. Stable operations are ensured by ultra-high gasoline injection pressure and tailored injection patterns in order to design the most proper local fuel distribution. In this context, engine simulations by means of CFD codes can provide insights on the design of the injection parameters, and emphasis must be placed on the capture of spray-wall impingement behaviour under those non-conventional conditions. This paper aims to analyse the spray-wall impingement behaviour of ultra-high gasoline spray using a combined experimental-CFD approach.

The need to reduce carbon dioxide emissions from motor vehicles pushes the European Union towards drastic choices on future mobility. Despite this, the engines of the “future” have not yet been defined: the choice of engine type will undoubtedly depend on the type of application (journey length, availability of recharging/refueling facilities), practical availability of alternative fuels, and electricity to recharge the batteries. The electrification of vehicles (passenger and transportation cars) may be unsuitable for several aspects: the gravimetric energy density could be too low if the vehicle has to be lightweight, must achieve a high degree of autonomy, or needs a very short refueling time.

Current modelling capability for engine icing accretion prediction is still limited for App. C. To further validate icing codes in complex engine geometries, it is necessary to perform additional experimental work in relevant geometrical and environmental conditions. Within the frame of ICE GENESIS [1], an experiment has been setup to replicate the condition at the inlet of an engine first stage compressor. This paper describes the choices for the design of the engine compressor model, the setup within the icing wind tunnel and the methodology employed to obtain the results. Additionally, more effort has been focused on obtaining accurate ice shapes using a 3D scanning system. Results of 3D scans are given.

The simulation of natural-like snow conditions in a controlled environment such as an Icing Wind Tunnel (IWT) is a key component for safe, efficient and cost-effective design and certification of future aircraft and rotorcraft. Current capabilities do not sufficiently match the properties of natural snow, especially in terms of size and morphology. Within the Horizon 2020 project ICE GENESIS, a new technology has been developed aiming to better recreate natural snowflakes. The focus of the newly developed system was the generation of falling snow in a temperature range of +1°C to -4°C. Ground measurements and flight test campaigns have been performed to better characterize these conditions and provide requirements for wind tunnel facilities. The calibration results of the new snow generation system as well as snow accretion data on a NACA0012 test article with a chord length of 0.377 m are presented.

In the current literature, the research studies on the trajectory tracking control and stability control strategy for autonomous vehicles in limited condition mostly focus on the yaw plane control, but few of the studies have considered the combined control performance of trajectory tracking, yaw and roll stability, and the roll stability is critical under the extreme cornering condition for autonomous vehicles. Aiming at the above shortages, this study designs the model predictive control (MPC) strategy for the autonomous vehicles under the limited handling condition, which integrates the front and rear wheel active steering control, four-wheel independent drive and braking control and active suspension control to comprehensively improve the trajectory tracking accuracy, yaw plane stability and roll plane stability of the vehicle under the extreme condition.

This study aims to provide an understanding of self-similarity in the turbulent wake generated by a Fastback DrivAer automotive model and assess the impact of upstream disturbances on the wake. The disturbances are generated using a circular cylinder placed five cylinder diameters upstream. Multiple ‘cylinder-model’ positions were tested by offsetting the lateral positioning of the cylinder with respect to the centreline of the model. Data was obtained at cross-planes in the wake going from 25% to 100% car length. Wind tunnel data has been obtained using a total pressure probe rake and a four-hole cobra probe. Data has also been obtained using RANS based simulations with k – ε realisable turbulence model. Mean axial-component velocity profiles were analysed with momentum thickness (θ) and vorticity thickness (δω) used as the scaling parameters. It was seen that self-similarity marginally exists in the wake depending on the upstream conditions and the scaling parameter.

The rise of autonomous technologies may reflect on new vehicle traffic characteristics, likely reducing vehicle-to-vehicle proximity and emerging platooning formations. Energy consumption, stability, and surface contamination are relevant factors that are sensitive to aerodynamic interference while platooning. From the experimental perspective, most wind tunnels were originally designed to host isolated models, and these constraints often limit the investigation of multiple full-body vehicle formations (e.g. test section length, moving ground dimensions, standard testing points). This paper introduces the ‘MultiWake’ model - a parametric bluff-body device based on a morphing concept, which can emulate the aerodynamic wake characteristics of different vehicle classes.

The continued electrification of aircraft is required such that ambitious decarbonisation targets can be met. A significant challenge presented with this trend is the increased reliance on electrical systems to perform flight-critical operations in a manner that has not been seen in previous generations of aircraft. The power electronic converter is a key enabling technology in aircraft electrification. Its prevalence is such that the failure rate of flight critical-loads is closely linked with that of the associated power electronic converters. As such, there is a clear need to better understand the impact of improvements in both the reliability and failure estimation of novel power electronic converters at a systems level in future aerospace applications. Accordingly, this paper presents key highlights from literature on power converter research, summarising advances in reliability-enhancing features and more accurate Physics-of-Failure modelling methods.

Due to the ever increasingly stringent emission regulations for passenger vehicles, the efficiency and performance increase of Spark Ignition (SI) engines have been under the focus of the engine manufacturers. The quest for efficiency and performance increase has led to the development of increasingly complex powertrains and control strategies. The development process requires novel methods that feature a smooth transition between the real and the virtual prototypes. Furthermore, to reduce the development time and cost, developing an engine simulator with a low computational effort and good accuracy, which predicts the engine behavior on the entire operating range, plays a crucial role. This work proposes an Artificial Intelligence-based engine simulator for a Spark Ignition engine. The simulator relies on Neural Networks for the calculation of the main combustion metrics. In the first part of this paper, the data acquired at the engine test cell are analyzed.

In the past years, stringent emission regulations for Internal Combustion (IC) engines produced a large amount of research aimed at the development of innovative combustion methodologies suitable to simultaneously reduce fuel consumption and engine-out emissions. Previous research demonstrates that the goal can be obtained through the so-called Low Temperature Combustions (LTC), which combine the benefits of compression-ignited engines, such as high compression ratio and unthrottled lean operation, with a properly premixed air-fuel mixture, usually obtained injecting gasoline-like fuels with high volatility and longer ignition delay. Gasoline Partially Premixed Combustion (PPC) is a promising LTC technique, mainly characterized by the high-pressure direct-injection of gasoline and the spontaneous ignition of the premixed air-fuel mixture through compression, which showed a good potential for the simultaneous reduction of fuel consumption and emissions in CI engines.

Dents on the aircraft skin are frequent and may easily go undetected during airworthiness checks, as their inspection process is tedious and extremely subject to human factors and environmental conditions. Nowadays, 3D scanning technologies are being proposed for more reliable, human-independent measurements, yet the process of inspection and reporting remains laborious and time consuming because data acquisition and validation are still carried out by the engineer. For full automation of dent inspection, the acquired point cloud data must be analysed via a reliable segmentation algorithm, releasing humans from the search and evaluation of damage. This paper reports on two developments towards automated dent inspection. The first is a method to generate a synthetic dataset of dented surfaces to train a fully convolutional neural network. The training of machine learning algorithms needs a substantial volume of dent data, which is not readily available.

This paper presents the design concept behind a novel remote visual inspection robotic system for fighter jet aircraft wing fuel tank inspection. This work is part of a larger research project which focuses on design, simulation, physical prototyping and experimental validation of a robotic system. Whereas this paper specifically focuses on the development concept of locomotion design choice for the robot. Therefore without an effective mobility method the robot will not be able to fulfill its purpose to access the hazardous confined spaces of the fuel tank. Aircraft wing fuel tank inspection is a challenging area of maintenance which requires a considerable amount of preparation and involvement of several tasks in order to conduct effective Visual and Non Destructive Inspection. The environment of an aircraft wing fuel tank poses several challenges due to both physical and atmospheric constraints which can be detrimental to human personal.

Internal combustion engines are the primary transportation mover for today society and they will likely continue to be for decades to come. Hybridization is the most common solution to reduce the petrol-fuels consumption and to respect the new raw emission limits. The gasoline engines designed for running together with an electric motor need to have a very high thermal efficiency because they must work at high loads, where engine thermal efficiency is close to the maximum one. Therefore, the technical solutions bringing to thermal efficiency enhancement were adopted on HVs (Hybrid Vehicles) prior to conventional vehicles. In these days, these solutions are going to be adopted on conventional vehicles too. The purpose of this work was to trace development guidelines useful for engine designers, based on the target power and focused on the maximization of the engine thermal efficiency, following the engine rightsizing concept.

This work focuses on the development and validation of a data-driven model capable of predicting the maximum in-cylinder pressure during the operation of an internal combustion engine, with the least possible computational effort. The model is based on two parameters, one that represents engine load and another one the combustion phase. Experimental data from four different gasoline engines, two turbocharged Gasoline Direct Injection Spark Ignition, a Naturally Aspirated SI and a Gasoline Compression Ignition engine, was used to calibrate and validate the model. Some of these engines were equipped with technologies such as Low-Pressure Exhaust Gas Recirculation and Water Injection or a compression ignition type of combustion in the case of the GCI engine. A vast amount of engine points were explored in order to cover as much as possible of the operating range when considering automotive applications and thus confirming the broad validity of the model.