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High-frequency pressure probes were used to map the airflow around a full-scale truck during on-road testing and around a model-scale truck during wind tunnel testing. Several configurations were tested during each type of testing. Results are presented for on-road ‘pass-by’ tests and detail velocity and coefficient of pressure variation alongside the truck at different heights. The wind tunnel data are results of flow mapping about a 10% scale model and show the velocity and coefficient of pressure distribution under and around the model truck for different configurations.

Over the last decade, many methods have been proposed for estimating the in-cylinder combustion pressure or the torque from instantaneous crankshaft speed measurements. However, such approaches are typically computationally expensive. In this paper, an entirely different approach is presented to allow the real-time estimation of the in-cylinder pressures based on crankshaft speed measurements. The technical implementation of the method will be presented, as well as extensive results obtained for a V-6 S.I. engine while varying spark timing, engine speed, engine load and EGR. The method allows to estimate the in-cylinder pressure with an average estimation error of the order of 1 to 2% of the peak pressure. It is very general in its formulation, is statistically robust in the presence of noise, and computationally inexpensive.

The air entrainment process of a compressed natural gas transient fuel jet was investigated in a constant-volume chamber using Schlieren and particle image velocimetry (PIV) techniques. A new method of calculating air entrainment around a gaseous fuel jet is proposed using Schlieren and PIV imaging techniques. This method offers an alternative to calculation of an alternative to calculation of entrainment using LIF technique in gaseous fuel jets. Several Jet-ambient pressure ratios were tested. In each test, nitrogen was used to fill the chamber as an air surrogate before the jet of natural gas was injected. Schlieren high speed videography and PIV experiments were performed at the same conditions. Schlieren mask images were used to accurately identify the jet boundary which was then superimposed onto a PIV image. Vectors adjacent to the Schlieren mask in the PIV image were used to calculate the spatial distribution of the air entrainment at the jet boundary.

The paper presents experimental analysis of the airflow around the differential center housing of a rear drive full-scale passenger car. The study included investigation of local airflow total and static pressure, as well as surface flow visualization. Estimation of the local airflow velocity is based on the measured pressure coefficients. The experiments were carried out at different test facilities: in a climatic wind tunnel, in a full-scale wind tunnel and on-road. Influence of side wind was modeled by the yawing of the car in the full-scale wind tunnel. The results show the asymmetrical structure of the flow in both, vertical and horizontal planes. Estimated longitudinal relative local velocity decreases from maximum Vr ≈ 0.4 at the lower surface of the center housing, to about Vr ≈ 0 above the upper surface. Side wind increases airflow velocity around the center housing within the investigated yaw range ± 20°

This paper describes the design and development of a virtual environment conceived to support flight operations of an Unmanned Air Vehicle (UAV) used for wind mapping in the proximity of existing or planned wind farms. The virtual environment can be used in pre-flight briefings aiming to define a trajectory from a list of waypoints, to change and eventually re-plan the mission in case of intersection with no fly zones, to simulate the mission, and to preview images/videos taken from the UAV on-board cameras. During flight, the tool can be used to compute the wind speed along the trajectory by analyzing the data streaming from the UAV. The integration of Augmented Reality (AR) techniques in the flight environment provides assistance in remotely piloted landings, and allows visualizing flight and environmental information that are critical to the mission.

Highway safety remains a significant concern, especially in mixed traffic scenarios involving heavy-duty vehicles (HDV) and smaller passenger cars. The vulnerability of HDVs following closely behind smaller cars is evident in incidents involving the lead vehicle, potentially leading to catastrophic rear-end collisions. This paper explores how automatic speed enforcement systems, using speed cameras, can mitigate risks for HDVs in such critical situations. While historical crash data consistently demonstrates the reduction of accidents near speed cameras, this paper goes beyond the conventional notion of crash occurrence reduction. Instead, it investigates the profound impact of driver behavior changes within desired travel speed distribution, especially around speed cameras, and their contribution to the safety of trailing vehicles, with a specific focus on heavy-duty trucks in accident-prone scenarios.

Metallic open-cell foams have proven to be valuable for many engineering applications. Their success is mainly related to mechanical strength, low density, high specific surface, good thermal exchange, low flow resistance and sound absorption properties. The present work aims to investigate three principal aspects of real foams: the geometrical characterization, the flow regime characterization, the effects of the pore size and the porosity on the pressure drop. The first aspect is very important, since the geometrical properties depend on other parameters, such as porosity, cell/pore size and specific surface. A statistical evaluation of the cell size of a foam sample is necessary to define both its geometrical characteristics and the flow pattern at a given input velocity. To this purpose, a procedure which statistically computes the number of cells and pores with a given size has been implemented in order to obtain the diameter distribution.

The maturity reached in the development of Unmanned Air Vehicles (UAVs) systems is making them more and more attractive for a vast number of civil missions. Clearly, the introduction of UAVs in the civil airspace requiring practical and effective regulation is one of the most critical issues being currently discussed. As several civil air authorities report in their regulations “Sense and Avoid” or “Detect and Avoid” capabilities are critical to the successful integration of UAV into the civil airspace. One possible approach to achieve this capability, specifically for operations beyond the Line-of-Sight, would be to equip air vehicles with a vision-based system using cameras to monitor the surrounding air space and to classify other air vehicles flying in close proximity. This paper presents an image-based application for the supervised classification of air vehicles.

Flow fields and fuel distribution play a critical role in developing the combustion process inside the cylinders of piston engines. This has prompted the development of measurement and diagnostic capabilities including laser techniques like Doppler Global Velocimetry (DGV). The paper provides an overview of the basics of DGV and the type of results that can be obtained. It also includes a short comparison to Particle Image Velocimetry (PIV) which is a popular alternative method. Furthermore, it is shown that DGV can be used simultaneously in combination with droplet distribution visualization inside cylinders based on Mie scattering.

Roundabout is a unique approach of managing traffic at intersections because it relies on driver’s instincts of safety. Roundabouts are considered safer than other ways of intersection traffic management due to low speed limits, smoother merging, and reduced fatal accidents. Despite their benefits and increasing usage, there is lack of clear understanding of the roundabouts, particularly due to scarcity of data and simulation models and the complexity of the structure. Real-time and offline traffic data recorded at a roundabout provides a basis for 1) identification of the safety issues, 2) understanding unexpected and risky driver behavior, 3) proposing potential mobility solutions, and 4) developing simulation models. The processed data may be used in controlling metered roundabouts, communicating with connected and automated vehicles (CAVs) etc. In this paper an approach to obtain useful traffic information from video feed data at a roundabout is presented.

The focus of internal combustion (IC) engine research is the improvement of fuel economy and the reduction of the tailpipe emissions of CO2 and other regulated pollutants. Promising solutions to this challenge include the use of both direct-injection (DI) and alternative fuels such as liquefied petroleum gas (LPG). This study uses Mie-scattering and schlieren imaging to resolve the liquid and vapor phases of propane and iso-octane, which serve as surrogates for LPG and gasoline respectively. These fuels are imaged in a constant volume chamber at conditions that are relevant to both naturally aspirated and boosted, gasoline direct injection (GDI) engines. It is observed that propane and iso-octane have different spray behaviors across these conditions. Iso-octane is subject to conventional spray breakup and evaporation in nearly all cases, while propane is heavily flash-boiling throughout the GDI operating map.

This study is focused on exploring the possibilities of using camera and route planner images for autonomous driving in an end-to-mid learning fashion. The overall idea is to clone the humans’ driving behavior, in particular, their use of vision for ‘driving’ and map for ‘navigating’. The notion is that we humans use our vision to ‘drive’ and sometimes, we also use a map such as Google/Apple maps to find direction in order to ‘navigate’. We replicated this notion by using end-to-mid imitation learning. In particular, we imitated human driving behavior by using camera and route planner images for predicting the desired waypoints and by using a dedicated control to follow those predicted waypoints. Besides, this work also places emphasis on using minimal and cheaper sensors such as camera and basic map for autonomous driving rather than expensive sensors such Lidar or HD Maps as we humans do not use such sophisticated sensors for driving.

Shock absorbers and damper systems are important parts of automobiles and motorcycles because they have effects on safety, ride comfort, and handling. In particular, for vehicle safety, shock absorber system plays a fundamental role in maintaining the contact between tire and road. Generally, to assure the best trade-off between safety and ride comfort, a fine experimental tuning on all shock absorber components is necessary. Inside a common damper system the presence of several conjugated actions made by springs, oil and pressurized air requires a significant experimental support and a great number of prototypes and test. Aimed to reduce the design and tuning phases of a damper system, it is necessary to join these phases together with a numerical modelling phase. The aim of this paper is to present the development of a mono-dimensional (1D) model for simulating dynamic behaviour of damper system.