1D-3D coupled approach for the evaluation of the in-cylinder conditions for Gasoline Compression Ignition Combustion

Nowadays, progressive improvements of engine performance must be performed to reduce fuel consumption, which directly affects the amount of CO2 released in the atmosphere. For this purpose, considering modern technologies in the automotive scenario, Gasoline Compression Ignition (GCI) combustion might represent one promising solution, since it experiences high thermal efficiency of Compression Ignited (CI) engines and pollutant emission mitigation. This paper shows the first step of a project aimed at reproducing the combustion behavior of a Diesel engine running with GCI combustion by means of CFD simulations. In particular, this work presents a methodology used to reconstruct the mixing process inside the cylinder before the combustion event, since those engines are dramatically sensitive to the global and local mixture quality. Firstly, a reverse-engineering procedure aimed at generating the CAD model of the engine was performed. Afterwards, the discharge coefficients of the intake and exhaust valves through specifically designed 3D CFD simulations were determined, which was necessary due to the customized intake/exhaust line. Eventually, to reasonably reconstruct the in-cylinder state, the Rate of Heat Release (RoHR) curve, calculated from the analysis of the in-cylinder pressure signal running the engine in GCI mode, was imposed in GT-Power by means of a combination of Wiebe functions with the purpose of generating representative trends of pressure, temperature, and mass flow to properly define the domains of the CFD model

ATLAS pixel detector electronics and sensors

The silicon pixel tracking system for the ATLAS experiment at the Large Hadron Collider is described and the performance requirements are summarized. Detailed descriptions of the pixel detector electronics and the silicon sensors are given. The design, fabrication, assembly and performance of the pixel detector modules are presented. Data obtained from test beams as well as studies using cosmic rays are also discussed

Vertically-Advised Federated Learning for Multi-Strategic Stock Predictions through Stochastic Attention-based LSTM

In recent years, stock price forecasting has become a challenging task commonly used to evaluate the performance of various machine learning solutions. This work explores a Federated Learning (FL) framework within a competitive collaboration scenario with the aim of training a centralised model advised by non-recoverable decentralised strategies so that no exchange of private data is required. The proposed Vertically-Advised Federated Learning (VAFL) framework combines elements from both horizontal and vertical FL, as each client trains two independent models. Furthermore, a novel forecasting architecture, based on a stochastic variant of an Attention-based Long Short Term Memory (LSTM) network, is proposed and validated on a simulated scenario based on real data from the stock market

Experimental characterization of the injected mass variation in a high-pressure GDI injector operating with a multiple injection strategy

Increasingly stringent limits to pollutants released by Internal Combustion Engines pushed the automotive research to develop technologies to reduce fuel consumption and emissions. Higher injection pressures are beneficial to accelerate the atomization phase, reducing the particulate matter and unburned hydrocarbon emissions. However, the spray protrusion inside the combustion chamber is enhanced and, consequently, the generation of a thick wall film, which tends to increase the latter emissions. Thus, multiple-injection strategies might be beneficial for both the atomization rate and the spray penetration, owing to a stratified charge inside the chamber. This paper investigates the effect of the adoption of multiple-injection strategies on the behaviour of a GDI injector operating in high injection pressure conditions. The resulting injected mass is influenced by electrical phenomena on the excitation circuit, which mainly depend on the relative time between the end of the first injection and the start of the following. Hence, the total amount of fuel injected with the multiple-injection pattern will differ from its nominal value. In this work, a specific experimental layout was developed to characterize the behaviour of the injector in different operating conditions and quantify the deviation between actual and nominal injected mass. The impact of the magnetized coils on the overall injected mass has been captured referring to the modification of the shape of the driving current profile with respect to the nominal one. Then, a correlation which considers the electric charge variation on the coils has been implemented to model the phenomenon and, consequently, to counterbalance the electro-magnetic effect on the injected mass. The resulting strategy successfully allowed to reduce the difference between the actual and target fuel mass from up to 30% to almost 5%, owing to its implementation on the injection control system to automatically correct the injection commands and compensate the fuel mass deviations