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Modeling combustion of transportation fuels remains a difficult task due to the extremely large number of species constituting commercial gasoline and diesel. However, for this purpose, multi-component surrogate fuel models with a reduced number of key species and dedicated reaction subsets can be used to reproduce the physical and chemical traits of diesel and gasoline, also allowing to perform CFD calculations. Recently, a detailed surrogate fuel kinetic model, named C3 mechanism, was developed by merging high-fidelity sub-mechanisms from different research groups, i.e. C0-C4 chemistry (NUI Galway), linear C6-C7 and iso-octane chemistry (Lawrence Livermore National Laboratory), and monocyclic aromatic hydrocarbons (MAHs) and polycyclic aromatic hydrocarbons (PAHs) (ITV-RWTH Aachen and CRECK modelling Lab-Politecnico di Milano).

Nowadays quasi-3D approaches are included in many commercial and research 1D numerical codes, in order to increase their simulation accuracy in presence of complex shape 3D volumes, e.g. plenums and silencers. In particular, these are regarded as valuable approaches for application during the design phase of an engine, for their capability of predicting non-planar waves motion and, on the other hand, for their low requirements in terms of computational runtime. However, the generation of a high-quality quasi-3D computational grid is not always straightforward, especially in case of complex elements, and can be a time-consuming operation, making the quasi-3D tool a less attractive option. In this work, a quasi-3D module has been implemented on the basis of the open-source CFD code OpenFOAM and coupled with the 1D code GASDYN.

The present paper investigates possible enhancement of ESP performance associated with the use of smart tires. In particular a novel control logic based on a direct feedback on the longitudinal forces developed by the four tires is considered. The control logic was developed using a simulation tool including a 14 dofs vehicle model and a smart tires emulator. Performance of the control strategy was evaluated in a series of handling maneuvers. The same maneuvers were performed on a HiL test bench interfacing the same vehicle model with a production ESP ECU. Results of the two logics were analyzed and compared.

In the paper some methods are presented, with the corresponding practical examples, related to MonteCarlo (MC) techniques for thermal model/test correlation purposes. The MonteCarlo techniques applied to model correlation are intended to be used as an alternative to empirical ‘manual’ correlation techniques, gradients methods, matrix methods based on least square fit minimization. First of all, Design Of Experiments (DoE) tools are used to determine the model response to uncertain parameters and the confidence level of such a response. A sensitivity map is built, allowing the design of the test to maximize the response of the system to the uncertain parameters. Techniques derived from the extreme statistics are used to extrapolate data beyond test limits, with a sufficient confidence in the queue behaviour.

We present a framework for the robust optimization of the heat flux distribution for an anti-ice electro-thermal ice protection system (AI-ETIPS) and iced airfoil performance analysis under uncertain conditions. The considered uncertainty regards a lack of knowledge concerning the characteristics of the cloud i.e. the liquid water content and the median volume diameter of water droplets, and the accuracy of measuring devices i.e., the static temperature probe, uncertain parameters are modeled as uniform random variables. A forward uncertainty propagation analysis is carried out using a Monte Carlo approach. The optimization framework relies on a gradient-free algorithm (Mesh Adaptive Direct Search) and three different problem formulations are considered in this work. Two bi-objective deterministic optimizations aim to minimize power consumption and either minimize ice formations or the iced airfoil drag coefficient.

In the ANNIE project - Applications of Neural Networks to Integrated Ergonomics - BE96-3433, a tool for integrating ergonomics into the design process is developed. This paper presents some features in the current ANNIE as applied to the design of car interiors. A variant of the ERGOMan mannequin with vision is controlled by a hybrid system for neuro-fuzzy simulation. It is trained by using an Elite system for registration of movements. An example of a trajectory generated by the system is shown. A fuzzy model is used for comfort evaluation. An experiment was performed to test its feasibility and it showed very promising results.

A multi-objective optimal design of a brushless DC electric motor for a brake system application is presented. Fifteen design variables are considered for the definition of the stator and rotor geometry, pole pieces and permanent magnets included. Target performance indices (peak torque, efficiency, rotor mass and inertia) are defined together with design constraints that refer to components stress levels and temperature thresholds, not to be surpassed after heavy duty cycles. The mathematical models used for optimization refer to electromagnetic field and related currents computation, to thermo-fluid dynamic simulation, to local stress and vibration assessment. An Artificial Neural Network model, trained with an iterative procedure, is employed for global approximation purposes. This allows to reduce the number of simulation runs needed to find the optimal configurations. Some of the Pareto-optimal solutions resulting from the optimal design process are analysed.

Brake calipers for high-end cars are typically realized using Aluminum alloys, with Silicon as the most common alloying element. Despite the excellent castability and machinability of Aluminum-Silicon alloys (AlSix), anodization is often required in order to increase its corrosion resistance. This is particularly true in Chlorides-rich environments where Aluminum can easily corrode. Even if anodization process is known for almost 100 years, anodization of AlSix -based materials is particularly challenging due to the presence of eutectic Silicon precipitates. These show a poor electric conductivity and a slow oxidation kinetics, leading to inhomogeneous anodic layers. Continuous research and process optimization are required in order to develop anodic layers with enhanced morphological and electrochemical properties, targeting a prolonged resistance of brake calipers under endurance corrosive tests (e.g. >1000 hours Neutral Salt Spray (NSS) tests).

In the rapidly changing scenario of the energy transition, data-driven tools for kinetic mechanism development and testing can greatly support the evaluation of the combustion properties of new potential e-fuels. Despite the effectiveness of kinetic mechanism generation and optimization procedures and the increased availability of experimental data, integrated methodologies combining data analysis, kinetic simulations, chemical lumping, and kinetic mechanism optimization are still lacking. This paper presents an integrated workflow that combines recently developed automated tools for kinetic mechanism development and testing, from data collection to kinetic model reduction and optimization. The proposed methodology is applied to build a consistent, efficient, and well-performing kinetic mechanism for the combustion of oxymethylene ethers (OMEs), which are promising synthetic e-fuels for transportation.