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The front camera module is a fundamental component of a modern vehicle’s active safety architecture. The module supports many active safety features. Perception of the road environment, requests for driver notification or alert, and requests for vehicle actuation are among the camera software’s key functions. This paper presents a novel method of testing these functions virtually. First, the front camera module software is compiled and packaged in a Docker container capable of running on a standard Linux computer as a software in the loop (SiL). This container is then integrated with the active safety simulation tool that represents the vehicle plant model and allows modeling of test scenarios. Then the following simulation components form a closed loop: First, the active safety simulation tool generates a video data stream (VDS). Using an internet protocol, the tool sends the VDS to the camera SiL and other vehicle channels.

The establishment of highly-diluted combustion strategies is one of the major challenges that the next generation of sustainable internal combustion engines must face. The desirable use of high EGR rates and of lean mixtures clashes with the tolerable combustion stability. To this aim, the development of numerical models able to reproduce the degree of combustion variability is crucial to allow the virtual exploration and optimization of a wide number of innovative combustion strategies. In this study ignition experiments using a conventional coil system are carried out in a closed volume combustion vessel with side-oriented flow generated by a speed-controlled fan. Acquisitions for four combinations of premixed propane/air mixture quality (Φ=0.9,1.2), dilution rate (20%-30%) and lateral flow velocity (1-5 m/s) are used to assess the modelling capabilities of a newly developed spark-ignition model for large-eddy simulation (GLIM, GruMo-UniMORE LES Ignition Model).

The comfort assessment of seats in the automotive industry has historically been accomplished by subjective ratings. This approach is expensive and time consuming since it involves multiple prototype seats and numerous people in supporting processes. In order to create a more efficient and robust method, objective metrics must be developed and utilized to establish measurable boundaries for seat performance. Objective measurements already widely accepted, such as IFD (Indentation Force Deflection) or CFD (Compression Force Deflection) [1], have significant shortcomings in defining seat comfort. The most obvious deficiency of these component level tests is that they only deal with a seats' foam rather than the system response. Consequently, these tests fail to take into account significant factors that affect seat comfort such as trim, suspension, attachments and other components.

Stochastic Preignition (SPI) is an abnormal combustion phenomenon for internal combustion engines (ICE), which has been a significant impact to automotive companies developing high efficiency, turbocharged, direct fuel injection, spark ignited engines. It is becoming clearer what fuel properties are related to the cause of SPI, whether directly with fuel preparation in the cylinder, or mechanisms related to the deposit build-up which contributes to initial and follow-on SPI events. The purpose of this paper is to provide a summary of global market gasoline fuel properties with special attention given to properties and specific compounds from the fuel and fuel additives that can contribute to SPI and the deposit build-up in engines. Based on a review of the global fuel quality, it appears that the fuel quality has not caught up to meet the technology requirements for fuel economy from modern technology engines.

In the context of vehicle electrification, improving vehicle aerodynamics is not only critical for efficiency and range, but also for driving experience. In order to balance the necessary trade-offs between drag and downforce without significant impact on the vehicle styling, we see an increasing amount of active aerodynamic solutions on high-end passenger vehicles. Active rear spoilers are one of the most common active aerodynamic features. They deploy at high vehicle speed when additional downforce is required [1, 2]. For a vehicle with an active rear spoiler, the aerodynamic performance is typically predicted through simulations or physical testing at different static spoiler positions. These positions range from fully stowed to fully deployed. However, this approach does not provide any information regarding the transient effects during the deployment of the rear spoiler, which can be critical to understanding key performance aspects of the system.

In recent years, there has been a sustained effort by the automotive OEMs and suppliers to improve the vehicle driveline efficiency. This has been in response to customer demands for greater vehicle fuel economy and increasingly stringent government regulations. The automotive rear axle is one of the major sources of power loss in the driveline, and hence represents an area where power loss improvements can have a significant impact on overall vehicle fuel economy. Both the friction induced mechanical losses and the spin losses vary significantly with the operating temperature of the lubricant. Also, the preloads in the bearings can vary due to temperature fluctuations. The temperatures of the lubricant, the gear tooth contacting surfaces, and the bearing contact surfaces are critical to the overall axle performance in terms of power losses, fatigue life, and wear.

The focus of this study is to validate the predictive capability of a recently developed physiology based thermal comfort modeling tool in a realistic thermal environment of a vehicle passenger compartment. Human subject test data for thermal sensation and comfort was obtained in a climatic wind tunnel for a cross-over vehicle in a relatively warm thermal environment including solar load. A CFD/thermal model that simulates the vehicle operating conditions in the tunnel, is used to provide the necessary inputs required by the stand-alone thermal comfort tool. Comparison of the local and the overall thermal sensation and comfort levels between the human subject test and the tool's predictions shows a reasonably good agreement. The next step is to use this modeling technique in designing and developing energy-efficient HVAC systems without compromising thermal comfort of the vehicle occupants.

Compartment noise has gained significant importance to meet customer expectation. One of the sources of noise is air intake noise. Intake noise is produced by both opening and closing of the inlet valve. This makes source noise critical to the development of air induction system. The new approach has been thought for noise analysis of Air Induction System (AIS) to identify source noise using 1D-3D coupling. It is very difficult to simulate engine and air induction system in Computational Fluid Dynamics (CFD) due to complexities in geometry. The objective of the present study is to predict the pulsed noise and flow noise using 1D-3D coupling. The engine with 1D code and AIS with 3D CFD code is simulated. Engine pulsation from GT-Power is provided as an input boundary condition to ANSYS Fluent. GT-Power exchanges boundary values to 3D computation domain at each CFD time step through special connections. The CFD code is run with implicit discretisation scheme and SAS turbulence model.

Vehicle manufacturers have developed new vehicle and diesel engine technologies compatible with B6-B20 biodiesel blends meeting ASTM D7467, “Standard Specification for Diesel Fuel Oil, Biodiesel Blend (B6 to B20).” However, recent U.S. market place fuel surveys have shown that many retail biodiesel samples are out of specification. A vehicle designed to use biodiesel blends is likely to encounter occasional use of poor quality biodiesel fuel; and therefore understanding the effects of bad marketplace biodiesel fuels on engine and fuel system performance is critical to develop durable automotive technologies. The results presented herein are from vehicle evaluation studies with both on-specification and off-specification bio-based fuels. These studies focused on the performance of fuel injection equipment, engine, engine oil, emissions and emissions system durability.

Miller Cycle engines employ a high expansion ratio to achieve high part-load efficiency, while minimizing knock sensitivity by using valve events that limit the effective compression ratio. The Miller effect may be achieved using either early or late intake valve closure. Combustion systems for these engines must be carefully designed to obtain adequate trapped charge to achieve full-load objectives as well as charge motion characteristics supporting good mixture preparation and flame propagation. This paper summarizes the results of a holistic project tasked with developing robust combustion systems for both early and late intake valve closure strategies. Based on best practices from conventional engines and preliminary Miller cycle requirements, a series of combustion systems was designed. These were analyzed using 3-dimensional computational fluid dynamics and those showing favorable combustion characteristics were experimentally evaluated using a modular single cylinder engine.

One of the major problems that the automotive industry faces is reducing friction to increase efficiency. Researchers have shown that 30% of the fuel energy was consumed to overcome the friction forces between the moving parts of any automobile, Holmberg et al. [1]. The interface of the piston pin and pin bore is one of the areas that generate high friction under severe working conditions of high temperature and lack of lubrication. In this research, experimental investigation and theoretical simulation have been carried out to analyze the motion of the floating pin against pin bore. In the experimental study, the focus was on analyzing the floating pin motion by using a bench test rig to simulate the floating pin motion in an internal combustion engine. A motion data acquisition system was developed to capture and record the pin motion. Thousands of images were recorded and later analyzed by a code written by MATLAB.

This paper examines the effect of rear effective backlight angle on vehicle contamination using contamination simulation results of a commercial vehicle. Highly-resolved time accurate computational fluid dynamics simulations were performed using a commercial Lattice-Boltzmann solver, to compare the rear end contamination with five different rear effective backlight angles. Additional aerodynamics simulations presented good correlation with published experimental data. The contamination results were compared with the aerodynamics simulation results in order to find trends between the two simulation types for different effective backlight angles.

This work reports a CFD study on a 2-stroke (2-S) opposed piston high speed direct injection (HSDI) Diesel engine. The engine main features (bore, stroke, port timings, et cetera) are defined in a previous stage of the project, while the current analysis is focused on the assembly made up of scavenge ports, manifold and cylinder. The first step of the study consists in the construction of a parametric mesh on a simplified geometry. Two geometric parameters and three different operating conditions are considered. A CFD-3D simulation by using a customized version of the KIVA-4 code is performed on a set of 243 different cases, sweeping all the most interesting combinations of geometric parameters and operating conditions. The post-processing of this huge amount of data allow us to define the most effective geometric configuration, named baseline.

In this paper, a finite element methodology is given in which finite element models of a three-weld Al-Cu plate is created with support and loading conditions emulating those seen in an optical lab. Harmonic response is sought for the models under the presumption that various defective welds are present. The numerical results are carefully examined to determine the guideline frequency range so the actual optical experiment can be carried out more efficiently.

Prior to making rotational vibration measurements with a laser vibrometer, it is good practice to establish that the instrument is operating properly. This can be accomplished by comparative measurement of a rotational vibration source with known amplitude and frequency. This paper describes the design and development of a rotational vibration apparatus with known amplitude and frequency to be used as a reference for comparison to concurrent and co-located measurements made by a rotational laser vibrometer (RLV). The comparative measurements acquired with the apparatus are helpful to verify proper laser vibrometer operation in between regular calibration intervals, and/or whenever the functionality of the vibrometer is suspect. In the subject apparatus, a Cardan shaft with variable input speed and angle is used to provide output torsional vibration with variable frequency and amplitude.

With the introduction of hybrid vehicles and the associated elimination of engine and exhaust masking noises, sounds from other sources is becoming more noticeable. Fuel tank sloshing is one of these sources. Fuel sloshing occurs when a vehicle is accelerated in any direction and can create noise that may be perceived as a quality issue by the customer. To reduce slosh noise, a fuel tank has to be carefully designed. Reduction in slosh noise using test- based methods can be very costly and timely. This paper shows how, using the combination of CFD (Computational Fluid Dynamic), FE (Finite Element) and Acoustic simulation methods, the radiated fuel tank slosh noise performance can be evaluated using CAE methods. Although the de-coupled fluid /structure interaction (FSI) method was used for the examples in this paper, the acoustic simulation method is not limited to the decoupled FSI method.

Analysis-driven pre-calibration of a modern automotive engine is extremely valuable in significantly reducing hardware investments and accelerating engine designs compliant with stricter emission regulations. Advanced modelling tools, such as a Virtual Engine Model (VEM) using Computational Fluid Dynamics (CFD), are often used within the framework of a Design of Experiments for Powertrain Engineering (DEPE) with the goal of streamlining significant portions of the calibration process. The success of the methodology largely relies on the accuracy of analytical predictions, especially engine-out emissions. Results show excellent agreements in engine performance parameters (with R2 > 98%) and good agreements in NOx and combustion noise (with R2 > 87%), while the Carbon Monoxide (CO), Unburned Hydrocarbons (HC) and Smoke emissions predictions remain a challenge even with a large n-heptane mechanism consisting of 144 species and 900 reactions and refined mesh resolution.

A Machine Learning (ML) approach is presented to correlate in-cylinder images of early flame kernel development within a spark-ignited (SI) gasoline engine to early-, mid-, and late-stage flame propagation. The objective of this study was to train machine learning models to analyze the relevance of flame surface features on subsequent burn rates. Ultimately, an approach of this nature can be generalized to flame images from a variety of sources. The prediction of combustion phasing was formulated as a regression problem to train predictive models to supplement observations of early flame kernel growth. High-speed images were captured from an optically accessible SI engine for 357 cycles under pre-mixed operation. A subset of these images was used to train three models: a linear regression model, a deep Convolutional Neural Network (CNN) based on the InceptionV3 architecture and a CNN built with assisted learning on the VGG19 architecture.

Cast Al-Si alloys are widely used in automotive industry to produce structural components, such as engine block and cylinder head, because of the increasing demands in reducing mass for improved fuel efficiency. The fatigue performance of the castings is critical in their application. Porosity is highly detrimental to the fatigue behavior of cast Al-Si alloys. Therefore, accurate measurement of pore sizes is important in order to develop the correlations between porosity and fatigue strength. However, quantification of porosity is challenging and shows large variation depending on the measurement methods, particularly for micro-shrinkage porosity due to the torturous and complex morphology. The conventional metallographic image analysis method in the 2D polished surface often underestimates the actual pore size particularly when the porosity morphology is complex.

More stringent emissions regulations are continually being proposed to mitigate adverse human health and environmental impacts of internal combustion engines. With that in mind, it has been proposed that vehicular particulate matter (PM) emissions should be regulated based on particle number in addition to particle mass. One aspect of this project is to study different sample handling methods for number-based aerosol measurements, specifically, two different methods for removing volatile organic compounds (VOCs). One method is a thermodenuder (TD) and the other is an evaporative chamber/diluter (EvCh). These sample-handling methods have been implemented in an engine test cell with a spark-ignited direct injection (SIDI) engine. The engine was designed for stoichiometric, homogeneous combustion.